From c3149215174e5927020868abb9233274bf6bd8d2 Mon Sep 17 00:00:00 2001 From: zyliang2001 Date: Sun, 1 Dec 2024 21:22:03 -0800 Subject: [PATCH] clean and add lasso and ridge --- ... 00_ablation_regression_ranking_script.sh} | 2 +- .../00_ablation_regression_script.sh | 10 - .../00_ablation_regression_script2.sh | 10 - .../00_ablation_regression_script3.sh | 10 - .../00_ablation_regression_script4.sh | 10 - ...0_ablation_regression_selection_script.sh} | 2 +- ...00_ablation_regression_stability_script.sh | 2 +- ...0_ablation_regression_stability_script2.sh | 10 - feature_importance/00_ablation_script_regr.sh | 10 - .../00_ablation_script_regr_ranking.sh | 12 + .../00_ablation_script_regr_selection.sh | 12 + .../00_ablation_script_regr_stability.sh | 10 +- ...> 00_run_ablation_regression_selection.py} | 75 +- .../00_run_ablation_regression_stability.py | 43 +- .../00_run_feature_ranking_simulation.py | 68 +- ...blation_results_visulization_ranking.ipynb | 2701 +++++------------ ...blation_results_visulization_retrain.ipynb | 2293 -------------- ...ation_results_visulization_selection.ipynb | 1238 ++++++++ ...ation_results_visulization_stability.ipynb | 1787 ++--------- feature_importance/debug_ablation.ipynb | 47 +- .../dgp.py | 0 .../models.py | 25 + .../dgp.py | 3 +- .../models.py | 25 + .../models.py | 52 - .../dgp.py | 20 +- .../models.py | 25 + .../dgp.py | 3 +- .../models.py | 25 + .../models.py | 52 - .../models.py | 52 - .../models.py | 29 - .../dgp.py | 0 .../real_data_regression_parkinsons/models.py | 25 + .../dgp.py | 38 + .../models.py | 25 + .../dgp.py | 41 + .../models.py | 25 + .../dgp.py | 18 +- .../models.py | 25 + .../models.py | 52 - .../dgp.py | 0 .../models.py | 25 + .../dgp.py | 38 + .../models.py | 25 + .../dgp.py | 41 + .../models.py | 25 + .../dgp.py | 39 + .../models.py | 25 + .../models.py | 52 - .../real_data_regression_satellite/models.py | 39 - .../real_data_regression_temperature/dgp.py | 20 + .../models.py | 25 + .../dgp.py | 52 + .../models.py | 26 + .../dgp.py | 55 + .../models.py | 27 + .../dgp.py | 20 +- .../models.py | 26 + .../models.py | 53 - .../scripts/competing_methods_local.py | 408 +-- 61 files changed, 3370 insertions(+), 6563 deletions(-) rename feature_importance/{00_ablation_regression_stability_script3.sh => 00_ablation_regression_ranking_script.sh} (57%) delete mode 100755 feature_importance/00_ablation_regression_script.sh delete mode 100755 feature_importance/00_ablation_regression_script2.sh delete mode 100755 feature_importance/00_ablation_regression_script3.sh delete mode 100755 feature_importance/00_ablation_regression_script4.sh rename feature_importance/{00_ablation_regression_stability_script4.sh => 00_ablation_regression_selection_script.sh} (56%) delete mode 100755 feature_importance/00_ablation_regression_stability_script2.sh delete mode 100755 feature_importance/00_ablation_script_regr.sh create mode 100755 feature_importance/00_ablation_script_regr_ranking.sh create mode 100755 feature_importance/00_ablation_script_regr_selection.sh rename feature_importance/{00_run_ablation_regression_retrain.py => 00_run_ablation_regression_selection.py} (87%) delete mode 100644 feature_importance/ablation_results_visulization_retrain.ipynb create mode 100644 feature_importance/ablation_results_visulization_selection.ipynb rename feature_importance/fi_config/mdi_local/{real_data_regression_CCLE_PD_0325901_retrain => real_data_regression_CCLE_PD_0325901}/dgp.py (100%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_CCLE_PD_0325901_linear_retrain => real_data_regression_CCLE_PD_0325901_linear}/dgp.py (96%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_CCLE_topotecan_retrain => real_data_regression_CCLE_PD_0325901_lss}/dgp.py (77%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_CCLE_PD_0325901_poly_retrain => real_data_regression_CCLE_PD_0325901_poly}/dgp.py (96%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_parkinsons_retrain => real_data_regression_parkinsons}/dgp.py (100%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_temperature_retrain => real_data_regression_parkinsons_poly}/dgp.py (67%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_performance_retrain => real_data_regression_performance}/dgp.py (100%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_satellite/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/models.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/dgp.py create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/models.py rename feature_importance/fi_config/mdi_local/{real_data_regression_satellite => real_data_regression_temperature_poly}/dgp.py (76%) create mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/models.py delete mode 100644 feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py diff --git a/feature_importance/00_ablation_regression_stability_script3.sh b/feature_importance/00_ablation_regression_ranking_script.sh similarity index 57% rename from feature_importance/00_ablation_regression_stability_script3.sh rename to feature_importance/00_ablation_regression_ranking_script.sh index b94b0da..24b24f2 100755 --- a/feature_importance/00_ablation_regression_stability_script3.sh +++ b/feature_importance/00_ablation_regression_ranking_script.sh @@ -4,7 +4,7 @@ #SBATCH --partition=yugroup source activate mdi # Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_stability.py --nreps 1 --config mdi_local.real_data_regression_temperature_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name temperature_stability" +command="00_run_feature_ranking_simulation.py --nreps 1 --config mdi_local.real_data_regression_${1}_${2} --split_seed 1 --y_seed ${3} --ignore_cache --create_rmd --folder_name ${1}_${2}" # Execute the command python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_script.sh b/feature_importance/00_ablation_regression_script.sh deleted file mode 100755 index 9d90092..0000000 --- a/feature_importance/00_ablation_regression_script.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -#SBATCH --mail-user=zhongyuan_liang@berkeley.edu -#SBATCH --mail-type=ALL -#SBATCH --partition=yugroup -source activate mdi -# Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_retrain.py --nreps 1 --config mdi_local.real_data_regression_parkinsons_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name parkinsons_retrain --fit_model True --absolute_masking True" - -# Execute the command -python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_script2.sh b/feature_importance/00_ablation_regression_script2.sh deleted file mode 100755 index d86a68f..0000000 --- a/feature_importance/00_ablation_regression_script2.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -#SBATCH --mail-user=zhongyuan_liang@berkeley.edu -#SBATCH --mail-type=ALL -#SBATCH --partition=yugroup -source activate mdi -# Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_retrain.py --nreps 1 --config mdi_local.real_data_regression_performance_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name performance_retrain --fit_model True --absolute_masking True" - -# Execute the command -python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_script3.sh b/feature_importance/00_ablation_regression_script3.sh deleted file mode 100755 index 0178d93..0000000 --- a/feature_importance/00_ablation_regression_script3.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -#SBATCH --mail-user=zhongyuan_liang@berkeley.edu -#SBATCH --mail-type=ALL -#SBATCH --partition=yugroup -source activate mdi -# Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_retrain.py --nreps 1 --config mdi_local.real_data_regression_temperature_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name temperature_retrain --fit_model True --absolute_masking True" - -# Execute the command -python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_script4.sh b/feature_importance/00_ablation_regression_script4.sh deleted file mode 100755 index 93c70c2..0000000 --- a/feature_importance/00_ablation_regression_script4.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -#SBATCH --mail-user=zhongyuan_liang@berkeley.edu -#SBATCH --mail-type=ALL -#SBATCH --partition=yugroup -source activate mdi -# Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_retrain.py --nreps 1 --config mdi_local.real_data_regression_CCLE_PD_0325901_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name CCLE_PD_0325901_retrain --fit_model True --absolute_masking True" - -# Execute the command -python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_stability_script4.sh b/feature_importance/00_ablation_regression_selection_script.sh similarity index 56% rename from feature_importance/00_ablation_regression_stability_script4.sh rename to feature_importance/00_ablation_regression_selection_script.sh index 978614d..bf3e2eb 100755 --- a/feature_importance/00_ablation_regression_stability_script4.sh +++ b/feature_importance/00_ablation_regression_selection_script.sh @@ -4,7 +4,7 @@ #SBATCH --partition=yugroup source activate mdi # Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_stability.py --nreps 1 --config mdi_local.real_data_regression_CCLE_PD_0325901_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name CCLE_PD_0325901_stability" +command="00_run_ablation_regression_selection.py --nreps 1 --config mdi_local.real_data_regression_${1} --split_seed ${2} --rf_seed ${3} --ignore_cache --create_rmd --folder_name ${1}_selection --fit_model True" # Execute the command python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_stability_script.sh b/feature_importance/00_ablation_regression_stability_script.sh index 7aa857d..cb98b11 100755 --- a/feature_importance/00_ablation_regression_stability_script.sh +++ b/feature_importance/00_ablation_regression_stability_script.sh @@ -4,7 +4,7 @@ #SBATCH --partition=yugroup source activate mdi # Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_stability.py --nreps 1 --config mdi_local.real_data_regression_parkinsons_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name parkinsons_stability" +command="00_run_ablation_regression_stability.py --nreps 1 --config mdi_local.real_data_regression_${1} --split_seed ${2} --ignore_cache --create_rmd --folder_name ${1}_stability" # Execute the command python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_regression_stability_script2.sh b/feature_importance/00_ablation_regression_stability_script2.sh deleted file mode 100755 index df168c9..0000000 --- a/feature_importance/00_ablation_regression_stability_script2.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -#SBATCH --mail-user=zhongyuan_liang@berkeley.edu -#SBATCH --mail-type=ALL -#SBATCH --partition=yugroup -source activate mdi -# Need to specify --result_name --ablate_features(default all features) --fitted(default not fitted) -command="00_run_ablation_regression_stability.py --nreps 1 --config mdi_local.real_data_regression_performance_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name performance_stability" - -# Execute the command -python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_script_regr.sh b/feature_importance/00_ablation_script_regr.sh deleted file mode 100755 index 6c670ab..0000000 --- a/feature_importance/00_ablation_script_regr.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash - -slurm_script="00_ablation_regression_script4.sh" - -for split_seed in {1..3}; do - for rf_seed in {1..5}; do - sbatch $slurm_script $split_seed $rf_seed # Submit SLURM job with both split_seed and rf_seed as arguments - sleep 2 - done -done \ No newline at end of file diff --git a/feature_importance/00_ablation_script_regr_ranking.sh b/feature_importance/00_ablation_script_regr_ranking.sh new file mode 100755 index 0000000..ed9dff2 --- /dev/null +++ b/feature_importance/00_ablation_script_regr_ranking.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +slurm_script="00_ablation_regression_ranking_script.sh" + +for data_name in "temperature" "performance" "parkinsons" "CCLE_PD_0325901"; do + for dgp in "linear" "lss" "poly"; do + for y_seed in {1..10}; do + sbatch $slurm_script $data_name $dgp $y_seed + sleep 2 + done + done +done diff --git a/feature_importance/00_ablation_script_regr_selection.sh b/feature_importance/00_ablation_script_regr_selection.sh new file mode 100755 index 0000000..77c3fb9 --- /dev/null +++ b/feature_importance/00_ablation_script_regr_selection.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +slurm_script="00_ablation_regression_selection_script.sh" + +for data_name in "temperature" "performance" "parkinsons" "CCLE_PD_0325901"; do + for split_seed in {1..3}; do + for rf_seed in {1..3}; do + sbatch $slurm_script $data_name $split_seed $rf_seed + sleep 2 + done + done +done \ No newline at end of file diff --git a/feature_importance/00_ablation_script_regr_stability.sh b/feature_importance/00_ablation_script_regr_stability.sh index a337d7f..945340e 100755 --- a/feature_importance/00_ablation_script_regr_stability.sh +++ b/feature_importance/00_ablation_script_regr_stability.sh @@ -1,8 +1,10 @@ #!/bin/bash -slurm_script="00_ablation_regression_stability_script4.sh" +slurm_script="00_ablation_regression_stability_script.sh" -for split_seed in {1..3}; do - sbatch $slurm_script $split_seed # Submit SLURM job with both split_seed and rf_seed as arguments - sleep 2 +for data_name in "temperature" "performance" "parkinsons" "CCLE_PD_0325901"; do + for split_seed in {1..3}; do + sbatch $slurm_script $data_name $split_seed + sleep 2 + done done \ No newline at end of file diff --git a/feature_importance/00_run_ablation_regression_retrain.py b/feature_importance/00_run_ablation_regression_selection.py similarity index 87% rename from feature_importance/00_run_ablation_regression_retrain.py rename to feature_importance/00_run_ablation_regression_selection.py index ed3817d..d301f96 100644 --- a/feature_importance/00_run_ablation_regression_retrain.py +++ b/feature_importance/00_run_ablation_regression_selection.py @@ -22,7 +22,7 @@ from sklearn.linear_model import LinearRegression import xgboost as xgb from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier -from sklearn.linear_model import RidgeCV +from sklearn.linear_model import RidgeCV, LassoCV sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -60,7 +60,6 @@ def compare_estimators(estimators: List[ModelConfig], # initialize results results = defaultdict(lambda: []) - feature_importance_list = {"positive": {}, "negative": {}, "absolute": {}} # loop over model estimators for model in estimators: @@ -90,55 +89,31 @@ def compare_estimators(estimators: List[ModelConfig], if args.fit_model: print("Fitting Models") # fit RF model - start_rf = time.time() est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=args.rf_seed) est.fit(X_train, y_train) - end_rf = time.time() - # fit default RF_plus model - start_rf_plus = time.time() rf_plus_base = RandomForestPlusRegressor(rf_model=est) rf_plus_base.fit(X_train, y_train) - end_rf_plus = time.time() - # fit oob RF_plus model - start_rf_plus_oob = time.time() - rf_plus_base_oob = RandomForestPlusRegressor(rf_model=est, fit_on="oob") - rf_plus_base_oob.fit(X_train, y_train) - end_rf_plus_oob = time.time() - - #fit inbag RF_plus model - start_rf_plus_inbag = time.time() - rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est, include_raw=False, fit_on="inbag", prediction_model=LinearRegression()) - rf_plus_base_inbag.fit(X_train, y_train) - end_rf_plus_inbag = time.time() - - - # fit default RF_plus model rf_plus_base_ridge = RandomForestPlusRegressor(rf_model=est, prediction_model=RidgeCV(cv=5)) rf_plus_base_ridge.fit(X_train, y_train) - rf_plus_base_oob_ridge = RandomForestPlusRegressor(rf_model=est, fit_on="oob", prediction_model=RidgeCV(cv=5)) - rf_plus_base_oob_ridge.fit(X_train, y_train) - rf_plus_base_inbag_ridge = RandomForestPlusRegressor(rf_model=est, include_raw=False, fit_on="inbag", prediction_model=RidgeCV(cv=5)) - rf_plus_base_inbag_ridge.fit(X_train, y_train) - + rf_plus_base_lasso = RandomForestPlusRegressor(rf_model=est, prediction_model=LassoCV(cv=5, max_iter=5000)) + rf_plus_base_lasso.fit(X_train, y_train) - # get test results test_all_mse_rf = mean_squared_error(y_test, est.predict(X_test)) test_all_r2_rf = r2_score(y_test, est.predict(X_test)) test_all_mse_rf_plus = mean_squared_error(y_test, rf_plus_base.predict(X_test)) test_all_r2_rf_plus = r2_score(y_test, rf_plus_base.predict(X_test)) - test_all_mse_rf_plus_oob = mean_squared_error(y_test, rf_plus_base_oob.predict(X_test)) - test_all_r2_rf_plus_oob = r2_score(y_test, rf_plus_base_oob.predict(X_test)) - test_all_mse_rf_plus_inbag = mean_squared_error(y_test, rf_plus_base_inbag.predict(X_test)) - test_all_r2_rf_plus_inbag = r2_score(y_test, rf_plus_base_inbag.predict(X_test)) + test_all_mse_rf_plus_ridge = mean_squared_error(y_test, rf_plus_base_ridge.predict(X_test)) + test_all_r2_rf_plus_ridge = r2_score(y_test, rf_plus_base_ridge.predict(X_test)) + test_all_mse_rf_plus_lasso = mean_squared_error(y_test, rf_plus_base_lasso.predict(X_test)) + test_all_r2_rf_plus_lasso = r2_score(y_test, rf_plus_base_lasso.predict(X_test)) fitted_results = { - "Model": ["RF", "RF_plus", "RF_plus_oob", "RF_plus_inbag"], - "MSE": [test_all_mse_rf, test_all_mse_rf_plus, test_all_mse_rf_plus_oob, test_all_mse_rf_plus_inbag], - "R2": [test_all_r2_rf, test_all_r2_rf_plus, test_all_r2_rf_plus_oob, test_all_r2_rf_plus_inbag], - "Time": [end_rf - start_rf, end_rf_plus - start_rf_plus, end_rf_plus_oob - start_rf_plus_oob, end_rf_plus_inbag - start_rf_plus_inbag] + "Model": ["RF", "RF_plus", "RF_plus_ridge", "RF_plus_lasso"], + "MSE": [test_all_mse_rf, test_all_mse_rf_plus, test_all_mse_rf_plus_ridge, test_all_mse_rf_plus_lasso], + "R2": [test_all_r2_rf, test_all_r2_rf_plus, test_all_r2_rf_plus_ridge, test_all_r2_rf_plus_lasso] } os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}", exist_ok=True) @@ -166,23 +141,15 @@ def compare_estimators(estimators: List[ModelConfig], loaded_model = None elif fi_est.base_model == "RF": loaded_model = est - elif fi_est.base_model == "RFPlus_oob": - loaded_model = rf_plus_base_oob - elif fi_est.base_model == "RFPlus_inbag": - loaded_model = rf_plus_base_inbag elif fi_est.base_model == "RFPlus_default": loaded_model = rf_plus_base elif fi_est.base_model == "RFPlus_ridge": loaded_model = rf_plus_base_ridge - elif fi_est.base_model == "RFPlus_oob_ridge": - loaded_model = rf_plus_base_oob_ridge - elif fi_est.base_model == "RFPlus_inbag_ridge": - loaded_model = rf_plus_base_inbag_ridge + elif fi_est.base_model == "RFPlus_lasso": + loaded_model = rf_plus_base_lasso - m= "absolute" - start = time.time() print(f"Compute feature importance") - local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train, fit=loaded_model, mode="absolute") + local_fi_score_train, _ = fi_est.cls(X_train=X_train, y_train=y_train, X_test=X_test, fit=loaded_model, mode="absolute") train_fi_mean = np.mean(local_fi_score_train, axis=0) print(f"Train FI Mean: {train_fi_mean}") if fi_est.ascending: @@ -190,10 +157,9 @@ def compare_estimators(estimators: List[ModelConfig], else: sorted_feature = np.argsort(train_fi_mean) print(f"Sorted Feature: {sorted_feature}") - end = time.time() - metric_results[f'fi_time_{m}'] = end - start ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=args.rf_seed), + "xgboost_Regressor": xgb.XGBRegressor(random_state=args.rf_seed), "Linear_Regressor": LinearRegression()} if X_train.shape[1] > 20: mask_ratio = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9] @@ -214,9 +180,7 @@ def compare_estimators(estimators: List[ModelConfig], metric_results[f'{a_model}_MSE_top_{mask}'] = mean_squared_error(y_test, y_pred) metric_results[f'{a_model}_R2_top_{mask}'] = r2_score(y_test, y_pred) - - # initialize results with metadata and metric results - kwargs: dict = model.kwargs # dict + kwargs: dict = model.kwargs for k in kwargs: results[k].append(kwargs[k]) for k in fi_kwargs: @@ -228,7 +192,7 @@ def compare_estimators(estimators: List[ModelConfig], results[met_name].append(met_val) # for key, value in results.items(): # print(f"{key}: {len(value)}") - return results, feature_importance_list + return results def run_comparison(path: str, @@ -263,7 +227,7 @@ def run_comparison(path: str, if len(fi_estimators) == 0: return - results, fi_lst = compare_estimators(estimators=estimators, + results = compare_estimators(estimators=estimators, fi_estimators=fi_estimators, X=X, y=y, support=support, metrics=metrics, @@ -282,8 +246,6 @@ def run_comparison(path: str, if col in df.columns: df = df.drop(columns=[col]) - pkl.dump(fi_lst, open(feature_importance_all, 'wb')) - for model_comparison_file, fi_estimator in zip(model_comparison_files, fi_estimators): output_dict = { # metadata @@ -360,9 +322,6 @@ def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp ### Newly added arguments parser.add_argument('--folder_name', type=str, default=None) parser.add_argument('--fit_model', type=bool, default=False) - parser.add_argument('--absolute_masking', type=bool, default=False) - parser.add_argument('--positive_masking', type=bool, default=False) - parser.add_argument('--negative_masking', type=bool, default=False) parser.add_argument('--num_features_masked', type=int, default=None) parser.add_argument('--rf_seed', type=int, default=0) diff --git a/feature_importance/00_run_ablation_regression_stability.py b/feature_importance/00_run_ablation_regression_stability.py index 51711a3..9034aa4 100644 --- a/feature_importance/00_run_ablation_regression_stability.py +++ b/feature_importance/00_run_ablation_regression_stability.py @@ -22,7 +22,7 @@ from sklearn.linear_model import LinearRegression import xgboost as xgb from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier -from sklearn.linear_model import RidgeCV +from sklearn.linear_model import RidgeCV, LassoCV sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -52,7 +52,6 @@ def compare_estimators(estimators: List[ModelConfig], # initialize results results = defaultdict(lambda: []) - feature_importance_list = {"positive": {}, "negative": {}, "absolute": {}} # loop over model estimators for model in estimators: @@ -78,17 +77,20 @@ def compare_estimators(estimators: List[ModelConfig], X_test = X y_train = y y_test = y - - top_features = [3, 5, 10] + + top_features_ratio = [0.05, 0.1, 0.2, 0.4] + top_features = [int(X_train.shape[1] * ratio) for ratio in top_features_ratio] top_features_dict = {} for fi_est in fi_ests: top_features_dict[fi_est.name] = {} for num_feature in top_features: - top_features_dict[fi_est.name][num_feature] = {"train": [], "test": []} + top_features_dict[fi_est.name][num_feature] = {"train": [], "test": [], "all": []} for i in range(X_train.shape[0]): top_features_dict[fi_est.name][num_feature]["train"].append([]) for i in range(X_test.shape[0]): top_features_dict[fi_est.name][num_feature]["test"].append([]) + for i in range(X.shape[0]): + top_features_dict[fi_est.name][num_feature]["all"].append([]) for rf_seed in range(5): est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=rf_seed) @@ -97,6 +99,12 @@ def compare_estimators(estimators: List[ModelConfig], rf_plus_base = RandomForestPlusRegressor(rf_model=est) rf_plus_base.fit(X_train, y_train) + rf_plus_base_ridge = RandomForestPlusRegressor(rf_model=est, prediction_model=RidgeCV(cv=5)) + rf_plus_base_ridge.fit(X_train, y_train) + + rf_plus_base_lasso = RandomForestPlusRegressor(rf_model=est, prediction_model=LassoCV(cv=5, max_iter=5000)) + rf_plus_base_lasso.fit(X_train, y_train) + for fi_est in tqdm(fi_ests): if fi_est.base_model == "None": loaded_model = None @@ -104,6 +112,10 @@ def compare_estimators(estimators: List[ModelConfig], loaded_model = est elif fi_est.base_model == "RFPlus_default": loaded_model = rf_plus_base + elif fi_est.base_model == "RFPlus_ridge": + loaded_model = rf_plus_base_ridge + elif fi_est.base_model == "RFPlus_lasso": + loaded_model = rf_plus_base_lasso local_fi_score_train, local_fi_score_test = fi_est.cls(X_train=X_train, y_train=y_train, X_test=X_test, fit=loaded_model, mode="absolute") @@ -117,10 +129,12 @@ def compare_estimators(estimators: List[ModelConfig], for i in range(X_train.shape[0]): for num_feature in top_features: top_features_dict[fi_est.name][num_feature]["train"][i].extend(sorted_feature_train[i][:num_feature].tolist()) + top_features_dict[fi_est.name][num_feature]["all"][i].extend(sorted_feature_train[i][:num_feature].tolist()) for i in range(X_test.shape[0]): for num_feature in top_features: top_features_dict[fi_est.name][num_feature]["test"][i].extend(sorted_feature_test[i][:num_feature].tolist()) + top_features_dict[fi_est.name][num_feature]["all"][X_train.shape[0]+i].extend(sorted_feature_test[i][:num_feature].tolist()) for fi_est in tqdm(fi_ests): @@ -133,17 +147,24 @@ def compare_estimators(estimators: List[ModelConfig], 'data_split_seed': args.split_seed, } - for num_feature in top_features: + for r in range(len(top_features_ratio)): + metric_results[f"top_{int(top_features_ratio[r]*100)}"] = top_features[r] + num_feature = top_features[r] total_train = 0 for i in range(X_train.shape[0]): total_train += len(set(top_features_dict[fi_est.name][num_feature]["train"][i])) - metric_results[f"avg_{num_feature}_features_train"] = total_train / X_train.shape[0] + metric_results[f"avg_{int(top_features_ratio[r]*100)}_features_train"] = total_train / X_train.shape[0] total_test = 0 for i in range(X_test.shape[0]): total_test += len(set(top_features_dict[fi_est.name][num_feature]["test"][i])) - metric_results[f"avg_{num_feature}_features_test"] = total_test / X_test.shape[0] + metric_results[f"avg_{int(top_features_ratio[r]*100)}_features_test"] = total_test / X_test.shape[0] + total_all = 0 + for i in range(X.shape[0]): + total_all += len(set(top_features_dict[fi_est.name][num_feature]["all"][i])) + metric_results[f"avg_{int(top_features_ratio[r]*100)}_features_all"] = total_all / X.shape[0] + # initialize results with metadata and metric results kwargs: dict = model.kwargs # dict for k in kwargs: @@ -157,7 +178,7 @@ def compare_estimators(estimators: List[ModelConfig], results[met_name].append(met_val) # for key, value in results.items(): # print(f"{key}: {len(value)}") - return results, feature_importance_list + return results def run_comparison(path: str, @@ -192,7 +213,7 @@ def run_comparison(path: str, if len(fi_estimators) == 0: return - results, fi_lst = compare_estimators(estimators=estimators, + results = compare_estimators(estimators=estimators, fi_estimators=fi_estimators, X=X, y=y, support=support, metrics=metrics, @@ -211,8 +232,6 @@ def run_comparison(path: str, if col in df.columns: df = df.drop(columns=[col]) - pkl.dump(fi_lst, open(feature_importance_all, 'wb')) - for model_comparison_file, fi_estimator in zip(model_comparison_files, fi_estimators): output_dict = { # metadata diff --git a/feature_importance/00_run_feature_ranking_simulation.py b/feature_importance/00_run_feature_ranking_simulation.py index 6a4996c..cafd146 100644 --- a/feature_importance/00_run_feature_ranking_simulation.py +++ b/feature_importance/00_run_feature_ranking_simulation.py @@ -22,7 +22,6 @@ from sklearn.linear_model import LinearRegression import xgboost as xgb from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier -from sklearn.linear_model import Ridge sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -30,7 +29,7 @@ from util import ModelConfig, FIModelConfig, tp, fp, neg, pos, specificity_score, auroc_score, auprc_score, compute_nsg_feat_corr_w_sig_subspace, apply_splitting_strategy import dill warnings.filterwarnings("ignore", message="Bins whose width") - +from sklearn.linear_model import RidgeCV, LassoCV def compare_estimators(estimators: List[ModelConfig], fi_estimators: List[FIModelConfig], X, y, support, @@ -47,7 +46,6 @@ def compare_estimators(estimators: List[ModelConfig], # initialize results results = defaultdict(lambda: []) - feature_importance_list = {"absolute": {}} # loop over model estimators for model in estimators: @@ -91,23 +89,32 @@ def compare_estimators(estimators: List[ModelConfig], rf_plus_base.fit(X_train, y_train) end_rf_plus = time.time() + rf_plus_base_ridge = RandomForestPlusRegressor(rf_model=est, prediction_model=RidgeCV(cv=5)) + rf_plus_base_ridge.fit(X_train, y_train) + + rf_plus_base_lasso = RandomForestPlusRegressor(rf_model=est, prediction_model=LassoCV(cv=5, max_iter=8000)) + rf_plus_base_lasso.fit(X_train, y_train) + # get test results test_all_mse_rf = mean_squared_error(y_test, est.predict(X_test)) test_all_r2_rf = r2_score(y_test, est.predict(X_test)) test_all_mse_rf_plus = mean_squared_error(y_test, rf_plus_base.predict(X_test)) test_all_r2_rf_plus = r2_score(y_test, rf_plus_base.predict(X_test)) + test_all_mse_rf_plus_ridge = mean_squared_error(y_test, rf_plus_base_ridge.predict(X_test)) + test_all_r2_rf_plus_ridge = r2_score(y_test, rf_plus_base_ridge.predict(X_test)) + test_all_mse_rf_plus_lasso = mean_squared_error(y_test, rf_plus_base_lasso.predict(X_test)) + test_all_r2_rf_plus_lasso = r2_score(y_test, rf_plus_base_lasso.predict(X_test)) fitted_results = { - "Model": ["RF", "RF_plus"], - "MSE": [test_all_mse_rf, test_all_mse_rf_plus], - "R2": [test_all_r2_rf, test_all_r2_rf_plus], - "Time": [end_rf - start_rf, end_rf_plus - start_rf_plus], - "Y_seed": [args.y_seed, args.y_seed], - "Split_seed": [args.split_seed, args.split_seed] + "Model": ["RF", "RF_plus", "RF_plus_ridge", "RF_plus_lasso"], + "MSE": [test_all_mse_rf, test_all_mse_rf_plus, test_all_mse_rf_plus_ridge, test_all_mse_rf_plus_lasso], + "R2": [test_all_r2_rf, test_all_r2_rf_plus, test_all_r2_rf_plus_ridge, test_all_r2_rf_plus_lasso], + "Y_seed": [args.y_seed, args.y_seed, args.y_seed, args.y_seed], + "Split_seed": [args.split_seed, args.split_seed, args.split_seed, args.split_seed], } temp = "" for vary_name in vary_setting: - fitted_results[vary_name] = [vary_setting[vary_name]] * 3 + fitted_results[vary_name] = [vary_setting[vary_name]] * 4 temp += f"{vary_name}_{vary_setting[vary_name]}_" print(fitted_results) @@ -130,32 +137,31 @@ def compare_estimators(estimators: List[ModelConfig], if fi_est.base_model == "None": loaded_model = None elif fi_est.base_model == "RF": - loaded_model = est + loaded_model = est elif fi_est.base_model == "RFPlus_default": loaded_model = rf_plus_base + elif fi_est.base_model == "RFPlus_ridge": + loaded_model = rf_plus_base_ridge + elif fi_est.base_model == "RFPlus_lasso": + loaded_model = rf_plus_base_lasso local_fi_score_train, local_fi_score_test = fi_est.cls(X_train=X_train, y_train=y_train, X_test=X_test, fit=loaded_model, mode="absolute") - feature_importance_list["absolute"][fi_est.name] = [local_fi_score_train, local_fi_score_test] all_fi_data = {"train": local_fi_score_train, "test": local_fi_score_test} for d in all_fi_data: fi_data = all_fi_data[d] - if not isinstance(fi_data, np.ndarray): - metric_results[f'auroc_{d}'] = None - metric_results[f'auprc_{d}'] = None - else: - auroc = [] - auprc = [] - for i in range(fi_data.shape[0]): - fi_data_i = fi_data[i] - if fi_est.ascending: - auroc.append(roc_auc_score(support, fi_data_i)) - auprc.append(average_precision_score(support, fi_data_i)) - else: - auroc.append(roc_auc_score(support, -1*fi_data_i)) - auprc.append(average_precision_score(support, -1*fi_data_i)) - metric_results[f'auroc_{d}'] = np.array(auroc).mean() - metric_results[f'auprc_{d}'] = np.array(auprc).mean() + auroc = [] + auprc = [] + for i in range(fi_data.shape[0]): + fi_data_i = fi_data[i] + if fi_est.ascending: + auroc.append(roc_auc_score(support, fi_data_i)) + auprc.append(average_precision_score(support, fi_data_i)) + else: + auroc.append(roc_auc_score(support, -1*fi_data_i)) + auprc.append(average_precision_score(support, -1*fi_data_i)) + metric_results[f'auroc_{d}'] = np.array(auroc).mean() + metric_results[f'auprc_{d}'] = np.array(auprc).mean() # initialize results with metadata and metric results kwargs: dict = model.kwargs # dict @@ -168,7 +174,7 @@ def compare_estimators(estimators: List[ModelConfig], results[k].append(None) for met_name, met_val in metric_results.items(): results[met_name].append(met_val) - return results, feature_importance_list + return results def run_comparison(path: str, @@ -204,7 +210,7 @@ def run_comparison(path: str, if len(fi_estimators) == 0: return - results, fi_lst = compare_estimators(estimators=estimators, + results = compare_estimators(estimators=estimators, fi_estimators=fi_estimators, X=X, y=y, support=support, metrics=metrics, @@ -224,8 +230,6 @@ def run_comparison(path: str, if col in df.columns: df = df.drop(columns=[col]) - pkl.dump(fi_lst, open(feature_importance_all, 'wb')) - for model_comparison_file, fi_estimator in zip(model_comparison_files, fi_estimators): output_dict = { # metadata diff --git a/feature_importance/ablation_results_visulization_ranking.ipynb b/feature_importance/ablation_results_visulization_ranking.ipynb index 5f9231e..c63a5a8 100644 --- a/feature_importance/ablation_results_visulization_ranking.ipynb +++ b/feature_importance/ablation_results_visulization_ranking.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 51, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -17,75 +17,23 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# load pickled data\n", - "with open('CCLE_rank.pkl', 'rb') as f:\n", - " ccle_rank = pickle.load(f)\n", - "with open('parkinsons_rank.pkl', 'rb') as f:\n", - " parkinsons_rank = pickle.load(f)\n", - "with open('performance_rank.pkl', 'rb') as f:\n", - " performance_rank = pickle.load(f)\n", - "with open('temperature_rank.pkl', 'rb') as f:\n", - " temperature_rank = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# dictionaries = [ccle_rank, parkinsons_rank, performance_rank, temperature_rank]\n", - "\n", - "# average_dict = {key: sum(d[key] for d in dictionaries) / len(dictionaries) for key in ccle_rank.keys()}\n", - "\n", - "# sorted_keys = sorted(average_dict, key=average_dict.get)\n", - "\n", - "# # Display sorted keys and their corresponding values\n", - "# sorted_average_dict = {key: average_dict[key] for key in sorted_keys}\n", - "\n", - "# for k,v in sorted_average_dict.items():\n", - "# print(k, v)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "task = \"regression\" #\"classification\" #\"regression\"\n", - "ablation_directory =\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_retrain/varying_sample_row_n\"\n", - "#####Regression\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_CCLE_PD_0325901_retrain/CCLE_PD_0325901_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_parkinsons_retrain/parkinsons_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_performance_retrain/performance_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_retrain/varying_sample_row_n\"\n", - "\n", - "#####Classification\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_juvenile_retrain/juvenile_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_csi_pecarn_retrain/csi_pecarn_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_credit_g_retrain/credit_g_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_Ionosphere_retrain/Ionosphere_retrain/varying_sample_row_n\"\n", + "dgp = \"poly\" # \"linear\", \"poly\", \"lss\"\n", + "data = \"temperature\"\n", + "ablation_directory = f\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_{data}_{dgp}/{data}_{dgp}/varying_heritability\"\n", + "folder_names = [folder for folder in os.listdir(ablation_directory) if os.path.isdir(os.path.join(ablation_directory, folder))]\n", + "experiments_seeds = []\n", + "for folder_name in folder_names:\n", + " experiments_seeds.append(int(folder_name[4:]))\n", "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "rf_seeds = [1,2,3,4,5]\n", - "for split_seed in split_seeds:\n", - " for rf_seed in rf_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}rf_seed_{rf_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", - "\n", + "for seed in experiments_seeds:\n", + " df = pd.read_csv(os.path.join(ablation_directory, f\"seed{seed}/results.csv\"))\n", + " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", "\n", - "# rf_plus_directory = f'/scratch/users/zhongyuan_liang/saved_models/{task_name}'\n", + "# rf_plus_directory = f'/scratch/users/zhongyuan_liang/saved_models/auroc/linear/'\n", "# combined_df_rf_plus = pd.DataFrame()\n", "# for file in os.listdir(rf_plus_directory):\n", "# if file.endswith(\".csv\"):\n", @@ -95,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -119,9 +67,9 @@ " \n", " \n", " \n", - " sample_row_n\n", - " sample_row_n_name\n", " rep\n", + " heritability\n", + " heritability_name\n", " n_estimators\n", " min_samples_leaf\n", " max_features\n", @@ -132,63 +80,19 @@ " test_size\n", " num_features\n", " data_split_seed\n", - " rf_seed\n", - " num_features_masked\n", - " fi_time_absolute\n", - " num_features_selected_0.01\n", - " RF_Regressor_MSE_top_0.01\n", - " RF_Regressor_R2_top_0.01\n", - " Linear_Regressor_MSE_top_0.01\n", - " Linear_Regressor_R2_top_0.01\n", - " num_features_selected_0.05\n", - " RF_Regressor_MSE_top_0.05\n", - " RF_Regressor_R2_top_0.05\n", - " Linear_Regressor_MSE_top_0.05\n", - " Linear_Regressor_R2_top_0.05\n", - " num_features_selected_0.1\n", - " RF_Regressor_MSE_top_0.1\n", - " RF_Regressor_R2_top_0.1\n", - " Linear_Regressor_MSE_top_0.1\n", - " Linear_Regressor_R2_top_0.1\n", - " num_features_selected_0.15\n", - " RF_Regressor_MSE_top_0.15\n", - " RF_Regressor_R2_top_0.15\n", - " Linear_Regressor_MSE_top_0.15\n", - " Linear_Regressor_R2_top_0.15\n", - " num_features_selected_0.25\n", - " RF_Regressor_MSE_top_0.25\n", - " RF_Regressor_R2_top_0.25\n", - " Linear_Regressor_MSE_top_0.25\n", - " Linear_Regressor_R2_top_0.25\n", - " num_features_selected_0.4\n", - " RF_Regressor_MSE_top_0.4\n", - " RF_Regressor_R2_top_0.4\n", - " Linear_Regressor_MSE_top_0.4\n", - " Linear_Regressor_R2_top_0.4\n", - " num_features_selected_0.5\n", - " RF_Regressor_MSE_top_0.5\n", - " RF_Regressor_R2_top_0.5\n", - " Linear_Regressor_MSE_top_0.5\n", - " Linear_Regressor_R2_top_0.5\n", - " num_features_selected_0.7\n", - " RF_Regressor_MSE_top_0.7\n", - " RF_Regressor_R2_top_0.7\n", - " Linear_Regressor_MSE_top_0.7\n", - " Linear_Regressor_R2_top_0.7\n", - " num_features_selected_0.9\n", - " RF_Regressor_MSE_top_0.9\n", - " RF_Regressor_R2_top_0.9\n", - " Linear_Regressor_MSE_top_0.9\n", - " Linear_Regressor_R2_top_0.9\n", + " auroc_train\n", + " auprc_train\n", + " auroc_test\n", + " auprc_test\n", " split_seed\n", " \n", " \n", " \n", " \n", " 0\n", - " NaN\n", - " keep_all_rows\n", " 0\n", + " 0.1\n", + " 0.1\n", " 100\n", " 5\n", " 0.33\n", @@ -199,1031 +103,770 @@ " 337\n", " 46\n", " 1\n", - " 1\n", - " 46\n", - " 104.601947\n", - " 1\n", - " 0.065978\n", - " 0.585859\n", - " 0.080756\n", - " 0.493104\n", - " 3\n", - " 0.057773\n", - " 0.637365\n", - " 0.071752\n", - " 0.549618\n", - " 5\n", - " 0.076612\n", - " 0.519111\n", - " 0.073049\n", - " 0.541476\n", - " 7\n", - " 0.059147\n", - " 0.628740\n", - " 0.073312\n", - " 0.539826\n", - " 12\n", - " 0.057959\n", - " 0.636197\n", - " 0.073617\n", - " 0.537912\n", - " 19\n", - " 0.057859\n", - " 0.636823\n", - " 0.071591\n", - " 0.550629\n", - " 23\n", - " 0.055291\n", - " 0.652944\n", - " 0.071319\n", - " 0.552335\n", - " 33\n", - " 0.054722\n", - " 0.656512\n", - " 0.068056\n", - " 0.572816\n", - " 42\n", - " 0.055254\n", - " 0.653174\n", - " 0.067633\n", - " 0.575476\n", + " 0.681314\n", + " 0.416192\n", + " 0.682381\n", + " 0.415301\n", " 1\n", " \n", " \n", " 1\n", - " NaN\n", - " keep_all_rows\n", " 0\n", + " 0.1\n", + " 0.1\n", " 100\n", " 5\n", " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_all_RFPlus\n", + " Local_MDI+_Alo_fit_on_all_RFPlus\n", " 683\n", " 337\n", " 46\n", " 1\n", - " 1\n", - " 46\n", - " 6.051740\n", - " 1\n", - " 0.058014\n", - " 0.635849\n", - " 0.072183\n", - " 0.546910\n", - " 3\n", - " 0.057286\n", - " 0.640420\n", - " 0.071752\n", - " 0.549618\n", - " 5\n", - " 0.055986\n", - " 0.648579\n", - " 0.070452\n", - " 0.557777\n", - " 7\n", - " 0.057412\n", - " 0.639631\n", - " 0.069463\n", - " 0.563983\n", - " 12\n", - " 0.053869\n", - " 0.661870\n", - " 0.067566\n", - " 0.575892\n", - " 19\n", - " 0.054150\n", - " 0.660104\n", - " 0.066602\n", - " 0.581942\n", - " 23\n", - " 0.055168\n", - " 0.653714\n", - " 0.066852\n", - " 0.580377\n", - " 33\n", - " 0.056201\n", - " 0.647229\n", - " 0.067106\n", - " 0.578780\n", - " 42\n", - " 0.055829\n", - " 0.649568\n", - " 0.067379\n", - " 0.577068\n", + " 0.636920\n", + " 0.305613\n", + " 0.634310\n", + " 0.303273\n", " 1\n", " \n", " \n", " 2\n", - " NaN\n", - " keep_all_rows\n", " 0\n", + " 0.1\n", + " 0.1\n", " 100\n", " 5\n", " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_all_average_RFPlus\n", + " Local_MDI+_Alo_fit_on_all_ranking_RFPlus\n", " 683\n", " 337\n", " 46\n", " 1\n", + " 0.737274\n", + " 0.393953\n", + " 0.735108\n", + " 0.391181\n", " 1\n", - " 46\n", - " 6.443466\n", - " 1\n", - " 0.058014\n", - " 0.635849\n", - " 0.072183\n", - " 0.546910\n", - " 3\n", - " 0.057286\n", - " 0.640420\n", - " 0.071752\n", - " 0.549618\n", + " \n", + " \n", + " 3\n", + " 0\n", + " 0.1\n", + " 0.1\n", + " 100\n", " 5\n", - " 0.055486\n", - " 0.651717\n", - " 0.070881\n", - " 0.555083\n", - " 7\n", - " 0.057270\n", - " 0.640520\n", - " 0.069463\n", - " 0.563983\n", - " 12\n", - " 0.054145\n", - " 0.660137\n", - " 0.067566\n", - " 0.575892\n", - " 19\n", - " 0.054853\n", - " 0.655689\n", - " 0.067253\n", - " 0.577856\n", - " 23\n", - " 0.055981\n", - " 0.648609\n", - " 0.067493\n", - " 0.576351\n", - " 33\n", - " 0.055092\n", - " 0.654192\n", - " 0.067219\n", - " 0.578070\n", + " 0.33\n", " 42\n", - " 0.055283\n", - " 0.652991\n", - " 0.067379\n", - " 0.577068\n", + " RF\n", + " Local_MDI+_MDI_fit_on_all_ranking_RFPlus\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 0.737168\n", + " 0.394115\n", + " 0.735108\n", + " 0.391181\n", " 1\n", " \n", " \n", - " 3\n", - " NaN\n", - " keep_all_rows\n", + " 4\n", " 0\n", + " 0.1\n", + " 0.1\n", " 100\n", " 5\n", " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_all_error_metric_RFPlus\n", + " Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus\n", " 683\n", " 337\n", " 46\n", " 1\n", + " 0.889184\n", + " 0.618186\n", + " 0.889954\n", + " 0.619344\n", " 1\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 315\n", + " 0\n", + " 0.8\n", + " 0.8\n", + " 100\n", + " 5\n", + " 0.33\n", + " 42\n", + " RF\n", + " Local_MDI+_MDI_fit_on_all_ranking_RFPlus\n", + " 683\n", + " 337\n", " 46\n", - " 6.669140\n", " 1\n", - " 0.065016\n", - " 0.591902\n", - " 0.081005\n", - " 0.491539\n", - " 3\n", - " 0.058308\n", - " 0.634004\n", - " 0.071752\n", - " 0.549618\n", + " 0.852883\n", + " 0.610872\n", + " 0.853926\n", + " 0.609349\n", + " 1\n", + " \n", + " \n", + " 316\n", + " 0\n", + " 0.8\n", + " 0.8\n", + " 100\n", " 5\n", - " 0.056910\n", - " 0.642778\n", - " 0.071403\n", - " 0.551811\n", - " 7\n", - " 0.057027\n", - " 0.642049\n", - " 0.070910\n", - " 0.554904\n", - " 12\n", - " 0.055956\n", - " 0.648766\n", - " 0.067925\n", - " 0.573638\n", - " 19\n", - " 0.056132\n", - " 0.647663\n", - " 0.067211\n", - " 0.578121\n", - " 23\n", - " 0.055026\n", - " 0.654609\n", - " 0.067716\n", - " 0.574955\n", - " 33\n", - " 0.054757\n", - " 0.656293\n", - " 0.066362\n", - " 0.583450\n", + " 0.33\n", " 42\n", - " 0.055137\n", - " 0.653908\n", - " 0.067558\n", - " 0.575946\n", + " RF\n", + " Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 0.883059\n", + " 0.679782\n", + " 0.883185\n", + " 0.679966\n", " 1\n", " \n", " \n", - " 4\n", - " NaN\n", - " keep_all_rows\n", + " 317\n", " 0\n", + " 0.8\n", + " 0.8\n", " 100\n", " 5\n", " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_all_error_metric_average_RFPlus\n", + " Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus\n", " 683\n", " 337\n", " 46\n", " 1\n", + " 0.804036\n", + " 0.590667\n", + " 0.806386\n", + " 0.591153\n", " 1\n", + " \n", + " \n", + " 318\n", + " 0\n", + " 0.8\n", + " 0.8\n", + " 100\n", + " 5\n", + " 0.33\n", + " 42\n", + " RF\n", + " Random\n", + " 683\n", + " 337\n", " 46\n", - " 7.259469\n", " 1\n", - " 0.065016\n", - " 0.591902\n", - " 0.081005\n", - " 0.491539\n", - " 3\n", - " 0.058308\n", - " 0.634004\n", - " 0.071752\n", - " 0.549618\n", + " 0.494766\n", + " 0.198741\n", + " 0.506726\n", + " 0.200490\n", + " 1\n", + " \n", + " \n", + " 319\n", + " 0\n", + " 0.8\n", + " 0.8\n", + " 100\n", " 5\n", - " 0.056910\n", - " 0.642778\n", - " 0.071403\n", - " 0.551811\n", - " 7\n", - " 0.057027\n", - " 0.642049\n", - " 0.070910\n", - " 0.554904\n", - " 12\n", - " 0.055956\n", - " 0.648766\n", - " 0.067925\n", - " 0.573638\n", - " 19\n", - " 0.056132\n", - " 0.647663\n", - " 0.067211\n", - " 0.578121\n", - " 23\n", - " 0.055026\n", - " 0.654609\n", - " 0.067716\n", - " 0.574955\n", - " 33\n", - " 0.054757\n", - " 0.656293\n", - " 0.066362\n", - " 0.583450\n", + " 0.33\n", " 42\n", - " 0.055137\n", - " 0.653908\n", - " 0.067558\n", - " 0.575946\n", + " RF\n", + " TreeSHAP_RF\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 0.829380\n", + " 0.629527\n", + " 0.833148\n", + " 0.632111\n", " 1\n", " \n", " \n", "\n", + "

320 rows × 18 columns

\n", "" ], "text/plain": [ - " sample_row_n sample_row_n_name rep n_estimators min_samples_leaf \\\n", - "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", - "\n", - " max_features random_state model \\\n", - "0 0.33 42 RF \n", - "1 0.33 42 RF \n", - "2 0.33 42 RF \n", - "3 0.33 42 RF \n", - "4 0.33 42 RF \n", - "\n", - " fi train_size test_size \\\n", - "0 LIME_RF 683 337 \n", - "1 Local_MDI+_fit_on_all_RFPlus 683 337 \n", - "2 Local_MDI+_fit_on_all_average_RFPlus 683 337 \n", - "3 Local_MDI+_fit_on_all_error_metric_RFPlus 683 337 \n", - "4 Local_MDI+_fit_on_all_error_metric_average_RFPlus 683 337 \n", - "\n", - " num_features data_split_seed rf_seed num_features_masked \\\n", - "0 46 1 1 46 \n", - "1 46 1 1 46 \n", - "2 46 1 1 46 \n", - "3 46 1 1 46 \n", - "4 46 1 1 46 \n", - "\n", - " fi_time_absolute num_features_selected_0.01 RF_Regressor_MSE_top_0.01 \\\n", - "0 104.601947 1 0.065978 \n", - "1 6.051740 1 0.058014 \n", - "2 6.443466 1 0.058014 \n", - "3 6.669140 1 0.065016 \n", - "4 7.259469 1 0.065016 \n", - "\n", - " RF_Regressor_R2_top_0.01 Linear_Regressor_MSE_top_0.01 \\\n", - "0 0.585859 0.080756 \n", - "1 0.635849 0.072183 \n", - "2 0.635849 0.072183 \n", - "3 0.591902 0.081005 \n", - "4 0.591902 0.081005 \n", - "\n", - " Linear_Regressor_R2_top_0.01 num_features_selected_0.05 \\\n", - "0 0.493104 3 \n", - "1 0.546910 3 \n", - "2 0.546910 3 \n", - "3 0.491539 3 \n", - "4 0.491539 3 \n", - "\n", - " RF_Regressor_MSE_top_0.05 RF_Regressor_R2_top_0.05 \\\n", - "0 0.057773 0.637365 \n", - "1 0.057286 0.640420 \n", - "2 0.057286 0.640420 \n", - "3 0.058308 0.634004 \n", - "4 0.058308 0.634004 \n", - "\n", - " Linear_Regressor_MSE_top_0.05 Linear_Regressor_R2_top_0.05 \\\n", - "0 0.071752 0.549618 \n", - "1 0.071752 0.549618 \n", - "2 0.071752 0.549618 \n", - "3 0.071752 0.549618 \n", - "4 0.071752 0.549618 \n", - "\n", - " num_features_selected_0.1 RF_Regressor_MSE_top_0.1 \\\n", - "0 5 0.076612 \n", - "1 5 0.055986 \n", - "2 5 0.055486 \n", - "3 5 0.056910 \n", - "4 5 0.056910 \n", - "\n", - " RF_Regressor_R2_top_0.1 Linear_Regressor_MSE_top_0.1 \\\n", - "0 0.519111 0.073049 \n", - "1 0.648579 0.070452 \n", - "2 0.651717 0.070881 \n", - "3 0.642778 0.071403 \n", - "4 0.642778 0.071403 \n", - "\n", - " Linear_Regressor_R2_top_0.1 num_features_selected_0.15 \\\n", - "0 0.541476 7 \n", - "1 0.557777 7 \n", - "2 0.555083 7 \n", - "3 0.551811 7 \n", - "4 0.551811 7 \n", - "\n", - " RF_Regressor_MSE_top_0.15 RF_Regressor_R2_top_0.15 \\\n", - "0 0.059147 0.628740 \n", - "1 0.057412 0.639631 \n", - "2 0.057270 0.640520 \n", - "3 0.057027 0.642049 \n", - "4 0.057027 0.642049 \n", - "\n", - " Linear_Regressor_MSE_top_0.15 Linear_Regressor_R2_top_0.15 \\\n", - "0 0.073312 0.539826 \n", - "1 0.069463 0.563983 \n", - "2 0.069463 0.563983 \n", - "3 0.070910 0.554904 \n", - "4 0.070910 0.554904 \n", + " rep heritability heritability_name n_estimators min_samples_leaf \\\n", + "0 0 0.1 0.1 100 5 \n", + "1 0 0.1 0.1 100 5 \n", + "2 0 0.1 0.1 100 5 \n", + "3 0 0.1 0.1 100 5 \n", + "4 0 0.1 0.1 100 5 \n", + ".. ... ... ... ... ... \n", + "315 0 0.8 0.8 100 5 \n", + "316 0 0.8 0.8 100 5 \n", + "317 0 0.8 0.8 100 5 \n", + "318 0 0.8 0.8 100 5 \n", + "319 0 0.8 0.8 100 5 \n", "\n", - " num_features_selected_0.25 RF_Regressor_MSE_top_0.25 \\\n", - "0 12 0.057959 \n", - "1 12 0.053869 \n", - "2 12 0.054145 \n", - "3 12 0.055956 \n", - "4 12 0.055956 \n", + " max_features random_state model \\\n", + "0 0.33 42 RF \n", + "1 0.33 42 RF \n", + "2 0.33 42 RF \n", + "3 0.33 42 RF \n", + "4 0.33 42 RF \n", + ".. ... ... ... \n", + "315 0.33 42 RF \n", + "316 0.33 42 RF \n", + "317 0.33 42 RF \n", + "318 0.33 42 RF \n", + "319 0.33 42 RF \n", "\n", - " RF_Regressor_R2_top_0.25 Linear_Regressor_MSE_top_0.25 \\\n", - "0 0.636197 0.073617 \n", - "1 0.661870 0.067566 \n", - "2 0.660137 0.067566 \n", - "3 0.648766 0.067925 \n", - "4 0.648766 0.067925 \n", + " fi train_size test_size \\\n", + "0 LIME_RF 683 337 \n", + "1 Local_MDI+_Alo_fit_on_all_RFPlus 683 337 \n", + "2 Local_MDI+_Alo_fit_on_all_ranking_RFPlus 683 337 \n", + "3 Local_MDI+_MDI_fit_on_all_ranking_RFPlus 683 337 \n", + "4 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus 683 337 \n", + ".. ... ... ... \n", + "315 Local_MDI+_MDI_fit_on_all_ranking_RFPlus 683 337 \n", + "316 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus 683 337 \n", + "317 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus 683 337 \n", + "318 Random 683 337 \n", + "319 TreeSHAP_RF 683 337 \n", "\n", - " Linear_Regressor_R2_top_0.25 num_features_selected_0.4 \\\n", - "0 0.537912 19 \n", - "1 0.575892 19 \n", - "2 0.575892 19 \n", - "3 0.573638 19 \n", - "4 0.573638 19 \n", + " num_features data_split_seed auroc_train auprc_train auroc_test \\\n", + "0 46 1 0.681314 0.416192 0.682381 \n", + "1 46 1 0.636920 0.305613 0.634310 \n", + "2 46 1 0.737274 0.393953 0.735108 \n", + "3 46 1 0.737168 0.394115 0.735108 \n", + "4 46 1 0.889184 0.618186 0.889954 \n", + ".. ... ... ... ... ... \n", + "315 46 1 0.852883 0.610872 0.853926 \n", + "316 46 1 0.883059 0.679782 0.883185 \n", + "317 46 1 0.804036 0.590667 0.806386 \n", + "318 46 1 0.494766 0.198741 0.506726 \n", + "319 46 1 0.829380 0.629527 0.833148 \n", "\n", - " RF_Regressor_MSE_top_0.4 RF_Regressor_R2_top_0.4 \\\n", - "0 0.057859 0.636823 \n", - "1 0.054150 0.660104 \n", - "2 0.054853 0.655689 \n", - "3 0.056132 0.647663 \n", - "4 0.056132 0.647663 \n", + " auprc_test split_seed \n", + "0 0.415301 1 \n", + "1 0.303273 1 \n", + "2 0.391181 1 \n", + "3 0.391181 1 \n", + "4 0.619344 1 \n", + ".. ... ... \n", + "315 0.609349 1 \n", + "316 0.679966 1 \n", + "317 0.591153 1 \n", + "318 0.200490 1 \n", + "319 0.632111 1 \n", "\n", - " Linear_Regressor_MSE_top_0.4 Linear_Regressor_R2_top_0.4 \\\n", - "0 0.071591 0.550629 \n", - "1 0.066602 0.581942 \n", - "2 0.067253 0.577856 \n", - "3 0.067211 0.578121 \n", - "4 0.067211 0.578121 \n", - "\n", - " num_features_selected_0.5 RF_Regressor_MSE_top_0.5 \\\n", - "0 23 0.055291 \n", - "1 23 0.055168 \n", - "2 23 0.055981 \n", - "3 23 0.055026 \n", - "4 23 0.055026 \n", - "\n", - " RF_Regressor_R2_top_0.5 Linear_Regressor_MSE_top_0.5 \\\n", - "0 0.652944 0.071319 \n", - "1 0.653714 0.066852 \n", - "2 0.648609 0.067493 \n", - "3 0.654609 0.067716 \n", - "4 0.654609 0.067716 \n", - "\n", - " Linear_Regressor_R2_top_0.5 num_features_selected_0.7 \\\n", - "0 0.552335 33 \n", - "1 0.580377 33 \n", - "2 0.576351 33 \n", - "3 0.574955 33 \n", - "4 0.574955 33 \n", - "\n", - " RF_Regressor_MSE_top_0.7 RF_Regressor_R2_top_0.7 \\\n", - "0 0.054722 0.656512 \n", - "1 0.056201 0.647229 \n", - "2 0.055092 0.654192 \n", - "3 0.054757 0.656293 \n", - "4 0.054757 0.656293 \n", - "\n", - " Linear_Regressor_MSE_top_0.7 Linear_Regressor_R2_top_0.7 \\\n", - "0 0.068056 0.572816 \n", - "1 0.067106 0.578780 \n", - "2 0.067219 0.578070 \n", - "3 0.066362 0.583450 \n", - "4 0.066362 0.583450 \n", - "\n", - " num_features_selected_0.9 RF_Regressor_MSE_top_0.9 \\\n", - "0 42 0.055254 \n", - "1 42 0.055829 \n", - "2 42 0.055283 \n", - "3 42 0.055137 \n", - "4 42 0.055137 \n", - "\n", - " RF_Regressor_R2_top_0.9 Linear_Regressor_MSE_top_0.9 \\\n", - "0 0.653174 0.067633 \n", - "1 0.649568 0.067379 \n", - "2 0.652991 0.067379 \n", - "3 0.653908 0.067558 \n", - "4 0.653908 0.067558 \n", - "\n", - " Linear_Regressor_R2_top_0.9 split_seed \n", - "0 0.575476 1 \n", - "1 0.577068 1 \n", - "2 0.577068 1 \n", - "3 0.575946 1 \n", - "4 0.575946 1 " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "#combined_df = combined_df[(combined_df['heritability'] == 0.8) & (combined_df['n_train'] == 750)]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# df = pd.DataFrame(combined_df_rf_plus)\n", - "# averages = df.groupby('Model').mean().reset_index()\n", - "# pd.DataFrame(averages)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([46])" + "[320 rows x 18 columns]" ] }, - "execution_count": 58, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "combined_df[\"num_features\"].unique()" + "combined_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Summarise the Ablation Data" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The training size is 683 and the test size is 337\n" - ] - } - ], - "source": [ - "train_size = combined_df[\"train_size\"].unique()[0]\n", - "test_size = combined_df[\"test_size\"].unique()[0]\n", - "print(f\"The training size is {train_size} and the test size is {test_size}\")" + "##### Plot AUROC/RBO Performance" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['LIME_RF', 'Local_MDI+_fit_on_all_RFPlus',\n", - " 'Local_MDI+_fit_on_all_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', 'Random',\n", - " 'TreeSHAP_RF'], dtype=object)" + "array([683])" ] }, - "execution_count": 60, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "combined_df[\"fi\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the Ablation Data Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "methods = ['LIME_RF', \n", - "# 'Local_MDI+_fit_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus',\n", - " # 'Random',\n", - " 'TreeSHAP_RF']\n", - "\n", - "num_features = combined_df['num_features_masked'].drop_duplicates().values[0]\n", - "metrics = {\"regression\": [\"MSE\", \"R2\"], \"classification\": [\"AUROC\", \"LogLoss\"]} #MSE\n", - "ablation_models = {\"regression\": [\"RF_Regressor\"],#, \"Linear_Regressor\"],\n", - " \"classification\": [\"RF_Classifier\", \"Logistic_Regression\"]}" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "color_map = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - "}\n", - "\n", - "# color_map = {\n", - "# 'LIME_RF': '#1f77b4', # bold blue\n", - "# 'Local_MDI+_fit_on_all_RFPlus': '#ff7f0e', # vibrant orange\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus': '#2ca02c', # bright green\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus': '#d62728', # bright red\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus': '#9467bd', # bold purple\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus': '#8c564b', # strong brown\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus': '#e377c2', # pink\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus': '#bcbd22', # lime green\n", - "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#17becf', # cyan\n", - "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#7f7f7f', # medium gray\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus': '#bc5a34', # burnt orange\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#000000', # black\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus': '#7fbc41', # moss green\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus': '#ff9896', # light coral\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus': '#aec7e8', # light blue\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus': '#9edae5', # light cyan\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus': '#b29189', # warm taupe\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus': '#c49c94', # peach\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus': '#dbdb8d', # soft yellow-green\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus': '#393b79', # dark blue\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus': '#637939', # dark olive green\n", - "# 'Local_MDI+_fit_on_oob_RFPlus': '#8c6d31', # earthy brown\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus': '#843c39', # dark brick red\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus': '#7b4173', # deep purple\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus': '#6b6ecf', # muted indigo\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus': '#5254a3', # steel blue\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus': '#8ca252', # olive\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus': '#bd9e39', # mustard yellow\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#d6616b', # muted pink\n", - "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#ce6dbd', # bright magenta\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus': '#de9ed6', # soft magenta\n", - "# 'Random': '#ad494a', # warm red\n", - "# 'TreeSHAP_RF': '#6baed6', # sky blue\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "if num_features > 20:\n", - " all_ratios = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "else:\n", - " all_ratios = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "num_features_selected = []\n", - "for r in all_ratios:\n", - " num_features_selected.append(combined_df[f\"num_features_selected_{r}\"].unique()[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Summary of results" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# results = {}\n", - "# for a_model in [\"RF_Regressor\"]:\n", - "# for metric in [\"MSE\"]:\n", - "# for m in methods:\n", - "# results[m] = []\n", - "# for m in methods:\n", - "# for k in all_ratios:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", - "\n", - "# filtered_sums = {\n", - "# key: sum(values[:5]) \n", - "# for key, values in results.items()\n", - "# }\n", - "# sorted(filtered_sums, key=filtered_sums.get)\n", - "\n", - "# import pickle\n", - "\n", - "# list_dict = {element: index + 1 for index, element in enumerate(sorted(filtered_sums, key=filtered_sums.get))}\n", - "\n", - "# with open(\"temperature_rank.pkl\", \"wb\") as file:\n", - "# pickle.dump(list_dict, file)\n", - "\n", - "# print(\"Dictionary saved as pickle file:\", list_dict)" + "combined_df[\"train_size\"].unique()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", - "\n", - " # excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", - " # filtered_sums = {\n", - " # key: sum(values[:5]) \n", - " # for key, values in results.items() if key not in excluded_keys\n", - " # }\n", - " # if metric == \"MSE\" or metric == \"LogLoss\":\n", - " # top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]\n", - " # else:\n", - " # top_3_keys =sorted(filtered_sums, key=filtered_sums.get, reverse=True)[:3]\n", - " # top_3_keys.extend(['LIME_RF', 'TreeSHAP_RF'])\n", - "\n", - " ax = axs[j]#, j]\n", - " for m in methods:#top_3_keys:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(\n", - " xlabel='Number of features selected',\n", - " ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}'\n", - " )\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./Ionosphere.png\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'LIME_RF': [0.5946887264464954,\n", - " 0.6302589415485504,\n", - " 0.5718717500808281,\n", - " 0.6186357071039745,\n", - " 0.6278066492913282,\n", - " 0.622186952873706,\n", - " 0.6241569295279662,\n", - " 0.6274335884484686,\n", - " 0.627492009166962],\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': [0.6121779642427637,\n", - " 0.6301353845294378,\n", - " 0.6486432934362347,\n", - " 0.6438457191404946,\n", - " 0.6406776436965793,\n", - " 0.6330689546234465,\n", - " 0.6324295722924849,\n", - " 0.6276463015207704,\n", - " 0.6265642527241945],\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': [0.6121779642427637,\n", - " 0.6300061937591899,\n", - " 0.6482813626398071,\n", - " 0.6441404694297874,\n", - " 0.6406839693372415,\n", - " 0.634527928971809,\n", - " 0.6327065258181216,\n", - " 0.6278504115268281,\n", - " 0.6282659921707162],\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': [0.6103185406161723,\n", - " 0.6312721204988823,\n", - " 0.6471158702702111,\n", - " 0.6512827262116713,\n", - " 0.6459821694674747,\n", - " 0.6387281907206704,\n", - " 0.6364020112407093,\n", - " 0.6313339086114025,\n", - " 0.6287347727058703],\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': [0.6103185406161723,\n", - " 0.6324691941383397,\n", - " 0.6474859425847125,\n", - " 0.6519436466077243,\n", - " 0.645953714697287,\n", - " 0.6396961786766258,\n", - " 0.6364869324666584,\n", - " 0.6320759548046145,\n", - " 0.6279736840582796],\n", - " 'TreeSHAP_RF': [0.5950663359815324,\n", - " 0.6302617408471165,\n", - " 0.6302016944510044,\n", - " 0.623038731721038,\n", - " 0.627766623206575,\n", - " 0.6280610005484434,\n", - " 0.6273085770879256,\n", - " 0.6270701633577266,\n", - " 0.6292354176970724]}" + "array([337])" ] }, - "execution_count": 66, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results" + "combined_df[\"test_size\"].unique()" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ - "# Filtered keys to exclude\n", - "excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", - "\n", - "# Compute the sum of the first five numbers for each key (excluding the specified keys)\n", - "filtered_sums = {\n", - " key: sum(values[:5]) \n", - " for key, values in results.items() if key not in excluded_keys\n", - "}\n", - "\n", - "# Sort the keys by their sum and extract the top 3 keys with the lowest sums\n", - "top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]" + "result_df = combined_df.groupby(['train_size', 'heritability', 'fi'])[[\"auroc_train\", \"auroc_test\"]].mean().reset_index()" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
train_sizeheritabilityfiauroc_trainauroc_test
06830.1LIME_RF0.6593420.656334
16830.1Local_MDI+_Alo_fit_on_all_RFPlus0.6802390.677669
26830.1Local_MDI+_Alo_fit_on_all_ranking_RFPlus0.7465270.746115
36830.1Local_MDI+_MDI_fit_on_all_ranking_RFPlus0.7466350.746115
46830.1Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus0.8586100.858519
56830.1Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus0.7445810.743816
66830.1Random0.4982940.500946
76830.1TreeSHAP_RF0.7545560.756916
86830.2LIME_RF0.6810020.677893
96830.2Local_MDI+_Alo_fit_on_all_RFPlus0.7006340.699259
106830.2Local_MDI+_Alo_fit_on_all_ranking_RFPlus0.7625700.762211
116830.2Local_MDI+_MDI_fit_on_all_ranking_RFPlus0.7627210.762211
126830.2Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus0.8635760.863857
136830.2Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus0.7515940.751532
146830.2Random0.4982940.500946
156830.2TreeSHAP_RF0.7800990.781517
166830.4LIME_RF0.6650640.661197
176830.4Local_MDI+_Alo_fit_on_all_RFPlus0.7387080.737663
186830.4Local_MDI+_Alo_fit_on_all_ranking_RFPlus0.7954550.795869
196830.4Local_MDI+_MDI_fit_on_all_ranking_RFPlus0.7955410.795869
206830.4Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus0.8688990.868650
216830.4Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus0.7757560.776800
226830.4Random0.4982940.500946
236830.4TreeSHAP_RF0.7973820.800403
246830.8LIME_RF0.6523440.647506
256830.8Local_MDI+_Alo_fit_on_all_RFPlus0.7797340.778307
266830.8Local_MDI+_Alo_fit_on_all_ranking_RFPlus0.8330710.834196
276830.8Local_MDI+_MDI_fit_on_all_ranking_RFPlus0.8332470.834196
286830.8Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus0.9005480.900792
296830.8Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus0.8011160.803070
306830.8Random0.4982940.500946
316830.8TreeSHAP_RF0.8220490.821715
\n", + "
" + ], "text/plain": [ - "['Local_MDI+_fit_on_all_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_3_keys" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" + " train_size heritability fi \\\n", + "0 683 0.1 LIME_RF \n", + "1 683 0.1 Local_MDI+_Alo_fit_on_all_RFPlus \n", + "2 683 0.1 Local_MDI+_Alo_fit_on_all_ranking_RFPlus \n", + "3 683 0.1 Local_MDI+_MDI_fit_on_all_ranking_RFPlus \n", + "4 683 0.1 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus \n", + "5 683 0.1 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus \n", + "6 683 0.1 Random \n", + "7 683 0.1 TreeSHAP_RF \n", + "8 683 0.2 LIME_RF \n", + "9 683 0.2 Local_MDI+_Alo_fit_on_all_RFPlus \n", + "10 683 0.2 Local_MDI+_Alo_fit_on_all_ranking_RFPlus \n", + "11 683 0.2 Local_MDI+_MDI_fit_on_all_ranking_RFPlus \n", + "12 683 0.2 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus \n", + "13 683 0.2 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus \n", + "14 683 0.2 Random \n", + "15 683 0.2 TreeSHAP_RF \n", + "16 683 0.4 LIME_RF \n", + "17 683 0.4 Local_MDI+_Alo_fit_on_all_RFPlus \n", + "18 683 0.4 Local_MDI+_Alo_fit_on_all_ranking_RFPlus \n", + "19 683 0.4 Local_MDI+_MDI_fit_on_all_ranking_RFPlus \n", + "20 683 0.4 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus \n", + "21 683 0.4 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus \n", + "22 683 0.4 Random \n", + "23 683 0.4 TreeSHAP_RF \n", + "24 683 0.8 LIME_RF \n", + "25 683 0.8 Local_MDI+_Alo_fit_on_all_RFPlus \n", + "26 683 0.8 Local_MDI+_Alo_fit_on_all_ranking_RFPlus \n", + "27 683 0.8 Local_MDI+_MDI_fit_on_all_ranking_RFPlus \n", + "28 683 0.8 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus \n", + "29 683 0.8 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus \n", + "30 683 0.8 Random \n", + "31 683 0.8 TreeSHAP_RF \n", + "\n", + " auroc_train auroc_test \n", + "0 0.659342 0.656334 \n", + "1 0.680239 0.677669 \n", + "2 0.746527 0.746115 \n", + "3 0.746635 0.746115 \n", + "4 0.858610 0.858519 \n", + "5 0.744581 0.743816 \n", + "6 0.498294 0.500946 \n", + "7 0.754556 0.756916 \n", + "8 0.681002 0.677893 \n", + "9 0.700634 0.699259 \n", + "10 0.762570 0.762211 \n", + "11 0.762721 0.762211 \n", + "12 0.863576 0.863857 \n", + "13 0.751594 0.751532 \n", + "14 0.498294 0.500946 \n", + "15 0.780099 0.781517 \n", + "16 0.665064 0.661197 \n", + "17 0.738708 0.737663 \n", + "18 0.795455 0.795869 \n", + "19 0.795541 0.795869 \n", + "20 0.868899 0.868650 \n", + "21 0.775756 0.776800 \n", + "22 0.498294 0.500946 \n", + "23 0.797382 0.800403 \n", + "24 0.652344 0.647506 \n", + "25 0.779734 0.778307 \n", + "26 0.833071 0.834196 \n", + "27 0.833247 0.834196 \n", + "28 0.900548 0.900792 \n", + "29 0.801116 0.803070 \n", + "30 0.498294 0.500946 \n", + "31 0.822049 0.821715 " ] }, + "execution_count": 67, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_top_{k}\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", - "plt.show()" + "result_df" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "methods = [\n", + " #'Random',\n", + " 'LIME_RF', \n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus',\n", + " 'TreeSHAP_RF',\n", + " 'Local_MDI+_Alo_fit_on_all_RFPlus'\n", + " ]\n", + "color_map = {\n", + " 'Random': 'gray', # Assign a default neutral color for Random\n", + " 'LIME_RF': '#71BEB7',\n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus': '#FF5733', # Example bright color\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus': '#33FF57', # Example greenish color\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus': '#3357FF', # Example blueish color\n", + " 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus': '#FF33A1', # Example pinkish color\n", + " 'TreeSHAP_RF': 'orange',\n", + " 'Local_MDI+_Alo_fit_on_all_RFPlus': '#8E44AD' # Example purple color\n", + "}\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# method_names = {'TreeSHAP_RF': 'SHAP', 'Local_MDI+_fit_on_all_ranking_RFPlus': \"LMDI+\", 'LIME_RF': 'LIME'}" + ] }, { "cell_type": "code", @@ -1231,1040 +874,116 @@ "metadata": {}, "outputs": [ { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[70], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39mfor\u001b[39;00m i, a_model \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(ablation_models[task]):\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m j, metric \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(metrics[task]):\n\u001b[1;32m 4\u001b[0m \u001b[39m# Initialize a new figure for each plot\u001b[39;00m\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "assert False\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " # Initialize a new figure for each plot\n", - " fig, ax = plt.subplots(figsize=(18, 8))\n", - " \n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " \n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " \n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features masked', ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " \n", - " # Add legend only once for each figure\n", - " if j == 0:\n", - " ax.legend()\n", - " \n", - " plt.tight_layout()\n", - " # Optionally save each plot as a separate file\n", - " # plt.savefig(f\"./{task_name}_{task}_model_{a_model}_metric_{metric}.png\")\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", + "# Set global parameters for plots\n", + "plt.rcParams['axes.labelsize'] = 28\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training Subset Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Define marker size\n", + "marker_size = 7\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Create a single subplot for AUROC Test\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Define the DataFrame\n", + "df = result_df\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", + "# List of methods with dotted line style\n", + "dotted_methods = ['LIME_RF', 'TreeSHAP_RF']\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test subset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Plot AUROC Test\n", + "for method in methods:\n", + " subset = df[df['fi'] == method]\n", + " ax.plot(\n", + " subset['heritability'], subset['auroc_train'],\n", + " label=method, linestyle=\"solid\", color=color_map[method], marker='o', markersize=marker_size\n", + " )\n", "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", + "# Set labels, title, and legend\n", + "ax.set_title(data+\" \"+dgp, fontsize=16)\n", + "ax.set_xlabel('PVE', fontsize=14)\n", + "ax.set_ylabel('AUROC', fontsize=14)\n", + "ax.legend(fontsize=10, title_fontsize=12, loc='best')\n", "\n", + "# Adjust layout and show plot\n", "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", + "# plt.savefig('auroc_lss.png')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# # results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - "# for k in range(num_features+1):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# #plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", + "# Set global parameters for plots\n", + "plt.rcParams['axes.labelsize'] = 28\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_train_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Subset Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# Define marker size\n", + "marker_size = 7\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# Create a single subplot for AUROC Test\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test_subset:\n", - "# results[m] = []\n", - "# for m in methods_test_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", + "# Define the DataFrame\n", + "df = result_df\n", "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_subset_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# List of methods with dotted line style\n", + "dotted_methods = ['LIME_RF', 'TreeSHAP_RF']\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# Plot AUROC Test\n", + "for method in methods:\n", + " subset = df[df['fi'] == method]\n", + " ax.plot(\n", + " subset['heritability'], subset['auroc_test'],\n", + " label=method, linestyle=\"solid\", color=color_map[method], marker='o', markersize=marker_size\n", + " )\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", + "# Set labels, title, and legend\n", + "ax.set_title(data+\" \"+dgp, fontsize=16)\n", + "ax.set_xlabel('PVE', fontsize=14)\n", + "ax.set_ylabel('AUROC', fontsize=14)\n", + "ax.legend(fontsize=10, title_fontsize=12, loc='best')\n", "\n", + "# Adjust layout and show plot\n", "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test:\n", - "# results[m] = []\n", - "# for m in methods_test:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_addition.png\")\n", - "# plt.show()" + "# plt.savefig('auroc_lss.png')\n", + "plt.show()\n" ] } ], diff --git a/feature_importance/ablation_results_visulization_retrain.ipynb b/feature_importance/ablation_results_visulization_retrain.ipynb deleted file mode 100644 index 5f9231e..0000000 --- a/feature_importance/ablation_results_visulization_retrain.ipynb +++ /dev/null @@ -1,2293 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import os\n", - "import pickle\n", - "import seaborn as sns\n", - "pd.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# load pickled data\n", - "with open('CCLE_rank.pkl', 'rb') as f:\n", - " ccle_rank = pickle.load(f)\n", - "with open('parkinsons_rank.pkl', 'rb') as f:\n", - " parkinsons_rank = pickle.load(f)\n", - "with open('performance_rank.pkl', 'rb') as f:\n", - " performance_rank = pickle.load(f)\n", - "with open('temperature_rank.pkl', 'rb') as f:\n", - " temperature_rank = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# dictionaries = [ccle_rank, parkinsons_rank, performance_rank, temperature_rank]\n", - "\n", - "# average_dict = {key: sum(d[key] for d in dictionaries) / len(dictionaries) for key in ccle_rank.keys()}\n", - "\n", - "# sorted_keys = sorted(average_dict, key=average_dict.get)\n", - "\n", - "# # Display sorted keys and their corresponding values\n", - "# sorted_average_dict = {key: average_dict[key] for key in sorted_keys}\n", - "\n", - "# for k,v in sorted_average_dict.items():\n", - "# print(k, v)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "task = \"regression\" #\"classification\" #\"regression\"\n", - "ablation_directory =\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_retrain/varying_sample_row_n\"\n", - "#####Regression\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_CCLE_PD_0325901_retrain/CCLE_PD_0325901_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_parkinsons_retrain/parkinsons_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_performance_retrain/performance_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_retrain/varying_sample_row_n\"\n", - "\n", - "#####Classification\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_juvenile_retrain/juvenile_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_csi_pecarn_retrain/csi_pecarn_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_credit_g_retrain/credit_g_retrain/varying_sample_row_n\"\n", - "\n", - "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_Ionosphere_retrain/Ionosphere_retrain/varying_sample_row_n\"\n", - "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "rf_seeds = [1,2,3,4,5]\n", - "for split_seed in split_seeds:\n", - " for rf_seed in rf_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}rf_seed_{rf_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", - "\n", - "\n", - "# rf_plus_directory = f'/scratch/users/zhongyuan_liang/saved_models/{task_name}'\n", - "# combined_df_rf_plus = pd.DataFrame()\n", - "# for file in os.listdir(rf_plus_directory):\n", - "# if file.endswith(\".csv\"):\n", - "# df = pd.read_csv(os.path.join(rf_plus_directory, file))\n", - "# combined_df_rf_plus = pd.concat([combined_df_rf_plus, df], ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sample_row_nsample_row_n_namerepn_estimatorsmin_samples_leafmax_featuresrandom_statemodelfitrain_sizetest_sizenum_featuresdata_split_seedrf_seednum_features_maskedfi_time_absolutenum_features_selected_0.01RF_Regressor_MSE_top_0.01RF_Regressor_R2_top_0.01Linear_Regressor_MSE_top_0.01Linear_Regressor_R2_top_0.01num_features_selected_0.05RF_Regressor_MSE_top_0.05RF_Regressor_R2_top_0.05Linear_Regressor_MSE_top_0.05Linear_Regressor_R2_top_0.05num_features_selected_0.1RF_Regressor_MSE_top_0.1RF_Regressor_R2_top_0.1Linear_Regressor_MSE_top_0.1Linear_Regressor_R2_top_0.1num_features_selected_0.15RF_Regressor_MSE_top_0.15RF_Regressor_R2_top_0.15Linear_Regressor_MSE_top_0.15Linear_Regressor_R2_top_0.15num_features_selected_0.25RF_Regressor_MSE_top_0.25RF_Regressor_R2_top_0.25Linear_Regressor_MSE_top_0.25Linear_Regressor_R2_top_0.25num_features_selected_0.4RF_Regressor_MSE_top_0.4RF_Regressor_R2_top_0.4Linear_Regressor_MSE_top_0.4Linear_Regressor_R2_top_0.4num_features_selected_0.5RF_Regressor_MSE_top_0.5RF_Regressor_R2_top_0.5Linear_Regressor_MSE_top_0.5Linear_Regressor_R2_top_0.5num_features_selected_0.7RF_Regressor_MSE_top_0.7RF_Regressor_R2_top_0.7Linear_Regressor_MSE_top_0.7Linear_Regressor_R2_top_0.7num_features_selected_0.9RF_Regressor_MSE_top_0.9RF_Regressor_R2_top_0.9Linear_Regressor_MSE_top_0.9Linear_Regressor_R2_top_0.9split_seed
0NaNkeep_all_rows010050.3342RFLIME_RF683337461146104.60194710.0659780.5858590.0807560.49310430.0577730.6373650.0717520.54961850.0766120.5191110.0730490.54147670.0591470.6287400.0733120.539826120.0579590.6361970.0736170.537912190.0578590.6368230.0715910.550629230.0552910.6529440.0713190.552335330.0547220.6565120.0680560.572816420.0552540.6531740.0676330.5754761
1NaNkeep_all_rows010050.3342RFLocal_MDI+_fit_on_all_RFPlus6833374611466.05174010.0580140.6358490.0721830.54691030.0572860.6404200.0717520.54961850.0559860.6485790.0704520.55777770.0574120.6396310.0694630.563983120.0538690.6618700.0675660.575892190.0541500.6601040.0666020.581942230.0551680.6537140.0668520.580377330.0562010.6472290.0671060.578780420.0558290.6495680.0673790.5770681
2NaNkeep_all_rows010050.3342RFLocal_MDI+_fit_on_all_average_RFPlus6833374611466.44346610.0580140.6358490.0721830.54691030.0572860.6404200.0717520.54961850.0554860.6517170.0708810.55508370.0572700.6405200.0694630.563983120.0541450.6601370.0675660.575892190.0548530.6556890.0672530.577856230.0559810.6486090.0674930.576351330.0550920.6541920.0672190.578070420.0552830.6529910.0673790.5770681
3NaNkeep_all_rows010050.3342RFLocal_MDI+_fit_on_all_error_metric_RFPlus6833374611466.66914010.0650160.5919020.0810050.49153930.0583080.6340040.0717520.54961850.0569100.6427780.0714030.55181170.0570270.6420490.0709100.554904120.0559560.6487660.0679250.573638190.0561320.6476630.0672110.578121230.0550260.6546090.0677160.574955330.0547570.6562930.0663620.583450420.0551370.6539080.0675580.5759461
4NaNkeep_all_rows010050.3342RFLocal_MDI+_fit_on_all_error_metric_average_RFPlus6833374611467.25946910.0650160.5919020.0810050.49153930.0583080.6340040.0717520.54961850.0569100.6427780.0714030.55181170.0570270.6420490.0709100.554904120.0559560.6487660.0679250.573638190.0561320.6476630.0672110.578121230.0550260.6546090.0677160.574955330.0547570.6562930.0663620.583450420.0551370.6539080.0675580.5759461
\n", - "
" - ], - "text/plain": [ - " sample_row_n sample_row_n_name rep n_estimators min_samples_leaf \\\n", - "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", - "\n", - " max_features random_state model \\\n", - "0 0.33 42 RF \n", - "1 0.33 42 RF \n", - "2 0.33 42 RF \n", - "3 0.33 42 RF \n", - "4 0.33 42 RF \n", - "\n", - " fi train_size test_size \\\n", - "0 LIME_RF 683 337 \n", - "1 Local_MDI+_fit_on_all_RFPlus 683 337 \n", - "2 Local_MDI+_fit_on_all_average_RFPlus 683 337 \n", - "3 Local_MDI+_fit_on_all_error_metric_RFPlus 683 337 \n", - "4 Local_MDI+_fit_on_all_error_metric_average_RFPlus 683 337 \n", - "\n", - " num_features data_split_seed rf_seed num_features_masked \\\n", - "0 46 1 1 46 \n", - "1 46 1 1 46 \n", - "2 46 1 1 46 \n", - "3 46 1 1 46 \n", - "4 46 1 1 46 \n", - "\n", - " fi_time_absolute num_features_selected_0.01 RF_Regressor_MSE_top_0.01 \\\n", - "0 104.601947 1 0.065978 \n", - "1 6.051740 1 0.058014 \n", - "2 6.443466 1 0.058014 \n", - "3 6.669140 1 0.065016 \n", - "4 7.259469 1 0.065016 \n", - "\n", - " RF_Regressor_R2_top_0.01 Linear_Regressor_MSE_top_0.01 \\\n", - "0 0.585859 0.080756 \n", - "1 0.635849 0.072183 \n", - "2 0.635849 0.072183 \n", - "3 0.591902 0.081005 \n", - "4 0.591902 0.081005 \n", - "\n", - " Linear_Regressor_R2_top_0.01 num_features_selected_0.05 \\\n", - "0 0.493104 3 \n", - "1 0.546910 3 \n", - "2 0.546910 3 \n", - "3 0.491539 3 \n", - "4 0.491539 3 \n", - "\n", - " RF_Regressor_MSE_top_0.05 RF_Regressor_R2_top_0.05 \\\n", - "0 0.057773 0.637365 \n", - "1 0.057286 0.640420 \n", - "2 0.057286 0.640420 \n", - "3 0.058308 0.634004 \n", - "4 0.058308 0.634004 \n", - "\n", - " Linear_Regressor_MSE_top_0.05 Linear_Regressor_R2_top_0.05 \\\n", - "0 0.071752 0.549618 \n", - "1 0.071752 0.549618 \n", - "2 0.071752 0.549618 \n", - "3 0.071752 0.549618 \n", - "4 0.071752 0.549618 \n", - "\n", - " num_features_selected_0.1 RF_Regressor_MSE_top_0.1 \\\n", - "0 5 0.076612 \n", - "1 5 0.055986 \n", - "2 5 0.055486 \n", - "3 5 0.056910 \n", - "4 5 0.056910 \n", - "\n", - " RF_Regressor_R2_top_0.1 Linear_Regressor_MSE_top_0.1 \\\n", - "0 0.519111 0.073049 \n", - "1 0.648579 0.070452 \n", - "2 0.651717 0.070881 \n", - "3 0.642778 0.071403 \n", - "4 0.642778 0.071403 \n", - "\n", - " Linear_Regressor_R2_top_0.1 num_features_selected_0.15 \\\n", - "0 0.541476 7 \n", - "1 0.557777 7 \n", - "2 0.555083 7 \n", - "3 0.551811 7 \n", - "4 0.551811 7 \n", - "\n", - " RF_Regressor_MSE_top_0.15 RF_Regressor_R2_top_0.15 \\\n", - "0 0.059147 0.628740 \n", - "1 0.057412 0.639631 \n", - "2 0.057270 0.640520 \n", - "3 0.057027 0.642049 \n", - "4 0.057027 0.642049 \n", - "\n", - " Linear_Regressor_MSE_top_0.15 Linear_Regressor_R2_top_0.15 \\\n", - "0 0.073312 0.539826 \n", - "1 0.069463 0.563983 \n", - "2 0.069463 0.563983 \n", - "3 0.070910 0.554904 \n", - "4 0.070910 0.554904 \n", - "\n", - " num_features_selected_0.25 RF_Regressor_MSE_top_0.25 \\\n", - "0 12 0.057959 \n", - "1 12 0.053869 \n", - "2 12 0.054145 \n", - "3 12 0.055956 \n", - "4 12 0.055956 \n", - "\n", - " RF_Regressor_R2_top_0.25 Linear_Regressor_MSE_top_0.25 \\\n", - "0 0.636197 0.073617 \n", - "1 0.661870 0.067566 \n", - "2 0.660137 0.067566 \n", - "3 0.648766 0.067925 \n", - "4 0.648766 0.067925 \n", - "\n", - " Linear_Regressor_R2_top_0.25 num_features_selected_0.4 \\\n", - "0 0.537912 19 \n", - "1 0.575892 19 \n", - "2 0.575892 19 \n", - "3 0.573638 19 \n", - "4 0.573638 19 \n", - "\n", - " RF_Regressor_MSE_top_0.4 RF_Regressor_R2_top_0.4 \\\n", - "0 0.057859 0.636823 \n", - "1 0.054150 0.660104 \n", - "2 0.054853 0.655689 \n", - "3 0.056132 0.647663 \n", - "4 0.056132 0.647663 \n", - "\n", - " Linear_Regressor_MSE_top_0.4 Linear_Regressor_R2_top_0.4 \\\n", - "0 0.071591 0.550629 \n", - "1 0.066602 0.581942 \n", - "2 0.067253 0.577856 \n", - "3 0.067211 0.578121 \n", - "4 0.067211 0.578121 \n", - "\n", - " num_features_selected_0.5 RF_Regressor_MSE_top_0.5 \\\n", - "0 23 0.055291 \n", - "1 23 0.055168 \n", - "2 23 0.055981 \n", - "3 23 0.055026 \n", - "4 23 0.055026 \n", - "\n", - " RF_Regressor_R2_top_0.5 Linear_Regressor_MSE_top_0.5 \\\n", - "0 0.652944 0.071319 \n", - "1 0.653714 0.066852 \n", - "2 0.648609 0.067493 \n", - "3 0.654609 0.067716 \n", - "4 0.654609 0.067716 \n", - "\n", - " Linear_Regressor_R2_top_0.5 num_features_selected_0.7 \\\n", - "0 0.552335 33 \n", - "1 0.580377 33 \n", - "2 0.576351 33 \n", - "3 0.574955 33 \n", - "4 0.574955 33 \n", - "\n", - " RF_Regressor_MSE_top_0.7 RF_Regressor_R2_top_0.7 \\\n", - "0 0.054722 0.656512 \n", - "1 0.056201 0.647229 \n", - "2 0.055092 0.654192 \n", - "3 0.054757 0.656293 \n", - "4 0.054757 0.656293 \n", - "\n", - " Linear_Regressor_MSE_top_0.7 Linear_Regressor_R2_top_0.7 \\\n", - "0 0.068056 0.572816 \n", - "1 0.067106 0.578780 \n", - "2 0.067219 0.578070 \n", - "3 0.066362 0.583450 \n", - "4 0.066362 0.583450 \n", - "\n", - " num_features_selected_0.9 RF_Regressor_MSE_top_0.9 \\\n", - "0 42 0.055254 \n", - "1 42 0.055829 \n", - "2 42 0.055283 \n", - "3 42 0.055137 \n", - "4 42 0.055137 \n", - "\n", - " RF_Regressor_R2_top_0.9 Linear_Regressor_MSE_top_0.9 \\\n", - "0 0.653174 0.067633 \n", - "1 0.649568 0.067379 \n", - "2 0.652991 0.067379 \n", - "3 0.653908 0.067558 \n", - "4 0.653908 0.067558 \n", - "\n", - " Linear_Regressor_R2_top_0.9 split_seed \n", - "0 0.575476 1 \n", - "1 0.577068 1 \n", - "2 0.577068 1 \n", - "3 0.575946 1 \n", - "4 0.575946 1 " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "#combined_df = combined_df[(combined_df['heritability'] == 0.8) & (combined_df['n_train'] == 750)]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# df = pd.DataFrame(combined_df_rf_plus)\n", - "# averages = df.groupby('Model').mean().reset_index()\n", - "# pd.DataFrame(averages)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([46])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df[\"num_features\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summarise the Ablation Data" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The training size is 683 and the test size is 337\n" - ] - } - ], - "source": [ - "train_size = combined_df[\"train_size\"].unique()[0]\n", - "test_size = combined_df[\"test_size\"].unique()[0]\n", - "print(f\"The training size is {train_size} and the test size is {test_size}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['LIME_RF', 'Local_MDI+_fit_on_all_RFPlus',\n", - " 'Local_MDI+_fit_on_all_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', 'Random',\n", - " 'TreeSHAP_RF'], dtype=object)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df[\"fi\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the Ablation Data Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "methods = ['LIME_RF', \n", - "# 'Local_MDI+_fit_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus',\n", - " # 'Random',\n", - " 'TreeSHAP_RF']\n", - "\n", - "num_features = combined_df['num_features_masked'].drop_duplicates().values[0]\n", - "metrics = {\"regression\": [\"MSE\", \"R2\"], \"classification\": [\"AUROC\", \"LogLoss\"]} #MSE\n", - "ablation_models = {\"regression\": [\"RF_Regressor\"],#, \"Linear_Regressor\"],\n", - " \"classification\": [\"RF_Classifier\", \"Logistic_Regression\"]}" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "color_map = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - "}\n", - "\n", - "# color_map = {\n", - "# 'LIME_RF': '#1f77b4', # bold blue\n", - "# 'Local_MDI+_fit_on_all_RFPlus': '#ff7f0e', # vibrant orange\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus': '#2ca02c', # bright green\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus': '#d62728', # bright red\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus': '#9467bd', # bold purple\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus': '#8c564b', # strong brown\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus': '#e377c2', # pink\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus': '#bcbd22', # lime green\n", - "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#17becf', # cyan\n", - "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#7f7f7f', # medium gray\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus': '#bc5a34', # burnt orange\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#000000', # black\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus': '#7fbc41', # moss green\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus': '#ff9896', # light coral\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus': '#aec7e8', # light blue\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus': '#9edae5', # light cyan\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus': '#b29189', # warm taupe\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus': '#c49c94', # peach\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus': '#dbdb8d', # soft yellow-green\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus': '#393b79', # dark blue\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus': '#637939', # dark olive green\n", - "# 'Local_MDI+_fit_on_oob_RFPlus': '#8c6d31', # earthy brown\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus': '#843c39', # dark brick red\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus': '#7b4173', # deep purple\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus': '#6b6ecf', # muted indigo\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus': '#5254a3', # steel blue\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus': '#8ca252', # olive\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus': '#bd9e39', # mustard yellow\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#d6616b', # muted pink\n", - "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#ce6dbd', # bright magenta\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus': '#de9ed6', # soft magenta\n", - "# 'Random': '#ad494a', # warm red\n", - "# 'TreeSHAP_RF': '#6baed6', # sky blue\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "if num_features > 20:\n", - " all_ratios = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "else:\n", - " all_ratios = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "num_features_selected = []\n", - "for r in all_ratios:\n", - " num_features_selected.append(combined_df[f\"num_features_selected_{r}\"].unique()[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Summary of results" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# results = {}\n", - "# for a_model in [\"RF_Regressor\"]:\n", - "# for metric in [\"MSE\"]:\n", - "# for m in methods:\n", - "# results[m] = []\n", - "# for m in methods:\n", - "# for k in all_ratios:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", - "\n", - "# filtered_sums = {\n", - "# key: sum(values[:5]) \n", - "# for key, values in results.items()\n", - "# }\n", - "# sorted(filtered_sums, key=filtered_sums.get)\n", - "\n", - "# import pickle\n", - "\n", - "# list_dict = {element: index + 1 for index, element in enumerate(sorted(filtered_sums, key=filtered_sums.get))}\n", - "\n", - "# with open(\"temperature_rank.pkl\", \"wb\") as file:\n", - "# pickle.dump(list_dict, file)\n", - "\n", - "# print(\"Dictionary saved as pickle file:\", list_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", - "\n", - " # excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", - " # filtered_sums = {\n", - " # key: sum(values[:5]) \n", - " # for key, values in results.items() if key not in excluded_keys\n", - " # }\n", - " # if metric == \"MSE\" or metric == \"LogLoss\":\n", - " # top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]\n", - " # else:\n", - " # top_3_keys =sorted(filtered_sums, key=filtered_sums.get, reverse=True)[:3]\n", - " # top_3_keys.extend(['LIME_RF', 'TreeSHAP_RF'])\n", - "\n", - " ax = axs[j]#, j]\n", - " for m in methods:#top_3_keys:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(\n", - " xlabel='Number of features selected',\n", - " ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}'\n", - " )\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./Ionosphere.png\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'LIME_RF': [0.5946887264464954,\n", - " 0.6302589415485504,\n", - " 0.5718717500808281,\n", - " 0.6186357071039745,\n", - " 0.6278066492913282,\n", - " 0.622186952873706,\n", - " 0.6241569295279662,\n", - " 0.6274335884484686,\n", - " 0.627492009166962],\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': [0.6121779642427637,\n", - " 0.6301353845294378,\n", - " 0.6486432934362347,\n", - " 0.6438457191404946,\n", - " 0.6406776436965793,\n", - " 0.6330689546234465,\n", - " 0.6324295722924849,\n", - " 0.6276463015207704,\n", - " 0.6265642527241945],\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': [0.6121779642427637,\n", - " 0.6300061937591899,\n", - " 0.6482813626398071,\n", - " 0.6441404694297874,\n", - " 0.6406839693372415,\n", - " 0.634527928971809,\n", - " 0.6327065258181216,\n", - " 0.6278504115268281,\n", - " 0.6282659921707162],\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': [0.6103185406161723,\n", - " 0.6312721204988823,\n", - " 0.6471158702702111,\n", - " 0.6512827262116713,\n", - " 0.6459821694674747,\n", - " 0.6387281907206704,\n", - " 0.6364020112407093,\n", - " 0.6313339086114025,\n", - " 0.6287347727058703],\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': [0.6103185406161723,\n", - " 0.6324691941383397,\n", - " 0.6474859425847125,\n", - " 0.6519436466077243,\n", - " 0.645953714697287,\n", - " 0.6396961786766258,\n", - " 0.6364869324666584,\n", - " 0.6320759548046145,\n", - " 0.6279736840582796],\n", - " 'TreeSHAP_RF': [0.5950663359815324,\n", - " 0.6302617408471165,\n", - " 0.6302016944510044,\n", - " 0.623038731721038,\n", - " 0.627766623206575,\n", - " 0.6280610005484434,\n", - " 0.6273085770879256,\n", - " 0.6270701633577266,\n", - " 0.6292354176970724]}" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Filtered keys to exclude\n", - "excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", - "\n", - "# Compute the sum of the first five numbers for each key (excluding the specified keys)\n", - "filtered_sums = {\n", - " key: sum(values[:5]) \n", - " for key, values in results.items() if key not in excluded_keys\n", - "}\n", - "\n", - "# Sort the keys by their sum and extract the top 3 keys with the lowest sums\n", - "top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Local_MDI+_fit_on_all_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_3_keys" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_top_{k}\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[70], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39mfor\u001b[39;00m i, a_model \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(ablation_models[task]):\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m j, metric \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(metrics[task]):\n\u001b[1;32m 4\u001b[0m \u001b[39m# Initialize a new figure for each plot\u001b[39;00m\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "assert False\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " # Initialize a new figure for each plot\n", - " fig, ax = plt.subplots(figsize=(18, 8))\n", - " \n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " \n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " \n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features masked', ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " \n", - " # Add legend only once for each figure\n", - " if j == 0:\n", - " ax.legend()\n", - " \n", - " plt.tight_layout()\n", - " # Optionally save each plot as a separate file\n", - " # plt.savefig(f\"./{task_name}_{task}_model_{a_model}_metric_{metric}.png\")\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training Subset Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test subset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# # results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - "# for k in range(num_features+1):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# #plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_train_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Subset Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test_subset:\n", - "# results[m] = []\n", - "# for m in methods_test_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_subset_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test:\n", - "# results[m] = []\n", - "# for m in methods_test:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_addition.png\")\n", - "# plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/feature_importance/ablation_results_visulization_selection.ipynb b/feature_importance/ablation_results_visulization_selection.ipynb new file mode 100644 index 0000000..e828248 --- /dev/null +++ b/feature_importance/ablation_results_visulization_selection.ipynb @@ -0,0 +1,1238 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "import pickle\n", + "import seaborn as sns\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# # load pickled data\n", + "# with open('CCLE_rank.pkl', 'rb') as f:\n", + "# ccle_rank = pickle.load(f)\n", + "# with open('parkinsons_rank.pkl', 'rb') as f:\n", + "# parkinsons_rank = pickle.load(f)\n", + "# with open('performance_rank.pkl', 'rb') as f:\n", + "# performance_rank = pickle.load(f)\n", + "# with open('temperature_rank.pkl', 'rb') as f:\n", + "# temperature_rank = pickle.load(f)\n", + "\n", + "# dictionaries = [ccle_rank, parkinsons_rank, performance_rank, temperature_rank]\n", + "\n", + "# average_dict = {key: sum(d[key] for d in dictionaries) / len(dictionaries) for key in ccle_rank.keys()}\n", + "\n", + "# sorted_keys = sorted(average_dict, key=average_dict.get)\n", + "\n", + "# # Display sorted keys and their corresponding values\n", + "# sorted_average_dict = {key: average_dict[key] for key in sorted_keys}\n", + "\n", + "# for k,v in sorted_average_dict.items():\n", + "# print(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "task = \"regression\" #\"classification\" #\"regression\"\n", + "data = \"temperature\"\n", + "ablation_directory =f\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_{data}/{data}_selection/varying_sample_row_n\"\n", + "#####Regression\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_CCLE_PD_0325901_retrain/CCLE_PD_0325901_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_parkinsons_retrain/parkinsons_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_performance_retrain/performance_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_retrain/varying_sample_row_n\"\n", + "\n", + "#####Classification\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_juvenile_retrain/juvenile_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_csi_pecarn_retrain/csi_pecarn_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_credit_g_retrain/credit_g_retrain/varying_sample_row_n\"\n", + "\n", + "#\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_classification_Ionosphere_retrain/Ionosphere_retrain/varying_sample_row_n\"\n", + "combined_df = pd.DataFrame()\n", + "split_seeds = [1,2,3]\n", + "rf_seeds = [1,2,3]\n", + "for split_seed in split_seeds:\n", + " for rf_seed in rf_seeds:\n", + " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}rf_seed_{rf_seed}/results.csv\"))\n", + " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "\n", + "\n", + "# rf_plus_directory = f'/scratch/users/zhongyuan_liang/saved_models/{task_name}'\n", + "# combined_df_rf_plus = pd.DataFrame()\n", + "# for file in os.listdir(rf_plus_directory):\n", + "# if file.endswith(\".csv\"):\n", + "# df = pd.read_csv(os.path.join(rf_plus_directory, file))\n", + "# combined_df_rf_plus = pd.concat([combined_df_rf_plus, df], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sample_row_nsample_row_n_namerepn_estimatorsmin_samples_leafmax_featuresrandom_statemodelfitrain_sizetest_sizenum_featuresdata_split_seedrf_seednum_features_maskednum_features_selected_0.01RF_Regressor_MSE_top_0.01RF_Regressor_R2_top_0.01xgboost_Regressor_MSE_top_0.01xgboost_Regressor_R2_top_0.01Linear_Regressor_MSE_top_0.01Linear_Regressor_R2_top_0.01num_features_selected_0.05RF_Regressor_MSE_top_0.05RF_Regressor_R2_top_0.05xgboost_Regressor_MSE_top_0.05xgboost_Regressor_R2_top_0.05Linear_Regressor_MSE_top_0.05Linear_Regressor_R2_top_0.05num_features_selected_0.1RF_Regressor_MSE_top_0.1RF_Regressor_R2_top_0.1xgboost_Regressor_MSE_top_0.1xgboost_Regressor_R2_top_0.1Linear_Regressor_MSE_top_0.1Linear_Regressor_R2_top_0.1num_features_selected_0.15RF_Regressor_MSE_top_0.15RF_Regressor_R2_top_0.15xgboost_Regressor_MSE_top_0.15xgboost_Regressor_R2_top_0.15Linear_Regressor_MSE_top_0.15Linear_Regressor_R2_top_0.15num_features_selected_0.25RF_Regressor_MSE_top_0.25RF_Regressor_R2_top_0.25xgboost_Regressor_MSE_top_0.25xgboost_Regressor_R2_top_0.25Linear_Regressor_MSE_top_0.25Linear_Regressor_R2_top_0.25num_features_selected_0.4RF_Regressor_MSE_top_0.4RF_Regressor_R2_top_0.4xgboost_Regressor_MSE_top_0.4xgboost_Regressor_R2_top_0.4Linear_Regressor_MSE_top_0.4Linear_Regressor_R2_top_0.4num_features_selected_0.5RF_Regressor_MSE_top_0.5RF_Regressor_R2_top_0.5xgboost_Regressor_MSE_top_0.5xgboost_Regressor_R2_top_0.5Linear_Regressor_MSE_top_0.5Linear_Regressor_R2_top_0.5num_features_selected_0.7RF_Regressor_MSE_top_0.7RF_Regressor_R2_top_0.7xgboost_Regressor_MSE_top_0.7xgboost_Regressor_R2_top_0.7Linear_Regressor_MSE_top_0.7Linear_Regressor_R2_top_0.7num_features_selected_0.9RF_Regressor_MSE_top_0.9RF_Regressor_R2_top_0.9xgboost_Regressor_MSE_top_0.9xgboost_Regressor_R2_top_0.9Linear_Regressor_MSE_top_0.9Linear_Regressor_R2_top_0.9split_seed
0NaNkeep_all_rows010050.3342RFLIME_RF68333746114610.0659780.5858590.0724390.5453070.0807560.49310430.0577730.6373650.0769400.5170530.0717520.54961850.0766120.5191110.0765410.5195560.0730490.54147670.0591470.6287400.0785140.5071740.0733120.539826120.0569430.6425730.0745090.5323120.0735910.538075190.0578820.6366800.0656960.5876320.0716190.550452230.0553390.6526420.0704480.5578070.0711550.553366330.0557270.6502080.0712860.5525430.0680610.572788420.0557350.6501560.0719380.5484520.0676330.5754761
1NaNkeep_all_rows010050.3342RFLocal_MDI+_Alo_fit_on_all_ranking_RFPlus68333746114610.0580140.6358490.0724300.5453600.0721830.54691030.0572860.6404200.0773140.5147050.0717520.54961850.0570120.6421420.0731560.5408040.0704520.55777770.0546210.6571460.0703410.5584750.0684960.570054120.0553000.6528840.0691820.5657530.0679060.573758190.0553410.6526320.0663740.5833780.0665920.582008230.0549100.6553370.0664110.5831430.0670150.579353330.0545260.6577460.0713990.5518370.0663620.583450420.0544630.6581420.0710950.5537440.0672340.5779751
2NaNkeep_all_rows010050.3342RFLocal_MDI+_MDI_fit_on_all_ranking_RFPlus68333746114610.0580140.6358490.0724300.5453600.0721830.54691030.0572860.6404200.0773140.5147050.0717520.54961850.0570120.6421420.0731560.5408040.0704520.55777770.0546210.6571460.0703410.5584750.0684960.570054120.0553000.6528840.0691820.5657530.0679060.573758190.0553410.6526320.0663740.5833780.0665920.582008230.0549100.6553370.0664110.5831430.0670150.579353330.0545260.6577460.0713990.5518370.0663620.583450420.0544630.6581420.0710950.5537440.0672340.5779751
3NaNkeep_all_rows010050.3342RFLocal_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus68333746114610.0580140.6358490.0724300.5453600.0721830.54691030.0584700.6329860.0769020.5172910.0708000.55559750.0568300.6432860.0716470.5502790.0706370.55661570.0572590.6405910.0700920.5600400.0708640.555195120.0559770.6486350.0729160.5423090.0698180.561760190.0555210.6514960.0710540.5540030.0676920.575100230.0545610.6575230.0714390.5515820.0673500.577250330.0558540.6494090.0720780.5475700.0664200.583087420.0551440.6538690.0710860.5537980.0672340.5779751
4NaNkeep_all_rows010050.3342RFLocal_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus68333746114610.0659780.5858590.0724390.5453070.0807560.49310430.0578900.6366290.0760610.5225700.0717520.54961850.0566090.6446720.0717170.5498370.0706370.55661570.0537120.6628530.0696450.5628460.0683570.570926120.0538820.6617870.0686360.5691790.0680520.572841190.0548160.6559260.0646550.5941640.0675720.575856230.0546320.6570770.0708730.5551380.0667330.581124330.0553680.6524610.0726870.5437490.0663620.583450420.0552840.6529860.0724760.5450770.0672340.5779751
\n", + "
" + ], + "text/plain": [ + " sample_row_n sample_row_n_name rep n_estimators min_samples_leaf \\\n", + "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", + "\n", + " max_features random_state model \\\n", + "0 0.33 42 RF \n", + "1 0.33 42 RF \n", + "2 0.33 42 RF \n", + "3 0.33 42 RF \n", + "4 0.33 42 RF \n", + "\n", + " fi train_size test_size \\\n", + "0 LIME_RF 683 337 \n", + "1 Local_MDI+_Alo_fit_on_all_ranking_RFPlus 683 337 \n", + "2 Local_MDI+_MDI_fit_on_all_ranking_RFPlus 683 337 \n", + "3 Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus 683 337 \n", + "4 Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus 683 337 \n", + "\n", + " num_features data_split_seed rf_seed num_features_masked \\\n", + "0 46 1 1 46 \n", + "1 46 1 1 46 \n", + "2 46 1 1 46 \n", + "3 46 1 1 46 \n", + "4 46 1 1 46 \n", + "\n", + " num_features_selected_0.01 RF_Regressor_MSE_top_0.01 \\\n", + "0 1 0.065978 \n", + "1 1 0.058014 \n", + "2 1 0.058014 \n", + "3 1 0.058014 \n", + "4 1 0.065978 \n", + "\n", + " RF_Regressor_R2_top_0.01 xgboost_Regressor_MSE_top_0.01 \\\n", + "0 0.585859 0.072439 \n", + "1 0.635849 0.072430 \n", + "2 0.635849 0.072430 \n", + "3 0.635849 0.072430 \n", + "4 0.585859 0.072439 \n", + "\n", + " xgboost_Regressor_R2_top_0.01 Linear_Regressor_MSE_top_0.01 \\\n", + "0 0.545307 0.080756 \n", + "1 0.545360 0.072183 \n", + "2 0.545360 0.072183 \n", + "3 0.545360 0.072183 \n", + "4 0.545307 0.080756 \n", + "\n", + " Linear_Regressor_R2_top_0.01 num_features_selected_0.05 \\\n", + "0 0.493104 3 \n", + "1 0.546910 3 \n", + "2 0.546910 3 \n", + "3 0.546910 3 \n", + "4 0.493104 3 \n", + "\n", + " RF_Regressor_MSE_top_0.05 RF_Regressor_R2_top_0.05 \\\n", + "0 0.057773 0.637365 \n", + "1 0.057286 0.640420 \n", + "2 0.057286 0.640420 \n", + "3 0.058470 0.632986 \n", + "4 0.057890 0.636629 \n", + "\n", + " xgboost_Regressor_MSE_top_0.05 xgboost_Regressor_R2_top_0.05 \\\n", + "0 0.076940 0.517053 \n", + "1 0.077314 0.514705 \n", + "2 0.077314 0.514705 \n", + "3 0.076902 0.517291 \n", + "4 0.076061 0.522570 \n", + "\n", + " Linear_Regressor_MSE_top_0.05 Linear_Regressor_R2_top_0.05 \\\n", + "0 0.071752 0.549618 \n", + "1 0.071752 0.549618 \n", + "2 0.071752 0.549618 \n", + "3 0.070800 0.555597 \n", + "4 0.071752 0.549618 \n", + "\n", + " num_features_selected_0.1 RF_Regressor_MSE_top_0.1 \\\n", + "0 5 0.076612 \n", + "1 5 0.057012 \n", + "2 5 0.057012 \n", + "3 5 0.056830 \n", + "4 5 0.056609 \n", + "\n", + " RF_Regressor_R2_top_0.1 xgboost_Regressor_MSE_top_0.1 \\\n", + "0 0.519111 0.076541 \n", + "1 0.642142 0.073156 \n", + "2 0.642142 0.073156 \n", + "3 0.643286 0.071647 \n", + "4 0.644672 0.071717 \n", + "\n", + " xgboost_Regressor_R2_top_0.1 Linear_Regressor_MSE_top_0.1 \\\n", + "0 0.519556 0.073049 \n", + "1 0.540804 0.070452 \n", + "2 0.540804 0.070452 \n", + "3 0.550279 0.070637 \n", + "4 0.549837 0.070637 \n", + "\n", + " Linear_Regressor_R2_top_0.1 num_features_selected_0.15 \\\n", + "0 0.541476 7 \n", + "1 0.557777 7 \n", + "2 0.557777 7 \n", + "3 0.556615 7 \n", + "4 0.556615 7 \n", + "\n", + " RF_Regressor_MSE_top_0.15 RF_Regressor_R2_top_0.15 \\\n", + "0 0.059147 0.628740 \n", + "1 0.054621 0.657146 \n", + "2 0.054621 0.657146 \n", + "3 0.057259 0.640591 \n", + "4 0.053712 0.662853 \n", + "\n", + " xgboost_Regressor_MSE_top_0.15 xgboost_Regressor_R2_top_0.15 \\\n", + "0 0.078514 0.507174 \n", + "1 0.070341 0.558475 \n", + "2 0.070341 0.558475 \n", + "3 0.070092 0.560040 \n", + "4 0.069645 0.562846 \n", + "\n", + " Linear_Regressor_MSE_top_0.15 Linear_Regressor_R2_top_0.15 \\\n", + "0 0.073312 0.539826 \n", + "1 0.068496 0.570054 \n", + "2 0.068496 0.570054 \n", + "3 0.070864 0.555195 \n", + "4 0.068357 0.570926 \n", + "\n", + " num_features_selected_0.25 RF_Regressor_MSE_top_0.25 \\\n", + "0 12 0.056943 \n", + "1 12 0.055300 \n", + "2 12 0.055300 \n", + "3 12 0.055977 \n", + "4 12 0.053882 \n", + "\n", + " RF_Regressor_R2_top_0.25 xgboost_Regressor_MSE_top_0.25 \\\n", + "0 0.642573 0.074509 \n", + "1 0.652884 0.069182 \n", + "2 0.652884 0.069182 \n", + "3 0.648635 0.072916 \n", + "4 0.661787 0.068636 \n", + "\n", + " xgboost_Regressor_R2_top_0.25 Linear_Regressor_MSE_top_0.25 \\\n", + "0 0.532312 0.073591 \n", + "1 0.565753 0.067906 \n", + "2 0.565753 0.067906 \n", + "3 0.542309 0.069818 \n", + "4 0.569179 0.068052 \n", + "\n", + " Linear_Regressor_R2_top_0.25 num_features_selected_0.4 \\\n", + "0 0.538075 19 \n", + "1 0.573758 19 \n", + "2 0.573758 19 \n", + "3 0.561760 19 \n", + "4 0.572841 19 \n", + "\n", + " RF_Regressor_MSE_top_0.4 RF_Regressor_R2_top_0.4 \\\n", + "0 0.057882 0.636680 \n", + "1 0.055341 0.652632 \n", + "2 0.055341 0.652632 \n", + "3 0.055521 0.651496 \n", + "4 0.054816 0.655926 \n", + "\n", + " xgboost_Regressor_MSE_top_0.4 xgboost_Regressor_R2_top_0.4 \\\n", + "0 0.065696 0.587632 \n", + "1 0.066374 0.583378 \n", + "2 0.066374 0.583378 \n", + "3 0.071054 0.554003 \n", + "4 0.064655 0.594164 \n", + "\n", + " Linear_Regressor_MSE_top_0.4 Linear_Regressor_R2_top_0.4 \\\n", + "0 0.071619 0.550452 \n", + "1 0.066592 0.582008 \n", + "2 0.066592 0.582008 \n", + "3 0.067692 0.575100 \n", + "4 0.067572 0.575856 \n", + "\n", + " num_features_selected_0.5 RF_Regressor_MSE_top_0.5 \\\n", + "0 23 0.055339 \n", + "1 23 0.054910 \n", + "2 23 0.054910 \n", + "3 23 0.054561 \n", + "4 23 0.054632 \n", + "\n", + " RF_Regressor_R2_top_0.5 xgboost_Regressor_MSE_top_0.5 \\\n", + "0 0.652642 0.070448 \n", + "1 0.655337 0.066411 \n", + "2 0.655337 0.066411 \n", + "3 0.657523 0.071439 \n", + "4 0.657077 0.070873 \n", + "\n", + " xgboost_Regressor_R2_top_0.5 Linear_Regressor_MSE_top_0.5 \\\n", + "0 0.557807 0.071155 \n", + "1 0.583143 0.067015 \n", + "2 0.583143 0.067015 \n", + "3 0.551582 0.067350 \n", + "4 0.555138 0.066733 \n", + "\n", + " Linear_Regressor_R2_top_0.5 num_features_selected_0.7 \\\n", + "0 0.553366 33 \n", + "1 0.579353 33 \n", + "2 0.579353 33 \n", + "3 0.577250 33 \n", + "4 0.581124 33 \n", + "\n", + " RF_Regressor_MSE_top_0.7 RF_Regressor_R2_top_0.7 \\\n", + "0 0.055727 0.650208 \n", + "1 0.054526 0.657746 \n", + "2 0.054526 0.657746 \n", + "3 0.055854 0.649409 \n", + "4 0.055368 0.652461 \n", + "\n", + " xgboost_Regressor_MSE_top_0.7 xgboost_Regressor_R2_top_0.7 \\\n", + "0 0.071286 0.552543 \n", + "1 0.071399 0.551837 \n", + "2 0.071399 0.551837 \n", + "3 0.072078 0.547570 \n", + "4 0.072687 0.543749 \n", + "\n", + " Linear_Regressor_MSE_top_0.7 Linear_Regressor_R2_top_0.7 \\\n", + "0 0.068061 0.572788 \n", + "1 0.066362 0.583450 \n", + "2 0.066362 0.583450 \n", + "3 0.066420 0.583087 \n", + "4 0.066362 0.583450 \n", + "\n", + " num_features_selected_0.9 RF_Regressor_MSE_top_0.9 \\\n", + "0 42 0.055735 \n", + "1 42 0.054463 \n", + "2 42 0.054463 \n", + "3 42 0.055144 \n", + "4 42 0.055284 \n", + "\n", + " RF_Regressor_R2_top_0.9 xgboost_Regressor_MSE_top_0.9 \\\n", + "0 0.650156 0.071938 \n", + "1 0.658142 0.071095 \n", + "2 0.658142 0.071095 \n", + "3 0.653869 0.071086 \n", + "4 0.652986 0.072476 \n", + "\n", + " xgboost_Regressor_R2_top_0.9 Linear_Regressor_MSE_top_0.9 \\\n", + "0 0.548452 0.067633 \n", + "1 0.553744 0.067234 \n", + "2 0.553744 0.067234 \n", + "3 0.553798 0.067234 \n", + "4 0.545077 0.067234 \n", + "\n", + " Linear_Regressor_R2_top_0.9 split_seed \n", + "0 0.575476 1 \n", + "1 0.577975 1 \n", + "2 0.577975 1 \n", + "3 0.577975 1 \n", + "4 0.577975 1 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# df = pd.DataFrame(combined_df_rf_plus)\n", + "# averages = df.groupby('Model').mean().reset_index()\n", + "# pd.DataFrame(averages)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([46])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined_df[\"num_features\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarise the Ablation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The training size is 683 and the test size is 337\n" + ] + } + ], + "source": [ + "train_size = combined_df[\"train_size\"].unique()[0]\n", + "test_size = combined_df[\"test_size\"].unique()[0]\n", + "print(f\"The training size is {train_size} and the test size is {test_size}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the Ablation Data Performance" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "num_features = combined_df['num_features_masked'].drop_duplicates().values[0]\n", + "metrics = {\"regression\": [\"MSE\", \"R2\"], \"classification\": [\"AUROC\", \"LogLoss\"]} #MSE\n", + "ablation_models = {\"regression\": [\"RF_Regressor\", \"xgboost_Regressor\", \"Linear_Regressor\"],\n", + " \"classification\": [\"RF_Classifier\", \"Logistic_Regression\"]}" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "methods = [\n", + " # 'Random',\n", + " # 'LIME_RF', \n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus',\n", + " # 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus',\n", + " # 'TreeSHAP_RF'\n", + " ]\n", + "color_map = {\n", + " 'Random': 'gray', # Assign a default neutral color for Random\n", + " 'LIME_RF': '#71BEB7',\n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus': '#FF5733', # Example bright color\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus': '#33FF57', # Example greenish color\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus': '#3357FF', # Example blueish color\n", + " 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus': '#FF33A1', # Example pinkish color\n", + " 'TreeSHAP_RF': 'orange'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "if num_features > 20:\n", + " all_ratios = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", + "else:\n", + " all_ratios = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", + "num_features_selected = []\n", + "for r in all_ratios:\n", + " num_features_selected.append(combined_df[f\"num_features_selected_{r}\"].unique()[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Summary of results" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# results = {}\n", + "# for a_model in [\"RF_Regressor\"]:\n", + "# for metric in [\"MSE\"]:\n", + "# for m in methods:\n", + "# results[m] = []\n", + "# for m in methods:\n", + "# for k in all_ratios:\n", + "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", + "\n", + "# filtered_sums = {\n", + "# key: sum(values[:5]) \n", + "# for key, values in results.items()\n", + "# }\n", + "# sorted(filtered_sums, key=filtered_sums.get)\n", + "\n", + "# import pickle\n", + "\n", + "# list_dict = {element: index + 1 for index, element in enumerate(sorted(filtered_sums, key=filtered_sums.get))}\n", + "\n", + "# with open(\"temperature_rank.pkl\", \"wb\") as file:\n", + "# pickle.dump(list_dict, file)\n", + "\n", + "# print(\"Dictionary saved as pickle file:\", list_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# method_names = {'TreeSHAP_RF': 'SHAP', 'Local_MDI+_fit_on_all_ranking_RFPlus': \"LMDI+\", 'LIME_RF': 'LIME'}" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['axes.labelsize'] = 30\n", + "plt.rcParams['xtick.labelsize'] = 10\n", + "plt.rcParams['ytick.labelsize'] = 12\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", + "\n", + "# Define marker size\n", + "marker_size = 7\n", + "\n", + "# Determine the number of rows and columns for subplots\n", + "n_models = len(ablation_models[task])\n", + "n_metrics = len(metrics[task])\n", + "\n", + "# Create subplots with a grid of n_models rows and n_metrics columns\n", + "fig, axs = plt.subplots(\n", + " nrows=n_models,\n", + " ncols=n_metrics,\n", + " figsize=(7 * n_metrics, 5 * n_models) # Adjust figure size dynamically\n", + ")\n", + "\n", + "# Ensure axs is iterable, even if there's only one subplot\n", + "if n_models == 1 and n_metrics == 1:\n", + " axs = [[axs]] # Single subplot case\n", + "elif n_models == 1 or n_metrics == 1:\n", + " axs = [axs] # Single row or column\n", + "\n", + "# Iterate through models and metrics\n", + "for i, a_model in enumerate(ablation_models[task]):\n", + " for j, metric in enumerate(metrics[task]):\n", + " ax = axs[i][j] if n_models > 1 and n_metrics > 1 else axs[max(i, j)]\n", + " \n", + " results = {m: [] for m in methods}\n", + " for m in methods:\n", + " for k in all_ratios:\n", + " results[m].append(\n", + " combined_df[combined_df['fi'] == m][f\"{a_model}_{metric}_top_{k}\"].mean()\n", + " )\n", + "\n", + " for m in methods:\n", + " color = color_map[m]\n", + " ax.plot(\n", + " all_ratios, results[m],\n", + " label=m, linestyle='solid',\n", + " color=color, marker='o', markersize=marker_size\n", + " )\n", + "\n", + " # Set labels, title, and ticks for each subplot\n", + " ax.set_xticks(all_ratios)\n", + " ax.set_xlabel('Percentage of features selected', fontsize=14)\n", + " ax.set_ylabel(metric, fontsize=14)\n", + " ax.set_title(f\"Model: {a_model}, Metric: {metric}\", fontsize=14)\n", + " ax.legend(fontsize=10, title_fontsize=12)\n", + "\n", + "# Adjust layout and show plot\n", + "plt.tight_layout(rect=[0, 0, 1, 0.95]) # Leave space for the title\n", + "fig.suptitle(data, fontsize=16) # Add the global title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# # Set global parameters for plots\n", + "# plt.rcParams['axes.labelsize'] = 30\n", + "# plt.rcParams['xtick.labelsize'] = 10\n", + "# plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "# # Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "# plt.rcParams['axes.spines.right'] = False\n", + "# plt.rcParams['axes.spines.top'] = False\n", + "# plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", + "\n", + "# # Define marker size\n", + "# marker_size = 7\n", + "\n", + "# # Create a single-column subplot\n", + "# fig, axs = plt.subplots(len(ablation_models[task]), 1, figsize=(7, 5))\n", + "\n", + "# # Ensure axs is always iterable\n", + "# if len(ablation_models[task]) == 1:\n", + "# axs = [axs]\n", + "\n", + "# # Iterate through models\n", + "# for i, a_model in enumerate(ablation_models[task]):\n", + "# results = {m: [] for m in methods}\n", + "# for m in methods:\n", + "# for k in all_ratios:\n", + "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metrics[task][0]}_top_{k}\"].mean())\n", + "\n", + "# ax = axs[i]\n", + "# for m in methods:\n", + "# color = color_map[m]\n", + "# ax.plot(all_ratios, results[m], label=m, linestyle='solid', color=color, marker='o', markersize=marker_size)\n", + "\n", + "# # Set labels and title for the subplot\n", + "# ax.set_xticks(all_ratios)\n", + "# ax.set_xlabel('Percentage of features selected', fontsize=14)\n", + "# ax.set_ylabel(f\"{metrics[task][0]}\", fontsize=14)\n", + "# ax.legend(fontsize=10, title_fontsize=12)\n", + "\n", + "# # Adjust layout and show plot\n", + "# plt.tight_layout(rect=[0, 0, 1, 0.95]) # Leave space for the title\n", + "# fig.suptitle(f\"CCLE Dataset\", fontsize=16) # Add the global title\n", + "# plt.savefig(\"ccle_retrain.png\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", + "\n", + "# for i, a_model in enumerate(ablation_models[task]):\n", + "# for j, metric in enumerate(metrics[task]):\n", + "# results = {}\n", + "# for m in methods:\n", + "# results[m] = []\n", + "# for m in methods:\n", + "# for k in all_ratios:\n", + "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", + "\n", + "# # excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", + "# # filtered_sums = {\n", + "# # key: sum(values[:5]) \n", + "# # for key, values in results.items() if key not in excluded_keys\n", + "# # }\n", + "# # if metric == \"MSE\" or metric == \"LogLoss\":\n", + "# # top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]\n", + "# # else:\n", + "# # top_3_keys =sorted(filtered_sums, key=filtered_sums.get, reverse=True)[:3]\n", + "# # top_3_keys.extend(['LIME_RF', 'TreeSHAP_RF'])\n", + "\n", + "# ax = axs[j]#, j]\n", + "# for m in methods:#top_3_keys:\n", + "# color = color_map[m]\n", + "# if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", + "# ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", + "# else:\n", + "# ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", + "# ax.set_xticks(num_features_selected)\n", + "# ax.set(\n", + "# xlabel='Number of features selected',\n", + "# ylabel=f\"{metric}\",\n", + "# title=f'Ablation model = {a_model}'\n", + "# )\n", + "# ax.legend()\n", + "\n", + "# plt.tight_layout()\n", + "# #plt.savefig(f\"./Ionosphere.png\")\n", + "# plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/ablation_results_visulization_stability.ipynb b/feature_importance/ablation_results_visulization_stability.ipynb index ddd2f6f..338bca1 100644 --- a/feature_importance/ablation_results_visulization_stability.ipynb +++ b/feature_importance/ablation_results_visulization_stability.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 60, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -17,137 +17,127 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ - "ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_CCLE_PD_0325901_retrain/CCLE_PD_0325901_stability/varying_sample_row_n\"\n", - "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "for split_seed in split_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "# ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_CCLE_PD_0325901_retrain/CCLE_PD_0325901_stability/varying_sample_row_n\"\n", + "# combined_df = pd.DataFrame()\n", + "# split_seeds = [1,2,3]\n", + "# for split_seed in split_seeds:\n", + "# df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", + "# combined_df = pd.concat([combined_df, df], ignore_index=True)\n", "\n", - "ccle_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", - " \"avg_3_features_train\", \"avg_3_features_test\", \n", - " \"avg_5_features_train\", \"avg_5_features_test\", \n", - " \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", - "ccle_combined_df[\"dataset\"] = \"CCLE\"\n", + "# ccle_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", + "# \"avg_3_features_train\", \"avg_3_features_test\", \n", + "# \"avg_5_features_train\", \"avg_5_features_test\", \n", + "# \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", + "# ccle_combined_df[\"dataset\"] = \"CCLE\"\n", "\n", - "ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_performance_retrain/performance_stability/varying_sample_row_n\"\n", - "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "for split_seed in split_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "# ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_performance_retrain/performance_stability/varying_sample_row_n\"\n", + "# combined_df = pd.DataFrame()\n", + "# split_seeds = [1,2,3]\n", + "# for split_seed in split_seeds:\n", + "# df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", + "# combined_df = pd.concat([combined_df, df], ignore_index=True)\n", "\n", - "performance_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", - " \"avg_3_features_train\", \"avg_3_features_test\", \n", - " \"avg_5_features_train\", \"avg_5_features_test\", \n", - " \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", - "performance_combined_df[\"dataset\"] = \"Performance\"\n", + "# performance_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", + "# \"avg_3_features_train\", \"avg_3_features_test\", \n", + "# \"avg_5_features_train\", \"avg_5_features_test\", \n", + "# \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", + "# performance_combined_df[\"dataset\"] = \"Performance\"\n", "\n", - "ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_parkinsons_retrain/parkinsons_stability/varying_sample_row_n\"\n", - "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "for split_seed in split_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "# ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_parkinsons_retrain/parkinsons_stability/varying_sample_row_n\"\n", + "# combined_df = pd.DataFrame()\n", + "# split_seeds = [1,2,3]\n", + "# for split_seed in split_seeds:\n", + "# df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", + "# combined_df = pd.concat([combined_df, df], ignore_index=True)\n", "\n", - "parkinsons_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", - " \"avg_3_features_train\", \"avg_3_features_test\", \n", - " \"avg_5_features_train\", \"avg_5_features_test\", \n", - " \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", - "parkinsons_combined_df[\"dataset\"] = \"Parkinsons\"\n", + "# parkinsons_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", + "# \"avg_3_features_train\", \"avg_3_features_test\", \n", + "# \"avg_5_features_train\", \"avg_5_features_test\", \n", + "# \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", + "# parkinsons_combined_df[\"dataset\"] = \"Parkinsons\"\n", "\n", - "ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature_retrain/temperature_stability/varying_sample_row_n\"\n", - "combined_df = pd.DataFrame()\n", - "split_seeds = [1,2,3]\n", - "for split_seed in split_seeds:\n", - " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", - " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "# ablation_directory = \"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_temperature/temperature_stability/varying_sample_row_n\"\n", + "# combined_df = pd.DataFrame()\n", + "# split_seeds = [1,2,3]\n", + "# for split_seed in split_seeds:\n", + "# df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", + "# combined_df = pd.concat([combined_df, df], ignore_index=True)\n", "\n", - "temperature_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", - " \"avg_3_features_train\", \"avg_3_features_test\", \n", - " \"avg_5_features_train\", \"avg_5_features_test\", \n", - " \"avg_10_features_train\", \"avg_10_features_test\"]].mean().reset_index()\n", - "temperature_combined_df[\"dataset\"] = \"Temperature\"\n", + "# temperature_combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", + "# \"avg_5_features_train\", \"avg_5_features_test\", \"avg_5_features_all\",\n", + "# \"avg_10_features_train\", \"avg_10_features_test\", \"avg_10_features_all\",\n", + "# \"avg_20_features_train\", \"avg_20_features_test\", \"avg_20_features_all\",\n", + "# \"avg_40_features_train\", \"avg_40_features_test\", \"avg_40_features_all\",\n", + "# ]].mean().reset_index()\n", + "# temperature_combined_df[\"dataset\"] = \"Temperature\"\n", "\n", - "combined_df_all = pd.concat([ccle_combined_df, performance_combined_df, parkinsons_combined_df, temperature_combined_df], ignore_index=True)" + "# combined_df_all = pd.concat([ccle_combined_df, performance_combined_df, parkinsons_combined_df, temperature_combined_df], ignore_index=True)\n", + "# combined_df_all = combined_df_all[combined_df_all[\"fi\"] != \"Random\"] " ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "combined_df_all = combined_df_all[combined_df_all[\"fi\"] != \"Random\"] " + "data = \"temperature\"\n", + "ablation_directory = f\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/results/mdi_local.real_data_regression_{data}/{data}_stability/varying_sample_row_n\"\n", + "combined_df = pd.DataFrame()\n", + "split_seeds = [1,2,3]\n", + "for split_seed in split_seeds:\n", + " df = pd.read_csv(os.path.join(ablation_directory, f\"split_seed_{split_seed}/results.csv\"))\n", + " combined_df = pd.concat([combined_df, df], ignore_index=True)\n", + "\n", + "combined_df = combined_df.groupby(\"fi\")[[\"train_size\", \"test_size\", \"num_features\", \n", + " \"avg_5_features_train\", \"avg_5_features_test\", \"avg_5_features_all\",\n", + " \"avg_10_features_train\", \"avg_10_features_test\", \"avg_10_features_all\",\n", + " \"avg_20_features_train\", \"avg_20_features_test\", \"avg_20_features_all\",\n", + " \"avg_40_features_train\", \"avg_40_features_test\", \"avg_40_features_all\",\n", + " ]].mean().reset_index()" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 47, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from matplotlib.ticker import MaxNLocator\n", - "palette = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - " # 'Random': '#ad494a', # warm red\n", + "methods = [\n", + " 'Random',\n", + " 'LIME_RF', \n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus',\n", + " 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus',\n", + " 'TreeSHAP_RF'\n", + " ]\n", + "color_map = {\n", + " 'Random': 'gray', # Assign a default neutral color for Random\n", + " 'LIME_RF': '#71BEB7',\n", + " 'Local_MDI+_Alo_fit_on_all_ranking_RFPlus': '#FF5733', # Example bright color\n", + " 'Local_MDI+_MDI_fit_on_all_ranking_RFPlus': '#33FF57', # Example greenish color\n", + " 'Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus': '#3357FF', # Example blueish color\n", + " 'Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus': '#FF33A1', # Example pinkish color\n", + " 'TreeSHAP_RF': 'orange'\n", "}\n", - "\n", - "sns.set(style=\"whitegrid\")\n", - "plt.figure(figsize=(10, 4)) \n", - "sns.scatterplot(\n", - " data=combined_df_all,\n", - " x='avg_3_features_train',\n", - " y='dataset',\n", - " hue='fi',\n", - " palette=palette,\n", - " s=100 # Size of the dots\n", - ")\n", - "\n", - "# Customize the legend\n", - "plt.legend(title='Method', loc='lower right')\n", - "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", - "plt.xlabel('Number of Distinct Features in Top 3 Across Training-Test Splits')\n", - "plt.ylabel('Dataset')\n", - "\n", - "plt.yticks(fontsize=10) \n", - "plt.tight_layout()\n", - "plt.show()" + "# method_names = {'TreeSHAP_RF': 'SHAP', 'Local_MDI+_fit_on_all_ranking_RFPlus': \"LMDI+\", 'LIME_RF': 'LIME'}" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -155,52 +145,49 @@ } ], "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", + "plt.rcParams['axes.labelsize'] = 28\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - "palette = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - " # 'Random': '#ad494a', # warm red\n", - "}\n", + "# Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", + "\n", + "# Increase data marker size\n", + "marker_size = 7\n", + "df = combined_df\n", + "df = df[df['fi'].isin(methods)]\n", "\n", - "sns.set(style=\"whitegrid\")\n", - "plt.figure(figsize=(10, 4)) \n", - "sns.scatterplot(\n", - " data=combined_df_all,\n", - " x='avg_5_features_train',\n", - " y='dataset',\n", - " hue='fi',\n", - " palette=palette,\n", - " s=100 # Size of the dots\n", - ")\n", + "x = [5,10,20,40]\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", - "# Customize the legend\n", - "plt.legend(title='Method', loc='lower right')\n", - "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", - "plt.xlabel('Number of Distinct Features in Top 5 Across Training-Test Splits')\n", - "plt.ylabel('Dataset')\n", + "for index, row in df.iterrows():\n", + " y = [row['avg_5_features_all'], row['avg_10_features_all'], row['avg_20_features_all'], row['avg_40_features_all']]\n", + " ax.plot(x, y, '-o', label=row['fi'], color=color_map[row['fi']], markersize=7)\n", + "\n", + "ax.set_xlabel('Pecentage of features considered', fontsize=14)\n", + "ax.set_ylabel('Number of unique features in top k', fontsize=14)\n", + "ax.set_title(data, fontsize=16)\n", + "ax.legend(fontsize=10, title_fontsize=12)\n", + "ax.set_xticks(x) \n", + "ax.set_xticklabels(x) \n", "\n", - "plt.yticks(fontsize=10) \n", "plt.tight_layout()\n", + "# plt.savefig('performance_dataset_stability.png')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -208,1452 +195,212 @@ } ], "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", + "plt.rcParams['axes.labelsize'] = 28\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - "palette = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - " # 'Random': '#ad494a', # warm red\n", - "}\n", + "# Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", + "\n", + "# Increase data marker size\n", + "marker_size = 7\n", + "df = combined_df\n", + "df = df[df['fi'].isin(methods)]\n", "\n", - "sns.set(style=\"whitegrid\")\n", - "plt.figure(figsize=(10, 4)) \n", - "sns.scatterplot(\n", - " data=combined_df_all,\n", - " x='avg_10_features_train',\n", - " y='dataset',\n", - " hue='fi',\n", - " palette=palette,\n", - " s=100 # Size of the dots\n", - ")\n", + "x = [5,10,20,40]\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", - "# Customize the legend\n", - "plt.legend(title='Method', loc='lower right')\n", - "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", - "plt.xlabel('Number of Distinct Features in Top 10 Across Training-Test Splits')\n", - "plt.ylabel('Dataset')\n", + "for index, row in df.iterrows():\n", + " y = [row['avg_5_features_train'], row['avg_10_features_train'], row['avg_20_features_train'], row['avg_40_features_train']]\n", + " ax.plot(x, y, '-o', label=row['fi'], color=color_map[row['fi']], markersize=7)\n", + "\n", + "ax.set_xlabel('Pecentage of features considered', fontsize=14)\n", + "ax.set_ylabel('Number of unique features in top k', fontsize=14)\n", + "ax.set_title(data, fontsize=16)\n", + "ax.legend(fontsize=10, title_fontsize=12)\n", + "ax.set_xticks(x) \n", + "ax.set_xticklabels(x) \n", "\n", - "plt.yticks(fontsize=10) \n", "plt.tight_layout()\n", + "# plt.savefig('performance_dataset_stability.png')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summarise the Ablation Data" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The training size is 683.0 and the test size is 337.0\n" - ] - } - ], - "source": [ - "train_size = combined_df[\"train_size\"].unique()[0]\n", - "test_size = combined_df[\"test_size\"].unique()[0]\n", - "print(f\"The training size is {train_size} and the test size is {test_size}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { + "image/png": "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", "text/plain": [ - "array(['LIME_RF', 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus', 'Random', 'TreeSHAP_RF'],\n", - " dtype=object)" + "
" ] }, - "execution_count": 26, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df[\"fi\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the Ablation Data Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'num_features_masked'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'num_features_masked'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 35\u001b[0m\n\u001b[1;32m 1\u001b[0m methods \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mLIME_RF\u001b[39m\u001b[39m'\u001b[39m, \n\u001b[1;32m 2\u001b[0m \u001b[39m# 'Local_MDI+_fit_on_all_RFPlus',\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39m# 'Local_MDI+_fit_on_all_average_RFPlus',\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[39m# 'Random',\u001b[39;00m\n\u001b[1;32m 33\u001b[0m \u001b[39m'\u001b[39m\u001b[39mTreeSHAP_RF\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m---> 35\u001b[0m num_features \u001b[39m=\u001b[39m combined_df[\u001b[39m'\u001b[39;49m\u001b[39mnum_features_masked\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39mdrop_duplicates()\u001b[39m.\u001b[39mvalues[\u001b[39m0\u001b[39m]\n\u001b[1;32m 36\u001b[0m metrics \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mregression\u001b[39m\u001b[39m\"\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mMSE\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mR2\u001b[39m\u001b[39m\"\u001b[39m], \u001b[39m\"\u001b[39m\u001b[39mclassification\u001b[39m\u001b[39m\"\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mAUROC\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mLogLoss\u001b[39m\u001b[39m\"\u001b[39m]} \u001b[39m#MSE\u001b[39;00m\n\u001b[1;32m 37\u001b[0m ablation_models \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mregression\u001b[39m\u001b[39m\"\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mRF_Regressor\u001b[39m\u001b[39m\"\u001b[39m],\u001b[39m#, \"Linear_Regressor\"],\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mclassification\u001b[39m\u001b[39m\"\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mRF_Classifier\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mLogistic_Regression\u001b[39m\u001b[39m\"\u001b[39m]}\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4088\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 4089\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4091\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4092\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'num_features_masked'" - ] + "output_type": "display_data" } ], "source": [ - "methods = ['LIME_RF', \n", - "# 'Local_MDI+_fit_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus',\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus',\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus',\n", - " # 'Random',\n", - " 'TreeSHAP_RF']\n", - "\n", - "num_features = combined_df['num_features_masked'].drop_duplicates().values[0]\n", - "metrics = {\"regression\": [\"MSE\", \"R2\"], \"classification\": [\"AUROC\", \"LogLoss\"]} #MSE\n", - "ablation_models = {\"regression\": [\"RF_Regressor\"],#, \"Linear_Regressor\"],\n", - " \"classification\": [\"RF_Classifier\", \"Logistic_Regression\"]}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "color_map = {\n", - " 'LIME_RF': '#1f77b4', # Bold blue\n", - " 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", - " 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", - " 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", - " 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", - " 'TreeSHAP_RF': '#9467bd', # Bold purple\n", - "}\n", - "\n", - "# color_map = {\n", - "# 'LIME_RF': '#1f77b4', # bold blue\n", - "# 'Local_MDI+_fit_on_all_RFPlus': '#ff7f0e', # vibrant orange\n", - "# 'Local_MDI+_fit_on_all_average_RFPlus': '#2ca02c', # bright green\n", - "# 'Local_MDI+_fit_on_all_error_metric_RFPlus': '#d62728', # bright red\n", - "# 'Local_MDI+_fit_on_all_error_metric_average_RFPlus': '#9467bd', # bold purple\n", - "# 'Local_MDI+_fit_on_all_error_metric_ranking_RFPlus': '#8c564b', # strong brown\n", - "# 'Local_MDI+_fit_on_all_l2_norm_RFPlus': '#e377c2', # pink\n", - "# 'Local_MDI+_fit_on_all_l2_norm_average_RFPlus': '#bcbd22', # lime green\n", - "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#17becf', # cyan\n", - "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#7f7f7f', # medium gray\n", - "# 'Local_MDI+_fit_on_all_ranking_ridge_RFPlus': '#bc5a34', # burnt orange\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#000000', # black\n", - "# 'Local_MDI+_fit_on_inbag_average_RFPlus': '#7fbc41', # moss green\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_RFPlus': '#ff9896', # light coral\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_average_RFPlus': '#aec7e8', # light blue\n", - "# 'Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus': '#9edae5', # light cyan\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_RFPlus': '#b29189', # warm taupe\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus': '#c49c94', # peach\n", - "# 'Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus': '#dbdb8d', # soft yellow-green\n", - "# 'Local_MDI+_fit_on_inbag_ranking_RFPlus': '#393b79', # dark blue\n", - "# 'Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus': '#637939', # dark olive green\n", - "# 'Local_MDI+_fit_on_oob_RFPlus': '#8c6d31', # earthy brown\n", - "# 'Local_MDI+_fit_on_oob_average_RFPlus': '#843c39', # dark brick red\n", - "# 'Local_MDI+_fit_on_oob_error_metric_RFPlus': '#7b4173', # deep purple\n", - "# 'Local_MDI+_fit_on_oob_error_metric_average_RFPlus': '#6b6ecf', # muted indigo\n", - "# 'Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus': '#5254a3', # steel blue\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_RFPlus': '#8ca252', # olive\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_average_RFPlus': '#bd9e39', # mustard yellow\n", - "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#d6616b', # muted pink\n", - "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#ce6dbd', # bright magenta\n", - "# 'Local_MDI+_fit_on_oob_ranking_ridge_RFPlus': '#de9ed6', # soft magenta\n", - "# 'Random': '#ad494a', # warm red\n", - "# 'TreeSHAP_RF': '#6baed6', # sky blue\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if num_features > 20:\n", - " all_ratios = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "else:\n", - " all_ratios = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9]\n", - "num_features_selected = []\n", - "for r in all_ratios:\n", - " num_features_selected.append(combined_df[f\"num_features_selected_{r}\"].unique()[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Summary of results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# results = {}\n", - "# for a_model in [\"RF_Regressor\"]:\n", - "# for metric in [\"MSE\"]:\n", - "# for m in methods:\n", - "# results[m] = []\n", - "# for m in methods:\n", - "# for k in all_ratios:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", - "\n", - "# filtered_sums = {\n", - "# key: sum(values[:5]) \n", - "# for key, values in results.items()\n", - "# }\n", - "# sorted(filtered_sums, key=filtered_sums.get)\n", - "\n", - "# import pickle\n", - "\n", - "# list_dict = {element: index + 1 for index, element in enumerate(sorted(filtered_sums, key=filtered_sums.get))}\n", - "\n", - "# with open(\"temperature_rank.pkl\", \"wb\") as file:\n", - "# pickle.dump(list_dict, file)\n", - "\n", - "# print(\"Dictionary saved as pickle file:\", list_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model + f\"_{metric}_top_{k}\"].mean())\n", + "plt.rcParams['axes.labelsize'] = 28\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", "\n", - " # excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", - " # filtered_sums = {\n", - " # key: sum(values[:5]) \n", - " # for key, values in results.items() if key not in excluded_keys\n", - " # }\n", - " # if metric == \"MSE\" or metric == \"LogLoss\":\n", - " # top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]\n", - " # else:\n", - " # top_3_keys =sorted(filtered_sums, key=filtered_sums.get, reverse=True)[:3]\n", - " # top_3_keys.extend(['LIME_RF', 'TreeSHAP_RF'])\n", + "# Remove chartjunk: Remove right and top spines, and change edge color to light grey\n", + "plt.rcParams['axes.spines.right'] = False\n", + "plt.rcParams['axes.spines.top'] = False\n", + "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n", "\n", - " ax = axs[j]#, j]\n", - " for m in methods:#top_3_keys:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(\n", - " xlabel='Number of features selected',\n", - " ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}'\n", - " )\n", - " ax.legend()\n", + "# Increase data marker size\n", + "marker_size = 7\n", + "df = temperature_combined_df\n", + "df = df[df['fi'].isin(methods)]\n", "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./Ionosphere.png\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Filtered keys to exclude\n", - "excluded_keys = {'LIME_RF', 'TreeSHAP_RF'}\n", + "x = [5,10,20,40]\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", - "# Compute the sum of the first five numbers for each key (excluding the specified keys)\n", - "filtered_sums = {\n", - " key: sum(values[:5]) \n", - " for key, values in results.items() if key not in excluded_keys\n", - "}\n", + "for index, row in df.iterrows():\n", + " y = [row['avg_5_features_test'], row['avg_10_features_test'], row['avg_20_features_test'], row['avg_40_features_test']]\n", + " ax.plot(x, y, '-o', label=row['fi'], color=color_map[row['fi']], markersize=7)\n", "\n", - "# Sort the keys by their sum and extract the top 3 keys with the lowest sums\n", - "top_3_keys = sorted(filtered_sums, key=filtered_sums.get)[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "top_3_keys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in all_ratios:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_top_{k}\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(num_features_selected, results[m], label=m, linestyle='dashed', color=color, marker='o')\n", - " else:\n", - " ax.plot(num_features_selected, results[m], label=m, color=color, marker='o')\n", - " ax.set_xticks(num_features_selected)\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", + "ax.set_xlabel('Pecentage of features considered', fontsize=14)\n", + "ax.set_ylabel('Number of unique features in top k', fontsize=14)\n", + "# ax.set_title('Performance Dataset', fontsize=16)\n", + "ax.legend(fontsize=10, title_fontsize=12)\n", + "ax.set_xticks(x) \n", + "ax.set_xticklabels(x) \n", "\n", "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", + "# plt.savefig('performance_dataset_stability.png')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert False\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " # Initialize a new figure for each plot\n", - " fig, ax = plt.subplots(figsize=(18, 8))\n", - " \n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " \n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " \n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features masked', ylabel=f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " \n", - " # Add legend only once for each figure\n", - " if j == 0:\n", - " ax.legend()\n", - " \n", - " plt.tight_layout()\n", - " # Optionally save each plot as a separate file\n", - " # plt.savefig(f\"./{task_name}_{task}_model_{a_model}_metric_{metric}.png\")\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 5))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods:\n", - " results[m] = []\n", - " for m in methods:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[j] \n", - " for m in methods:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"LIME_RF\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color, marker='o', markersize=4)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color, marker='o', markersize=4)\n", - " ax.set_xticks(range(num_features+1))\n", - " ax.set(xlabel='Number of features selected', ylabel= f\"{metric}\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0 and j==0:\n", - " ax.legend()\n", + "# import pandas as pd\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", + "# from matplotlib.ticker import MaxNLocator\n", + "# palette = {\n", + "# 'LIME_RF': '#1f77b4', # Bold blue\n", + "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", + "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", + "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", + "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", + "# 'TreeSHAP_RF': '#9467bd', # Bold purple\n", + "# # 'Random': '#ad494a', # warm red\n", + "# }\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training Subset Data" + "# sns.set(style=\"whitegrid\")\n", + "# plt.figure(figsize=(10, 4)) \n", + "# sns.scatterplot(\n", + "# data=combined_df_all,\n", + "# x='avg_3_features_train',\n", + "# y='dataset',\n", + "# hue='fi',\n", + "# palette=palette,\n", + "# s=100 # Size of the dots\n", + "# )\n", + "\n", + "# # Customize the legend\n", + "# plt.legend(title='Method', loc='lower right')\n", + "# plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", + "# plt.xlabel('Number of Distinct Features in Top 3 Across Training-Test Splits')\n", + "# plt.ylabel('Dataset')\n", + "\n", + "# plt.yticks(fontsize=10) \n", + "# plt.tight_layout()\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", + "# import pandas as pd\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" + "# palette = {\n", + "# 'LIME_RF': '#1f77b4', # Bold blue\n", + "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", + "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", + "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", + "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", + "# 'TreeSHAP_RF': '#9467bd', # Bold purple\n", + "# # 'Random': '#ad494a', # warm red\n", + "# }\n", + "\n", + "# sns.set(style=\"whitegrid\")\n", + "# plt.figure(figsize=(10, 4)) \n", + "# sns.scatterplot(\n", + "# data=combined_df_all,\n", + "# x='avg_5_features_train',\n", + "# y='dataset',\n", + "# hue='fi',\n", + "# palette=palette,\n", + "# s=100 # Size of the dots\n", + "# )\n", + "\n", + "# # Customize the legend\n", + "# plt.legend(title='Method', loc='lower right')\n", + "# plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", + "# plt.xlabel('Number of Distinct Features in Top 5 Across Training-Test Splits')\n", + "# plt.ylabel('Dataset')\n", + "\n", + "# plt.yticks(fontsize=10) \n", + "# plt.tight_layout()\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", + "# import pandas as pd\n", + "# import matplotlib.pyplot as plt\n", + "# import seaborn as sns\n", "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_{metric}_after_ablation_{k}_absolute\"].mean())\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"metric\",\n", - " title=f'Ablation model = {a_model}')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "# plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test subset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_subset_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_positive\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " for k in range(num_features+1):\n", - " results[m].append(np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_test_delta_MSE_after_ablation_{k}_negative\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# # results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - "# for k in range(num_features+1):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+f\"_train_subset_delta_MSE_after_ablation_{k}_absolute\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# #plt.savefig(f\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_train_subset:\n", - " results[m] = []\n", - " for m in methods_train_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_train_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Train size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_train_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_train_subset:\n", - "# results[m] = []\n", - "# for m in methods_train_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_train_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_train_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Train size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_train_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Subset Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test_subset:\n", - " results[m] = []\n", - " for m in methods_test_subset:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test_subset:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = 100')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_subset_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test_subset:\n", - "# results[m] = []\n", - "# for m in methods_test_subset:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_subset_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test_subset:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = 100')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", - "\n", - "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_subset_addition.png\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Test Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_absolute\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_absolute\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_absolute.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_positive\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_positive\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_positive.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "for i, a_model in enumerate(ablation_models[task]):\n", - " for j, metric in enumerate(metrics[task]):\n", - " results = {}\n", - " for m in methods_test:\n", - " results[m] = []\n", - " for m in methods_test:\n", - " if metric == \"MSE\":\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean()))\n", - " for k in range(num_features):\n", - " results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean()))\n", - " else:\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_negative\"].mean())\n", - " for k in range(num_features):\n", - " results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_negative\"].mean())\n", - " ax = axs[i, j]\n", - " for m in methods_test:\n", - " color = color_map[m]\n", - " if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - " else:\n", - " ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - " if metric == \"MSE\":\n", - " ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " else:\n", - " ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - " title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - " if i == 0 and j == 0:\n", - " ax.legend()\n", - "\n", - "plt.tight_layout()\n", - "#plt.savefig(f\"./{task_name}_{task}_test_removal_negative.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", - "# for i, a_model in enumerate(ablation_models[task]):\n", - "# for j, metric in enumerate(metrics[task]):\n", - "# results = {}\n", - "# for m in methods_test:\n", - "# results[m] = []\n", - "# for m in methods_test:\n", - "# if metric == \"MSE\":\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean()))\n", - "# for k in range(num_features):\n", - "# results[m].append(-1*np.sqrt(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean()))\n", - "# else:\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_before_ablation_addition\"].mean())\n", - "# for k in range(num_features):\n", - "# results[m].append(combined_df[combined_df['fi'] == m][a_model+\"_test_\"+metric+f\"_after_ablation_{k+1}_addition\"].mean())\n", - "# ax = axs[i, j]\n", - "# for m in methods_test:\n", - "# color = color_map[m]\n", - "# if m in [\"TreeSHAP_RF\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - "# ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\n", - "# else:\n", - "# ax.plot(range(num_features+1), results[m], label=m, color=color)\n", - "# if metric == \"MSE\":\n", - "# ax.set(xlabel='Number of features ablated', ylabel= f\"Negative Root({metric})\",\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# else:\n", - "# ax.set(xlabel='Number of features ablated', ylabel=metric,\n", - "# title=f'Ablation model = {a_model}, Test size = {test_size}')\n", - "# if i == 0 and j == 0:\n", - "# ax.legend()\n", + "# palette = {\n", + "# 'LIME_RF': '#1f77b4', # Bold blue\n", + "# 'Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus': '#ff7f0e', # Vibrant orange\n", + "# 'Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus': '#2ca02c', # Bright green\n", + "# 'Local_MDI+_fit_on_oob_ranking_RFPlus': '#d62728', # Bright red\n", + "# 'Local_MDI+_fit_on_all_ranking_RFPlus': '#e377c2', # Pink\n", + "# 'TreeSHAP_RF': '#9467bd', # Bold purple\n", + "# # 'Random': '#ad494a', # warm red\n", + "# }\n", "\n", + "# sns.set(style=\"whitegrid\")\n", + "# plt.figure(figsize=(10, 4)) \n", + "# sns.scatterplot(\n", + "# data=combined_df_all,\n", + "# x='avg_10_features_train',\n", + "# y='dataset',\n", + "# hue='fi',\n", + "# palette=palette,\n", + "# s=100 # Size of the dots\n", + "# )\n", + "\n", + "# # Customize the legend\n", + "# plt.legend(title='Method', loc='lower right')\n", + "# plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n", + "# plt.xlabel('Number of Distinct Features in Top 10 Across Training-Test Splits')\n", + "# plt.ylabel('Dataset')\n", + "\n", + "# plt.yticks(fontsize=10) \n", "# plt.tight_layout()\n", - "# # #plt.savefig(f\"./{task_name}_{task}_test_addition.png\")\n", "# plt.show()" ] } diff --git a/feature_importance/debug_ablation.ipynb b/feature_importance/debug_ablation.ipynb index aa9b03b..51c456e 100644 --- a/feature_importance/debug_ablation.ipynb +++ b/feature_importance/debug_ablation.ipynb @@ -50,10 +50,55 @@ "# X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute, dataset_format=\"array\")" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'imodels.imodels'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mimodels\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mimodels\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mimportance\u001b[39;00m \u001b[39mimport\u001b[39;00m RandomForestPlusRegressor\n\u001b[1;32m 3\u001b[0m rf_plus_model \u001b[39m=\u001b[39m RandomForestPlusRegressor()\n\u001b[1;32m 4\u001b[0m rf_plus_model\u001b[39m.\u001b[39mfit(X, y)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'imodels.imodels'" + ] + } + ], + "source": [ + "from imodels.imodels.importance import RandomForestPlusRegressor\n", + "\n", + "rf_plus_model = RandomForestPlusRegressor()\n", + "rf_plus_model.fit(X, y)\n", + "mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X, y)" + ] + }, { "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 3, 1, 2])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argsort(-1*np.array([5,2,1,4]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)" @@ -61,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/models.py new file mode 100644 index 0000000..0b8e113 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/dgp.py similarity index 96% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/dgp.py index bd290cd..5db2e4d 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/dgp.py @@ -7,7 +7,8 @@ X_PARAMS_DICT = { "source": "csv", "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_PD-0325901_top500.csv", - "sample_row_n": None + "sample_row_n": None, + "normalize": True } # X_PARAMS_DICT = { # "source": "imodels", diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/models.py new file mode 100644 index 0000000..0b8e113 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py deleted file mode 100644 index 4b60f25..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py +++ /dev/null @@ -1,52 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/dgp.py similarity index 77% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/dgp.py index 6978ee9..b161012 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/dgp.py @@ -6,8 +6,9 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { "source": "csv", - "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_Topotecan_top500.csv", - "sample_row_n": None + "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_PD-0325901_top500.csv", + "sample_row_n": None, + "normalize": True } # X_PARAMS_DICT = { # "source": "imodels", @@ -25,11 +26,16 @@ # "sample_row_n": None # } -Y_DGP = sample_real_data_y +Y_DGP = lss_model Y_PARAMS_DICT = { - "source": "csv", - "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_Topotecan.csv" + "beta": 1, + "sigma": None, + "heritability": 0.4, + "tau": 0, + "m": 3, + "r": 2 } + # Y_PARAMS_DICT = { # "source": "imodels", # "data_name": "satellite_image" @@ -45,5 +51,5 @@ # } # vary one parameter -VARY_PARAM_NAME = "sample_row_n" -VARY_PARAM_VALS = {"keep_all_rows": None} \ No newline at end of file +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/models.py new file mode 100644 index 0000000..0b8e113 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_lss/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/dgp.py similarity index 96% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/dgp.py index ed847a7..0af7812 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/dgp.py @@ -7,7 +7,8 @@ X_PARAMS_DICT = { "source": "csv", "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_PD-0325901_top500.csv", - "sample_row_n": None + "sample_row_n": None, + "normalize": True } # X_PARAMS_DICT = { # "source": "imodels", diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/models.py new file mode 100644 index 0000000..0b8e113 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py deleted file mode 100644 index 4b60f25..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py +++ /dev/null @@ -1,52 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/models.py deleted file mode 100644 index 4b60f25..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/models.py +++ /dev/null @@ -1,52 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py deleted file mode 100644 index a82071a..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py +++ /dev/null @@ -1,29 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus_l2_norm', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_RFPlus_l2_norm', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus_l2_norm_sign', LFI_evaluation_RFPlus_inbag_l2_norm_sign_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus_l2_norm_sign', LFI_evaluation_RFPlus_oob_l2_norm_sign_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_RFPlus_l2_norm_sign', LFI_evaluation_RFPlus_all_l2_norm_sign_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/dgp.py new file mode 100644 index 0000000..6b70488 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/dgp.py @@ -0,0 +1,38 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 189, + "sample_row_n": None, + "normalize": True +} + + +Y_DGP = linear_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "s": 5, +} +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_linear/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/dgp.py new file mode 100644 index 0000000..0927e87 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/dgp.py @@ -0,0 +1,41 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 189, + "sample_row_n": None, + "normalize": True +} + + +Y_DGP = lss_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "tau": 0, + "m": 3, + "r": 2 +} + +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_lss/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/dgp.py similarity index 67% rename from feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/dgp.py index f27c705..c08cf5c 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/dgp.py @@ -6,16 +6,20 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { "source": "uci", - "data_id": 925, - "sample_row_n": None + "data_id": 189, + "sample_row_n": None, + "normalize": True } -Y_DGP = sample_real_data_y +Y_DGP = hierarchical_poly Y_PARAMS_DICT = { - "source": "uci", - "data_id": 925 + "m": 3, + "r": 2, + "beta": 1, + "heritability": 0.4, } + # Y_PARAMS_DICT = { # "source": "imodels", # "data_name": "satellite_image" @@ -31,5 +35,5 @@ # } # vary one parameter -VARY_PARAM_NAME = "sample_row_n" -VARY_PARAM_VALS = {"keep_all_rows": None} \ No newline at end of file +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_poly/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/models.py deleted file mode 100644 index 4b60f25..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/models.py +++ /dev/null @@ -1,52 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_performance/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/dgp.py new file mode 100644 index 0000000..b297894 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/dgp.py @@ -0,0 +1,38 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 320, + "sample_row_n": None, + "normalize": True +} + + +Y_DGP = linear_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "s": 5, +} +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_linear/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/dgp.py new file mode 100644 index 0000000..7a865e0 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/dgp.py @@ -0,0 +1,41 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 320, + "sample_row_n": None, + "normalize": True +} + + +Y_DGP = lss_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "tau": 0, + "m": 3, + "r": 2 +} + +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_lss/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/dgp.py new file mode 100644 index 0000000..fd8cb19 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/dgp.py @@ -0,0 +1,39 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 320, + "sample_row_n": None, + "normalize": True +} + + +Y_DGP = hierarchical_poly +Y_PARAMS_DICT = { + "m": 3, + "r": 2, + "beta": 1, + "heritability": 0.4, +} + +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/models.py new file mode 100644 index 0000000..af85347 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_poly/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/models.py deleted file mode 100644 index 4b60f25..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/models.py +++ /dev/null @@ -1,52 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_satellite/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_satellite/models.py deleted file mode 100644 index b5c76eb..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_satellite/models.py +++ /dev/null @@ -1,39 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestRegressor -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_OOB_RFPlus', LFI_evaluation_RFPlus_oob, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus', LFI_evaluation_RFPlus_all, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus', LFI_evaluation_RFPlus_oob, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus_l2_norm', LFI_evaluation_RFPlus_inbag_l2_norm, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm', LFI_evaluation_RFPlus_all_l2_norm, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus_avg_leaf', LFI_evaluation_RFPlus_inbag_avg_leaf, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_OOB_RFPlus_avg_leaf', LFI_evaluation_RFPlus_oob_avg_leaf, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf', LFI_evaluation_RFPlus_all_avg_leaf, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf', LFI_evaluation_RFPlus_oob_avg_leaf, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf', LFI_evaluation_RFPlus_inbag_l2_norm_avg_leaf, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf', LFI_evaluation_RFPlus_oob_l2_norm_avg_leaf, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf', LFI_evaluation_RFPlus_all_l2_norm_avg_leaf, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf', LFI_evaluation_RFPlus_oob_l2_norm_avg_leaf, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Kernel_SHAP_RF_plus', kernel_shap_evaluation_RF_plus, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF_plus', lime_evaluation_RF_plus, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Oracle_test_RFPlus', LFI_evaluation_oracle_RF_plus, base_model="RFPlus_default", model_type='tree', splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_global_MDI_plus_RFPlus', LFI_global_MDI_plus_RF_Plus, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature/dgp.py new file mode 100644 index 0000000..80d20e0 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature/dgp.py @@ -0,0 +1,20 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 925, + "sample_row_n": None +} + +Y_DGP = sample_real_data_y +Y_PARAMS_DICT = { + "source": "uci", + "data_id": 925 +} + +VARY_PARAM_NAME = "sample_row_n" +VARY_PARAM_VALS = {"keep_all_rows": None} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature/models.py new file mode 100644 index 0000000..0b8e113 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature/models.py @@ -0,0 +1,25 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/dgp.py new file mode 100644 index 0000000..f918d5c --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/dgp.py @@ -0,0 +1,52 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 925, + "sample_row_n": None, + "normalize": True +} +# X_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image", +# "sample_row_n": None +# } +# X_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588, +# "sample_row_n": None +# } +# X_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_PD-0325901_top1000.csv", +# "sample_row_n": None +# } + +Y_DGP = linear_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "s": 5, +} +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/models.py new file mode 100644 index 0000000..68386d0 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_linear/models.py @@ -0,0 +1,26 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_RFPlus', LFI_evaluation_AloRFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/dgp.py new file mode 100644 index 0000000..ff4cdca --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/dgp.py @@ -0,0 +1,55 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 925, + "sample_row_n": None, + "normalize": True +} +# X_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image", +# "sample_row_n": None +# } +# X_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588, +# "sample_row_n": None +# } +# X_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/X_ccle_rnaseq_PD-0325901_top1000.csv", +# "sample_row_n": None +# } + +Y_DGP = lss_model +Y_PARAMS_DICT = { + "beta": 1, + "sigma": None, + "heritability": 0.4, + "tau": 0, + "m": 3, + "r": 2 +} + +# Y_PARAMS_DICT = { +# "source": "imodels", +# "data_name": "satellite_image" +# } +# Y_PARAMS_DICT = { +# "source": "openml", +# "data_id": 588 +# } + +# Y_PARAMS_DICT = { +# "source": "csv", +# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_PD-0325901.csv", +# } + +# vary one parameter +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/models.py new file mode 100644 index 0000000..5da8745 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_lss/models.py @@ -0,0 +1,27 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_RFPlus', LFI_evaluation_AloRFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], +] + diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_satellite/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/dgp.py similarity index 76% rename from feature_importance/fi_config/mdi_local/real_data_regression_satellite/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/dgp.py index 8906c68..3f6040e 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_satellite/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/dgp.py @@ -5,9 +5,10 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { - "source": "imodels", - "data_name": "satellite_image", - "sample_row_n": None + "source": "uci", + "data_id": 925, + "sample_row_n": None, + "normalize": True } # X_PARAMS_DICT = { # "source": "imodels", @@ -25,11 +26,14 @@ # "sample_row_n": None # } -Y_DGP = sample_real_data_y +Y_DGP = hierarchical_poly Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "satellite_image" + "m": 3, + "r": 2, + "beta": 1, + "heritability": 0.4, } + # Y_PARAMS_DICT = { # "source": "imodels", # "data_name": "satellite_image" @@ -45,5 +49,5 @@ # } # vary one parameter -VARY_PARAM_NAME = "sample_row_n" -VARY_PARAM_VALS = {"keep_all_rows": None} \ No newline at end of file +VARY_PARAM_NAME = ["heritability"] +VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}} \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/models.py new file mode 100644 index 0000000..68386d0 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_poly/models.py @@ -0,0 +1,26 @@ +import copy +import numpy as np +# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV +# from sklearn.utils.extmath import softmax +from feature_importance.util import ModelConfig, FIModelConfig +from sklearn.ensemble import RandomForestClassifier +from feature_importance.scripts.competing_methods_local import * +from sklearn.linear_model import Ridge + + +ESTIMATORS = [ + [ModelConfig('RF', RandomForestRegressor, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] +] + + +FI_ESTIMATORS = [ + [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], + [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_ranking_RFPlus', LFI_evaluation_AloRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_ridge_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_MDI_lasso_fit_on_all_ranking_RFPlus', LFI_evaluation_MDIRFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_lasso", splitting_strategy = "train-test")], + [FIModelConfig('Local_MDI+_Alo_fit_on_all_RFPlus', LFI_evaluation_AloRFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], +] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py deleted file mode 100644 index d1ddbd7..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py +++ /dev/null @@ -1,53 +0,0 @@ -import copy -import numpy as np -# from sklearn.linear_model import RidgeClassifierCV, LogisticRegressionCV -# from sklearn.utils.extmath import softmax -from feature_importance.util import ModelConfig, FIModelConfig -from sklearn.ensemble import RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestRegressor, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] -] - - -FI_ESTIMATORS = [ - [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('LIME_RF', lime_evaluation_RF_retrain, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random_retrain, model_type='tree', base_model="None", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_RFPlus', LFI_evaluation_RFPlus_inbag_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_RFPlus', LFI_evaluation_RFPlus_oob_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_RFPlus', LFI_evaluation_RFPlus_all_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_RFPlus', LFI_evaluation_RFPlus_all_error_metric_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_average_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_average_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_average_RFPlus', LFI_evaluation_RFPlus_all_error_metric_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_error_metric_ranking_RFPlus', LFI_evaluation_RFPlus_all_error_metric_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_average_RFPlus', LFI_evaluation_RFPlus_inbag_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_average_RFPlus', LFI_evaluation_RFPlus_oob_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_average_RFPlus', LFI_evaluation_RFPlus_all_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_RFPlus', LFI_evaluation_RFPlus_oob_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_ranking_RFPlus', LFI_evaluation_RFPlus_all_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_inbag_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_oob_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_oob_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_ranking_ridge_RFPlus', LFI_evaluation_RFPlus_all_ranking_ridge_retrain, model_type='tree', base_model="RFPlus_ridge", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_average_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_average_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_inbag_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_inbag", splitting_strategy = "train-test")], - # [FIModelConfig('Local_MDI+_fit_on_oob_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_oob", splitting_strategy = "train-test")], - [FIModelConfig('Local_MDI+_fit_on_all_l2_norm_ranking_RFPlus', LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test")], - -] - diff --git a/feature_importance/scripts/competing_methods_local.py b/feature_importance/scripts/competing_methods_local.py index e3ea4f9..594d9b3 100644 --- a/feature_importance/scripts/competing_methods_local.py +++ b/feature_importance/scripts/competing_methods_local.py @@ -30,14 +30,14 @@ # masked_feature_importance[~mask] = sys.maxsize - 1 # return masked_feature_importance - ##############################################################################################3 def random_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): local_fi_score_train = np.random.randn(*X_train.shape) local_fi_score_test = np.random.randn(*X_test.shape) if mode == "absolute": return np.abs(local_fi_score_train), np.abs(local_fi_score_test) - + else: + return local_fi_score_train, local_fi_score_test def tree_shap_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): """ @@ -54,37 +54,12 @@ def tree_shap_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="ab if sklearn.base.is_classifier(fit): if mode == "absolute": return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_test[:,:,1]) + else: + return local_fi_score_train[:,:,1], local_fi_score_test[:,:,1] if mode == "absolute": return np.abs(local_fi_score_train), np.abs(local_fi_score_test) - - -# def lime_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): -# result = np.zeros((X_train.shape[0], X_train.shape[1])) -# if sklearn.base.is_classifier(fit): -# task = "classification" -# else: -# task = "regression" -# if task == "classification": -# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) -# num_features = X_train.shape[1] -# for i in range(X_train.shape[0]): -# exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, 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] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) -# elif task == "regression": -# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) -# num_features = X_train.shape[1] -# for i in range(X_train.shape[0]): -# exp = explainer.explain_instance(X_train[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] = sorted_feature_importance[j][1] -# if mode == "absolute": -# lime_values = np.abs(result) -# return lime_values + else: + return local_fi_score_train, local_fi_score_test def lime_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): train_result = np.zeros((X_train.shape[0], X_train.shape[1])) @@ -114,15 +89,80 @@ def lime_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="absolut for j in range(num_features): test_result[i, j] = sorted_feature_importance[j][1] if mode == "absolute": - train_lime_values = np.abs(train_result) - test_lime_values = np.abs(test_result) + return np.abs(train_result), np.abs(test_result) + else: + return train_result, test_result + +def LFI_evaluation_AloRFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_test) + else: + return local_fi_score_train, local_fi_score_test + +def LFI_evaluation_AloRFPlus_all_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = False) + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, ranking = False) + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_test) + else: + return local_fi_score_train, local_fi_score_test + +def LFI_evaluation_MDIRFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_test) else: - train_lime_values = train_result - test_lime_values = test_result - return train_lime_values, test_lime_values + return local_fi_score_train, local_fi_score_test + +# def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=False, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), np.abs(local_fi_score_test) +# else: +# return local_fi_score_train, local_fi_score_test +# def lime_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): +# result = np.zeros((X_train.shape[0], X_train.shape[1])) +# if sklearn.base.is_classifier(fit): +# task = "classification" +# else: +# task = "regression" +# if task == "classification": +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) +# num_features = X_train.shape[1] +# for i in range(X_train.shape[0]): +# exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, 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] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) +# elif task == "regression": +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) +# num_features = X_train.shape[1] +# for i in range(X_train.shape[0]): +# exp = explainer.explain_instance(X_train[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] = sorted_feature_importance[j][1] +# if mode == "absolute": +# lime_values = np.abs(result) +# return lime_values + # def LFI_evaluation_RFPlus_inbag_retrain(X_train, y_train, fit=None, mode="absolute"): # assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) # rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") @@ -253,13 +293,13 @@ def lime_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="absolut # if mode == "absolute": # return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, ranking = True) - if mode == "absolute": - return np.abs(local_fi_score_train), np.abs(local_fi_score_test) +# def LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, ranking = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), np.abs(local_fi_score_test) # def LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): # assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) @@ -366,13 +406,13 @@ def LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=None # if mode == "absolute": # return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=False, ranking=True) - if mode == "absolute": - return np.abs(local_fi_score_train), np.abs(local_fi_score_test) +# def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=False, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), np.abs(local_fi_score_test) @@ -390,84 +430,84 @@ def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, X_test, -#### Baseline Methods -def random(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - local_fi_score_train = np.random.randn(*X_train.shape) - local_fi_score_train_subset = np.random.randn(*X_train_subset.shape) - local_fi_score_test = np.random.randn(*X_test.shape) - local_fi_score_test_subset = np.random.randn(*X_test_subset.shape) - if mode == "absolute": - return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset - # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") - # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") - # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") - # return local_fi_score_train, np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# #### Baseline Methods +# def random(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# local_fi_score_train = np.random.randn(*X_train.shape) +# local_fi_score_train_subset = np.random.randn(*X_train_subset.shape) +# local_fi_score_test = np.random.randn(*X_test.shape) +# local_fi_score_test_subset = np.random.randn(*X_test_subset.shape) +# if mode == "absolute": +# return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# # return local_fi_score_train, np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) -def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - """ - 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) - local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) - local_fi_score_train_subset = explainer.shap_values(X_train_subset, check_additivity=False) - local_fi_score_test = explainer.shap_values(X_test, check_additivity=False) - local_fi_score_test_subset = explainer.shap_values(X_test_subset, check_additivity=False) - if sklearn.base.is_classifier(fit): - if mode == "absolute": - #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) - return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_train_subset[:,:,1]), np.abs(local_fi_score_test[:,:,1]), np.abs(local_fi_score_test_subset[:,:,1]) - else: - return local_fi_score_train[:,:,1], local_fi_score_train_subset[:,:,1], local_fi_score_test[:,:,1], local_fi_score_test_subset[:,:,1] - else: - if mode == "absolute": - return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") - # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") - # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") - return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset +# def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# """ +# 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) +# local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) +# local_fi_score_train_subset = explainer.shap_values(X_train_subset, check_additivity=False) +# local_fi_score_test = explainer.shap_values(X_test, check_additivity=False) +# local_fi_score_test_subset = explainer.shap_values(X_test_subset, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# if mode == "absolute": +# #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) +# return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_train_subset[:,:,1]), np.abs(local_fi_score_test[:,:,1]), np.abs(local_fi_score_test_subset[:,:,1]) +# else: +# return local_fi_score_train[:,:,1], local_fi_score_train_subset[:,:,1], local_fi_score_test[:,:,1], local_fi_score_test_subset[:,:,1] +# else: +# if mode == "absolute": +# return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset -def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - if train_only: - result = np.zeros((X_train.shape[0], X_train.shape[1])) - if sklearn.base.is_classifier(fit): - task = "classification" - else: - task = "regression" +# def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# if train_only: +# result = np.zeros((X_train.shape[0], X_train.shape[1])) +# if sklearn.base.is_classifier(fit): +# task = "classification" +# else: +# task = "regression" - if task == "classification": - explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) - num_features = X_train.shape[1] - for i in range(X_train.shape[0]): - exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, 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] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) - elif task == "regression": - explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) - num_features = X_train.shape[1] - for i in range(X_train.shape[0]): - exp = explainer.explain_instance(X_train[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] = sorted_feature_importance[j][1] - if mode == "absolute": - lime_values = np.abs(result) - else: - lime_values = result +# if task == "classification": +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) +# num_features = X_train.shape[1] +# for i in range(X_train.shape[0]): +# exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, 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] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) +# elif task == "regression": +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) +# num_features = X_train.shape[1] +# for i in range(X_train.shape[0]): +# exp = explainer.explain_instance(X_train[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] = sorted_feature_importance[j][1] +# if mode == "absolute": +# lime_values = np.abs(result) +# else: +# lime_values = result - return lime_values, None, None, None +# return lime_values, None, None, None @@ -530,52 +570,52 @@ def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, # return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset -def LFI_evaluation_RFPlus_oob(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) - local_fi_score_train_subset = None - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) - local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) - if mode == "absolute": - return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset +# def LFI_evaluation_RFPlus_oob(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset -def LFI_evaluation_RFPlus_all(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) - local_fi_score_train_subset = None - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) - local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) - if mode == "absolute": - return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset +# def LFI_evaluation_RFPlus_all(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset -def LFI_evaluation_RFPlus_oob_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert train_only == True - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] - if mode == "absolute": - return np.abs(local_fi_score_train), None, None, None - else: - return local_fi_score_train, None, None, None +# def LFI_evaluation_RFPlus_oob_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert train_only == True +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, None, None +# else: +# return local_fi_score_train, None, None, None -def LFI_evaluation_RFPlus_all_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert train_only == True - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") - local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] - if mode == "absolute": - return np.abs(local_fi_score_train), None, None, None - else: - return local_fi_score_train, None, None, None +# def LFI_evaluation_RFPlus_all_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert train_only == True +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, None, None +# else: +# return local_fi_score_train, None, None, None # ##### Average Leaf @@ -624,30 +664,30 @@ def LFI_evaluation_RFPlus_all_error_metric(X_train, y_train, X_train_subset, y_t # return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset -def LFI_evaluation_RFPlus_oob_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) - local_fi_score_train_subset = None - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) - local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) - if mode == "absolute": - return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset +# def LFI_evaluation_RFPlus_oob_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset -def LFI_evaluation_RFPlus_all_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) - local_fi_score_train_subset = None - local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) - local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) - if mode == "absolute": - return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) - else: - return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset +# def LFI_evaluation_RFPlus_all_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset # ##### l2 norm