diff --git a/feature_importance/00_ablation_classification_script.sh b/feature_importance/00_ablation_classification_script.sh new file mode 100755 index 0000000..42d4285 --- /dev/null +++ b/feature_importance/00_ablation_classification_script.sh @@ -0,0 +1,10 @@ +#!/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_classification_retrain.py --nreps 1 --config mdi_local.real_data_classification_credit_g_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name credit_g_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_classification_script2.sh b/feature_importance/00_ablation_classification_script2.sh new file mode 100755 index 0000000..9ab0204 --- /dev/null +++ b/feature_importance/00_ablation_classification_script2.sh @@ -0,0 +1,10 @@ +#!/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_classification_retrain.py --nreps 1 --config mdi_local.real_data_classification_csi_pecarn_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name csi_pecarn_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_classification_script3.sh b/feature_importance/00_ablation_classification_script3.sh new file mode 100755 index 0000000..e474649 --- /dev/null +++ b/feature_importance/00_ablation_classification_script3.sh @@ -0,0 +1,10 @@ +#!/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_classification_retrain.py --nreps 1 --config mdi_local.real_data_classification_juvenile_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name juvenile_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_classification_script4.sh b/feature_importance/00_ablation_classification_script4.sh new file mode 100755 index 0000000..eeb382c --- /dev/null +++ b/feature_importance/00_ablation_classification_script4.sh @@ -0,0 +1,10 @@ +#!/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_classification_retrain.py --nreps 1 --config mdi_local.real_data_classification_Ionosphere_retrain --split_seed ${1} --rf_seed ${2} --ignore_cache --create_rmd --folder_name Ionosphere_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_script.sh b/feature_importance/00_ablation_regression_script.sh index e62eb02..9d90092 100755 --- a/feature_importance/00_ablation_regression_script.sh +++ b/feature_importance/00_ablation_regression_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_retrain.py --nreps 1 --config mdi_local.real_data_regression_retrain --split_seed 0 --rf_seed ${1} --ignore_cache --create_rmd --folder_name linear_retrain --fit_model True --absolute_masking True" +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 new file mode 100755 index 0000000..d86a68f --- /dev/null +++ b/feature_importance/00_ablation_regression_script2.sh @@ -0,0 +1,10 @@ +#!/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 new file mode 100755 index 0000000..0178d93 --- /dev/null +++ b/feature_importance/00_ablation_regression_script3.sh @@ -0,0 +1,10 @@ +#!/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 new file mode 100755 index 0000000..93c70c2 --- /dev/null +++ b/feature_importance/00_ablation_regression_script4.sh @@ -0,0 +1,10 @@ +#!/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_script.sh b/feature_importance/00_ablation_regression_stability_script.sh new file mode 100755 index 0000000..7aa857d --- /dev/null +++ b/feature_importance/00_ablation_regression_stability_script.sh @@ -0,0 +1,10 @@ +#!/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_parkinsons_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name parkinsons_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 new file mode 100755 index 0000000..df168c9 --- /dev/null +++ b/feature_importance/00_ablation_regression_stability_script2.sh @@ -0,0 +1,10 @@ +#!/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_regression_stability_script3.sh b/feature_importance/00_ablation_regression_stability_script3.sh new file mode 100755 index 0000000..b94b0da --- /dev/null +++ b/feature_importance/00_ablation_regression_stability_script3.sh @@ -0,0 +1,10 @@ +#!/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_temperature_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name temperature_stability" + +# 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_stability_script4.sh new file mode 100755 index 0000000..978614d --- /dev/null +++ b/feature_importance/00_ablation_regression_stability_script4.sh @@ -0,0 +1,10 @@ +#!/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_CCLE_PD_0325901_retrain --split_seed ${1} --ignore_cache --create_rmd --folder_name CCLE_PD_0325901_stability" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/00_ablation_script_class.sh b/feature_importance/00_ablation_script_class.sh new file mode 100755 index 0000000..252d70d --- /dev/null +++ b/feature_importance/00_ablation_script_class.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +slurm_script="00_ablation_classification_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 diff --git a/feature_importance/00_ablation_script_regr.sh b/feature_importance/00_ablation_script_regr.sh index 8c8f0e3..6c670ab 100755 --- a/feature_importance/00_ablation_script_regr.sh +++ b/feature_importance/00_ablation_script_regr.sh @@ -1,9 +1,10 @@ #!/bin/bash -slurm_script="00_ablation_regression_script.sh" +slurm_script="00_ablation_regression_script4.sh" -for rep in {1..2} -do - sbatch $slurm_script $rep # Submit SLURM job using the specified script - sleep 2 +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_stability.sh b/feature_importance/00_ablation_script_regr_stability.sh new file mode 100755 index 0000000..a337d7f --- /dev/null +++ b/feature_importance/00_ablation_script_regr_stability.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +slurm_script="00_ablation_regression_stability_script4.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 +done \ No newline at end of file diff --git a/feature_importance/00_run_ablation_classification_retrain.py b/feature_importance/00_run_ablation_classification_retrain.py index 3e7a613..31bb8d5 100644 --- a/feature_importance/00_run_ablation_classification_retrain.py +++ b/feature_importance/00_run_ablation_classification_retrain.py @@ -20,6 +20,7 @@ from sklearn import preprocessing from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.linear_model import LogisticRegressionCV +from sklearn.linear_model import LinearRegression from sklearn.svm import SVC import xgboost as xgb from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier @@ -36,90 +37,13 @@ # python 01_run_ablation_classification.py --nreps 5 --config mdi_local.real_data_classification --split_seed 331 --ignore_cache --create_rmd --result_name diabetes_classification -# def generate_random_shuffle(data, seed): -# """ -# Randomly shuffle each column of the data. -# """ -# np.random.seed(seed) -# return np.array([np.random.permutation(data[:, i]) for i in range(data.shape[1])]).T - - -# def ablation(data, feature_importance, mode, num_features, seed): -# """ -# Replace the top num_features max feature importance data with random shuffle for each sample -# """ -# assert mode in ["max", "min"] -# fi = feature_importance.to_numpy() -# shuffle = generate_random_shuffle(data, seed) -# if mode == "max": -# indices = np.argsort(-fi) -# else: -# indices = np.argsort(fi) -# data_copy = data.copy() -# for i in range(data.shape[0]): -# for j in range(num_features): -# data_copy[i, indices[i,j]] = shuffle[i, indices[i,j]] -# return data_copy - -# def ablation_removal(train_mean, data, feature_importance_rank, feature_index): -# """ -# Replace the top num_features max feature importance data with mean value for each sample -# """ -# data_copy = data.copy() -# for i in range(data.shape[0]): -# data_copy[i, feature_importance_rank[i,feature_index]] = train_mean[feature_importance_rank[i,feature_index]] -# return data_copy - -# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): -# """ -# Initialize the data with mean values and add the top num_features max feature importance data for each sample -# """ -# data_copy = data_ablation.copy() -# for i in range(data.shape[0]): -# data_copy[i, feature_importance_rank[i,feature_index]] = data[i, feature_importance_rank[i,feature_index]] -# return data_copy - -def ablation_removal(train_mean, data, feature_importance, feature_importance_rank, feature_index, mode): - if mode == "absolute": - return ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index) - else: - return ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index) - - -def ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index): - """ - Replace the top num_features max feature importance data with mean value for each sample - """ - data_copy = data.copy() - indices = feature_importance_rank[:, feature_index] - data_copy[np.arange(data.shape[0]), indices] = train_mean[indices] - return data_copy - -def ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index): - data_copy = data.copy() - indices = feature_importance_rank[:, feature_index] - sum = 0 - for i in range(data.shape[0]): - if feature_importance[i, indices[i]] != 0 and feature_importance[i, indices[i]] < sys.maxsize - 1: - sum += 1 - data_copy[i, indices[i]] = train_mean[indices[i]] - print("Remove sum: ", sum) - return data_copy - -# def delta_mae(y_true, y_pred_1, y_pred_2): -# mae_before = np.abs(y_true - y_pred_1) -# mae_after = np.abs(y_true - y_pred_2) -# absolute_delta_mae = np.mean(np.abs(mae_before - mae_after)) -# return absolute_delta_mae - -# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): -# """ -# Initialize the data with mean values and add the top num_features max feature importance data for each sample -# """ -# data_copy = data_ablation.copy() -# indices = feature_importance_rank[:, feature_index] -# data_copy[np.arange(data.shape[0]), indices] = data[np.arange(data.shape[0]), indices] -# return data_copy +def select_top_features(array, sorted_indices, percentage): + array = copy.deepcopy(array) + num_features = array.shape[1] + num_selected = int(np.ceil(num_features * percentage)) + selected_indices = sorted_indices[:num_selected] + selected_array = array[:, selected_indices] + return num_selected, selected_array def compare_estimators(estimators: List[ModelConfig], @@ -141,7 +65,7 @@ def compare_estimators(estimators: List[ModelConfig], # loop over model estimators for model in estimators: - est = model.cls(**model.kwargs) + # est = model.cls(**model.kwargs) # get kwargs for all fi_ests fi_kwargs = {} @@ -170,6 +94,7 @@ def compare_estimators(estimators: List[ModelConfig], print("Fitting Models") # fit RF model start_rf = time.time() + est = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=args.rf_seed) est.fit(X_train, y_train) end_rf = time.time() @@ -185,13 +110,13 @@ def compare_estimators(estimators: List[ModelConfig], 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() - # est_regressor = RandomForestRegressor(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=42) - # est_regressor.fit(X_train, y_train) - # rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est_regressor, include_raw=False, fit_on="inbag", prediction_model=Ridge(alpha=1e-6)) - # rf_plus_base_inbag.fit(X_train, y_train) - # end_rf_plus_inbag = time.time() + #fit inbag RF_plus model + start_rf_plus_inbag = time.time() + est_regressor = RandomForestRegressor(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=args.rf_seed) + est_regressor.fit(X_train, y_train) + rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est_regressor, include_raw=False, fit_on="inbag", prediction_model=LinearRegression()) + rf_plus_base_inbag.fit(X_train, y_train) + end_rf_plus_inbag = time.time() # get test results test_all_auc_rf = roc_auc_score(y_test, est.predict_proba(X_test)[:, 1]) @@ -203,289 +128,83 @@ def compare_estimators(estimators: List[ModelConfig], test_all_auc_rf_plus_oob = roc_auc_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1]) test_all_auprc_rf_plus_oob = average_precision_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1]) test_all_f1_rf_plus_oob = f1_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1] > 0.5) + # test_all_auc_rf_plus_inbag = roc_auc_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1]) + # test_all_auprc_rf_plus_inbag = average_precision_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1]) + # test_all_f1_rf_plus_inbag = f1_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1] > 0.5) fitted_results = { - "Model": ["RF", "RF_plus", "RF_plus_oob"], - "AUC": [test_all_auc_rf, test_all_auc_rf_plus, test_all_auc_rf_plus_oob], - "AUPRC": [test_all_auprc_rf, test_all_auprc_rf_plus, test_all_auprc_rf_plus_oob], - "F1": [test_all_f1_rf, test_all_f1_rf_plus, test_all_f1_rf_plus_oob], - "Time": [end_rf - start_rf, end_rf_plus - start_rf_plus, end_rf_plus_oob - start_rf_plus_oob] + "Model": ["RF", "RF_plus", "RF_plus_oob"],#, "RF_plus_inbag"], + "AUC": [test_all_auc_rf, test_all_auc_rf_plus, test_all_auc_rf_plus_oob],#, test_all_auc_rf_plus_inbag], + "AUPRC": [test_all_auprc_rf, test_all_auprc_rf_plus, test_all_auprc_rf_plus_oob],#, test_all_auprc_rf_plus_inbag], + "F1": [test_all_f1_rf, test_all_f1_rf_plus, test_all_f1_rf_plus_oob],#, test_all_f1_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] } os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}", exist_ok=True) results_df = pd.DataFrame(fitted_results) - results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_{args.split_seed}.csv", index=False) + results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_rf_seed_{args.rf_seed}_split_seed_{args.split_seed}.csv", index=False) - - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RF_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(est, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_default_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base_oob, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base_inbag, file) - - if args.absolute_masking or args.positive_masking or args.negative_masking: - np.random.seed(42) - if X_train.shape[0] > 100: - indices_train = np.random.choice(X_train.shape[0], 100, replace=False) - X_train_subset = X_train[indices_train] - y_train_subset = y_train[indices_train] - else: - indices_train = np.arange(X_train.shape[0]) - X_train_subset = X_train - y_train_subset = y_train - - if X_test.shape[0] > 100: - indices_test = np.random.choice(X_test.shape[0], 100, replace=False) - X_test_subset = X_test[indices_test] - y_test_subset = y_test[indices_test] - else: - indices_test = np.arange(X_test.shape[0]) - X_test_subset = X_test - y_test_subset = y_test - if args.num_features_masked is None: num_features_masked = X_train.shape[1] else: num_features_masked = args.num_features_masked - for fi_est in tqdm(fi_ests): metric_results = { 'model': model.name, 'fi': fi_est.name, 'train_size': X_train.shape[0], - 'train_subset_size': X_train_subset.shape[0], 'test_size': X_test.shape[0], - 'test_subset_size': X_test_subset.shape[0], 'num_features': X_train.shape[1], 'data_split_seed': args.split_seed, + 'rf_seed': args.rf_seed, 'num_features_masked': num_features_masked } - for i in range(X_train_subset.shape[0]): - metric_results[f'sample_train_{i}'] = indices_train[i] - for i in range(X_test_subset.shape[0]): - metric_results[f'sample_test_{i}'] = indices_test[i] - print("Load Models") - start = time.time() - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_default_{args.split_seed}.dill", 'rb') as file: - # rf_plus_base = dill.load(file) - # if fi_est.base_model == "None": - # loaded_model = None - # elif fi_est.base_model == "RF": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RF_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_oob": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_inbag": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_default": - # loaded_model = rf_plus_base - rf_plus_base = rf_plus_base if fi_est.base_model == "None": 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_inbag": + loaded_model = rf_plus_base_inbag elif fi_est.base_model == "RFPlus_default": loaded_model = rf_plus_base - end = time.time() - metric_results['load_model_time'] = end - start - print(f"done with loading models: {end - start}") - + m= "absolute" start = time.time() print(f"Compute feature importance") - # Compute feature importance - local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset = fi_est.cls(X_train=X_train, y_train=y_train, X_train_subset = X_train_subset, y_train_subset=y_train_subset, - X_test=X_test, y_test=y_test, X_test_subset=X_test_subset, y_test_subset=y_test_subset, - fit=loaded_model, mode="absolute") - if fi_est.name.startswith("Local_MDI+"): - local_fi_score_train_subset = local_fi_score_train[indices_train] - - m= "absolute" - #feature_importance_list[m][fi_est.name] = [local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset] + local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train, 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: + sorted_feature = np.argsort(-train_fi_mean) + 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 - print(f"done with feature importance {m}: {end - start}") - # prepare ablations - print("start ablation") - ablation_models = {"RF_Classifier": RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42), - # "LogisticCV": LogisticRegressionCV(random_state=42, max_iter=2000), - # "SVM": SVC(random_state=42, probability=True), - # "XGBoost_Classifier": xgb.XGBClassifier(random_state=42), - #"RF_Plus_Classifier": rf_plus_base - } - start = time.time() - for a_model in ablation_models: - ablation_models[a_model].fit(X_train_subset, y_train_subset) - end = time.time() - metric_results['ablation_model_fit_time'] = end - start - print(f"done with ablation model fit: {end - start}") - local_fi_score_train_subset[local_fi_score_train_subset == float("-inf")] = -sys.maxsize - 1 - local_fi_score_train_subset[local_fi_score_train_subset == float("inf")] = sys.maxsize - 1 - if fi_est.ascending: - local_fi_score_train_subset_rank = np.argsort(-local_fi_score_train_subset) + ablation_models = {"RF_Classifier": RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=args.rf_seed), + "Logistic_Regression": LogisticRegressionCV(cv=5, max_iter=5000),} + 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] else: - local_fi_score_train_subset_rank = np.argsort(local_fi_score_train_subset) - - train_mean = np.mean(X_train_subset, axis=0) - - for a_model in ablation_models: - print(f"start ablation removal: {a_model}") - ablation_est = ablation_models[a_model] - y_pred_before = ablation_est.predict(X_test) - metric_results[f'{a_model}_log_loss_after_ablation_0_{m}'] = log_loss(y_test, y_pred_before) - X_temp = copy.deepcopy(X_train_subset) - for i in range(num_features_masked): - ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score_train_subset, local_fi_score_train_subset_rank, i, m) - ablation_est.fit(ablation_X_data, y_train_subset) - y_pred = ablation_est.predict(X_test) - metric_results[f'{a_model}_log_loss_after_ablation_{i+1}_{m}'] = log_loss(y_test, y_pred) - X_temp = ablation_X_data - - # all_fi = [local_fi_score_train_subset, local_fi_score_test_subset, local_fi_score_test] - # all_fi_rank = [None, None, None] - # for i in range(len(all_fi)): - # fi = all_fi[i] - # if isinstance(fi, np.ndarray): - # fi[fi == float("-inf")] = -sys.maxsize - 1 - # fi[fi == float("inf")] = sys.maxsize - 1 - # if fi_est.ascending: - # all_fi_rank[i] = np.argsort(-fi) - # else: - # all_fi_rank[i] = np.argsort(fi) - - # ablation_datas = {"train_subset": (X_train_subset, y_train_subset, all_fi[0], all_fi_rank[0]), - # "test_subset": (X_test_subset, y_test_subset, all_fi[1], all_fi_rank[1]), - # "test": (X_test, y_test, all_fi[2], all_fi_rank[2])} - # train_mean = np.mean(X_train, axis=0) - - # print("start ablation") - # # Start ablation 1: Feature removal - # for ablation_data in ablation_datas: - # start = time.time() - # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] - # if not isinstance(local_fi_score, np.ndarray): - # for a_model in ablation_models: - # for i in range(num_features_masked+1): - # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i}_{m}'] = None - # else: - # for a_model in ablation_models: - # print(f"start ablation removal: {ablation_data} {a_model}") - # ablation_est = ablation_models[a_model] - # y_pred_before = ablation_est.predict_proba(X_data)[:, 1] - # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_0_{m}'] = 0 - # X_temp = copy.deepcopy(X_data) - # for i in range(num_features_masked): - # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) - # y_pred = ablation_est.predict_proba(ablation_X_data)[:, 1] - # if i == 0: - # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i+1}_{m}'] = delta_mae(y_data, y_pred_before, y_pred) - # else: - # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i+1}_{m}'] = delta_mae(y_data, y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i}_{m}'] - # X_temp = ablation_X_data - # y_pred_before = y_pred - # end = time.time() - # print(f"done with ablation removal {m}: {ablation_data} {end - start}") - # metric_results[f'{ablation_data}_ablation_removal_{m}_time'] = end - start - - - - # Start ablation 1: Feature removal - # for ablation_data in ablation_datas: - # start = time.time() - # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] - # if not isinstance(local_fi_score, np.ndarray): - # for a_model in ablation_models: - # metric_results[f'{a_model}_{ablation_data}_AUROC_before_ablation_{m}'] = None - # metric_results[f'{a_model}_{ablation_data}_AUPRC_before_ablation_{m}'] = None - # metric_results[f'{a_model}_{ablation_data}_F1_before_ablation_{m}'] = None - # for i in range(num_features_masked): - # for a_model in ablation_models: - # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_{m}'] = None - # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_{m}'] = None - # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_{m}'] = None - # else: - # for a_model in ablation_models: - # print(f"start ablation removal: {ablation_data} {a_model}") - # ablation_est = ablation_models[a_model] - # y_pred = ablation_est.predict(X_data) - # metric_results[a_model + f'_{ablation_data}_AUROC_before_ablation_{m}'] = roc_auc_score(y_data, y_pred) - # metric_results[a_model + f'_{ablation_data}_AUPRC_before_ablation_{m}'] = average_precision_score(y_data, y_pred) - # metric_results[a_model + f'_{ablation_data}_F1_before_ablation_{m}'] = f1_score(y_data, y_pred > 0.5) - # ablation_results_auroc_list = [0] * num_features_masked - # ablation_results_auprc_list = [0] * num_features_masked - # ablation_results_f1_list = [0] * num_features_masked - # X_temp = X_data.copy() - # for i in range(num_features_masked): - # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) - # ablation_results_auroc_list[i] = roc_auc_score(y_data, ablation_est.predict(ablation_X_data)) - # ablation_results_auprc_list[i] = average_precision_score(y_data, ablation_est.predict(ablation_X_data)) - # ablation_results_f1_list[i] = f1_score(y_data, ablation_est.predict(ablation_X_data) > 0.5) - # X_temp = ablation_X_data - # for i in range(num_features_masked): - # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_{m}'] = ablation_results_auroc_list[i] - # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_{m}'] = ablation_results_auprc_list[i] - # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_{m}'] = ablation_results_f1_list[i] - # end = time.time() - # print(f"done with ablation removal: {ablation_data} {end - start}") - # metric_results[f'{ablation_data}_ablation_removal_time'] = end - start - - # # Start ablation 2: Feature addition - # for ablation_data in ablation_datas: - # start = time.time() - # X_data, y_data, local_fi_score_data = ablation_datas[ablation_data] - # if not isinstance(local_fi_score_data, np.ndarray): - # for a_model in ablation_models: - # metric_results[f'{a_model}_{ablation_data}_AUROC_before_ablation_addition'] = None - # metric_results[f'{a_model}_{ablation_data}_AUPRC_before_ablation_addition'] = None - # metric_results[f'{a_model}_{ablation_data}_F1_before_ablation_addition'] = None - # for i in range(num_ablate_features): - # for a_model in ablation_models: - # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_addition'] = None - # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_addition'] = None - # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_addition'] = None - # else: - # for a_model in ablation_models: - # print(f"start ablation addtion: {ablation_data} {a_model}") - # ablation_est = ablation_models[a_model] - # X_temp = np.array([train_mean_list] * X_data.shape[0]).copy() - # y_pred = ablation_est.predict(X_temp) - # metric_results[a_model + f'_{ablation_data}_AUROC_before_ablation_addition'] = roc_auc_score(y_data, y_pred) - # metric_results[a_model + f'_{ablation_data}_AUPRC_before_ablation_addition'] = average_precision_score(y_data, y_pred) - # metric_results[a_model + f'_{ablation_data}_F1_before_ablation_addition'] = f1_score(y_data, y_pred > 0.5) - # imp_vals = copy.deepcopy(local_fi_score_data) - # ablation_results_auroc_list = [0] * num_ablate_features - # ablation_results_auprc_list = [0] * num_ablate_features - # ablation_results_f1_list = [0] * num_ablate_features - # for i in range(num_ablate_features): - # ablation_X_data = ablation_addition(X_temp, X_data, imp_vals, i) - # ablation_results_auroc_list[i] = roc_auc_score(y_data, ablation_est.predict(ablation_X_data)) - # ablation_results_auprc_list[i] = average_precision_score(y_data, ablation_est.predict(ablation_X_data)) - # ablation_results_f1_list[i] = f1_score(y_data, ablation_est.predict(ablation_X_data) > 0.5) - # X_temp = ablation_X_data - # for i in range(num_ablate_features): - # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_addition'] = ablation_results_auroc_list[i] - # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_addition'] = ablation_results_auprc_list[i] - # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_addition'] = ablation_results_f1_list[i] - - # end = time.time() - # print(f"done with ablation addtion: {ablation_data} {end - start}") - # metric_results[f'{ablation_data}_ablation_addition_time'] = end - start - + mask_ratio = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9] + print(f"X_train_0: {X_train[0]}") + for mask in mask_ratio: + print(f"Mask ratio: {mask}") + num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) + print(f"Train shape: {X_train_masked.shape}") + num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + print(f"Test shape: {X_test_masked.shape}") + print(f"X_train_masked_0: {X_train_masked[0]}") + metric_results[f'num_features_selected_{mask}'] = num_features_selected + for a_model in ablation_models: + ablation_models[a_model].fit(X_train_masked, y_train) + y_pred = ablation_models[a_model].predict_proba(X_test_masked)[:, 1] + metric_results[f'{a_model}_LogLoss_top_{mask}'] = log_loss(y_test, y_pred) + metric_results[f'{a_model}_AUROC_top_{mask}'] = roc_auc_score(y_test, y_pred) # initialize results with metadata and metric results kwargs: dict = model.kwargs # dict @@ -634,6 +353,7 @@ def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp 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) # for multiple reruns, should support varying split_seed parser.add_argument('--ignore_cache', action='store_true', default=False) @@ -687,9 +407,9 @@ def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp results_dir = oj(args.results_path, args.config, args.folder_name) if isinstance(vary_param_name, list): - path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.split_seed)) + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "split_seed_" + str(args.split_seed)+"rf_seed_" + str(args.rf_seed)) else: - path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) + path = oj(results_dir, "varying_" + vary_param_name, "split_seed_" + str(args.split_seed)+"rf_seed_" + str(args.rf_seed)) os.makedirs(path, exist_ok=True) eval_out = defaultdict(list) diff --git a/feature_importance/00_run_ablation_regression_retrain.py b/feature_importance/00_run_ablation_regression_retrain.py index a14c100..ed3817d 100644 --- a/feature_importance/00_run_ablation_regression_retrain.py +++ b/feature_importance/00_run_ablation_regression_retrain.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 Ridge +from sklearn.linear_model import RidgeCV sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -36,75 +36,6 @@ #RUN THE FILE # python 01_run_ablation_regression.py --nreps 5 --config mdi_local.real_data_regression --split_seed 331 --ignore_cache --create_rmd --result_name diabetes_regression - -# def generate_random_shuffle(data, seed): -# """ -# Randomly shuffle each column of the data. -# """ -# np.random.seed(seed) -# return np.array([np.random.permutation(data[:, i]) for i in range(data.shape[1])]).T - - -# def ablation(data, feature_importance, mode, num_features, seed): -# """ -# Replace the top num_features max feature importance data with random shuffle for each sample -# """ -# assert mode in ["max", "min"] -# fi = feature_importance.to_numpy() -# shuffle = generate_random_shuffle(data, seed) -# if mode == "max": -# indices = np.argsort(-fi) -# else: -# indices = np.argsort(fi) -# data_copy = data.copy() -# for i in range(data.shape[0]): -# for j in range(num_features): -# data_copy[i, indices[i,j]] = shuffle[i, indices[i,j]] -# return data_copy - -# def ablation_removal(train_mean, data, feature_importance_rank, feature_index): -# """ -# Replace the top num_features max feature importance data with mean value for each sample -# """ -# data_copy = data.copy() -# for i in range(data.shape[0]): -# data_copy[i, feature_importance_rank[i,feature_index]] = train_mean[feature_importance_rank[i,feature_index]] -# return data_copy - -# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): -# """ -# Initialize the data with mean values and add the top num_features max feature importance data for each sample -# """ -# data_copy = data_ablation.copy() -# for i in range(data.shape[0]): -# data_copy[i, feature_importance_rank[i,feature_index]] = data[i, feature_importance_rank[i,feature_index]] -# return data_copy -def ablation_removal(train_mean, data, feature_importance, feature_importance_rank, feature_index, mode): - if mode == "absolute": - return ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index) - # else: - # return ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index) - -def ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index): - """ - Replace the top num_features max feature importance data with mean value for each sample - """ - data_copy = data.copy() - indices = feature_importance_rank[:, feature_index] - data_copy[np.arange(data.shape[0]), indices] = train_mean[indices] - return data_copy - -# def ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index): -# data_copy = data.copy() -# indices = feature_importance_rank[:, feature_index] -# sum = 0 -# for i in range(data.shape[0]): -# if feature_importance[i, indices[i]] != 0 and feature_importance[i, indices[i]] < sys.maxsize - 1: -# sum += 1 -# data_copy[i, indices[i]] = train_mean[indices[i]] -# print("Remove sum: ", sum) -# return data_copy - def select_top_features(array, sorted_indices, percentage): array = copy.deepcopy(array) num_features = array.shape[1] @@ -113,24 +44,6 @@ def select_top_features(array, sorted_indices, percentage): selected_array = array[:, selected_indices] return num_selected, selected_array -# def delta_mse(y_true, y_pred_1, y_pred_2): -# mse_before = (y_true - y_pred_1) ** 2 -# mse_after = (y_true - y_pred_2) ** 2 -# absolute_delta_mse = np.mean(np.abs(mse_before - mse_after)) -# return absolute_delta_mse - -# def delta_y_pred(y_pred_1, y_pred_2): -# return np.mean(np.abs(y_pred_1 - y_pred_2)) - -# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): -# """ -# Initialize the data with mean values and add the top num_features max feature importance data for each sample -# """ -# data_copy = data_ablation.copy() -# indices = feature_importance_rank[:, feature_index] -# data_copy[np.arange(data.shape[0]), indices] = data[np.arange(data.shape[0]), indices] -# return data_copy - def compare_estimators(estimators: List[ModelConfig], fi_estimators: List[FIModelConfig], @@ -200,6 +113,17 @@ def compare_estimators(estimators: List[ModelConfig], 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) + + + # 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)) @@ -214,46 +138,12 @@ def compare_estimators(estimators: List[ModelConfig], "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] + "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] } os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}", exist_ok=True) results_df = pd.DataFrame(fitted_results) - results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_rf_seed_{args.rf_seed}.csv", index=False) - - - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RF_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(est, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_default_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base_oob, file) - # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill" - # with open(pickle_file, 'wb') as file: - # dill.dump(rf_plus_base_inbag, file) - - # if args.absolute_masking or args.positive_masking or args.negative_masking: - # np.random.seed(42) - # if X_train.shape[0] > 100: - # indices_train = np.random.choice(X_train.shape[0], 100, replace=False) - # X_train_subset = X_train[indices_train] - # y_train_subset = y_train[indices_train] - # else: - # indices_train = np.arange(X_train.shape[0]) - # X_train_subset = X_train - # y_train_subset = y_train - - # if X_test.shape[0] > 100: - # indices_test = np.random.choice(X_test.shape[0], 100, replace=False) - # X_test_subset = X_test[indices_test] - # y_test_subset = y_test[indices_test] - # else: - # indices_test = np.arange(X_test.shape[0]) - # X_test_subset = X_test - # y_test_subset = y_test + results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_rf_seed_{args.rf_seed}_split_seed_{args.split_seed}.csv", index=False) if args.num_features_masked is None: num_features_masked = X_train.shape[1] @@ -266,37 +156,12 @@ def compare_estimators(estimators: List[ModelConfig], 'model': model.name, 'fi': fi_est.name, 'train_size': X_train.shape[0], - # 'train_subset_size': X_train_subset.shape[0], 'test_size': X_test.shape[0], - # 'test_subset_size': X_test_subset.shape[0], 'num_features': X_train.shape[1], 'data_split_seed': args.split_seed, 'rf_seed': args.rf_seed, 'num_features_masked': num_features_masked } - # for i in range(X_train_subset.shape[0]): - # metric_results[f'sample_train_{i}'] = indices_train[i] - # for i in range(X_test_subset.shape[0]): - # metric_results[f'sample_test_{i}'] = indices_test[i] - - print("Load Models") - start = time.time() - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_default_{args.split_seed}.dill", 'rb') as file: - # rf_plus_base = dill.load(file) - # if fi_est.base_model == "None": - # loaded_model = None - # elif fi_est.base_model == "RF": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RF_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_oob": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_inbag": - # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill", 'rb') as file: - # loaded_model = dill.load(file) - # elif fi_est.base_model == "RFPlus_default": - # loaded_model = rf_plus_base - #rf_plus_base = rf_plus_base if fi_est.base_model == "None": loaded_model = None elif fi_est.base_model == "RF": @@ -307,166 +172,48 @@ def compare_estimators(estimators: List[ModelConfig], loaded_model = rf_plus_base_inbag elif fi_est.base_model == "RFPlus_default": loaded_model = rf_plus_base - end = time.time() - metric_results['load_model_time'] = end - start - print(f"done with loading models: {end - start}") - + 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 - start = time.time() - print(f"Compute feature importance") - # Compute feature importance - local_fi_score_train, _, _, _ = fi_est.cls(X_train=X_train, y_train=y_train, fit=loaded_model, mode="absolute") - # if fi_est.name.startswith("Local_MDI+"): - # local_fi_score_train_subset = local_fi_score_train[indices_train] - m= "absolute" - #feature_importance_list[m][fi_est.name] = [local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset] - end = time.time() - metric_results[f'fi_time_{m}'] = end - start - print(f"done with feature importance {m}: {end - start}") - # prepare ablations - print("prepare ablation") - ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=args.rf_seed), - # "Linear": LinearRegression(), - # "XGB_Regressor": xgb.XGBRegressor(random_state=42), - # 'Kernel_Ridge': KernelRidge(), - #"RF_Plus_Regressor": rf_plus_base - } start = time.time() - for a_model in ablation_models: - ablation_models[a_model].fit(X_train, y_train) - end = time.time() - metric_results['ablation_model_fit_time'] = end - start - print(f"done with ablation model fit: {end - start}") - - # all_fi = [local_fi_score_train_subset, local_fi_score_test_subset, local_fi_score_test] - # all_fi_rank = [None, None, None] - # for i in range(len(all_fi)): - # fi = all_fi[i] - # if isinstance(fi, np.ndarray): - # fi[fi == float("-inf")] = -sys.maxsize - 1 - # fi[fi == float("inf")] = sys.maxsize - 1 - # if fi_est.ascending: - # all_fi_rank[i] = np.argsort(-fi) - # else: - # all_fi_rank[i] = np.argsort(fi) - local_fi_score_train[local_fi_score_train == float("-inf")] = -sys.maxsize - 1 - local_fi_score_train[local_fi_score_train == float("inf")] = sys.maxsize - 1 - if fi_est.ascending: - local_fi_score_train_rank = np.argsort(-local_fi_score_train) - else: - local_fi_score_train_rank = np.argsort(local_fi_score_train) + 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") train_fi_mean = np.mean(local_fi_score_train, axis=0) + print(f"Train FI Mean: {train_fi_mean}") if fi_est.ascending: sorted_feature = np.argsort(-train_fi_mean) else: sorted_feature = np.argsort(train_fi_mean) - train_mean = np.mean(X_train, axis=0) - - for a_model in ablation_models: - print(f"start ablation removal: {a_model}") - ablation_est = ablation_models[a_model] - y_pred_before = ablation_est.predict(X_test) - metric_results[f'{a_model}_MSE_after_ablation_0_{m}'] = mean_squared_error(y_test, y_pred_before) - metric_results[f'{a_model}_R2_after_ablation_0_{m}'] = r2_score(y_test, y_pred_before) - X_temp = copy.deepcopy(X_train) - print(f"Train 0: X_temp[0]") - for i in range(num_features_masked): - print(f"Masking {i}") - ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score_train, local_fi_score_train_rank, i, m) - print(f"Train 0: X_temp[0]") - ablation_est.fit(ablation_X_data, y_train) - y_pred = ablation_est.predict(X_test) - metric_results[f'{a_model}_MSE_after_ablation_{i+1}_{m}'] = mean_squared_error(y_test, y_pred) - metric_results[f'{a_model}_R2_after_ablation_{i+1}_{m}'] = r2_score(y_test, y_pred) - X_temp = ablation_X_data - - mask_ratio = [0.05, 0.1, 0.25, 0.5, 0.9] + 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), + "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] + else: + mask_ratio = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9] + print(f"X_train_0: {X_train[0]}") for mask in mask_ratio: print(f"Mask ratio: {mask}") - print(f"Train shape: {X_train.shape}") - num_features_masked, X_train_masked = select_top_features(X_train, sorted_feature, mask) + num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) print(f"Train shape: {X_train_masked.shape}") - num_features_masked, X_test_masked = select_top_features(X_test, sorted_feature, mask) - print(f"Test shape: {X_train_masked.shape}") - metric_results[f'num_features_masked_{mask}'] = num_features_masked + num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + print(f"Test shape: {X_test_masked.shape}") + print(f"X_train_masked_0: {X_train_masked[0]}") + metric_results[f'num_features_selected_{mask}'] = num_features_selected for a_model in ablation_models: ablation_models[a_model].fit(X_train_masked, y_train) y_pred = ablation_models[a_model].predict(X_test_masked) - metric_results[f'{a_model}_MSE_after_ablation_top_{mask}'] = mean_squared_error(y_test, y_pred) - metric_results[f'{a_model}_R2_after_ablation_top_{mask}'] = r2_score(y_test, y_pred) - - # ablation_datas = {"train_subset": (X_train_subset, y_train_subset, all_fi[0], all_fi_rank[0]), - # "test_subset": (X_test_subset, y_test_subset, all_fi[1], all_fi_rank[1]), - # "test": (X_test, y_test, all_fi[2], all_fi_rank[2])} - # train_mean = np.mean(X_train, axis=0) - - # print("start ablation") - # # Start ablation 1: Feature removal - # for ablation_data in ablation_datas: - # start = time.time() - # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] - # if not isinstance(local_fi_score, np.ndarray): - # for a_model in ablation_models: - # for i in range(num_features_masked+1): - # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i}_{m}'] = None - # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i}_{m}'] = None - # else: - # for a_model in ablation_models: - # print(f"start ablation removal: {ablation_data} {a_model}") - # ablation_est = ablation_models[a_model] - # y_pred_before = ablation_est.predict(X_data) - # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_0_{m}'] = 0 - # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_0_{m}'] = 0 - # X_temp = copy.deepcopy(X_data) - # for i in range(num_features_masked): - # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) - # y_pred = ablation_est.predict(ablation_X_data) - # if i == 0: - # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i+1}_{m}'] = delta_mse(y_data, y_pred_before, y_pred) - # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i+1}_{m}'] = delta_y_pred(y_pred_before, y_pred) - # else: - # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i+1}_{m}'] = delta_mse(y_data, y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i}_{m}'] - # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i+1}_{m}'] = delta_y_pred(y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i}_{m}' ] - # X_temp = ablation_X_data - # y_pred_before = y_pred - # end = time.time() - # print(f"done with ablation removal {m}: {ablation_data} {end - start}") - # metric_results[f'{ablation_data}_ablation_removal_{m}_time'] = end - start - # # Start ablation 2: Feature addition - # for ablation_data in ablation_datas: - # start = time.time() - # X_data, y_data, local_fi_score_data = ablation_datas[ablation_data] - # if not isinstance(local_fi_score_data, np.ndarray): - # for a_model in ablation_models: - # metric_results[a_model + f'_{ablation_data}_MSE_before_ablation_addition'] = None - # metric_results[a_model + f'_{ablation_data}_R_2_before_ablation_addition'] = None - # for i in range(num_ablate_features): - # for a_model in ablation_models: - # metric_results[f'{a_model}_{ablation_data}_MSE_after_ablation_{i+1}_addition'] = None - # metric_results[f'{a_model}_{ablation_data}_R_2_after_ablation_{i+1}_addition'] = None - # else: - # for a_model in ablation_models: - # print(f"start ablation addtion: {ablation_data} {a_model}") - # ablation_est = ablation_models[a_model] - # X_temp = np.array([train_mean_list] * X_data.shape[0]).copy() - # y_pred = ablation_est.predict(X_temp) - # metric_results[a_model + f'_{ablation_data}_MSE_before_ablation_addition'] = mean_squared_error(y_data, y_pred) - # metric_results[a_model + f'_{ablation_data}_R_2_before_ablation_addition'] = r2_score(y_data, y_pred) - # imp_vals = copy.deepcopy(local_fi_score_data) - # ablation_results_list = [0] * num_ablate_features - # ablation_results_list_r2 = [0] * num_ablate_features - # for i in range(num_ablate_features): - # ablation_X_data = ablation_addition(X_temp, X_data, imp_vals, i) - # ablation_results_list[i] = mean_squared_error(y_data, ablation_est.predict(ablation_X_data)) - # ablation_results_list_r2[i] = r2_score(y_data, ablation_est.predict(ablation_X_data)) - # X_temp = ablation_X_data - # for i in range(num_ablate_features): - # metric_results[f'{a_model}_{ablation_data}_MSE_after_ablation_{i+1}_addition'] = ablation_results_list[i] - # metric_results[f'{a_model}_{ablation_data}_R_2_after_ablation_{i+1}_addition'] = ablation_results_list_r2[i] - # end = time.time() - # print(f"done with ablation addtion: {ablation_data} {end - start}") - # metric_results[f'{ablation_data}_ablation_addition_time'] = end - start + 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 @@ -479,6 +226,8 @@ 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) + # for key, value in results.items(): + # print(f"{key}: {len(value)}") return results, feature_importance_list @@ -673,9 +422,9 @@ def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp # else: # path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) if isinstance(vary_param_name, list): - path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.rf_seed)) + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "split_seed_" + str(args.split_seed)+"rf_seed_" + str(args.rf_seed)) else: - path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.rf_seed)) + path = oj(results_dir, "varying_" + vary_param_name, "split_seed_" + str(args.split_seed)+"rf_seed_" + str(args.rf_seed)) os.makedirs(path, exist_ok=True) eval_out = defaultdict(list) diff --git a/feature_importance/00_run_ablation_regression_stability.py b/feature_importance/00_run_ablation_regression_stability.py new file mode 100644 index 0000000..51711a3 --- /dev/null +++ b/feature_importance/00_run_ablation_regression_stability.py @@ -0,0 +1,523 @@ +import copy +import os +from os.path import join as oj +import glob +import argparse +import pickle as pkl +import time +import warnings +from scipy import stats +import dask +from dask.distributed import Client +import numpy as np +import pandas as pd +from tqdm import tqdm +import sys +from collections import defaultdict +from typing import Callable, List, Tuple +import itertools +from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score +from sklearn import preprocessing +from sklearn.ensemble import RandomForestRegressor +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 +sys.path.append(".") +sys.path.append("..") +sys.path.append("../..") +import fi_config +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 +from sklearn.kernel_ridge import KernelRidge + +warnings.filterwarnings("ignore", message="Bins whose width") + +#RUN THE FILE +# python 01_run_ablation_regression.py --nreps 5 --config mdi_local.real_data_regression --split_seed 331 --ignore_cache --create_rmd --result_name diabetes_regression + + +def compare_estimators(estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + X, y, support: List, + metrics: List[Tuple[str, Callable]], + args, ) -> Tuple[dict, dict]: + """Calculates results given estimators, feature importance estimators, datasets, and metrics. + Called in run_comparison + """ + if type(estimators) != list: + raise Exception("First argument needs to be a list of Models") + if type(metrics) != list: + raise Exception("Argument metrics needs to be a list containing ('name', callable) pairs") + + # initialize results + results = defaultdict(lambda: []) + feature_importance_list = {"positive": {}, "negative": {}, "absolute": {}} + + # loop over model estimators + for model in estimators: + # est = model.cls(**model.kwargs) + + # get kwargs for all fi_ests + fi_kwargs = {} + for fi_est in fi_estimators: + fi_kwargs.update(fi_est.kwargs) + + # get groups of estimators for each splitting strategy + fi_ests_dict = defaultdict(list) + for fi_est in fi_estimators: + fi_ests_dict[fi_est.splitting_strategy].append(fi_est) + + # loop over splitting strategies + for splitting_strategy, fi_ests in fi_ests_dict.items(): + # implement provided splitting strategy + if splitting_strategy is not None: + X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, splitting_strategy, args.split_seed) + else: + X_train = X + X_test = X + y_train = y + y_test = y + + top_features = [3, 5, 10] + 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": []} + 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 rf_seed in range(5): + est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=rf_seed) + est.fit(X_train, y_train) + + rf_plus_base = RandomForestPlusRegressor(rf_model=est) + rf_plus_base.fit(X_train, y_train) + + for fi_est in tqdm(fi_ests): + if fi_est.base_model == "None": + loaded_model = None + elif fi_est.base_model == "RF": + loaded_model = est + elif fi_est.base_model == "RFPlus_default": + loaded_model = rf_plus_base + + 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") + + if fi_est.ascending: + sorted_feature_train = np.argsort(-local_fi_score_train) + sorted_feature_test = np.argsort(-local_fi_score_test) + else: + sorted_feature_train = np.argsort(local_fi_score_train) + sorted_feature_test = np.argsort(local_fi_score_test) + + 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()) + + 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()) + + + for fi_est in tqdm(fi_ests): + metric_results = { + 'model': model.name, + 'fi': fi_est.name, + 'train_size': X_train.shape[0], + 'test_size': X_test.shape[0], + 'num_features': X_train.shape[1], + 'data_split_seed': args.split_seed, + } + + for num_feature in top_features: + 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] + + 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] + + # initialize results with metadata and metric results + kwargs: dict = model.kwargs # dict + for k in kwargs: + results[k].append(kwargs[k]) + for k in fi_kwargs: + if k in fi_est.kwargs: + results[k].append(str(fi_est.kwargs[k])) + else: + results[k].append(None) + for met_name, met_val in metric_results.items(): + results[met_name].append(met_val) + # for key, value in results.items(): + # print(f"{key}: {len(value)}") + return results, feature_importance_list + + +def run_comparison(path: str, + X, y, support: List, + metrics: List[Tuple[str, Callable]], + estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + args): + estimator_name = estimators[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_estimators) \ + if fi_estimator.model_type in estimators[0].model_type] + model_comparison_files_all = [oj(path, f'{estimator_name}_{fi_estimator.name}_comparisons.pkl') \ + for fi_estimator in fi_estimators_all] + + feature_importance_all = oj(path, f'feature_importance.pkl') + + + if args.parallel_id is not None: + model_comparison_files_all = [f'_{args.parallel_id[0]}.'.join(model_comparison_file.split('.')) \ + for model_comparison_file in model_comparison_files_all] + + fi_estimators = [] + model_comparison_files = [] + for model_comparison_file, fi_estimator in zip(model_comparison_files_all, fi_estimators_all): + if os.path.isfile(model_comparison_file) and not args.ignore_cache: + print( + f'{estimator_name} with {fi_estimator.name} results already computed and cached. use --ignore_cache to recompute') + else: + fi_estimators.append(fi_estimator) + model_comparison_files.append(model_comparison_file) + + if len(fi_estimators) == 0: + return + + results, fi_lst = compare_estimators(estimators=estimators, + fi_estimators=fi_estimators, + X=X, y=y, support=support, + metrics=metrics, + args=args) + + estimators_list = [e.name for e in estimators] + metrics_list = [m[0] for m in metrics] + + df = pd.DataFrame.from_dict(results) + df['split_seed'] = args.split_seed + if args.nosave_cols is not None: + nosave_cols = np.unique([x.strip() for x in args.nosave_cols.split(",")]) + else: + nosave_cols = [] + for col in nosave_cols: + 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 + 'sim_name': args.config, + 'estimators': estimators_list, + 'fi_estimators': fi_estimator.name, + 'metrics': metrics_list, + + # actual values + 'df': df.loc[df.fi == fi_estimator.name], + } + pkl.dump(output_dict, open(model_comparison_file, 'wb')) + return df + + +def get_metrics(): + return [('rocauc', auroc_score), ('prauc', auprc_score)] + + +def reformat_results(results): + results = results.reset_index().drop(columns=['index']) + # fi_scores = pd.concat(results.pop('fi_scores').to_dict()). \ + # reset_index(level=0).rename(columns={'level_0': 'index'}) + # results_df = pd.merge(results, fi_scores, left_index=True, right_on="index") + # return results_df + return results + +def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, metrics, args): + os.makedirs(oj(path, val_name, "rep" + str(i)), exist_ok=True) + np.random.seed(i) + max_iter = 100 + iter = 0 + while iter <= max_iter: # regenerate data if y is constant + X = X_dgp(**X_params_dict) + y, support, beta = y_dgp(X, **y_params_dict, return_support=True) + if not all(y == y[0]): + break + iter += 1 + if iter > max_iter: + raise ValueError("Response y is constant.") + if args.omit_vars is not None: + omit_vars = np.unique([int(x.strip()) for x in args.omit_vars.split(",")]) + support = np.delete(support, omit_vars) + X = np.delete(X, omit_vars, axis=1) + del beta # note: beta is not currently supported when using omit_vars + + for est in ests: + results = run_comparison(path=oj(path, val_name, "rep" + str(i)), + X=X, y=y, support=support, + metrics=metrics, + estimators=est, + fi_estimators=fi_ests, + args=args) + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + + parser.add_argument('--nreps', type=int, default=2) + parser.add_argument('--model', type=str, default=None) # , default='c4') + parser.add_argument('--fi_model', type=str, default=None) # , default='c4') + parser.add_argument('--config', type=str, default='test') + parser.add_argument('--omit_vars', type=str, default=None) # comma-separated string of variables to omit + parser.add_argument('--nosave_cols', type=str, default="prediction_model") + + ### Newly added arguments + parser.add_argument('--folder_name', type=str, default=None) + + # for multiple reruns, should support varying split_seed + parser.add_argument('--ignore_cache', action='store_true', default=False) + parser.add_argument('--verbose', action='store_true', default=True) + parser.add_argument('--parallel', action='store_true', default=False) + parser.add_argument('--parallel_id', nargs='+', type=int, default=None) + parser.add_argument('--n_cores', type=int, default=None) + parser.add_argument('--split_seed', type=int, default=0) + parser.add_argument('--results_path', type=str, default=default_dir) + + # arguments for rmd output of results + parser.add_argument('--create_rmd', action='store_true', default=False) + parser.add_argument('--show_vars', type=int, default=None) + + args = parser.parse_args() + + if args.parallel: + if args.n_cores is None: + print(os.getenv("SLURM_CPUS_ON_NODE")) + n_cores = int(os.getenv("SLURM_CPUS_ON_NODE")) + else: + n_cores = args.n_cores + client = Client(n_workers=n_cores) + + ests, fi_ests, \ + X_dgp, X_params_dict, y_dgp, y_params_dict, \ + vary_param_name, vary_param_vals = fi_config.get_fi_configs(args.config) + + metrics = get_metrics() + + if args.model: + ests = list(filter(lambda x: args.model.lower() == x[0].name.lower(), ests)) + if args.fi_model: + fi_ests = list(filter(lambda x: args.fi_model.lower() == x[0].name.lower(), fi_ests)) + + if len(ests) == 0: + raise ValueError('No valid estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if len(fi_ests) == 0: + raise ValueError('No valid FI estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if args.verbose: + print('running', args.config, + 'ests', ests, + 'fi_ests', fi_ests) + print('\tsaving to', args.results_path) + + if args.omit_vars is not None: + #results_dir = oj(args.results_path, args.config + "_omitted_vars") + results_dir = oj(args.results_path, args.config + "_omitted_vars", args.folder_name) + else: + #results_dir = oj(args.results_path, args.config) + results_dir = oj(args.results_path, args.config, args.folder_name) + + # if isinstance(vary_param_name, list): + # path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.split_seed)) + # else: + # path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) + if isinstance(vary_param_name, list): + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "split_seed_" + str(args.split_seed)) + else: + path = oj(results_dir, "varying_" + vary_param_name, "split_seed_" + str(args.split_seed)) + os.makedirs(path, exist_ok=True) + + eval_out = defaultdict(list) + + vary_type = None + if isinstance(vary_param_name, list): # multiple parameters are being varied + # get parameters that are being varied over and identify whether it's a DGP/method/fi_method argument + keys, values = zip(*vary_param_vals.items()) + vary_param_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)] + vary_type = {} + for vary_param_dict in vary_param_dicts: + for param_name, param_val in vary_param_dict.items(): + if param_name in X_params_dict.keys() and param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif param_name in X_params_dict.keys(): + vary_type[param_name] = "dgp" + X_params_dict[param_name] = vary_param_vals[param_name][param_val] + elif param_name in y_params_dict.keys(): + vary_type[param_name] = "dgp" + y_params_dict[param_name] = vary_param_vals[param_name][param_val] + else: + est_kwargs = list( + itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if param_name in est_kwargs: + vary_type[param_name] = "est" + elif param_name in fi_est_kwargs: + vary_type[param_name] = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, + y_params_dict, y_dgp, ests, fi_ests, metrics, args) for i in + range(args.nreps)] + results = dask.compute(*futures) + else: + results = [ + run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + y_dgp, ests, fi_ests, metrics, args) for i in range(args.nreps)] + assert all(results) + + else: # only on parameter is being varied over + # get parameter that is being varied over and identify whether it's a DGP/method/fi_method argument + for val_name, val in vary_param_vals.items(): + if vary_param_name in X_params_dict.keys() and vary_param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif vary_param_name in X_params_dict.keys(): + vary_type = "dgp" + X_params_dict[vary_param_name] = val + elif vary_param_name in y_params_dict.keys(): + vary_type = "dgp" + y_params_dict[vary_param_name] = val + else: + est_kwargs = list(itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if vary_param_name in est_kwargs: + vary_type = "est" + elif vary_param_name in fi_est_kwargs: + vary_type = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, + fi_ests, metrics, args) for i in range(args.nreps)] + results = dask.compute(*futures) + else: + results = [run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, + metrics, args) for i in range(args.nreps)] + assert all(results) + + print('completed all experiments successfully!') + + # get model file names + model_comparison_files_all = [] + for est in ests: + estimator_name = est[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_ests) \ + if fi_estimator.model_type in est[0].model_type] + model_comparison_files = [f'{estimator_name}_{fi_estimator.name}_comparisons.pkl' for fi_estimator in + fi_estimators_all] + model_comparison_files_all += model_comparison_files + + # aggregate results + results_list = [] + if isinstance(vary_param_name, list): + for vary_param_dict in vary_param_dicts: + val_name = "_".join(vary_param_dict.values()) + + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + + for param_name, param_val in vary_param_dict.items(): + val = vary_param_vals[param_name][param_val] + if vary_type[param_name] == "dgp": + if np.isscalar(val): + results.insert(0, param_name, val) + else: + results.insert(0, param_name, [val for i in range(results.shape[0])]) + results.insert(1, param_name + "_name", param_val) + elif vary_type[param_name] == "est" or vary_type[param_name] == "fi_est": + results.insert(0, param_name + "_name", copy.deepcopy(results[param_name])) + results.insert(0, 'rep', i) + results_list.append(results) + else: + for val_name, val in vary_param_vals.items(): + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + if vary_type == "dgp": + if np.isscalar(val): + results.insert(0, vary_param_name, val) + else: + results.insert(0, vary_param_name, [val for i in range(results.shape[0])]) + results.insert(1, vary_param_name + "_name", val_name) + results.insert(2, 'rep', i) + elif vary_type == "est" or vary_type == "fi_est": + results.insert(0, vary_param_name + "_name", copy.deepcopy(results[vary_param_name])) + results.insert(1, 'rep', i) + results_list.append(results) + results_merged = pd.concat(results_list, axis=0) + pkl.dump(results_merged, open(oj(path, 'results.pkl'), 'wb')) + results_df = reformat_results(results_merged) + results_df.to_csv(oj(path, 'results.csv'), index=False) + + print('merged and saved all experiment results successfully!') + + # create R markdown summary of results + if args.create_rmd: + if args.show_vars is None: + show_vars = 'NULL' + else: + show_vars = args.show_vars + + if isinstance(vary_param_name, list): + vary_param_name = "; ".join(vary_param_name) + + sim_rmd = os.path.basename(results_dir) + '_simulation_results.Rmd' + os.system( + 'cp {} \'{}\''.format(oj("rmd", "simulation_results.Rmd"), sim_rmd) + ) + os.system( + 'Rscript -e "rmarkdown::render(\'{}\', params = list(results_dir = \'{}\', vary_param_name = \'{}\', seed = {}, keep_vars = {}), output_file = \'{}\', quiet = TRUE)"'.format( + sim_rmd, + results_dir, vary_param_name, str(args.split_seed), str(show_vars), + oj(path, "simulation_results.html")) + ) + os.system('rm \'{}\''.format(sim_rmd)) + print("created rmd of simulation results successfully!") \ No newline at end of file diff --git a/feature_importance/00_run_feature_ranking_simulation.py b/feature_importance/00_run_feature_ranking_simulation.py new file mode 100644 index 0000000..6a4996c --- /dev/null +++ b/feature_importance/00_run_feature_ranking_simulation.py @@ -0,0 +1,548 @@ +import copy +import os +from os.path import join as oj +import glob +import argparse +import pickle as pkl +import time +import warnings +from scipy import stats +import dask +from dask.distributed import Client +import numpy as np +import pandas as pd +from tqdm import tqdm +import sys +from collections import defaultdict +from typing import Callable, List, Tuple +import itertools +from sklearn.metrics import roc_auc_score, f1_score, average_precision_score, recall_score, precision_score, mean_squared_error, r2_score +from sklearn import preprocessing +from sklearn.ensemble import RandomForestRegressor +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("../..") +import fi_config +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") + +def compare_estimators(estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + X, y, support, + metrics: List[Tuple[str, Callable]], + args, + vary_setting) -> Tuple[dict, dict]: + """Calculates results given estimators, feature importance estimators, datasets, and metrics. + Called in run_comparison + """ + if type(estimators) != list: + raise Exception("First argument needs to be a list of Models") + if type(metrics) != list: + raise Exception("Argument metrics needs to be a list containing ('name', callable) pairs") + + # initialize results + results = defaultdict(lambda: []) + feature_importance_list = {"absolute": {}} + + # loop over model estimators + for model in estimators: + est = model.cls(**model.kwargs) + + # get kwargs for all fi_ests + fi_kwargs = {} + for fi_est in fi_estimators: + fi_kwargs.update(fi_est.kwargs) + + # get groups of estimators for each splitting strategy + fi_ests_dict = defaultdict(list) + for fi_est in fi_estimators: + fi_ests_dict[fi_est.splitting_strategy].append(fi_est) + + # loop over splitting strategies + for splitting_strategy, fi_ests in fi_ests_dict.items(): + # implement provided splitting strategy + if splitting_strategy is not None: + X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, splitting_strategy, args.split_seed) + else: + X_train = X + X_test = X + y_train = y + y_test = y + + # check if there are NA values in the data + if np.isnan(X_train).any() or np.isnan(y_train).any(): + raise ValueError("There are NA values in the data") + if np.isnan(X_test).any() or np.isnan(y_test).any(): + raise ValueError("There are NA values in the data") + + # fit RF model + start_rf = time.time() + 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() + + # 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)) + + 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] + } + temp = "" + for vary_name in vary_setting: + fitted_results[vary_name] = [vary_setting[vary_name]] * 3 + temp += f"{vary_name}_{vary_setting[vary_name]}_" + + print(fitted_results) + + os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}", exist_ok=True) + results_df = pd.DataFrame(fitted_results) + results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_fitted_summary_{args.y_seed}_{args.split_seed}_{temp}.csv", index=False) + + # loop over fi estimators + for fi_est in tqdm(fi_ests): + metric_results = { + 'model': model.name, + 'fi': fi_est.name, + 'train_size': X_train.shape[0], + 'test_size': X_test.shape[0], + 'num_features': X_train.shape[1], + 'data_split_seed': args.split_seed, + } + + if fi_est.base_model == "None": + loaded_model = None + elif fi_est.base_model == "RF": + loaded_model = est + elif fi_est.base_model == "RFPlus_default": + loaded_model = rf_plus_base + + 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() + + # initialize results with metadata and metric results + kwargs: dict = model.kwargs # dict + for k in kwargs: + results[k].append(kwargs[k]) + for k in fi_kwargs: + if k in fi_est.kwargs: + results[k].append(str(fi_est.kwargs[k])) + else: + results[k].append(None) + for met_name, met_val in metric_results.items(): + results[met_name].append(met_val) + return results, feature_importance_list + + +def run_comparison(path: str, + X, y, support: List, + metrics: List[Tuple[str, Callable]], + estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + args, + vary_setting): + estimator_name = estimators[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_estimators) \ + if fi_estimator.model_type in estimators[0].model_type] + model_comparison_files_all = [oj(path, f'{estimator_name}_{fi_estimator.name}_comparisons.pkl') \ + for fi_estimator in fi_estimators_all] + + feature_importance_all = oj(path, f'feature_importance.pkl') + + + if args.parallel_id is not None: + model_comparison_files_all = [f'_{args.parallel_id[0]}.'.join(model_comparison_file.split('.')) \ + for model_comparison_file in model_comparison_files_all] + + fi_estimators = [] + model_comparison_files = [] + for model_comparison_file, fi_estimator in zip(model_comparison_files_all, fi_estimators_all): + if os.path.isfile(model_comparison_file) and not args.ignore_cache: + print( + f'{estimator_name} with {fi_estimator.name} results already computed and cached. use --ignore_cache to recompute') + else: + fi_estimators.append(fi_estimator) + model_comparison_files.append(model_comparison_file) + + if len(fi_estimators) == 0: + return + + results, fi_lst = compare_estimators(estimators=estimators, + fi_estimators=fi_estimators, + X=X, y=y, support=support, + metrics=metrics, + args=args, + vary_setting=vary_setting) + + estimators_list = [e.name for e in estimators] + metrics_list = [m[0] for m in metrics] + + df = pd.DataFrame.from_dict(results) + df['split_seed'] = args.split_seed + if args.nosave_cols is not None: + nosave_cols = np.unique([x.strip() for x in args.nosave_cols.split(",")]) + else: + nosave_cols = [] + for col in nosave_cols: + 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 + 'sim_name': args.config, + 'estimators': estimators_list, + 'fi_estimators': fi_estimator.name, + 'metrics': metrics_list, + + # actual values + 'df': df.loc[df.fi == fi_estimator.name], + } + pkl.dump(output_dict, open(model_comparison_file, 'wb')) + return df + + +def get_metrics(): + return [('rocauc', auroc_score), ('prauc', auprc_score)] + + +def reformat_results(results): + results = results.reset_index().drop(columns=['index']) + # fi_scores = pd.concat(results.pop('fi_scores').to_dict()). \ + # reset_index(level=0).rename(columns={'level_0': 'index'}) + # results_df = pd.merge(results, fi_scores, left_index=True, right_on="index") + # return results_df + return results + +def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, metrics, args, vary_setting): + os.makedirs(oj(path, val_name, "rep" + str(i)), exist_ok=True) + np.random.seed(i) + max_iter = 100 + iter = 0 + while iter <= max_iter: # regenerate data if y is constant + X = X_dgp(**X_params_dict) + y, support, beta = y_dgp(X, **y_params_dict, seed = args.y_seed, return_support=True) + if not all(y == y[0]): + break + iter += 1 + if iter > max_iter: + raise ValueError("Response y is constant.") + if args.omit_vars is not None: + omit_vars = np.unique([int(x.strip()) for x in args.omit_vars.split(",")]) + support = np.delete(support, omit_vars) + X = np.delete(X, omit_vars, axis=1) + del beta # note: beta is not currently supported when using omit_vars + + for est in ests: + results = run_comparison(path=oj(path, val_name, "rep" + str(i)), + X=X, y=y, support=support, + metrics=metrics, + estimators=est, + fi_estimators=fi_ests, + args=args, + vary_setting=vary_setting) + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + + parser.add_argument('--nreps', type=int, default=2) + parser.add_argument('--model', type=str, default=None) # , default='c4') + parser.add_argument('--fi_model', type=str, default=None) # , default='c4') + parser.add_argument('--config', type=str, default='test') + parser.add_argument('--omit_vars', type=str, default=None) # comma-separated string of variables to omit + parser.add_argument('--nosave_cols', type=str, default="prediction_model") + + ### Newly added arguments + parser.add_argument('--folder_name', type=str, default=None) + + # for multiple reruns, should support varying split_seed + parser.add_argument('--ignore_cache', action='store_true', default=False) + parser.add_argument('--verbose', action='store_true', default=True) + parser.add_argument('--parallel', action='store_true', default=False) + parser.add_argument('--parallel_id', nargs='+', type=int, default=None) + parser.add_argument('--n_cores', type=int, default=None) + parser.add_argument('--split_seed', type=int, default=0) + parser.add_argument('--results_path', type=str, default=default_dir) + parser.add_argument('--y_seed', type=int, default=0) + + # arguments for rmd output of results + parser.add_argument('--create_rmd', action='store_true', default=False) + parser.add_argument('--show_vars', type=int, default=None) + + args = parser.parse_args() + + if args.parallel: + if args.n_cores is None: + print(os.getenv("SLURM_CPUS_ON_NODE")) + n_cores = int(os.getenv("SLURM_CPUS_ON_NODE")) + else: + n_cores = args.n_cores + client = Client(n_workers=n_cores) + + ests, fi_ests, \ + X_dgp, X_params_dict, y_dgp, y_params_dict, \ + vary_param_name, vary_param_vals = fi_config.get_fi_configs(args.config) + + metrics = get_metrics() + + if args.model: + ests = list(filter(lambda x: args.model.lower() == x[0].name.lower(), ests)) + if args.fi_model: + fi_ests = list(filter(lambda x: args.fi_model.lower() == x[0].name.lower(), fi_ests)) + + if len(ests) == 0: + raise ValueError('No valid estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if len(fi_ests) == 0: + raise ValueError('No valid FI estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if args.verbose: + print('running', args.config, + 'ests', ests, + 'fi_ests', fi_ests) + print('\tsaving to', args.results_path) + + if args.omit_vars is not None: + #results_dir = oj(args.results_path, args.config + "_omitted_vars") + results_dir = oj(args.results_path, args.config + "_omitted_vars", args.folder_name) + else: + #results_dir = oj(args.results_path, args.config) + results_dir = oj(args.results_path, args.config, args.folder_name) + + if isinstance(vary_param_name, list): + #path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.split_seed)) + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.y_seed)+ str(args.split_seed)) + else: + #path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) + path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.y_seed)+ str(args.split_seed)) + os.makedirs(path, exist_ok=True) + + eval_out = defaultdict(list) + + vary_type = None + if isinstance(vary_param_name, list): # multiple parameters are being varied + # get parameters that are being varied over and identify whether it's a DGP/method/fi_method argument + keys, values = zip(*vary_param_vals.items()) + vary_param_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)] + vary_type = {} + #### Added + vary_setting = {} + #### + for vary_param_dict in vary_param_dicts: + for param_name, param_val in vary_param_dict.items(): + if param_name in X_params_dict.keys() and param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif param_name in X_params_dict.keys(): + vary_type[param_name] = "dgp" + X_params_dict[param_name] = vary_param_vals[param_name][param_val] + elif param_name in y_params_dict.keys(): + vary_type[param_name] = "dgp" + y_params_dict[param_name] = vary_param_vals[param_name][param_val] + else: + est_kwargs = list( + itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if param_name in est_kwargs: + vary_type[param_name] = "est" + elif param_name in fi_est_kwargs: + vary_type[param_name] = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + #### Added + vary_setting[param_name] = param_val + #### + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, + y_params_dict, y_dgp, ests, fi_ests, metrics, args, vary_setting) for i in + range(args.nreps)] + results = dask.compute(*futures) + else: + # results = [ + # run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + # y_dgp, ests, fi_ests, metrics, args) for i in range(args.nreps)] + results = [ + run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + y_dgp, ests, fi_ests, metrics, args, vary_setting) for i in range(args.nreps)] + assert all(results) + + else: # only on parameter is being varied over + # get parameter that is being varied over and identify whether it's a DGP/method/fi_method argument + for val_name, val in vary_param_vals.items(): + if vary_param_name in X_params_dict.keys() and vary_param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif vary_param_name in X_params_dict.keys(): + vary_type = "dgp" + X_params_dict[vary_param_name] = val + elif vary_param_name in y_params_dict.keys(): + vary_type = "dgp" + y_params_dict[vary_param_name] = val + else: + est_kwargs = list(itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if vary_param_name in est_kwargs: + vary_type = "est" + elif vary_param_name in fi_est_kwargs: + vary_type = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, + fi_ests, metrics, args) for i in range(args.nreps)] + results = dask.compute(*futures) + else: + results = [ + run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + y_dgp, ests, fi_ests, metrics, args) for i in range(args.nreps)] + # results = [run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, + # metrics, args) for i in range(args.nreps)] + assert all(results) + + print('completed all experiments successfully!') + + # get model file names + model_comparison_files_all = [] + for est in ests: + estimator_name = est[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_ests) \ + if fi_estimator.model_type in est[0].model_type] + model_comparison_files = [f'{estimator_name}_{fi_estimator.name}_comparisons.pkl' for fi_estimator in + fi_estimators_all] + model_comparison_files_all += model_comparison_files + + # aggregate results + results_list = [] + if isinstance(vary_param_name, list): + for vary_param_dict in vary_param_dicts: + val_name = "_".join(vary_param_dict.values()) + + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + + for param_name, param_val in vary_param_dict.items(): + val = vary_param_vals[param_name][param_val] + if vary_type[param_name] == "dgp": + if np.isscalar(val): + results.insert(0, param_name, val) + else: + results.insert(0, param_name, [val for i in range(results.shape[0])]) + results.insert(1, param_name + "_name", param_val) + elif vary_type[param_name] == "est" or vary_type[param_name] == "fi_est": + results.insert(0, param_name + "_name", copy.deepcopy(results[param_name])) + results.insert(0, 'rep', i) + results_list.append(results) + else: + for val_name, val in vary_param_vals.items(): + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + if vary_type == "dgp": + if np.isscalar(val): + results.insert(0, vary_param_name, val) + else: + results.insert(0, vary_param_name, [val for i in range(results.shape[0])]) + results.insert(1, vary_param_name + "_name", val_name) + results.insert(2, 'rep', i) + elif vary_type == "est" or vary_type == "fi_est": + results.insert(0, vary_param_name + "_name", copy.deepcopy(results[vary_param_name])) + results.insert(1, 'rep', i) + results_list.append(results) + results_merged = pd.concat(results_list, axis=0) + pkl.dump(results_merged, open(oj(path, 'results.pkl'), 'wb')) + results_df = reformat_results(results_merged) + results_df.to_csv(oj(path, 'results.csv'), index=False) + + print('merged and saved all experiment results successfully!') + + # create R markdown summary of results + if args.create_rmd: + if args.show_vars is None: + show_vars = 'NULL' + else: + show_vars = args.show_vars + + if isinstance(vary_param_name, list): + vary_param_name = "; ".join(vary_param_name) + + sim_rmd = os.path.basename(results_dir) + '_simulation_results.Rmd' + os.system( + 'cp {} \'{}\''.format(oj("rmd", "simulation_results.Rmd"), sim_rmd) + ) + os.system( + 'Rscript -e "rmarkdown::render(\'{}\', params = list(results_dir = \'{}\', vary_param_name = \'{}\', seed = {}, keep_vars = {}), output_file = \'{}\', quiet = TRUE)"'.format( + sim_rmd, + results_dir, vary_param_name, str(args.split_seed), str(show_vars), + oj(path, "simulation_results.html")) + ) + os.system('rm \'{}\''.format(sim_rmd)) + print("created rmd of simulation results successfully!") \ No newline at end of file diff --git a/feature_importance/OLD_00_run_ablation_classification_retrain.py b/feature_importance/OLD_00_run_ablation_classification_retrain.py new file mode 100644 index 0000000..8412a8e --- /dev/null +++ b/feature_importance/OLD_00_run_ablation_classification_retrain.py @@ -0,0 +1,936 @@ +import copy +import os +from os.path import join as oj +import glob +import argparse +import pickle as pkl +import time +import warnings +from scipy import stats +import dask +from dask.distributed import Client +import numpy as np +import pandas as pd +from tqdm import tqdm +import sys +from collections import defaultdict +from typing import Callable, List, Tuple +import itertools +from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, average_precision_score, log_loss +from sklearn import preprocessing +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.linear_model import LogisticRegressionCV +from sklearn.linear_model import LinearRegression +from sklearn.svm import SVC +import xgboost as xgb +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +sys.path.append(".") +sys.path.append("..") +sys.path.append("../..") +import fi_config +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 +from sklearn.linear_model import Ridge +warnings.filterwarnings("ignore", message="Bins whose width") +import dill + +#RUN THE FILE +# python 01_run_ablation_classification.py --nreps 5 --config mdi_local.real_data_classification --split_seed 331 --ignore_cache --create_rmd --result_name diabetes_classification + + +# def generate_random_shuffle(data, seed): +# """ +# Randomly shuffle each column of the data. +# """ +# np.random.seed(seed) +# return np.array([np.random.permutation(data[:, i]) for i in range(data.shape[1])]).T + + +# def ablation(data, feature_importance, mode, num_features, seed): +# """ +# Replace the top num_features max feature importance data with random shuffle for each sample +# """ +# assert mode in ["max", "min"] +# fi = feature_importance.to_numpy() +# shuffle = generate_random_shuffle(data, seed) +# if mode == "max": +# indices = np.argsort(-fi) +# else: +# indices = np.argsort(fi) +# data_copy = data.copy() +# for i in range(data.shape[0]): +# for j in range(num_features): +# data_copy[i, indices[i,j]] = shuffle[i, indices[i,j]] +# return data_copy + +# def ablation_removal(train_mean, data, feature_importance_rank, feature_index): +# """ +# Replace the top num_features max feature importance data with mean value for each sample +# """ +# data_copy = data.copy() +# for i in range(data.shape[0]): +# data_copy[i, feature_importance_rank[i,feature_index]] = train_mean[feature_importance_rank[i,feature_index]] +# return data_copy + +# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): +# """ +# Initialize the data with mean values and add the top num_features max feature importance data for each sample +# """ +# data_copy = data_ablation.copy() +# for i in range(data.shape[0]): +# data_copy[i, feature_importance_rank[i,feature_index]] = data[i, feature_importance_rank[i,feature_index]] +# return data_copy + + +# def ablation_removal(train_mean, data, feature_importance, feature_importance_rank, feature_index, mode): +# if mode == "absolute": +# return ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index) +# # else: +# # return ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index) + + +# def ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index): +# """ +# Replace the top num_features max feature importance data with mean value for each sample +# """ +# data_copy = data.copy() +# indices = feature_importance_rank[:, feature_index] +# data_copy[np.arange(data.shape[0]), indices] = train_mean[indices] +# return data_copy + + +def select_top_features(array, sorted_indices, percentage): + array = copy.deepcopy(array) + num_features = array.shape[1] + num_selected = int(np.ceil(num_features * percentage)) + selected_indices = sorted_indices[:num_selected] + selected_array = array[:, selected_indices] + return num_selected, selected_array + + +# def ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index): +# data_copy = data.copy() +# indices = feature_importance_rank[:, feature_index] +# sum = 0 +# for i in range(data.shape[0]): +# if feature_importance[i, indices[i]] != 0 and feature_importance[i, indices[i]] < sys.maxsize - 1: +# sum += 1 +# data_copy[i, indices[i]] = train_mean[indices[i]] +# print("Remove sum: ", sum) +# return data_copy + +# def delta_mae(y_true, y_pred_1, y_pred_2): +# mae_before = np.abs(y_true - y_pred_1) +# mae_after = np.abs(y_true - y_pred_2) +# absolute_delta_mae = np.mean(np.abs(mae_before - mae_after)) +# return absolute_delta_mae + +# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): +# """ +# Initialize the data with mean values and add the top num_features max feature importance data for each sample +# """ +# data_copy = data_ablation.copy() +# indices = feature_importance_rank[:, feature_index] +# data_copy[np.arange(data.shape[0]), indices] = data[np.arange(data.shape[0]), indices] +# return data_copy + + +def compare_estimators(estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + X, y, support: List, + metrics: List[Tuple[str, Callable]], + args, ) -> Tuple[dict, dict]: + """Calculates results given estimators, feature importance estimators, datasets, and metrics. + Called in run_comparison + """ + if type(estimators) != list: + raise Exception("First argument needs to be a list of Models") + if type(metrics) != list: + raise Exception("Argument metrics needs to be a list containing ('name', callable) pairs") + + # initialize results + results = defaultdict(lambda: []) + feature_importance_list = {"positive": {}, "negative": {}, "absolute": {}} + + # loop over model estimators + for model in estimators: + est = model.cls(**model.kwargs) + + # get kwargs for all fi_ests + fi_kwargs = {} + for fi_est in fi_estimators: + fi_kwargs.update(fi_est.kwargs) + + # get groups of estimators for each splitting strategy + fi_ests_dict = defaultdict(list) + for fi_est in fi_estimators: + fi_ests_dict[fi_est.splitting_strategy].append(fi_est) + + # loop over splitting strategies + for splitting_strategy, fi_ests in fi_ests_dict.items(): + # implement provided splitting strategy + if splitting_strategy is not None: + X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, splitting_strategy, args.split_seed) + else: + X_train = X + X_tune = X + X_test = X + y_train = y + y_tune = y + y_test = y + + if args.fit_model: + print("Fitting Models") + # fit RF model + start_rf = time.time() + est = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', 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 = RandomForestPlusClassifier(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 = RandomForestPlusClassifier(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() + est_regressor = RandomForestRegressor(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=args.rf_seed) + est_regressor.fit(X_train, y_train) + rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est_regressor, include_raw=False, fit_on="inbag", prediction_model=LinearRegression()) + rf_plus_base_inbag.fit(X_train, y_train) + end_rf_plus_inbag = time.time() + + # get test results + test_all_auc_rf = roc_auc_score(y_test, est.predict_proba(X_test)[:, 1]) + test_all_auprc_rf = average_precision_score(y_test, est.predict_proba(X_test)[:, 1]) + test_all_f1_rf = f1_score(y_test, est.predict_proba(X_test)[:, 1] > 0.5) + test_all_auc_rf_plus = roc_auc_score(y_test, rf_plus_base.predict_proba(X_test)[:, 1]) + test_all_auprc_rf_plus = average_precision_score(y_test, rf_plus_base.predict_proba(X_test)[:, 1]) + test_all_f1_rf_plus = f1_score(y_test, rf_plus_base.predict_proba(X_test)[:, 1] > 0.5) + test_all_auc_rf_plus_oob = roc_auc_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1]) + test_all_auprc_rf_plus_oob = average_precision_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1]) + test_all_f1_rf_plus_oob = f1_score(y_test, rf_plus_base_oob.predict_proba(X_test)[:, 1] > 0.5) + test_all_auc_rf_plus_inbag = roc_auc_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1]) + test_all_auprc_rf_plus_inbag = average_precision_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1]) + test_all_f1_rf_plus_inbag = f1_score(y_test, rf_plus_base_inbag.predict_proba(X_test)[:, 1] > 0.5) + + fitted_results = { + "Model": ["RF", "RF_plus", "RF_plus_oob", "RF_plus_inbag"], + "AUC": [test_all_auc_rf, test_all_auc_rf_plus, test_all_auc_rf_plus_oob, test_all_auc_rf_plus_inbag], + "AUPRC": [test_all_auprc_rf, test_all_auprc_rf_plus, test_all_auprc_rf_plus_oob, test_all_auprc_rf_plus_inbag], + "F1": [test_all_f1_rf, test_all_f1_rf_plus, test_all_f1_rf_plus_oob, test_all_f1_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] + } + + os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}", exist_ok=True) + results_df = pd.DataFrame(fitted_results) + results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_rf_seed_{args.rf_seed}_split_seed_{args.split_seed}.csv", index=False) + + + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RF_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(est, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_default_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base_oob, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base_inbag, file) + + # if args.absolute_masking or args.positive_masking or args.negative_masking: + # np.random.seed(42) + # if X_train.shape[0] > 100: + # indices_train = np.random.choice(X_train.shape[0], 100, replace=False) + # X_train_subset = X_train[indices_train] + # y_train_subset = y_train[indices_train] + # else: + # indices_train = np.arange(X_train.shape[0]) + # X_train_subset = X_train + # y_train_subset = y_train + + # if X_test.shape[0] > 100: + # indices_test = np.random.choice(X_test.shape[0], 100, replace=False) + # X_test_subset = X_test[indices_test] + # y_test_subset = y_test[indices_test] + # else: + # indices_test = np.arange(X_test.shape[0]) + # X_test_subset = X_test + # y_test_subset = y_test + + if args.num_features_masked is None: + num_features_masked = X_train.shape[1] + else: + num_features_masked = args.num_features_masked + + + for fi_est in tqdm(fi_ests): + metric_results = { + 'model': model.name, + 'fi': fi_est.name, + 'train_size': X_train.shape[0], + # 'train_subset_size': X_train_subset.shape[0], + 'test_size': X_test.shape[0], + # 'test_subset_size': X_test_subset.shape[0], + 'num_features': X_train.shape[1], + 'data_split_seed': args.split_seed, + 'rf_seed': args.rf_seed, + 'num_features_masked': num_features_masked + } + # for i in range(X_train_subset.shape[0]): + # metric_results[f'sample_train_{i}'] = indices_train[i] + # for i in range(X_test_subset.shape[0]): + # metric_results[f'sample_test_{i}'] = indices_test[i] + print("Load Models") + start = time.time() + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_default_{args.split_seed}.dill", 'rb') as file: + # rf_plus_base = dill.load(file) + # if fi_est.base_model == "None": + # loaded_model = None + # elif fi_est.base_model == "RF": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RF_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_oob": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_inbag": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_default": + # loaded_model = rf_plus_base + rf_plus_base = rf_plus_base + if fi_est.base_model == "None": + 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 + end = time.time() + metric_results['load_model_time'] = end - start + print(f"done with loading models: {end - start}") + + + start = time.time() + print(f"Compute feature importance") + # Compute feature importance + local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train, fit=loaded_model, mode="absolute") + # if fi_est.name.startswith("Local_MDI+"): + # local_fi_score_train_subset = local_fi_score_train[indices_train] + + m= "absolute" + #feature_importance_list[m][fi_est.name] = [local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset] + end = time.time() + metric_results[f'fi_time_{m}'] = end - start + print(f"done with feature importance {m}: {end - start}") + # prepare ablations + print("start ablation") + ablation_models = {"RF_Classifier": RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=args.rf_seed), + # "LogisticCV": LogisticRegressionCV(random_state=42, max_iter=2000), + # "SVM": SVC(random_state=42, probability=True), + # "XGBoost_Classifier": xgb.XGBClassifier(random_state=42), + #"RF_Plus_Classifier": rf_plus_base + } + + train_fi_mean = np.mean(local_fi_score_train, axis=0) + if fi_est.ascending: + sorted_feature = np.argsort(-train_fi_mean) + else: + sorted_feature = np.argsort(train_fi_mean) + + mask_ratio = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9] + for mask in mask_ratio: + print(f"Mask ratio: {mask}") + num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) + print(f"Train shape: {X_train_masked.shape}") + num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + print(f"Test shape: {X_train_masked.shape}") + metric_results[f'num_features_selected_{mask}'] = num_features_selected + for a_model in ablation_models: + ablation_models[a_model].fit(X_train_masked, y_train) + y_pred = ablation_models[a_model].predict_proba(X_test_masked)[:, 1] + metric_results[f'{a_model}_LogLoss_after_ablation_top_{mask}'] = log_loss(y_test, y_pred) + metric_results[f'{a_model}_AUROC_after_ablation_top_{mask}'] = roc_auc_score(y_test, y_pred) + + # start = time.time() + # for a_model in ablation_models: + # ablation_models[a_model].fit(X_train, y_train) + # end = time.time() + # metric_results['ablation_model_fit_time'] = end - start + # print(f"done with ablation model fit: {end - start}") + # all_fi = [local_fi_score_train_subset, local_fi_score_test_subset, local_fi_score_test] + # all_fi_rank = [None, None, None] + # for i in range(len(all_fi)): + # fi = all_fi[i] + # if isinstance(fi, np.ndarray): + # fi[fi == float("-inf")] = -sys.maxsize - 1 + # fi[fi == float("inf")] = sys.maxsize - 1 + # if fi_est.ascending: + # all_fi_rank[i] = np.argsort(-fi) + # else: + # all_fi_rank[i] = np.argsort(fi) + + # local_fi_score_train[local_fi_score_train == float("-inf")] = -sys.maxsize - 1 + # local_fi_score_train[local_fi_score_train == float("inf")] = sys.maxsize - 1 + # if fi_est.ascending: + # local_fi_score_train_rank = np.argsort(-local_fi_score_train) + # else: + # local_fi_score_train_rank = np.argsort(local_fi_score_train) + # train_fi_mean = np.mean(local_fi_score_train, axis=0) + # if fi_est.ascending: + # sorted_feature = np.argsort(-train_fi_mean) + # else: + # sorted_feature = np.argsort(train_fi_mean) + # train_mean = np.mean(X_train, axis=0) + + # for a_model in ablation_models: + # print(f"start ablation removal: {a_model}") + # ablation_est = ablation_models[a_model] + # y_pred_before = ablation_est.predict_proba(X_test)[:, 1] + # metric_results[f'{a_model}_LogLoss_after_ablation_0_{m}'] = log_loss(y_test, y_pred_before) + # metric_results[f'{a_model}_AUROC_after_ablation_0_{m}'] = roc_auc_score(y_test, y_pred_before) + # X_temp = copy.deepcopy(X_train) + # print(f"Train 0: {X_temp[0]}") + # for i in range(num_features_masked): + # print(f"Masking {i}") + # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score_train, local_fi_score_train_rank, i, m) + # print(f"Train 0: {X_temp[0]}") + # ablation_est.fit(ablation_X_data, y_train) + # y_pred = ablation_est.predict_proba(X_test)[:, 1] + # metric_results[f'{a_model}_LogLoss_after_ablation_{i+1}_{m}'] = log_loss(y_test, y_pred) + # metric_results[f'{a_model}_AUROC_after_ablation_{i+1}_{m}'] = roc_auc_score(y_test, y_pred) + # X_temp = ablation_X_data + + # mask_ratio = [0.01, 0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7, 0.9] + # for mask in mask_ratio: + # print(f"Mask ratio: {mask}") + # num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) + # print(f"Train shape: {X_train_masked.shape}") + # num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + # print(f"Test shape: {X_train_masked.shape}") + # metric_results[f'num_features_selected_{mask}'] = num_features_selected + # for a_model in ablation_models: + # ablation_models[a_model].fit(X_train_masked, y_train) + # y_pred = ablation_models[a_model].predict_proba(X_test_masked)[:, 1] + # metric_results[f'{a_model}_LogLoss_after_ablation_top_{mask}'] = log_loss(y_test, y_pred) + # metric_results[f'{a_model}_AUROC_after_ablation_top_{mask}'] = roc_auc_score(y_test, y_pred) + + + # all_fi = [local_fi_score_train_subset, local_fi_score_test_subset, local_fi_score_test] + # all_fi_rank = [None, None, None] + # for i in range(len(all_fi)): + # fi = all_fi[i] + # if isinstance(fi, np.ndarray): + # fi[fi == float("-inf")] = -sys.maxsize - 1 + # fi[fi == float("inf")] = sys.maxsize - 1 + # if fi_est.ascending: + # all_fi_rank[i] = np.argsort(-fi) + # else: + # all_fi_rank[i] = np.argsort(fi) + + # ablation_datas = {"train_subset": (X_train_subset, y_train_subset, all_fi[0], all_fi_rank[0]), + # "test_subset": (X_test_subset, y_test_subset, all_fi[1], all_fi_rank[1]), + # "test": (X_test, y_test, all_fi[2], all_fi_rank[2])} + # train_mean = np.mean(X_train, axis=0) + + # print("start ablation") + # # Start ablation 1: Feature removal + # for ablation_data in ablation_datas: + # start = time.time() + # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] + # if not isinstance(local_fi_score, np.ndarray): + # for a_model in ablation_models: + # for i in range(num_features_masked+1): + # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i}_{m}'] = None + # else: + # for a_model in ablation_models: + # print(f"start ablation removal: {ablation_data} {a_model}") + # ablation_est = ablation_models[a_model] + # y_pred_before = ablation_est.predict_proba(X_data)[:, 1] + # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_0_{m}'] = 0 + # X_temp = copy.deepcopy(X_data) + # for i in range(num_features_masked): + # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) + # y_pred = ablation_est.predict_proba(ablation_X_data)[:, 1] + # if i == 0: + # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i+1}_{m}'] = delta_mae(y_data, y_pred_before, y_pred) + # else: + # metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i+1}_{m}'] = delta_mae(y_data, y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_MAE_after_ablation_{i}_{m}'] + # X_temp = ablation_X_data + # y_pred_before = y_pred + # end = time.time() + # print(f"done with ablation removal {m}: {ablation_data} {end - start}") + # metric_results[f'{ablation_data}_ablation_removal_{m}_time'] = end - start + + + + # Start ablation 1: Feature removal + # for ablation_data in ablation_datas: + # start = time.time() + # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] + # if not isinstance(local_fi_score, np.ndarray): + # for a_model in ablation_models: + # metric_results[f'{a_model}_{ablation_data}_AUROC_before_ablation_{m}'] = None + # metric_results[f'{a_model}_{ablation_data}_AUPRC_before_ablation_{m}'] = None + # metric_results[f'{a_model}_{ablation_data}_F1_before_ablation_{m}'] = None + # for i in range(num_features_masked): + # for a_model in ablation_models: + # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_{m}'] = None + # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_{m}'] = None + # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_{m}'] = None + # else: + # for a_model in ablation_models: + # print(f"start ablation removal: {ablation_data} {a_model}") + # ablation_est = ablation_models[a_model] + # y_pred = ablation_est.predict(X_data) + # metric_results[a_model + f'_{ablation_data}_AUROC_before_ablation_{m}'] = roc_auc_score(y_data, y_pred) + # metric_results[a_model + f'_{ablation_data}_AUPRC_before_ablation_{m}'] = average_precision_score(y_data, y_pred) + # metric_results[a_model + f'_{ablation_data}_F1_before_ablation_{m}'] = f1_score(y_data, y_pred > 0.5) + # ablation_results_auroc_list = [0] * num_features_masked + # ablation_results_auprc_list = [0] * num_features_masked + # ablation_results_f1_list = [0] * num_features_masked + # X_temp = X_data.copy() + # for i in range(num_features_masked): + # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) + # ablation_results_auroc_list[i] = roc_auc_score(y_data, ablation_est.predict(ablation_X_data)) + # ablation_results_auprc_list[i] = average_precision_score(y_data, ablation_est.predict(ablation_X_data)) + # ablation_results_f1_list[i] = f1_score(y_data, ablation_est.predict(ablation_X_data) > 0.5) + # X_temp = ablation_X_data + # for i in range(num_features_masked): + # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_{m}'] = ablation_results_auroc_list[i] + # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_{m}'] = ablation_results_auprc_list[i] + # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_{m}'] = ablation_results_f1_list[i] + # end = time.time() + # print(f"done with ablation removal: {ablation_data} {end - start}") + # metric_results[f'{ablation_data}_ablation_removal_time'] = end - start + + # # Start ablation 2: Feature addition + # for ablation_data in ablation_datas: + # start = time.time() + # X_data, y_data, local_fi_score_data = ablation_datas[ablation_data] + # if not isinstance(local_fi_score_data, np.ndarray): + # for a_model in ablation_models: + # metric_results[f'{a_model}_{ablation_data}_AUROC_before_ablation_addition'] = None + # metric_results[f'{a_model}_{ablation_data}_AUPRC_before_ablation_addition'] = None + # metric_results[f'{a_model}_{ablation_data}_F1_before_ablation_addition'] = None + # for i in range(num_ablate_features): + # for a_model in ablation_models: + # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_addition'] = None + # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_addition'] = None + # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_addition'] = None + # else: + # for a_model in ablation_models: + # print(f"start ablation addtion: {ablation_data} {a_model}") + # ablation_est = ablation_models[a_model] + # X_temp = np.array([train_mean_list] * X_data.shape[0]).copy() + # y_pred = ablation_est.predict(X_temp) + # metric_results[a_model + f'_{ablation_data}_AUROC_before_ablation_addition'] = roc_auc_score(y_data, y_pred) + # metric_results[a_model + f'_{ablation_data}_AUPRC_before_ablation_addition'] = average_precision_score(y_data, y_pred) + # metric_results[a_model + f'_{ablation_data}_F1_before_ablation_addition'] = f1_score(y_data, y_pred > 0.5) + # imp_vals = copy.deepcopy(local_fi_score_data) + # ablation_results_auroc_list = [0] * num_ablate_features + # ablation_results_auprc_list = [0] * num_ablate_features + # ablation_results_f1_list = [0] * num_ablate_features + # for i in range(num_ablate_features): + # ablation_X_data = ablation_addition(X_temp, X_data, imp_vals, i) + # ablation_results_auroc_list[i] = roc_auc_score(y_data, ablation_est.predict(ablation_X_data)) + # ablation_results_auprc_list[i] = average_precision_score(y_data, ablation_est.predict(ablation_X_data)) + # ablation_results_f1_list[i] = f1_score(y_data, ablation_est.predict(ablation_X_data) > 0.5) + # X_temp = ablation_X_data + # for i in range(num_ablate_features): + # metric_results[f'{a_model}_{ablation_data}_AUROC_after_ablation_{i+1}_addition'] = ablation_results_auroc_list[i] + # metric_results[f'{a_model}_{ablation_data}_AUPRC_after_ablation_{i+1}_addition'] = ablation_results_auprc_list[i] + # metric_results[f'{a_model}_{ablation_data}_F1_after_ablation_{i+1}_addition'] = ablation_results_f1_list[i] + + # end = time.time() + # print(f"done with ablation addtion: {ablation_data} {end - start}") + # metric_results[f'{ablation_data}_ablation_addition_time'] = end - start + + + # initialize results with metadata and metric results + kwargs: dict = model.kwargs # dict + for k in kwargs: + results[k].append(kwargs[k]) + for k in fi_kwargs: + if k in fi_est.kwargs: + results[k].append(str(fi_est.kwargs[k])) + else: + results[k].append(None) + for met_name, met_val in metric_results.items(): + results[met_name].append(met_val) + return results, feature_importance_list + + +def run_comparison(path: str, + X, y, support: List, + metrics: List[Tuple[str, Callable]], + estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + args): + estimator_name = estimators[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_estimators) \ + if fi_estimator.model_type in estimators[0].model_type] + model_comparison_files_all = [oj(path, f'{estimator_name}_{fi_estimator.name}_comparisons.pkl') \ + for fi_estimator in fi_estimators_all] + + feature_importance_all = oj(path, f'feature_importance.pkl') + + + if args.parallel_id is not None: + model_comparison_files_all = [f'_{args.parallel_id[0]}.'.join(model_comparison_file.split('.')) \ + for model_comparison_file in model_comparison_files_all] + + fi_estimators = [] + model_comparison_files = [] + for model_comparison_file, fi_estimator in zip(model_comparison_files_all, fi_estimators_all): + if os.path.isfile(model_comparison_file) and not args.ignore_cache: + print( + f'{estimator_name} with {fi_estimator.name} results already computed and cached. use --ignore_cache to recompute') + else: + fi_estimators.append(fi_estimator) + model_comparison_files.append(model_comparison_file) + if len(fi_estimators) == 0: + return + results, fi_lst = compare_estimators(estimators=estimators, + fi_estimators=fi_estimators, + X=X, y=y, support=support, + metrics=metrics, + args=args) + + estimators_list = [e.name for e in estimators] + metrics_list = [m[0] for m in metrics] + + df = pd.DataFrame.from_dict(results) + df['split_seed'] = args.split_seed + if args.nosave_cols is not None: + nosave_cols = np.unique([x.strip() for x in args.nosave_cols.split(",")]) + else: + nosave_cols = [] + for col in nosave_cols: + 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 + 'sim_name': args.config, + 'estimators': estimators_list, + 'fi_estimators': fi_estimator.name, + 'metrics': metrics_list, + + # actual values + 'df': df.loc[df.fi == fi_estimator.name], + } + pkl.dump(output_dict, open(model_comparison_file, 'wb')) + return df + + +def get_metrics(): + return [('rocauc', auroc_score), ('prauc', auprc_score)] + + +def reformat_results(results): + results = results.reset_index().drop(columns=['index']) + # fi_scores = pd.concat(results.pop('fi_scores').to_dict()). \ + # reset_index(level=0).rename(columns={'level_0': 'index'}) + # results_df = pd.merge(results, fi_scores, left_index=True, right_on="index") + # return results_df + return results + + + +def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, metrics, args): + os.makedirs(oj(path, val_name, "rep" + str(i)), exist_ok=True) + np.random.seed(i) + max_iter = 100 + iter = 0 + while iter <= max_iter: # regenerate data if y is constant + X = X_dgp(**X_params_dict) + y, support, beta = y_dgp(X, **y_params_dict, return_support=True) + if not all(y == y[0]): + break + iter += 1 + if iter > max_iter: + raise ValueError("Response y is constant.") + if args.omit_vars is not None: + omit_vars = np.unique([int(x.strip()) for x in args.omit_vars.split(",")]) + support = np.delete(support, omit_vars) + X = np.delete(X, omit_vars, axis=1) + del beta # note: beta is not currently supported when using omit_vars + + for est in ests: + results = run_comparison(path=oj(path, val_name, "rep" + str(i)), + X=X, y=y, support=support, + metrics=metrics, + estimators=est, + fi_estimators=fi_ests, + args=args) + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + + parser.add_argument('--nreps', type=int, default=2) + parser.add_argument('--model', type=str, default=None) # , default='c4') + parser.add_argument('--fi_model', type=str, default=None) # , default='c4') + parser.add_argument('--config', type=str, default='test') + parser.add_argument('--omit_vars', type=str, default=None) # comma-separated string of variables to omit + parser.add_argument('--nosave_cols', type=str, default="prediction_model") + + ### 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) + + # for multiple reruns, should support varying split_seed + parser.add_argument('--ignore_cache', action='store_true', default=False) + parser.add_argument('--verbose', action='store_true', default=True) + parser.add_argument('--parallel', action='store_true', default=False) + parser.add_argument('--parallel_id', nargs='+', type=int, default=None) + parser.add_argument('--n_cores', type=int, default=None) + parser.add_argument('--split_seed', type=int, default=0) + parser.add_argument('--results_path', type=str, default=default_dir) + + # arguments for rmd output of results + parser.add_argument('--create_rmd', action='store_true', default=False) + parser.add_argument('--show_vars', type=int, default=None) + + args = parser.parse_args() + + if args.parallel: + if args.n_cores is None: + print(os.getenv("SLURM_CPUS_ON_NODE")) + n_cores = int(os.getenv("SLURM_CPUS_ON_NODE")) + else: + n_cores = args.n_cores + client = Client(n_workers=n_cores) + + ests, fi_ests, \ + X_dgp, X_params_dict, y_dgp, y_params_dict, \ + vary_param_name, vary_param_vals = fi_config.get_fi_configs(args.config) + + metrics = get_metrics() + + if args.model: + ests = list(filter(lambda x: args.model.lower() == x[0].name.lower(), ests)) + if args.fi_model: + fi_ests = list(filter(lambda x: args.fi_model.lower() == x[0].name.lower(), fi_ests)) + + if len(ests) == 0: + raise ValueError('No valid estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if len(fi_ests) == 0: + raise ValueError('No valid FI estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if args.verbose: + print('running', args.config, + 'ests', ests, + 'fi_ests', fi_ests) + print('\tsaving to', args.results_path) + + if args.omit_vars is not None: + #results_dir = oj(args.results_path, args.config + "_omitted_vars") + results_dir = oj(args.results_path, args.config + "_omitted_vars", args.folder_name) + else: + #results_dir = oj(args.results_path, args.config) + results_dir = oj(args.results_path, args.config, args.folder_name) + + if isinstance(vary_param_name, list): + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.split_seed)) + else: + path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) + os.makedirs(path, exist_ok=True) + + eval_out = defaultdict(list) + + vary_type = None + if isinstance(vary_param_name, list): # multiple parameters are being varied + # get parameters that are being varied over and identify whether it's a DGP/method/fi_method argument + keys, values = zip(*vary_param_vals.items()) + vary_param_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)] + vary_type = {} + for vary_param_dict in vary_param_dicts: + for param_name, param_val in vary_param_dict.items(): + if param_name in X_params_dict.keys() and param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif param_name in X_params_dict.keys(): + vary_type[param_name] = "dgp" + X_params_dict[param_name] = vary_param_vals[param_name][param_val] + elif param_name in y_params_dict.keys(): + vary_type[param_name] = "dgp" + y_params_dict[param_name] = vary_param_vals[param_name][param_val] + else: + est_kwargs = list( + itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if param_name in est_kwargs: + vary_type[param_name] = "est" + elif param_name in fi_est_kwargs: + vary_type[param_name] = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, + y_params_dict, y_dgp, ests, fi_ests, metrics, args) for i in + range(args.nreps)] + results = dask.compute(*futures) + else: + results = [ + run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + y_dgp, ests, fi_ests, metrics, args) for i in range(args.nreps)] + assert all(results) + + else: # only on parameter is being varied over + # get parameter that is being varied over and identify whether it's a DGP/method/fi_method argument + for val_name, val in vary_param_vals.items(): + if vary_param_name in X_params_dict.keys() and vary_param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif vary_param_name in X_params_dict.keys(): + vary_type = "dgp" + X_params_dict[vary_param_name] = val + elif vary_param_name in y_params_dict.keys(): + vary_type = "dgp" + y_params_dict[vary_param_name] = val + else: + est_kwargs = list(itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if vary_param_name in est_kwargs: + vary_type = "est" + elif vary_param_name in fi_est_kwargs: + vary_type = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, + fi_ests, metrics, args) for i in range(args.nreps)] + results = dask.compute(*futures) + else: + results = [run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, + metrics, args) for i in range(args.nreps)] + assert all(results) + + print('completed all experiments successfully!') + + # get model file names + model_comparison_files_all = [] + for est in ests: + estimator_name = est[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_ests) \ + if fi_estimator.model_type in est[0].model_type] + model_comparison_files = [f'{estimator_name}_{fi_estimator.name}_comparisons.pkl' for fi_estimator in + fi_estimators_all] + model_comparison_files_all += model_comparison_files + + # aggregate results + results_list = [] + if isinstance(vary_param_name, list): + for vary_param_dict in vary_param_dicts: + val_name = "_".join(vary_param_dict.values()) + + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + + for param_name, param_val in vary_param_dict.items(): + val = vary_param_vals[param_name][param_val] + if vary_type[param_name] == "dgp": + if np.isscalar(val): + results.insert(0, param_name, val) + else: + results.insert(0, param_name, [val for i in range(results.shape[0])]) + results.insert(1, param_name + "_name", param_val) + elif vary_type[param_name] == "est" or vary_type[param_name] == "fi_est": + results.insert(0, param_name + "_name", copy.deepcopy(results[param_name])) + results.insert(0, 'rep', i) + results_list.append(results) + else: + for val_name, val in vary_param_vals.items(): + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + if vary_type == "dgp": + if np.isscalar(val): + results.insert(0, vary_param_name, val) + else: + results.insert(0, vary_param_name, [val for i in range(results.shape[0])]) + results.insert(1, vary_param_name + "_name", val_name) + results.insert(2, 'rep', i) + elif vary_type == "est" or vary_type == "fi_est": + results.insert(0, vary_param_name + "_name", copy.deepcopy(results[vary_param_name])) + results.insert(1, 'rep', i) + results_list.append(results) + results_merged = pd.concat(results_list, axis=0) + pkl.dump(results_merged, open(oj(path, 'results.pkl'), 'wb')) + results_df = reformat_results(results_merged) + results_df.to_csv(oj(path, 'results.csv'), index=False) + + print('merged and saved all experiment results successfully!') + + # create R markdown summary of results + if args.create_rmd: + if args.show_vars is None: + show_vars = 'NULL' + else: + show_vars = args.show_vars + + if isinstance(vary_param_name, list): + vary_param_name = "; ".join(vary_param_name) + + sim_rmd = os.path.basename(results_dir) + '_simulation_results.Rmd' + os.system( + 'cp {} \'{}\''.format(oj("rmd", "simulation_results.Rmd"), sim_rmd) + ) + os.system( + 'Rscript -e "rmarkdown::render(\'{}\', params = list(results_dir = \'{}\', vary_param_name = \'{}\', seed = {}, keep_vars = {}), output_file = \'{}\', quiet = TRUE)"'.format( + sim_rmd, + results_dir, vary_param_name, str(args.split_seed), str(show_vars), + oj(path, "simulation_results.html")) + ) + os.system('rm \'{}\''.format(sim_rmd)) + print("created rmd of simulation results successfully!") \ No newline at end of file diff --git a/feature_importance/OLD_00_run_ablation_regression_retrain.py b/feature_importance/OLD_00_run_ablation_regression_retrain.py new file mode 100644 index 0000000..dad591f --- /dev/null +++ b/feature_importance/OLD_00_run_ablation_regression_retrain.py @@ -0,0 +1,882 @@ +import copy +import os +from os.path import join as oj +import glob +import argparse +import pickle as pkl +import time +import warnings +from scipy import stats +import dask +from dask.distributed import Client +import numpy as np +import pandas as pd +from tqdm import tqdm +import sys +from collections import defaultdict +from typing import Callable, List, Tuple +import itertools +from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score +from sklearn import preprocessing +from sklearn.ensemble import RandomForestRegressor +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("../..") +import fi_config +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 +from sklearn.kernel_ridge import KernelRidge + +warnings.filterwarnings("ignore", message="Bins whose width") + +#RUN THE FILE +# python 01_run_ablation_regression.py --nreps 5 --config mdi_local.real_data_regression --split_seed 331 --ignore_cache --create_rmd --result_name diabetes_regression + + +# def generate_random_shuffle(data, seed): +# """ +# Randomly shuffle each column of the data. +# """ +# np.random.seed(seed) +# return np.array([np.random.permutation(data[:, i]) for i in range(data.shape[1])]).T + + +# def ablation(data, feature_importance, mode, num_features, seed): +# """ +# Replace the top num_features max feature importance data with random shuffle for each sample +# """ +# assert mode in ["max", "min"] +# fi = feature_importance.to_numpy() +# shuffle = generate_random_shuffle(data, seed) +# if mode == "max": +# indices = np.argsort(-fi) +# else: +# indices = np.argsort(fi) +# data_copy = data.copy() +# for i in range(data.shape[0]): +# for j in range(num_features): +# data_copy[i, indices[i,j]] = shuffle[i, indices[i,j]] +# return data_copy + +# def ablation_removal(train_mean, data, feature_importance_rank, feature_index): +# """ +# Replace the top num_features max feature importance data with mean value for each sample +# """ +# data_copy = data.copy() +# for i in range(data.shape[0]): +# data_copy[i, feature_importance_rank[i,feature_index]] = train_mean[feature_importance_rank[i,feature_index]] +# return data_copy + +# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): +# """ +# Initialize the data with mean values and add the top num_features max feature importance data for each sample +# """ +# data_copy = data_ablation.copy() +# for i in range(data.shape[0]): +# data_copy[i, feature_importance_rank[i,feature_index]] = data[i, feature_importance_rank[i,feature_index]] +# return data_copy + + +# def ablation_removal(train_mean, data, feature_importance, feature_importance_rank, feature_index, mode): +# if mode == "absolute": +# return ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index) +# # else: +# # return ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index) + +# def ablation_removal_absolute(train_mean, data, feature_importance_rank, feature_index): +# """ +# Replace the top num_features max feature importance data with mean value for each sample +# """ +# data_copy = data.copy() +# indices = feature_importance_rank[:, feature_index] +# data_copy[np.arange(data.shape[0]), indices] = train_mean[indices] +# return data_copy + +# def ablation_removal_pos_neg(train_mean, data, feature_importance_rank, feature_importance, feature_index): +# data_copy = data.copy() +# indices = feature_importance_rank[:, feature_index] +# sum = 0 +# for i in range(data.shape[0]): +# if feature_importance[i, indices[i]] != 0 and feature_importance[i, indices[i]] < sys.maxsize - 1: +# sum += 1 +# data_copy[i, indices[i]] = train_mean[indices[i]] +# print("Remove sum: ", sum) +# return data_copy + +def select_top_features(array, sorted_indices, percentage): + array = copy.deepcopy(array) + num_features = array.shape[1] + num_selected = int(np.ceil(num_features * percentage)) + selected_indices = sorted_indices[:num_selected] + selected_array = array[:, selected_indices] + return num_selected, selected_array + +# def delta_mse(y_true, y_pred_1, y_pred_2): +# mse_before = (y_true - y_pred_1) ** 2 +# mse_after = (y_true - y_pred_2) ** 2 +# absolute_delta_mse = np.mean(np.abs(mse_before - mse_after)) +# return absolute_delta_mse + +# def delta_y_pred(y_pred_1, y_pred_2): +# return np.mean(np.abs(y_pred_1 - y_pred_2)) + +# def ablation_addition(data_ablation, data, feature_importance_rank, feature_index): +# """ +# Initialize the data with mean values and add the top num_features max feature importance data for each sample +# """ +# data_copy = data_ablation.copy() +# indices = feature_importance_rank[:, feature_index] +# data_copy[np.arange(data.shape[0]), indices] = data[np.arange(data.shape[0]), indices] +# return data_copy + + +def compare_estimators(estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + X, y, support: List, + metrics: List[Tuple[str, Callable]], + args, ) -> Tuple[dict, dict]: + """Calculates results given estimators, feature importance estimators, datasets, and metrics. + Called in run_comparison + """ + if type(estimators) != list: + raise Exception("First argument needs to be a list of Models") + if type(metrics) != list: + raise Exception("Argument metrics needs to be a list containing ('name', callable) pairs") + + # initialize results + results = defaultdict(lambda: []) + feature_importance_list = {"positive": {}, "negative": {}, "absolute": {}} + + # loop over model estimators + for model in estimators: + # est = model.cls(**model.kwargs) + + # get kwargs for all fi_ests + fi_kwargs = {} + for fi_est in fi_estimators: + fi_kwargs.update(fi_est.kwargs) + + # get groups of estimators for each splitting strategy + fi_ests_dict = defaultdict(list) + for fi_est in fi_estimators: + fi_ests_dict[fi_est.splitting_strategy].append(fi_est) + + # loop over splitting strategies + for splitting_strategy, fi_ests in fi_ests_dict.items(): + # implement provided splitting strategy + if splitting_strategy is not None: + X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, splitting_strategy, args.split_seed) + else: + X_train = X + X_test = X + y_train = y + y_test = y + + 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() + + # 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)) + + 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] + } + + os.makedirs(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}", exist_ok=True) + results_df = pd.DataFrame(fitted_results) + results_df.to_csv(f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_fitted_summary_rf_seed_{args.rf_seed}_split_seed_{args.split_seed}.csv", index=False) + + + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RF_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(est, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_default_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base_oob, file) + # pickle_file = f"/scratch/users/zhongyuan_liang/saved_models/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill" + # with open(pickle_file, 'wb') as file: + # dill.dump(rf_plus_base_inbag, file) + + # if args.absolute_masking or args.positive_masking or args.negative_masking: + # np.random.seed(42) + # if X_train.shape[0] > 100: + # indices_train = np.random.choice(X_train.shape[0], 100, replace=False) + # X_train_subset = X_train[indices_train] + # y_train_subset = y_train[indices_train] + # else: + # indices_train = np.arange(X_train.shape[0]) + # X_train_subset = X_train + # y_train_subset = y_train + + # if X_test.shape[0] > 100: + # indices_test = np.random.choice(X_test.shape[0], 100, replace=False) + # X_test_subset = X_test[indices_test] + # y_test_subset = y_test[indices_test] + # else: + # indices_test = np.arange(X_test.shape[0]) + # X_test_subset = X_test + # y_test_subset = y_test + + if args.num_features_masked is None: + num_features_masked = X_train.shape[1] + else: + num_features_masked = args.num_features_masked + + # loop over fi estimators + for fi_est in tqdm(fi_ests): + metric_results = { + 'model': model.name, + 'fi': fi_est.name, + 'train_size': X_train.shape[0], + # 'train_subset_size': X_train_subset.shape[0], + 'test_size': X_test.shape[0], + # 'test_subset_size': X_test_subset.shape[0], + 'num_features': X_train.shape[1], + 'data_split_seed': args.split_seed, + 'rf_seed': args.rf_seed, + 'num_features_masked': num_features_masked + } + # for i in range(X_train_subset.shape[0]): + # metric_results[f'sample_train_{i}'] = indices_train[i] + # for i in range(X_test_subset.shape[0]): + # metric_results[f'sample_test_{i}'] = indices_test[i] + + print("Load Models") + start = time.time() + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_default_{args.split_seed}.dill", 'rb') as file: + # rf_plus_base = dill.load(file) + # if fi_est.base_model == "None": + # loaded_model = None + # elif fi_est.base_model == "RF": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RF_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_oob": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_oob_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_inbag": + # with open(f"/scratch/users/zhongyuan_liang/saved_models/auroc/{args.folder_name}/RFPlus_inbag_{args.split_seed}.dill", 'rb') as file: + # loaded_model = dill.load(file) + # elif fi_est.base_model == "RFPlus_default": + # loaded_model = rf_plus_base + #rf_plus_base = rf_plus_base + if fi_est.base_model == "None": + 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 + end = time.time() + metric_results['load_model_time'] = end - start + print(f"done with loading models: {end - start}") + + + start = time.time() + print(f"Compute feature importance") + # Compute feature importance + local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train, fit=loaded_model, mode="absolute") + # if fi_est.name.startswith("Local_MDI+"): + # local_fi_score_train_subset = local_fi_score_train[indices_train] + + m= "absolute" + #feature_importance_list[m][fi_est.name] = [local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset] + end = time.time() + metric_results[f'fi_time_{m}'] = end - start + print(f"done with feature importance {m}: {end - start}") + # prepare ablations + print("prepare ablation") + ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=args.rf_seed), + # "Linear": LinearRegression(), + # "XGB_Regressor": xgb.XGBRegressor(random_state=42), + # 'Kernel_Ridge': KernelRidge(), + #"RF_Plus_Regressor": rf_plus_base + } + + train_fi_mean = np.mean(local_fi_score_train, axis=0) + if fi_est.ascending: + sorted_feature = np.argsort(-train_fi_mean) + else: + sorted_feature = np.argsort(train_fi_mean) + + + mask_ratio = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7,0.9] + for mask in mask_ratio: + print(f"Mask ratio: {mask}") + num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) + print(f"Train shape: {X_train_masked.shape}") + num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + print(f"Test shape: {X_train_masked.shape}") + metric_results[f'num_features_selected_{mask}'] = num_features_selected + for a_model in ablation_models: + ablation_models[a_model].fit(X_train_masked, y_train) + y_pred = ablation_models[a_model].predict(X_test_masked) + metric_results[f'{a_model}_MSE_after_ablation_top_{mask}'] = mean_squared_error(y_test, y_pred) + metric_results[f'{a_model}_R2_after_ablation_top_{mask}'] = r2_score(y_test, y_pred) + + + # start = time.time() + # for a_model in ablation_models: + # ablation_models[a_model].fit(X_train, y_train) + # end = time.time() + # metric_results['ablation_model_fit_time'] = end - start + # print(f"done with ablation model fit: {end - start}") + + # all_fi = [local_fi_score_train_subset, local_fi_score_test_subset, local_fi_score_test] + # all_fi_rank = [None, None, None] + # for i in range(len(all_fi)): + # fi = all_fi[i] + # if isinstance(fi, np.ndarray): + # fi[fi == float("-inf")] = -sys.maxsize - 1 + # fi[fi == float("inf")] = sys.maxsize - 1 + # if fi_est.ascending: + # all_fi_rank[i] = np.argsort(-fi) + # else: + # all_fi_rank[i] = np.argsort(fi) + # local_fi_score_train[local_fi_score_train == float("-inf")] = -sys.maxsize - 1 + # local_fi_score_train[local_fi_score_train == float("inf")] = sys.maxsize - 1 + # if fi_est.ascending: + # local_fi_score_train_rank = np.argsort(-local_fi_score_train) + # local_fi_score_test_rank = np.argsort(-local_fi_score_test) + # else: + # local_fi_score_train_rank = np.argsort(local_fi_score_train) + # local_fi_score_test_rank = np.argsort(local_fi_score_test) + # train_fi_mean = np.mean(local_fi_score_train, axis=0) + # if fi_est.ascending: + # sorted_feature = np.argsort(-train_fi_mean) + # else: + # sorted_feature = np.argsort(train_fi_mean) + # train_mean = np.mean(X_train, axis=0) + + # for a_model in ablation_models: + # print(f"start ablation removal: {a_model}") + # ablation_est = ablation_models[a_model] + # y_pred_before = ablation_est.predict(X_test) + # metric_results[f'{a_model}_MSE_after_ablation_0_{m}'] = mean_squared_error(y_test, y_pred_before) + # metric_results[f'{a_model}_R2_after_ablation_0_{m}'] = r2_score(y_test, y_pred_before) + # X_temp = copy.deepcopy(X_train) + # X_temp_test = copy.deepcopy(X_test) + # print(f"Train 0: {X_temp[0]}") + # for i in range(num_features_masked): + # print(f"Masking {i}") + # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score_train, local_fi_score_train_rank, i, m) + # print(f"Train 0: {X_temp[0]}") + # ablation_est.fit(ablation_X_data, y_train) + # ablation_X_data_test = ablation_removal(train_mean, X_temp_test, local_fi_score_test, local_fi_score_test_rank, i, m) + # y_pred = ablation_est.predict(ablation_X_data_test) + # metric_results[f'{a_model}_MSE_after_ablation_{i+1}_{m}'] = mean_squared_error(y_test, y_pred) + # metric_results[f'{a_model}_R2_after_ablation_{i+1}_{m}'] = r2_score(y_test, y_pred) + # X_temp = ablation_X_data + # X_temp_test = ablation_X_data_test + + # mask_ratio = [0.05, 0.1, 0.15, 0.25, 0.4, 0.5, 0.7,0.9] + # for mask in mask_ratio: + # print(f"Mask ratio: {mask}") + # num_features_selected, X_train_masked = select_top_features(X_train, sorted_feature, mask) + # print(f"Train shape: {X_train_masked.shape}") + # num_features_selected, X_test_masked = select_top_features(X_test, sorted_feature, mask) + # print(f"Test shape: {X_train_masked.shape}") + # metric_results[f'num_features_selected_{mask}'] = num_features_selected + # for a_model in ablation_models: + # ablation_models[a_model].fit(X_train_masked, y_train) + # y_pred = ablation_models[a_model].predict(X_test_masked) + # metric_results[f'{a_model}_MSE_after_ablation_top_{mask}'] = mean_squared_error(y_test, y_pred) + # metric_results[f'{a_model}_R2_after_ablation_top_{mask}'] = r2_score(y_test, y_pred) + + # ablation_datas = {"train_subset": (X_train_subset, y_train_subset, all_fi[0], all_fi_rank[0]), + # "test_subset": (X_test_subset, y_test_subset, all_fi[1], all_fi_rank[1]), + # "test": (X_test, y_test, all_fi[2], all_fi_rank[2])} + # train_mean = np.mean(X_train, axis=0) + + # print("start ablation") + # # Start ablation 1: Feature removal + # for ablation_data in ablation_datas: + # start = time.time() + # X_data, y_data, local_fi_score, local_fi_score_rank = ablation_datas[ablation_data] + # if not isinstance(local_fi_score, np.ndarray): + # for a_model in ablation_models: + # for i in range(num_features_masked+1): + # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i}_{m}'] = None + # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i}_{m}'] = None + # else: + # for a_model in ablation_models: + # print(f"start ablation removal: {ablation_data} {a_model}") + # ablation_est = ablation_models[a_model] + # y_pred_before = ablation_est.predict(X_data) + # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_0_{m}'] = 0 + # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_0_{m}'] = 0 + # X_temp = copy.deepcopy(X_data) + # for i in range(num_features_masked): + # ablation_X_data = ablation_removal(train_mean, X_temp, local_fi_score, local_fi_score_rank, i, m) + # y_pred = ablation_est.predict(ablation_X_data) + # if i == 0: + # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i+1}_{m}'] = delta_mse(y_data, y_pred_before, y_pred) + # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i+1}_{m}'] = delta_y_pred(y_pred_before, y_pred) + # else: + # metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i+1}_{m}'] = delta_mse(y_data, y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_MSE_after_ablation_{i}_{m}'] + # metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i+1}_{m}'] = delta_y_pred(y_pred_before, y_pred) + metric_results[f'{a_model}_{ablation_data}_delta_y_hat_after_ablation_{i}_{m}' ] + # X_temp = ablation_X_data + # y_pred_before = y_pred + # end = time.time() + # print(f"done with ablation removal {m}: {ablation_data} {end - start}") + # metric_results[f'{ablation_data}_ablation_removal_{m}_time'] = end - start + # # Start ablation 2: Feature addition + # for ablation_data in ablation_datas: + # start = time.time() + # X_data, y_data, local_fi_score_data = ablation_datas[ablation_data] + # if not isinstance(local_fi_score_data, np.ndarray): + # for a_model in ablation_models: + # metric_results[a_model + f'_{ablation_data}_MSE_before_ablation_addition'] = None + # metric_results[a_model + f'_{ablation_data}_R_2_before_ablation_addition'] = None + # for i in range(num_ablate_features): + # for a_model in ablation_models: + # metric_results[f'{a_model}_{ablation_data}_MSE_after_ablation_{i+1}_addition'] = None + # metric_results[f'{a_model}_{ablation_data}_R_2_after_ablation_{i+1}_addition'] = None + # else: + # for a_model in ablation_models: + # print(f"start ablation addtion: {ablation_data} {a_model}") + # ablation_est = ablation_models[a_model] + # X_temp = np.array([train_mean_list] * X_data.shape[0]).copy() + # y_pred = ablation_est.predict(X_temp) + # metric_results[a_model + f'_{ablation_data}_MSE_before_ablation_addition'] = mean_squared_error(y_data, y_pred) + # metric_results[a_model + f'_{ablation_data}_R_2_before_ablation_addition'] = r2_score(y_data, y_pred) + # imp_vals = copy.deepcopy(local_fi_score_data) + # ablation_results_list = [0] * num_ablate_features + # ablation_results_list_r2 = [0] * num_ablate_features + # for i in range(num_ablate_features): + # ablation_X_data = ablation_addition(X_temp, X_data, imp_vals, i) + # ablation_results_list[i] = mean_squared_error(y_data, ablation_est.predict(ablation_X_data)) + # ablation_results_list_r2[i] = r2_score(y_data, ablation_est.predict(ablation_X_data)) + # X_temp = ablation_X_data + # for i in range(num_ablate_features): + # metric_results[f'{a_model}_{ablation_data}_MSE_after_ablation_{i+1}_addition'] = ablation_results_list[i] + # metric_results[f'{a_model}_{ablation_data}_R_2_after_ablation_{i+1}_addition'] = ablation_results_list_r2[i] + # end = time.time() + # print(f"done with ablation addtion: {ablation_data} {end - start}") + # metric_results[f'{ablation_data}_ablation_addition_time'] = end - start + + # initialize results with metadata and metric results + kwargs: dict = model.kwargs # dict + for k in kwargs: + results[k].append(kwargs[k]) + for k in fi_kwargs: + if k in fi_est.kwargs: + results[k].append(str(fi_est.kwargs[k])) + else: + results[k].append(None) + for met_name, met_val in metric_results.items(): + results[met_name].append(met_val) + # for key, value in results.items(): + # print(f"{key}: {len(value)}") + return results, feature_importance_list + + +def run_comparison(path: str, + X, y, support: List, + metrics: List[Tuple[str, Callable]], + estimators: List[ModelConfig], + fi_estimators: List[FIModelConfig], + args): + estimator_name = estimators[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_estimators) \ + if fi_estimator.model_type in estimators[0].model_type] + model_comparison_files_all = [oj(path, f'{estimator_name}_{fi_estimator.name}_comparisons.pkl') \ + for fi_estimator in fi_estimators_all] + + feature_importance_all = oj(path, f'feature_importance.pkl') + + + if args.parallel_id is not None: + model_comparison_files_all = [f'_{args.parallel_id[0]}.'.join(model_comparison_file.split('.')) \ + for model_comparison_file in model_comparison_files_all] + + fi_estimators = [] + model_comparison_files = [] + for model_comparison_file, fi_estimator in zip(model_comparison_files_all, fi_estimators_all): + if os.path.isfile(model_comparison_file) and not args.ignore_cache: + print( + f'{estimator_name} with {fi_estimator.name} results already computed and cached. use --ignore_cache to recompute') + else: + fi_estimators.append(fi_estimator) + model_comparison_files.append(model_comparison_file) + + if len(fi_estimators) == 0: + return + + results, fi_lst = compare_estimators(estimators=estimators, + fi_estimators=fi_estimators, + X=X, y=y, support=support, + metrics=metrics, + args=args) + + estimators_list = [e.name for e in estimators] + metrics_list = [m[0] for m in metrics] + + df = pd.DataFrame.from_dict(results) + df['split_seed'] = args.split_seed + if args.nosave_cols is not None: + nosave_cols = np.unique([x.strip() for x in args.nosave_cols.split(",")]) + else: + nosave_cols = [] + for col in nosave_cols: + 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 + 'sim_name': args.config, + 'estimators': estimators_list, + 'fi_estimators': fi_estimator.name, + 'metrics': metrics_list, + + # actual values + 'df': df.loc[df.fi == fi_estimator.name], + } + pkl.dump(output_dict, open(model_comparison_file, 'wb')) + return df + + +def get_metrics(): + return [('rocauc', auroc_score), ('prauc', auprc_score)] + + +def reformat_results(results): + results = results.reset_index().drop(columns=['index']) + # fi_scores = pd.concat(results.pop('fi_scores').to_dict()). \ + # reset_index(level=0).rename(columns={'level_0': 'index'}) + # results_df = pd.merge(results, fi_scores, left_index=True, right_on="index") + # return results_df + return results + +def run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, metrics, args): + os.makedirs(oj(path, val_name, "rep" + str(i)), exist_ok=True) + np.random.seed(i) + max_iter = 100 + iter = 0 + while iter <= max_iter: # regenerate data if y is constant + X = X_dgp(**X_params_dict) + y, support, beta = y_dgp(X, **y_params_dict, return_support=True) + if not all(y == y[0]): + break + iter += 1 + if iter > max_iter: + raise ValueError("Response y is constant.") + if args.omit_vars is not None: + omit_vars = np.unique([int(x.strip()) for x in args.omit_vars.split(",")]) + support = np.delete(support, omit_vars) + X = np.delete(X, omit_vars, axis=1) + del beta # note: beta is not currently supported when using omit_vars + + for est in ests: + results = run_comparison(path=oj(path, val_name, "rep" + str(i)), + X=X, y=y, support=support, + metrics=metrics, + estimators=est, + fi_estimators=fi_ests, + args=args) + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + + parser.add_argument('--nreps', type=int, default=2) + parser.add_argument('--model', type=str, default=None) # , default='c4') + parser.add_argument('--fi_model', type=str, default=None) # , default='c4') + parser.add_argument('--config', type=str, default='test') + parser.add_argument('--omit_vars', type=str, default=None) # comma-separated string of variables to omit + parser.add_argument('--nosave_cols', type=str, default="prediction_model") + + ### 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) + + # for multiple reruns, should support varying split_seed + parser.add_argument('--ignore_cache', action='store_true', default=False) + parser.add_argument('--verbose', action='store_true', default=True) + parser.add_argument('--parallel', action='store_true', default=False) + parser.add_argument('--parallel_id', nargs='+', type=int, default=None) + parser.add_argument('--n_cores', type=int, default=None) + parser.add_argument('--split_seed', type=int, default=0) + parser.add_argument('--results_path', type=str, default=default_dir) + + # arguments for rmd output of results + parser.add_argument('--create_rmd', action='store_true', default=False) + parser.add_argument('--show_vars', type=int, default=None) + + args = parser.parse_args() + + if args.parallel: + if args.n_cores is None: + print(os.getenv("SLURM_CPUS_ON_NODE")) + n_cores = int(os.getenv("SLURM_CPUS_ON_NODE")) + else: + n_cores = args.n_cores + client = Client(n_workers=n_cores) + + ests, fi_ests, \ + X_dgp, X_params_dict, y_dgp, y_params_dict, \ + vary_param_name, vary_param_vals = fi_config.get_fi_configs(args.config) + + metrics = get_metrics() + + if args.model: + ests = list(filter(lambda x: args.model.lower() == x[0].name.lower(), ests)) + if args.fi_model: + fi_ests = list(filter(lambda x: args.fi_model.lower() == x[0].name.lower(), fi_ests)) + + if len(ests) == 0: + raise ValueError('No valid estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if len(fi_ests) == 0: + raise ValueError('No valid FI estimators', 'sim', args.config, 'models', args.model, 'fi', args.fi_model) + if args.verbose: + print('running', args.config, + 'ests', ests, + 'fi_ests', fi_ests) + print('\tsaving to', args.results_path) + + if args.omit_vars is not None: + #results_dir = oj(args.results_path, args.config + "_omitted_vars") + results_dir = oj(args.results_path, args.config + "_omitted_vars", args.folder_name) + else: + #results_dir = oj(args.results_path, args.config) + results_dir = oj(args.results_path, args.config, args.folder_name) + + # if isinstance(vary_param_name, list): + # path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.split_seed)) + # else: + # path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.split_seed)) + if isinstance(vary_param_name, list): + path = oj(results_dir, "varying_" + "_".join(vary_param_name), "seed" + str(args.rf_seed)) + else: + path = oj(results_dir, "varying_" + vary_param_name, "seed" + str(args.rf_seed)) + os.makedirs(path, exist_ok=True) + + eval_out = defaultdict(list) + + vary_type = None + if isinstance(vary_param_name, list): # multiple parameters are being varied + # get parameters that are being varied over and identify whether it's a DGP/method/fi_method argument + keys, values = zip(*vary_param_vals.items()) + vary_param_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)] + vary_type = {} + for vary_param_dict in vary_param_dicts: + for param_name, param_val in vary_param_dict.items(): + if param_name in X_params_dict.keys() and param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif param_name in X_params_dict.keys(): + vary_type[param_name] = "dgp" + X_params_dict[param_name] = vary_param_vals[param_name][param_val] + elif param_name in y_params_dict.keys(): + vary_type[param_name] = "dgp" + y_params_dict[param_name] = vary_param_vals[param_name][param_val] + else: + est_kwargs = list( + itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if param_name in est_kwargs: + vary_type[param_name] = "est" + elif param_name in fi_est_kwargs: + vary_type[param_name] = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, + y_params_dict, y_dgp, ests, fi_ests, metrics, args) for i in + range(args.nreps)] + results = dask.compute(*futures) + else: + results = [ + run_simulation(i, path, "_".join(vary_param_dict.values()), X_params_dict, X_dgp, y_params_dict, + y_dgp, ests, fi_ests, metrics, args) for i in range(args.nreps)] + assert all(results) + + else: # only on parameter is being varied over + # get parameter that is being varied over and identify whether it's a DGP/method/fi_method argument + for val_name, val in vary_param_vals.items(): + if vary_param_name in X_params_dict.keys() and vary_param_name in y_params_dict.keys(): + raise ValueError('Cannot vary over parameter in both X and y DGPs.') + elif vary_param_name in X_params_dict.keys(): + vary_type = "dgp" + X_params_dict[vary_param_name] = val + elif vary_param_name in y_params_dict.keys(): + vary_type = "dgp" + y_params_dict[vary_param_name] = val + else: + est_kwargs = list(itertools.chain(*[list(est.kwargs.keys()) for est in list(itertools.chain(*ests))])) + fi_est_kwargs = list( + itertools.chain(*[list(fi_est.kwargs.keys()) for fi_est in list(itertools.chain(*fi_ests))])) + if vary_param_name in est_kwargs: + vary_type = "est" + elif vary_param_name in fi_est_kwargs: + vary_type = "fi_est" + else: + raise ValueError('Invalid vary_param_name.') + + if args.parallel: + futures = [ + dask.delayed(run_simulation)(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, + fi_ests, metrics, args) for i in range(args.nreps)] + results = dask.compute(*futures) + else: + results = [run_simulation(i, path, val_name, X_params_dict, X_dgp, y_params_dict, y_dgp, ests, fi_ests, + metrics, args) for i in range(args.nreps)] + assert all(results) + + print('completed all experiments successfully!') + + # get model file names + model_comparison_files_all = [] + for est in ests: + estimator_name = est[0].name.split(' - ')[0] + fi_estimators_all = [fi_estimator for fi_estimator in itertools.chain(*fi_ests) \ + if fi_estimator.model_type in est[0].model_type] + model_comparison_files = [f'{estimator_name}_{fi_estimator.name}_comparisons.pkl' for fi_estimator in + fi_estimators_all] + model_comparison_files_all += model_comparison_files + + # aggregate results + results_list = [] + if isinstance(vary_param_name, list): + for vary_param_dict in vary_param_dicts: + val_name = "_".join(vary_param_dict.values()) + + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + + for param_name, param_val in vary_param_dict.items(): + val = vary_param_vals[param_name][param_val] + if vary_type[param_name] == "dgp": + if np.isscalar(val): + results.insert(0, param_name, val) + else: + results.insert(0, param_name, [val for i in range(results.shape[0])]) + results.insert(1, param_name + "_name", param_val) + elif vary_type[param_name] == "est" or vary_type[param_name] == "fi_est": + results.insert(0, param_name + "_name", copy.deepcopy(results[param_name])) + results.insert(0, 'rep', i) + results_list.append(results) + else: + for val_name, val in vary_param_vals.items(): + for i in range(args.nreps): + all_files = glob.glob(oj(path, val_name, 'rep' + str(i), '*')) + model_files = sorted([f for f in all_files if os.path.basename(f) in model_comparison_files_all]) + + if len(model_files) == 0: + print('No files found at ', oj(path, val_name, 'rep' + str(i))) + continue + + results = pd.concat( + [pkl.load(open(f, 'rb'))['df'] for f in model_files], + axis=0 + ) + if vary_type == "dgp": + if np.isscalar(val): + results.insert(0, vary_param_name, val) + else: + results.insert(0, vary_param_name, [val for i in range(results.shape[0])]) + results.insert(1, vary_param_name + "_name", val_name) + results.insert(2, 'rep', i) + elif vary_type == "est" or vary_type == "fi_est": + results.insert(0, vary_param_name + "_name", copy.deepcopy(results[vary_param_name])) + results.insert(1, 'rep', i) + results_list.append(results) + results_merged = pd.concat(results_list, axis=0) + pkl.dump(results_merged, open(oj(path, 'results.pkl'), 'wb')) + results_df = reformat_results(results_merged) + results_df.to_csv(oj(path, 'results.csv'), index=False) + + print('merged and saved all experiment results successfully!') + + # create R markdown summary of results + if args.create_rmd: + if args.show_vars is None: + show_vars = 'NULL' + else: + show_vars = args.show_vars + + if isinstance(vary_param_name, list): + vary_param_name = "; ".join(vary_param_name) + + sim_rmd = os.path.basename(results_dir) + '_simulation_results.Rmd' + os.system( + 'cp {} \'{}\''.format(oj("rmd", "simulation_results.Rmd"), sim_rmd) + ) + os.system( + 'Rscript -e "rmarkdown::render(\'{}\', params = list(results_dir = \'{}\', vary_param_name = \'{}\', seed = {}, keep_vars = {}), output_file = \'{}\', quiet = TRUE)"'.format( + sim_rmd, + results_dir, vary_param_name, str(args.split_seed), str(show_vars), + oj(path, "simulation_results.html")) + ) + os.system('rm \'{}\''.format(sim_rmd)) + print("created rmd of simulation results successfully!") \ No newline at end of file diff --git a/feature_importance/ablation_results_visulization_ranking.ipynb b/feature_importance/ablation_results_visulization_ranking.ipynb new file mode 100644 index 0000000..5f9231e --- /dev/null +++ b/feature_importance/ablation_results_visulization_ranking.ipynb @@ -0,0 +1,2293 @@ +{ + "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": [ + "
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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_retrain.ipynb b/feature_importance/ablation_results_visulization_retrain.ipynb index 6e43b6f..5f9231e 100644 --- a/feature_importance/ablation_results_visulization_retrain.ipynb +++ b/feature_importance/ablation_results_visulization_retrain.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -17,24 +17,73 @@ }, { "cell_type": "code", - "execution_count": 2, + "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_name = 'diabetes_retrain' #'diabetes_regr''csi_pecarn_pred_delta_mae' 'diabetes_classification_delta_mae' 'diabetes_delta_mse' 'credit_g_classification_delta_mae' 'concrete_delta_mse'\n", "task = \"regression\" #\"classification\" #\"regression\"\n", - "baseline = False\n", - "# ablation_directory = f'./results/mdi_local.real_data_{task}/{task_name}/varying_sample_row_n'\n", - "#ablation_directory = f'./results/mdi_local.synthetic_data_linear/{task_name}/varying_heritability_n'\n", - "ablation_directory = f'./results/mdi_local.real_data_{task}_{task_name}/{task_name}/varying_sample_row_n'\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", + "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", - "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", + "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", @@ -46,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -80,237 +129,57 @@ " model\n", " fi\n", " train_size\n", - " train_subset_size\n", " test_size\n", - " test_subset_size\n", " num_features\n", " data_split_seed\n", + " rf_seed\n", " num_features_masked\n", - " sample_train_0\n", - " sample_train_1\n", - " sample_train_2\n", - " sample_train_3\n", - " sample_train_4\n", - " sample_train_5\n", - " sample_train_6\n", - " sample_train_7\n", - " sample_train_8\n", - " sample_train_9\n", - " sample_train_10\n", - " sample_train_11\n", - " sample_train_12\n", - " sample_train_13\n", - " sample_train_14\n", - " sample_train_15\n", - " sample_train_16\n", - " sample_train_17\n", - " sample_train_18\n", - " sample_train_19\n", - " sample_train_20\n", - " sample_train_21\n", - " sample_train_22\n", - " sample_train_23\n", - " sample_train_24\n", - " sample_train_25\n", - " sample_train_26\n", - " sample_train_27\n", - " sample_train_28\n", - " sample_train_29\n", - " sample_train_30\n", - " sample_train_31\n", - " sample_train_32\n", - " sample_train_33\n", - " sample_train_34\n", - " sample_train_35\n", - " sample_train_36\n", - " sample_train_37\n", - " sample_train_38\n", - " sample_train_39\n", - " sample_train_40\n", - " sample_train_41\n", - " sample_train_42\n", - " sample_train_43\n", - " sample_train_44\n", - " sample_train_45\n", - " sample_train_46\n", - " sample_train_47\n", - " sample_train_48\n", - " sample_train_49\n", - " sample_train_50\n", - " sample_train_51\n", - " sample_train_52\n", - " sample_train_53\n", - " sample_train_54\n", - " sample_train_55\n", - " sample_train_56\n", - " sample_train_57\n", - " sample_train_58\n", - " sample_train_59\n", - " sample_train_60\n", - " sample_train_61\n", - " sample_train_62\n", - " sample_train_63\n", - " sample_train_64\n", - " sample_train_65\n", - " sample_train_66\n", - " sample_train_67\n", - " sample_train_68\n", - " sample_train_69\n", - " sample_train_70\n", - " sample_train_71\n", - " sample_train_72\n", - " sample_train_73\n", - " sample_train_74\n", - " sample_train_75\n", - " sample_train_76\n", - " sample_train_77\n", - " sample_train_78\n", - " sample_train_79\n", - " sample_train_80\n", - " sample_train_81\n", - " sample_train_82\n", - " sample_train_83\n", - " sample_train_84\n", - " sample_train_85\n", - " sample_train_86\n", - " sample_train_87\n", - " sample_train_88\n", - " sample_train_89\n", - " sample_train_90\n", - " sample_train_91\n", - " sample_train_92\n", - " sample_train_93\n", - " sample_train_94\n", - " sample_train_95\n", - " sample_train_96\n", - " sample_train_97\n", - " sample_train_98\n", - " sample_train_99\n", - " sample_test_0\n", - " sample_test_1\n", - " sample_test_2\n", - " sample_test_3\n", - " sample_test_4\n", - " sample_test_5\n", - " sample_test_6\n", - " sample_test_7\n", - " sample_test_8\n", - " sample_test_9\n", - " sample_test_10\n", - " sample_test_11\n", - " sample_test_12\n", - " sample_test_13\n", - " sample_test_14\n", - " sample_test_15\n", - " sample_test_16\n", - " sample_test_17\n", - " sample_test_18\n", - " sample_test_19\n", - " sample_test_20\n", - " sample_test_21\n", - " sample_test_22\n", - " sample_test_23\n", - " sample_test_24\n", - " sample_test_25\n", - " sample_test_26\n", - " sample_test_27\n", - " sample_test_28\n", - " sample_test_29\n", - " sample_test_30\n", - " sample_test_31\n", - " sample_test_32\n", - " sample_test_33\n", - " sample_test_34\n", - " sample_test_35\n", - " sample_test_36\n", - " sample_test_37\n", - " sample_test_38\n", - " sample_test_39\n", - " sample_test_40\n", - " sample_test_41\n", - " sample_test_42\n", - " sample_test_43\n", - " sample_test_44\n", - " sample_test_45\n", - " sample_test_46\n", - " sample_test_47\n", - " sample_test_48\n", - " sample_test_49\n", - " sample_test_50\n", - " sample_test_51\n", - " sample_test_52\n", - " sample_test_53\n", - " sample_test_54\n", - " sample_test_55\n", - " sample_test_56\n", - " sample_test_57\n", - " sample_test_58\n", - " sample_test_59\n", - " sample_test_60\n", - " sample_test_61\n", - " sample_test_62\n", - " sample_test_63\n", - " sample_test_64\n", - " sample_test_65\n", - " sample_test_66\n", - " sample_test_67\n", - " sample_test_68\n", - " sample_test_69\n", - " sample_test_70\n", - " sample_test_71\n", - " sample_test_72\n", - " sample_test_73\n", - " sample_test_74\n", - " sample_test_75\n", - " sample_test_76\n", - " sample_test_77\n", - " sample_test_78\n", - " sample_test_79\n", - " sample_test_80\n", - " sample_test_81\n", - " sample_test_82\n", - " sample_test_83\n", - " sample_test_84\n", - " sample_test_85\n", - " sample_test_86\n", - " sample_test_87\n", - " sample_test_88\n", - " sample_test_89\n", - " sample_test_90\n", - " sample_test_91\n", - " sample_test_92\n", - " sample_test_93\n", - " sample_test_94\n", - " sample_test_95\n", - " sample_test_96\n", - " sample_test_97\n", - " sample_test_98\n", - " sample_test_99\n", - " load_model_time\n", " fi_time_absolute\n", - " ablation_model_fit_time\n", - " RF_Regressor_MSE_after_ablation_0_absolute\n", - " RF_Regressor_MSE_after_ablation_1_absolute\n", - " RF_Regressor_MSE_after_ablation_2_absolute\n", - " RF_Regressor_MSE_after_ablation_3_absolute\n", - " RF_Regressor_MSE_after_ablation_4_absolute\n", - " RF_Regressor_MSE_after_ablation_5_absolute\n", - " RF_Regressor_MSE_after_ablation_6_absolute\n", - " RF_Regressor_MSE_after_ablation_7_absolute\n", - " RF_Regressor_MSE_after_ablation_8_absolute\n", - " RF_Regressor_MSE_after_ablation_9_absolute\n", - " RF_Regressor_MSE_after_ablation_10_absolute\n", - " Linear_MSE_after_ablation_0_absolute\n", - " Linear_MSE_after_ablation_1_absolute\n", - " Linear_MSE_after_ablation_2_absolute\n", - " Linear_MSE_after_ablation_3_absolute\n", - " Linear_MSE_after_ablation_4_absolute\n", - " Linear_MSE_after_ablation_5_absolute\n", - " Linear_MSE_after_ablation_6_absolute\n", - " Linear_MSE_after_ablation_7_absolute\n", - " Linear_MSE_after_ablation_8_absolute\n", - " Linear_MSE_after_ablation_9_absolute\n", - " Linear_MSE_after_ablation_10_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", " split_seed\n", " \n", " \n", @@ -325,240 +194,60 @@ " 0.33\n", " 42\n", " RF\n", - " Kernel_SHAP_RF_plus\n", - " 296\n", - " 100\n", - " 146\n", - " 100\n", - " 10\n", - " 4\n", - " 10\n", - " 274\n", - " 155\n", - " 84\n", - " 82\n", - " 261\n", - " 9\n", - " 42\n", - " 277\n", - " 282\n", - " 92\n", - " 148\n", - " 211\n", - " 60\n", - " 218\n", - " 262\n", + " LIME_RF\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 1\n", " 46\n", - " 45\n", - " 236\n", - " 228\n", - " 132\n", - " 143\n", - " 167\n", - " 152\n", - " 93\n", - " 113\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", - " 238\n", - " 251\n", - " 170\n", - " 186\n", - " 193\n", - " 33\n", - " 222\n", - " 216\n", - " 197\n", - " 73\n", - " 182\n", - " 119\n", - " 285\n", - " 202\n", - " 204\n", - " 179\n", - " 177\n", - " 111\n", - " 59\n", - " 226\n", - " 25\n", - " 77\n", - " 6\n", - " 175\n", - " 164\n", - " 140\n", - " 30\n", - " 22\n", - " 245\n", - " 24\n", - " 56\n", - " 144\n", - " 124\n", - " 97\n", - " 63\n", - " 17\n", - " 215\n", - " 219\n", - " 183\n", - " 114\n", - " 76\n", - " 284\n", - " 66\n", - " 178\n", - " 154\n", - " 75\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", - " 108\n", - " 79\n", - " 118\n", - " 72\n", - " 15\n", - " 10\n", - " 101\n", - " 68\n", - " 125\n", - " 37\n", - " 16\n", - " 293\n", - " 139\n", - " 266\n", - " 67\n", - " 90\n", - " 69\n", - " 288\n", - " 165\n", - " 126\n", - " 221\n", - " 173\n", - " 18\n", - " 172\n", - " 96\n", - " 146\n", - " 86\n", - " 69\n", - " 30\n", - " 39\n", - " 2\n", - " 124\n", - " 10\n", - " 68\n", - " 51\n", - " 71\n", - " 77\n", - " 102\n", - " 80\n", - " 76\n", - " 142\n", - " 127\n", - " 95\n", - " 70\n", - " 93\n", - " 67\n", - " 0\n", - " 105\n", - " 82\n", - " 136\n", - " 40\n", - " 54\n", - " 28\n", - " 74\n", - " 119\n", - " 18\n", - " 9\n", - " 58\n", - " 99\n", - " 73\n", - " 97\n", - " 128\n", - " 122\n", - " 55\n", - " 90\n", - " 129\n", - " 79\n", - " 4\n", - " 87\n", - " 83\n", - " 115\n", - " 81\n", - " 72\n", - " 144\n", - " 78\n", - " 126\n", - " 132\n", - " 106\n", - " 75\n", - " 61\n", - " 143\n", - " 131\n", - " 123\n", - " 89\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", - " 133\n", - " 14\n", - " 88\n", - " 140\n", - " 11\n", - " 13\n", - " 15\n", - " 139\n", - " 64\n", - " 19\n", - " 44\n", - " 35\n", - " 56\n", - " 6\n", - " 107\n", - " 12\n", - " 113\n", - " 141\n", - " 49\n", - " 25\n", - " 41\n", - " 38\n", - " 130\n", + " 0.054722\n", + " 0.656512\n", + " 0.068056\n", + " 0.572816\n", " 42\n", - " 8\n", - " 101\n", - " 125\n", + " 0.055254\n", + " 0.653174\n", + " 0.067633\n", + " 0.575476\n", " 1\n", - " 137\n", - " 65\n", - " 22\n", - " 85\n", - " 46\n", - " 103\n", - " 145\n", - " 111\n", - " 100\n", - " 57\n", - " 53\n", - " 109\n", - " 24\n", - " 17\n", - " 0.000002\n", - " 252.267561\n", - " 0.176501\n", - " 3176.637731\n", - " 3581.164414\n", - " 4187.246823\n", - " 4628.778988\n", - " 4850.998948\n", - " 5534.896348\n", - " 5997.868475\n", - " 6063.883313\n", - " 5634.395323\n", - " 5786.880003\n", - " 5786.262547\n", - " 2944.156549\n", - " 3825.876036\n", - " 4358.913641\n", - " 14063.612627\n", - " 22241.241600\n", - " 82141.497378\n", - " 1.357333e+05\n", - " 2.315023e+06\n", - " 8.156634e+05\n", - " 1.584653e+06\n", - " 1.320781e+64\n", - " 4\n", " \n", " \n", " 1\n", @@ -570,240 +259,60 @@ " 0.33\n", " 42\n", " RF\n", - " LIME_RF_plus\n", - " 296\n", - " 100\n", - " 146\n", - " 100\n", - " 10\n", - " 4\n", - " 10\n", - " 274\n", - " 155\n", - " 84\n", - " 82\n", - " 261\n", - " 9\n", - " 42\n", - " 277\n", - " 282\n", - " 92\n", - " 148\n", - " 211\n", - " 60\n", - " 218\n", - " 262\n", + " Local_MDI+_fit_on_all_RFPlus\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 1\n", " 46\n", - " 45\n", - " 236\n", - " 228\n", - " 132\n", - " 143\n", - " 167\n", - " 152\n", - " 93\n", - " 113\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", - " 238\n", - " 251\n", - " 170\n", - " 186\n", - " 193\n", - " 33\n", - " 222\n", - " 216\n", - " 197\n", - " 73\n", - " 182\n", - " 119\n", - " 285\n", - " 202\n", - " 204\n", - " 179\n", - " 177\n", - " 111\n", - " 59\n", - " 226\n", - " 25\n", - " 77\n", - " 6\n", - " 175\n", - " 164\n", - " 140\n", - " 30\n", - " 22\n", - " 245\n", - " 24\n", - " 56\n", - " 144\n", - " 124\n", - " 97\n", - " 63\n", - " 17\n", - " 215\n", - " 219\n", - " 183\n", - " 114\n", - " 76\n", - " 284\n", - " 66\n", - " 178\n", - " 154\n", - " 75\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", - " 108\n", - " 79\n", - " 118\n", - " 72\n", - " 15\n", - " 10\n", - " 101\n", - " 68\n", - " 125\n", - " 37\n", - " 16\n", - " 293\n", - " 139\n", - " 266\n", - " 67\n", - " 90\n", - " 69\n", - " 288\n", - " 165\n", - " 126\n", - " 221\n", - " 173\n", - " 18\n", - " 172\n", - " 96\n", - " 146\n", - " 86\n", - " 69\n", - " 30\n", - " 39\n", - " 2\n", - " 124\n", - " 10\n", - " 68\n", - " 51\n", - " 71\n", - " 77\n", - " 102\n", - " 80\n", - " 76\n", - " 142\n", - " 127\n", - " 95\n", - " 70\n", - " 93\n", - " 67\n", - " 0\n", - " 105\n", - " 82\n", - " 136\n", - " 40\n", - " 54\n", - " 28\n", - " 74\n", - " 119\n", - " 18\n", - " 9\n", - " 58\n", - " 99\n", - " 73\n", - " 97\n", - " 128\n", - " 122\n", - " 55\n", - " 90\n", - " 129\n", - " 79\n", - " 4\n", - " 87\n", - " 83\n", - " 115\n", - " 81\n", - " 72\n", - " 144\n", - " 78\n", - " 126\n", - " 132\n", - " 106\n", - " 75\n", - " 61\n", - " 143\n", - " 131\n", - " 123\n", - " 89\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", - " 133\n", - " 14\n", - " 88\n", - " 140\n", - " 11\n", - " 13\n", - " 15\n", - " 139\n", - " 64\n", - " 19\n", - " 44\n", - " 35\n", - " 56\n", - " 6\n", - " 107\n", - " 12\n", - " 113\n", - " 141\n", - " 49\n", - " 25\n", - " 41\n", - " 38\n", - " 130\n", + " 0.056201\n", + " 0.647229\n", + " 0.067106\n", + " 0.578780\n", " 42\n", - " 8\n", - " 101\n", - " 125\n", + " 0.055829\n", + " 0.649568\n", + " 0.067379\n", + " 0.577068\n", " 1\n", - " 137\n", - " 65\n", - " 22\n", - " 85\n", - " 46\n", - " 103\n", - " 145\n", - " 111\n", - " 100\n", - " 57\n", - " 53\n", - " 109\n", - " 24\n", - " 17\n", - " 0.000001\n", - " 317.494610\n", - " 0.181540\n", - " 3176.637731\n", - " 3588.997715\n", - " 3891.971280\n", - " 4232.248291\n", - " 4571.342820\n", - " 4784.506368\n", - " 4692.709394\n", - " 5421.989321\n", - " 5171.955995\n", - " 5917.403870\n", - " 5786.262547\n", - " 2944.156549\n", - " 4375.861942\n", - " 4817.180190\n", - " 4469.781983\n", - " 4319.525196\n", - " 7307.617928\n", - " 1.211935e+05\n", - " 1.255625e+04\n", - " 2.155933e+04\n", - " 2.057777e+04\n", - " 1.320781e+64\n", - " 4\n", " \n", " \n", " 2\n", @@ -815,240 +324,60 @@ " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_OOB_RFPlus\n", - " 296\n", - " 100\n", - " 146\n", - " 100\n", - " 10\n", - " 4\n", - " 10\n", - " 274\n", - " 155\n", - " 84\n", - " 82\n", - " 261\n", - " 9\n", - " 42\n", - " 277\n", - " 282\n", - " 92\n", - " 148\n", - " 211\n", - " 60\n", - " 218\n", - " 262\n", + " Local_MDI+_fit_on_all_average_RFPlus\n", + " 683\n", + " 337\n", + " 46\n", + " 1\n", + " 1\n", " 46\n", - " 45\n", - " 236\n", - " 228\n", - " 132\n", - " 143\n", - " 167\n", - " 152\n", - " 93\n", - " 113\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", " 5\n", - " 238\n", - " 251\n", - " 170\n", - " 186\n", - " 193\n", - " 33\n", - " 222\n", - " 216\n", - " 197\n", - " 73\n", - " 182\n", - " 119\n", - " 285\n", - " 202\n", - " 204\n", - " 179\n", - " 177\n", - " 111\n", - " 59\n", - " 226\n", - " 25\n", - " 77\n", - " 6\n", - " 175\n", - " 164\n", - " 140\n", - " 30\n", - " 22\n", - " 245\n", - " 24\n", - " 56\n", - " 144\n", - " 124\n", - " 97\n", - " 63\n", - " 17\n", - " 215\n", - " 219\n", - " 183\n", - " 114\n", - " 76\n", - " 284\n", - " 66\n", - " 178\n", - " 154\n", - " 75\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", - " 108\n", - " 79\n", - " 118\n", - " 72\n", - " 15\n", - " 10\n", - " 101\n", - " 68\n", - " 125\n", - " 37\n", - " 16\n", - " 293\n", - " 139\n", - " 266\n", - " 67\n", - " 90\n", - " 69\n", - " 288\n", - " 165\n", - " 126\n", - " 221\n", - " 173\n", - " 18\n", - " 172\n", - " 96\n", - " 146\n", - " 86\n", - " 69\n", - " 30\n", - " 39\n", - " 2\n", - " 124\n", - " 10\n", - " 68\n", - " 51\n", - " 71\n", - " 77\n", - " 102\n", - " 80\n", - " 76\n", - " 142\n", - " 127\n", - " 95\n", - " 70\n", - " 93\n", - " 67\n", - " 0\n", - " 105\n", - " 82\n", - " 136\n", - " 40\n", - " 54\n", - " 28\n", - " 74\n", - " 119\n", - " 18\n", - " 9\n", - " 58\n", - " 99\n", - " 73\n", - " 97\n", - " 128\n", - " 122\n", - " 55\n", - " 90\n", - " 129\n", - " 79\n", - " 4\n", - " 87\n", - " 83\n", - " 115\n", - " 81\n", - " 72\n", - " 144\n", - " 78\n", - " 126\n", - " 132\n", - " 106\n", - " 75\n", - " 61\n", - " 143\n", - " 131\n", - " 123\n", - " 89\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", - " 133\n", - " 14\n", - " 88\n", - " 140\n", - " 11\n", - " 13\n", - " 15\n", - " 139\n", - " 64\n", - " 19\n", - " 44\n", - " 35\n", - " 56\n", - " 6\n", - " 107\n", - " 12\n", - " 113\n", - " 141\n", - " 49\n", - " 25\n", - " 41\n", - " 38\n", - " 130\n", + " 0.055092\n", + " 0.654192\n", + " 0.067219\n", + " 0.578070\n", " 42\n", - " 8\n", - " 101\n", - " 125\n", + " 0.055283\n", + " 0.652991\n", + " 0.067379\n", + " 0.577068\n", " 1\n", - " 137\n", - " 65\n", - " 22\n", - " 85\n", - " 46\n", - " 103\n", - " 145\n", - " 111\n", - " 100\n", - " 57\n", - " 53\n", - " 109\n", - " 24\n", - " 17\n", - " 0.000001\n", - " 1.707188\n", - " 0.175033\n", - " 3176.637731\n", - " 3461.916046\n", - " 4062.669048\n", - " 4650.680048\n", - " 5119.488079\n", - " 5202.601165\n", - " 5692.855261\n", - " 5949.004684\n", - " 5678.288448\n", - " 6491.619009\n", - " 5786.262547\n", - " 2944.156549\n", - " 3755.282068\n", - " 6272.919551\n", - " 6096.738057\n", - " 256432.843616\n", - " 402230.172337\n", - " 1.918386e+06\n", - " 1.429838e+06\n", - " 4.144031e+06\n", - " 1.137612e+07\n", - " 1.320781e+64\n", - " 4\n", " \n", " \n", " 3\n", @@ -1060,240 +389,60 @@ " 0.33\n", " 42\n", " RF\n", - " Local_MDI+_fit_on_OOB_RFPlus_l2_norm\n", - " 296\n", - " 100\n", - " 146\n", - " 100\n", - " 10\n", - " 4\n", - " 10\n", - " 274\n", - " 155\n", - " 84\n", - " 82\n", - " 261\n", - " 9\n", - " 42\n", - " 277\n", - " 282\n", - " 92\n", - " 148\n", - " 211\n", - " 60\n", - " 218\n", - " 262\n", + " Local_MDI+_fit_on_all_error_metric_RFPlus\n", + " 683\n", + " 337\n", " 46\n", - " 45\n", - " 236\n", - " 228\n", - " 132\n", - " 143\n", - " 167\n", - " 152\n", - " 93\n", - " 113\n", + " 1\n", + " 1\n", + " 46\n", + " 6.669140\n", + " 1\n", + " 0.065016\n", + " 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sample_test_28 sample_test_29 sample_test_30 \\\n", - "0 119 18 9 58 \n", - "1 119 18 9 58 \n", - "2 119 18 9 58 \n", - "3 119 18 9 58 \n", - "4 119 18 9 58 \n", - "\n", - " sample_test_31 sample_test_32 sample_test_33 sample_test_34 \\\n", - "0 99 73 97 128 \n", - "1 99 73 97 128 \n", - "2 99 73 97 128 \n", - "3 99 73 97 128 \n", - "4 99 73 97 128 \n", - "\n", - " sample_test_35 sample_test_36 sample_test_37 sample_test_38 \\\n", - "0 122 55 90 129 \n", - "1 122 55 90 129 \n", - "2 122 55 90 129 \n", - "3 122 55 90 129 \n", - "4 122 55 90 129 \n", - "\n", - " sample_test_39 sample_test_40 sample_test_41 sample_test_42 \\\n", - "0 79 4 87 83 \n", - "1 79 4 87 83 \n", - "2 79 4 87 83 \n", - "3 79 4 87 83 \n", - "4 79 4 87 83 \n", - "\n", - " sample_test_43 sample_test_44 sample_test_45 sample_test_46 \\\n", - "0 115 81 72 144 \n", - "1 115 81 72 144 \n", - "2 115 81 72 144 \n", - "3 115 81 72 144 \n", - "4 115 81 72 144 \n", - "\n", - " sample_test_47 sample_test_48 sample_test_49 sample_test_50 \\\n", - "0 78 126 132 106 \n", - "1 78 126 132 106 \n", - "2 78 126 132 106 \n", - "3 78 126 132 106 \n", - "4 78 126 132 106 \n", - "\n", - " sample_test_51 sample_test_52 sample_test_53 sample_test_54 \\\n", - "0 75 61 143 131 \n", - "1 75 61 143 131 \n", - "2 75 61 143 131 \n", - "3 75 61 143 131 \n", - "4 75 61 143 131 \n", - "\n", - " sample_test_55 sample_test_56 sample_test_57 sample_test_58 \\\n", - "0 123 89 33 133 \n", - "1 123 89 33 133 \n", - "2 123 89 33 133 \n", - "3 123 89 33 133 \n", - "4 123 89 33 133 \n", - "\n", - " sample_test_59 sample_test_60 sample_test_61 sample_test_62 \\\n", - "0 14 88 140 11 \n", - "1 14 88 140 11 \n", - "2 14 88 140 11 \n", - "3 14 88 140 11 \n", - "4 14 88 140 11 \n", - "\n", - " sample_test_63 sample_test_64 sample_test_65 sample_test_66 \\\n", - "0 13 15 139 64 \n", - "1 13 15 139 64 \n", - "2 13 15 139 64 \n", - "3 13 15 139 64 \n", - "4 13 15 139 64 \n", - "\n", - " sample_test_67 sample_test_68 sample_test_69 sample_test_70 \\\n", - "0 19 44 35 56 \n", - "1 19 44 35 56 \n", - "2 19 44 35 56 \n", - "3 19 44 35 56 \n", - "4 19 44 35 56 \n", - "\n", - " sample_test_71 sample_test_72 sample_test_73 sample_test_74 \\\n", - "0 6 107 12 113 \n", - "1 6 107 12 113 \n", - "2 6 107 12 113 \n", - "3 6 107 12 113 \n", - "4 6 107 12 113 \n", - "\n", - " sample_test_75 sample_test_76 sample_test_77 sample_test_78 \\\n", - "0 141 49 25 41 \n", - "1 141 49 25 41 \n", - "2 141 49 25 41 \n", - "3 141 49 25 41 \n", - "4 141 49 25 41 \n", - "\n", - " sample_test_79 sample_test_80 sample_test_81 sample_test_82 \\\n", - "0 38 130 42 8 \n", - "1 38 130 42 8 \n", - "2 38 130 42 8 \n", - "3 38 130 42 8 \n", - "4 38 130 42 8 \n", - "\n", - " sample_test_83 sample_test_84 sample_test_85 sample_test_86 \\\n", - "0 101 125 1 137 \n", - "1 101 125 1 137 \n", - "2 101 125 1 137 \n", - "3 101 125 1 137 \n", - "4 101 125 1 137 \n", - "\n", - " sample_test_87 sample_test_88 sample_test_89 sample_test_90 \\\n", - "0 65 22 85 46 \n", - "1 65 22 85 46 \n", - "2 65 22 85 46 \n", - "3 65 22 85 46 \n", - "4 65 22 85 46 \n", - "\n", - " sample_test_91 sample_test_92 sample_test_93 sample_test_94 \\\n", - "0 103 145 111 100 \n", - "1 103 145 111 100 \n", - "2 103 145 111 100 \n", - "3 103 145 111 100 \n", - "4 103 145 111 100 \n", - "\n", - " sample_test_95 sample_test_96 sample_test_97 sample_test_98 \\\n", - "0 57 53 109 24 \n", - "1 57 53 109 24 \n", - "2 57 53 109 24 \n", - "3 57 53 109 24 \n", - "4 57 53 109 24 \n", - "\n", - " sample_test_99 load_model_time fi_time_absolute ablation_model_fit_time \\\n", - "0 17 0.000002 252.267561 0.176501 \n", - "1 17 0.000001 317.494610 0.181540 \n", - "2 17 0.000001 1.707188 0.175033 \n", - "3 17 0.000001 1.856085 0.173636 \n", - "4 17 0.000002 1.701588 0.175016 \n", - "\n", - " RF_Regressor_MSE_after_ablation_0_absolute \\\n", - "0 3176.637731 \n", - "1 3176.637731 \n", - "2 3176.637731 \n", - "3 3176.637731 \n", - "4 3176.637731 \n", - "\n", - " RF_Regressor_MSE_after_ablation_1_absolute \\\n", - "0 3581.164414 \n", - "1 3588.997715 \n", - "2 3461.916046 \n", - "3 3479.662240 \n", - "4 3469.447475 \n", - "\n", - " RF_Regressor_MSE_after_ablation_2_absolute \\\n", - "0 4187.246823 \n", - "1 3891.971280 \n", - "2 4062.669048 \n", - "3 4018.250551 \n", - "4 4143.341016 \n", - "\n", - " RF_Regressor_MSE_after_ablation_3_absolute \\\n", - "0 4628.778988 \n", - "1 4232.248291 \n", - "2 4650.680048 \n", - "3 4605.198529 \n", - "4 4374.707683 \n", - "\n", - " RF_Regressor_MSE_after_ablation_4_absolute \\\n", - "0 4850.998948 \n", - "1 4571.342820 \n", - "2 5119.488079 \n", - "3 5144.888824 \n", - "4 4958.397428 \n", - "\n", - " RF_Regressor_MSE_after_ablation_5_absolute \\\n", - "0 5534.896348 \n", - "1 4784.506368 \n", - "2 5202.601165 \n", - "3 5215.542607 \n", - "4 5653.224948 \n", - "\n", - " RF_Regressor_MSE_after_ablation_6_absolute \\\n", - "0 5997.868475 \n", - "1 4692.709394 \n", - "2 5692.855261 \n", - "3 5572.440257 \n", - "4 5496.830504 \n", - "\n", - " RF_Regressor_MSE_after_ablation_7_absolute \\\n", - "0 6063.883313 \n", - "1 5421.989321 \n", - "2 5949.004684 \n", - "3 5696.438211 \n", - "4 5633.101963 \n", - "\n", - " RF_Regressor_MSE_after_ablation_8_absolute \\\n", - "0 5634.395323 \n", - "1 5171.955995 \n", - "2 5678.288448 \n", - "3 5558.492422 \n", - "4 5803.725288 \n", - "\n", - " RF_Regressor_MSE_after_ablation_9_absolute \\\n", - "0 5786.880003 \n", - "1 5917.403870 \n", - "2 6491.619009 \n", - "3 5540.286780 \n", - "4 5407.044541 \n", - "\n", - " RF_Regressor_MSE_after_ablation_10_absolute \\\n", - "0 5786.262547 \n", - "1 5786.262547 \n", - "2 5786.262547 \n", - "3 5786.262547 \n", - "4 5786.262547 \n", - "\n", - " Linear_MSE_after_ablation_0_absolute Linear_MSE_after_ablation_1_absolute \\\n", - "0 2944.156549 3825.876036 \n", - "1 2944.156549 4375.861942 \n", - "2 2944.156549 3755.282068 \n", - "3 2944.156549 3922.792616 \n", - "4 2944.156549 3629.982447 \n", - "\n", - " Linear_MSE_after_ablation_2_absolute Linear_MSE_after_ablation_3_absolute \\\n", - "0 4358.913641 14063.612627 \n", - "1 4817.180190 4469.781983 \n", - "2 6272.919551 6096.738057 \n", - "3 6657.034144 18254.261187 \n", - "4 6580.224443 100112.918616 \n", - "\n", - " Linear_MSE_after_ablation_4_absolute Linear_MSE_after_ablation_5_absolute \\\n", - "0 22241.241600 82141.497378 \n", - "1 4319.525196 7307.617928 \n", - "2 256432.843616 402230.172337 \n", - "3 173834.710200 392140.165705 \n", - "4 29445.897691 134379.397173 \n", - "\n", - " Linear_MSE_after_ablation_6_absolute Linear_MSE_after_ablation_7_absolute \\\n", - "0 1.357333e+05 2.315023e+06 \n", - "1 1.211935e+05 1.255625e+04 \n", - "2 1.918386e+06 1.429838e+06 \n", - "3 1.931821e+06 3.560411e+06 \n", - "4 1.993457e+06 4.689103e+06 \n", - "\n", - " Linear_MSE_after_ablation_8_absolute Linear_MSE_after_ablation_9_absolute \\\n", - "0 8.156634e+05 1.584653e+06 \n", - "1 2.155933e+04 2.057777e+04 \n", - "2 4.144031e+06 1.137612e+07 \n", - "3 3.852496e+06 6.016587e+06 \n", - "4 1.740061e+09 1.847789e+09 \n", - "\n", - " Linear_MSE_after_ablation_10_absolute split_seed \n", - "0 1.320781e+64 4 \n", - "1 1.320781e+64 4 \n", - "2 1.320781e+64 4 \n", - "3 1.320781e+64 4 \n", - "4 1.320781e+64 4 " + " 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": 3, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2061,16 +715,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "# combined_df = combined_df[(combined_df['heritability'] == 0.8) & (combined_df['n'] == 1000)]" + "#combined_df = combined_df[(combined_df['heritability'] == 0.8) & (combined_df['n_train'] == 750)]" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -2079,70 +733,94 @@ "# pd.DataFrame(averages)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summarise the Ablation Data" - ] - }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 58, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The training size is 296 and the test size is 146\n" - ] + "data": { + "text/plain": [ + "array([46])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" } ], "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}\")" + "combined_df[\"num_features\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarise the Ablation Data" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "442\n", - "[10]\n" + "The training size is 683 and the test size is 337\n" ] } ], "source": [ - "print(train_size+test_size)\n", - "print(combined_df[\"num_features\"].unique())" + "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": 8, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['Kernel_SHAP_RF_plus', 'LIME_RF_plus',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm', 'Random',\n", + "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": 8, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -2151,26 +829,6 @@ "combined_df[\"fi\"].unique()" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_elements(list1, list2):\n", - " \"\"\"\n", - " Remove elements from list1 that are present in list2.\n", - " \n", - " Parameters:\n", - " list1 (list): The original list.\n", - " list2 (list): The list of elements to remove from list1.\n", - " \n", - " Returns:\n", - " list: A new list with elements from list1, excluding those found in list2.\n", - " \"\"\"\n", - " return [element for element in list1 if element not in list2]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2180,229 +838,165 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "# methods_train_subset = ['Kernel_SHAP_RF_plus', \n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - "# 'LIME_RF_plus','TreeSHAP_RF', 'Random']\n", - "# methods_test_subset = ['Kernel_SHAP_RF_plus', \n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - "# 'LIME_RF_plus','TreeSHAP_RF', 'Random']\n", - "# methods_test = [\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - "# # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - "# 'TreeSHAP_RF', 'Random']\n", - "\n", - "methods_train_subset = ['Kernel_SHAP_RF_plus', \n", - " # 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus',\n", - " # 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - " 'LIME_RF_plus','TreeSHAP_RF', 'Random']\n", - "methods_test_subset = ['Kernel_SHAP_RF_plus', \n", - " # 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus',\n", - " # 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - " 'LIME_RF_plus','TreeSHAP_RF', 'Random']\n", - "methods_test = [\n", - " #'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus',\n", - " # 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus',\n", - " # 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf',\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf',\n", - " # 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm',\n", - " 'TreeSHAP_RF', 'Random']\n", + "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\": [\"y_hat\"], \"classification\": [\"MAE\"]} #MSE\n", - "ablation_models = {\"regression\": [\"RF_Regressor\", \"Linear\"],# \"XGB_Regressor\", \"RF_Plus_Regressor\"], #\"Kernel_Ridge\",\n", - " \"classification\": [\"RF_Classifier\",\"LogisticCV\", \"SVM\", \"XGBoost_Classifier\", \"RF_Plus_Classifier\"]}" + "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": 11, + "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", - "# 'Kernel_SHAP_RF_plus': '#1f77b4', # blue\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus': '#ff7f0e', # orange\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus': '#2ca02c', # green\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus': '#d62728', # red\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#9467bd', # purple\n", - "# 'LIME_RF_plus': '#8c564b', # brown\n", - "# 'Oracle_test_RFPlus': '#e377c2', # pink\n", - "# 'Random': '#7f7f7f', # gray\n", - "# 'TreeSHAP_RF': '#bcbd22', # yellow\n", - "# 'Local_MDI+_global_MDI_plus_RFPlus': '#17becf' # cyan\n", - "# }\n", - "# color_map = {\n", - "# 'Kernel_SHAP_RF_plus': '#1f77b4', # blue\n", - "# 'LIME_RF_plus': '#ff7f0e', # orange\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_subtract_intercept': '#9467bd', # purple\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_subtract_intercept_avg_leaf': '#8c564b', # brown\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_subtract_intercept': '#2ca02c', # yellow\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_subtract_intercept_avg_leaf': '#bcbd22', # green\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_subtract_intercept': '#7f7f7f', # gray\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_subtract_intercept_avg_leaf': '#17becf', # cyan\n", - "# 'Random': '#000000', # black\n", - "# 'TreeSHAP_RF': '#d62728' # teal\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": 12, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "# color_map = {\n", - "# 'Kernel_SHAP_RF_plus': '#1f77b4', # blue\n", - "# 'LIME_RF_plus': '#ff7f0e', # orange\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_subtract_intercept': '#d62728', # red\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_subtract_intercept_avg_leaf': '#9467bd', # purple,\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus': '#17becf', # cyan\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf': '#e377c2', # pink,\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#00ff00', # lime\n", - "# 'Random': '#000000', # black\n", - "# 'TreeSHAP_RF': '#d62728', # teal,\n", - "# }" + "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": 13, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ - "color_map = {\n", - " 'Kernel_SHAP_RF_plus': '#1f77b4', # Blue\n", - " 'LIME_RF_plus': '#8c564b', # Brown\n", - " 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm': '#ff7f0e', # Orange\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm': '#2ca02c', # Green\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm': '#9467bd', # Purple\n", - " 'Local_MDI+_fit_on_OOB_RFPlus': '#ffbb78', # Light Orange\n", - " 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus': '#98df8a', # Light Green\n", - " 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus': '#c5b0d5', # Light Purple\n", - " 'Random': '#7f7f7f', # Gray\n", - " 'TreeSHAP_RF': '#e377c2', # Pink\n", - "}\n", - "# color_map = {\n", - "# 'Kernel_SHAP_RF_plus': '#1f77b4', # blue\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_avg_leaf': '#ff7f0e', # orange\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus': '#2ca02c', # green\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm_avg_leaf': '#d62728', # red\n", - "# 'Local_MDI+_fit_on_OOB_RFPlus_l2_norm': '#9467bd', # purple\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_avg_leaf': '#8c564b', # brown\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus': '#e377c2', # pink\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm_avg_leaf': '#7f7f7f', # gray\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_l2_norm': '#bcbd22', # yellow-green\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_avg_leaf': '#17becf', # cyan\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus': '#aec7e8', # light blue\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm_avg_leaf': '#ffbb78', # light orange\n", - "# 'Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_l2_norm': '#98df8a', # light green\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus_avg_leaf': '#ff9896', # light red\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus': '#c5b0d5', # light purple\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm_avg_leaf': '#c49c94', # light brown\n", - "# 'Local_MDI+_fit_on_inbag_RFPlus_l2_norm': '#f7b6d2', # light pink\n", - "# 'LIME_RF_plus': '#c7c7c7', # light gray\n", - "# 'TreeSHAP_RF': '#dbdb8d', # light yellow-green\n", - "# 'Random': '#9edae5' # light cyan\n", - "# }" + "# 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": 15, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -2410,55 +1004,346 @@ } ], "source": [ - "fig, axs = plt.subplots(len(ablation_models[task]), len(metrics[task]), figsize=(15, 20))\n", + "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_train_subset:\n", + " for m in methods:\n", " results[m] = []\n", - " for m in methods_train_subset:\n", - " for k in range(num_features+1):#(num_features+1):\n", - " results[m].append(np.log10(combined_df[combined_df['fi'] == m][a_model+f\"_MSE_after_ablation_{k}_absolute\"].mean()))\n", - " ax = axs[i]\n", - " for m in methods_train_subset:\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\", \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\", \"Random\"]:\n", - " ax.plot(range(num_features+1), results[m], label=m, linestyle='dashed', color=color)\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(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", + " 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\"./{task_name}_{task}_train_removal_absolute.png\")\n", - "plt.show()" + "#plt.savefig(f\"./Ionosphere.png\")\n", + "plt.show()\n" ] }, { - "cell_type": "markdown", + "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": [ - "#### Training Subset Data" + "# 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": null, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "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", @@ -2490,18 +1375,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", @@ -2533,18 +1407,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", @@ -2604,32 +1467,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must have same first dimension, but have shapes (9,) and (0,)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[51], line 15\u001b[0m\n\u001b[1;32m 13\u001b[0m color \u001b[39m=\u001b[39m color_map[m]\n\u001b[1;32m 14\u001b[0m \u001b[39mif\u001b[39;00m m \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mTreeSHAP_RF\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mKernel_SHAP_RF_plus\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mLIME_RF_plus\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mRandom\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m---> 15\u001b[0m ax\u001b[39m.\u001b[39;49mplot(\u001b[39mrange\u001b[39;49m(num_features\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m), results[m], label\u001b[39m=\u001b[39;49mm, linestyle\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdashed\u001b[39;49m\u001b[39m'\u001b[39;49m, color\u001b[39m=\u001b[39;49mcolor)\n\u001b[1;32m 16\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 17\u001b[0m ax\u001b[39m.\u001b[39mplot(\u001b[39mrange\u001b[39m(num_features\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m), results[m], label\u001b[39m=\u001b[39mm, color\u001b[39m=\u001b[39mcolor)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1482\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1483\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1721\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1722\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1723\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1724\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m 1725\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m 1726\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 301\u001b[0m this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m 302\u001b[0m args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m 304\u001b[0m axes, this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 496\u001b[0m axes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n\u001b[1;32m 498\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y must have same first dimension, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 500\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhave shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 501\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m y\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m 502\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y can be no greater than 2D, but have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 503\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (9,) and (0,)" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", @@ -2663,32 +1501,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must have same first dimension, but have shapes (9,) and (0,)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[52], line 15\u001b[0m\n\u001b[1;32m 13\u001b[0m color \u001b[39m=\u001b[39m color_map[m]\n\u001b[1;32m 14\u001b[0m \u001b[39mif\u001b[39;00m m \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mTreeSHAP_RF\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mKernel_SHAP_RF_plus\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mLIME_RF_plus\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mRandom\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m---> 15\u001b[0m ax\u001b[39m.\u001b[39;49mplot(\u001b[39mrange\u001b[39;49m(num_features\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m), results[m], label\u001b[39m=\u001b[39;49mm, linestyle\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdashed\u001b[39;49m\u001b[39m'\u001b[39;49m, color\u001b[39m=\u001b[39;49mcolor)\n\u001b[1;32m 16\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 17\u001b[0m ax\u001b[39m.\u001b[39mplot(\u001b[39mrange\u001b[39m(num_features\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m), results[m], label\u001b[39m=\u001b[39mm, color\u001b[39m=\u001b[39mcolor)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1724\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1482\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1483\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1721\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1722\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1723\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1724\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m 1725\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m 1726\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 301\u001b[0m this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m 302\u001b[0m args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m 304\u001b[0m axes, this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n", - "File \u001b[0;32m/scratch/users/zhongyuan_liang/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 496\u001b[0m axes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n\u001b[1;32m 498\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y must have same first dimension, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 500\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhave shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 501\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m y\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m 502\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y can be no greater than 2D, but have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 503\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (9,) and (0,)" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", @@ -2729,18 +1542,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", @@ -2774,39 +1576,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'RF_Regressor_test_subset_delta_MSE_after_ablation_0_positive'", - "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: 'RF_Regressor_test_subset_delta_MSE_after_ablation_0_positive'", - "\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[107], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39mif\u001b[39;00m metric \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mMSE\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 9\u001b[0m \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_features\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[0;32m---> 10\u001b[0m results[m]\u001b[39m.\u001b[39mappend(np\u001b[39m.\u001b[39msqrt(combined_df[combined_df[\u001b[39m'\u001b[39;49m\u001b[39mfi\u001b[39;49m\u001b[39m'\u001b[39;49m] \u001b[39m==\u001b[39;49m m][a_model\u001b[39m+\u001b[39;49m\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m_test_subset_delta_MSE_after_ablation_\u001b[39;49m\u001b[39m{\u001b[39;49;00mk\u001b[39m}\u001b[39;49;00m\u001b[39m_positive\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39mmean()))\n\u001b[1;32m 11\u001b[0m ax \u001b[39m=\u001b[39m axs[i]\n\u001b[1;32m 12\u001b[0m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m methods_train_subset:\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: 'RF_Regressor_test_subset_delta_MSE_after_ablation_0_positive'" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "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", diff --git a/feature_importance/ablation_results_visulization_stability.ipynb b/feature_importance/ablation_results_visulization_stability.ipynb new file mode 100644 index 0000000..ddd2f6f --- /dev/null +++ b/feature_importance/ablation_results_visulization_stability.ipynb @@ -0,0 +1,1683 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 60, + "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": 61, + "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", + "\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", + "\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", + "\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", + "\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", + "\n", + "combined_df_all = pd.concat([ccle_combined_df, performance_combined_df, parkinsons_combined_df, temperature_combined_df], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "combined_df_all = combined_df_all[combined_df_all[\"fi\"] != \"Random\"] " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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", + "}\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()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\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", + "\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": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\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", + "\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.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, + "metadata": {}, + "outputs": [ + { + "data": { + "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'" + ] + } + ], + "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", + "\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": 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", + "\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": 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", + "\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": 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, + "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/debug_ablation_average.ipynb b/feature_importance/debug_ablation_average.ipynb index 9336368..045c73e 100644 --- a/feature_importance/debug_ablation_average.ipynb +++ b/feature_importance/debug_ablation_average.ipynb @@ -2,103 +2,883 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import imodels\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import *\n", + "from sklearn.preprocessing import StandardScaler\n", + "import copy\n", + "import matplotlib.pyplot as plt\n", + "import openml\n", + "import sys\n", + "sys.path.append('..')\n", + "sys.path.append('../..')\n", + "sys.path.append('.')\n", + "sys.path.append('./scripts')\n", + "from competing_methods_local import *\n", + "from simulations_util import *" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "X = sample_real_data_X(source=\"uci\", data_id=189)\n", + "y = sample_real_data_y(source=\"uci\", data_id=189, return_support=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# apply train test split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 9.2s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 33.2s finished\n" + ] + } + ], + "source": [ + "rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)\n", + "rf.fit(X_train, y_train)\n", + "rf_plus_base = RandomForestPlusRegressor(rf_model=rf)\n", + "rf_plus_base.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_data, test_data = LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, X_test, fit=rf_plus_base, mode=\"absolute\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([13.16, 14.13, 5.62, 14.16, 9.41, 7.43, 6.22, 5.52, 7.24,\n", + " 4.43, 5.65, 7.56, 4.37, 4.72, 9.56, 15.22, 11.59, 13.48,\n", + " 11.53])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[15, 3, 1]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argsort(-1*train_data[0])[:3].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Example NumPy array\n", + "data = LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, fit=rf_plus_base, mode=\"absolute\")\n", + "data = np.argsort(data, axis=1) # Sort the indices of the features\n", + "\n", + "# Adjust the figure size\n", + "plt.figure(figsize=(12, 6)) # Width = 12 inches, Height = 6 inches\n", + "plt.imshow(data, cmap='viridis', interpolation='nearest', aspect='auto')\n", + "plt.colorbar() # Add a color bar to show the scale\n", + "plt.title(\"Heatmap of NumPy Array\")\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, fit=rf_plus_base, mode=\"absolute\")" + ] + }, + { + "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": [ + "rf_plus_mdi = RFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "temp1 = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)\n", + "temp_10 = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average=True)\n", + "temp2 = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train)\n", + "temp3 = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True)\n", + "temp4 = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "leaf_indices = rf.apply(X_train).flatten()\n", + "leaf_mapping = defaultdict(list)\n", + "for sample_idx, leaf_idx in enumerate(leaf_indices):\n", + " leaf_mapping[leaf_idx].append(sample_idx)\n", + "leaf_mapping[20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp3[148]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp3[82]" + ] + }, + { + "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": [ + "temp1[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(y_train[0] - rf_plus_base.predict(X_train[0].reshape(1, -1)) + temp1[0])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp2[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = (temp - y_train[:, np.newaxis, np.newaxis])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_train[:, np.newaxis, np.newaxis].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_train[20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(0.1676066)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp[20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result[20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_plus_base = RandomForestPlusRegressor(rf_model=rf)\n", + "rf_plus_base.fit(X_train, y_train)\n", + "rf_plus_base.score(X_test, y_test)" + ] + }, + { + "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": [ + "from ucimlrepo import fetch_ucirepo \n", + " \n", + "# fetch dataset \n", + "parkinsons_telemonitoring = fetch_ucirepo(id=189) \n", + " \n", + "# data (as pandas dataframes) \n", + "X = parkinsons_telemonitoring.data.features \n", + "y = parkinsons_telemonitoring.data.targets \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": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "categorical_cols = X.select_dtypes(include=[\"object\", \"category\"]).columns\n", + "numerical_cols = X.select_dtypes(include=[\"number\"]).columns\n", + "\n", + "# Step 2: Handle missing values (if any)\n", + "# Check if there are missing values in the numerical columns\n", + "if X[numerical_cols].isnull().any().any():\n", + " # Impute missing values in numerical columns with the mean\n", + " num_imputer = SimpleImputer(strategy=\"mean\")\n", + " X[numerical_cols] = num_imputer.fit_transform(X[numerical_cols])\n", + "\n", + "# Check if there are missing values in the categorical columns\n", + "if len(categorical_cols) > 0 and X[categorical_cols].isnull().any().any():\n", + " # Convert categorical columns to string to ensure consistent types\n", + " X[categorical_cols] = X[categorical_cols].astype(str)\n", + "\n", + " # Impute missing values in categorical columns with the most frequent value\n", + " cat_imputer = SimpleImputer(strategy=\"most_frequent\")\n", + " X[categorical_cols] = cat_imputer.fit_transform(X[categorical_cols])\n", + "\n", + "# Step 3: Encode categorical variables using OneHotEncoder (if any categorical columns)\n", + "if len(categorical_cols) > 0:\n", + " encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n", + " X_categorical = encoder.fit_transform(X[categorical_cols])\n", + "\n", + " # Convert encoded categorical data back to DataFrame\n", + " X_categorical_df = pd.DataFrame(\n", + " X_categorical,\n", + " columns=encoder.get_feature_names_out(categorical_cols),\n", + " index=X.index\n", + " )\n", + "\n", + " # Step 4: Concatenate numerical columns and the encoded categorical DataFrame\n", + " X = pd.concat([X[numerical_cols], X_categorical_df], axis=1)\n", + "else:\n", + " # If no categorical columns, we just use the numerical columns\n", + " X = X[numerical_cols]\n", + "X = X.to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if y.to_numpy().shape[1] > 1:\n", + " y = y.iloc[:, 0].to_numpy().flatten()\n", + "else:\n", + " y = y.to_numpy().flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit a random forest model\n", + "rf = RandomForestRegressor(n_estimators=100, max_depth=5)\n", + "rf.fit(X, y)\n", + "rf.score(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_plus_base = RandomForestPlusRegressor(rf_model=rf)\n", + "rf_plus_base.fit(X, y)\n", + "rf_plus_base.score(X, y)" + ] + }, + { + "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": [ + "# X, y, _ = imodels.get_clean_dataset(\"diabetes\")\n", + "X, y, _ = imodels.get_clean_dataset(\"diabetes_regr\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Debug\n", + "# RF Regressor\n", + "est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)\n", + "est.fit(X_train, y_train)\n", + "\n", + "# RFplus default(fit on all)\n", + "rf_plus_base = RandomForestPlusRegressor(rf_model=est)\n", + "rf_plus_base.fit(X_train, y_train)\n", + "\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "temp = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp[0,0,:].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/accounts/projects/binyu/zhongyuan_liang/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ - "import imodels\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score\n", - "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import *\n", - "from sklearn.preprocessing import StandardScaler\n", - "import copy\n", - "import matplotlib.pyplot as plt\n", - "import openml\n", - "import sys\n", - "sys.path.append('..')\n", - "sys.path.append('../..')\n", - "sys.path.append('.')\n", - "sys.path.append('./scripts')\n", - "from competing_methods_local import *\n", - "from simulations_util import *" + "r2_score([y_train[0]]*100, temp[1,1,:])" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "X = sample_real_data_X(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)" + "# # RF Regressor\n", + "# est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)\n", + "# est.fit(X_train, y_train)\n", + "\n", + "# # RFplus default(fit on all)\n", + "# rf_plus_base = RandomForestPlusRegressor(rf_model=est)\n", + "# rf_plus_base.fit(X_train, y_train)\n", + "\n", + "# # RFplus oob \n", + "# rf_plus_base_oob = RandomForestPlusRegressor(rf_model=est, fit_on=\"oob\")\n", + "# rf_plus_base_oob.fit(X_train, y_train)\n", + "\n", + "# #RFplus inbag RF\n", + "# rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est, include_raw=False, fit_on=\"inbag\", prediction_model=LinearRegression())\n", + "# rf_plus_base_inbag.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "y = sample_real_data_y(source=\"csv\", file_path=\"/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_Topotecan.csv\")[0]" + "# RF Classifier\n", + "est = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=42)\n", + "est.fit(X_train, y_train)\n", + "\n", + "# RFplus default(fit on all)\n", + "rf_plus_base = RandomForestPlusClassifier(rf_model=est)\n", + "rf_plus_base.fit(X_train, y_train)\n", + "\n", + "# RFplus oob \n", + "rf_plus_base_oob = RandomForestPlusClassifier(rf_model=est, fit_on=\"oob\")\n", + "rf_plus_base_oob.fit(X_train, y_train)\n", + "\n", + "rf_plus_base_inbag = RandomForestPlusClassifier(rf_model=est, include_raw=False, fit_on=\"inbag\")\n", + "rf_plus_base_inbag.fit(X_train, y_train)\n", + "\n", + "# #RFplus inbag RF\n", + "# est_regressor = RandomForestRegressor(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=42)\n", + "# est_regressor.fit(X_train, y_train)\n", + "# rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est_regressor, include_raw=False, fit_on=\"inbag\", prediction_model=LinearRegression())\n", + "# rf_plus_base_inbag.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# split the data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + "# Inbag LMDI+\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)\n", + "\n", + "# OOB LMDI+\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)\n", + "\n", + "# ALL LMDI+\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)\n", + "\n", + "# Inbag LMDI+ l2 norm with sign\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)\n", + "\n", + "# OOB LMDI+ l2 norm with sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)\n", + "\n", + "# ALL LMDI+ l2 norm with sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)\n", + "\n", + "# Inbag LMDI+ l2 norm without sign\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)\n", + "\n", + "# OOB LMDI+ l2 norm without sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)\n", + "\n", + "# ALL LMDI+ l2 norm without sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)\n", + "\n", + "# Inbag LMDI+ with ranking then average\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)\n", + "\n", + "# OOB LMDI+ with ranking then average\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)\n", + "\n", + "# ALL LMDI+ with ranking then average\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "X_train = X_train[:,:5]" + "# Inbag LMDI+ l2 norm with sign\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)\n", + "\n", + "# OOB LMDI+ l2 norm with sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)\n", + "\n", + "# ALL LMDI+ l2 norm with sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 4.8s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.7s finished\n" - ] - } - ], + "outputs": [], "source": [ - "est = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)\n", + "# Inbag LMDI+ l2 norm without sign\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)\n", + "\n", + "# OOB LMDI+ l2 norm without sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)\n", + "\n", + "# ALL LMDI+ l2 norm without sign\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Inbag LMDI+ with ranking then average\n", + "rf_plus_mdi = RFPlusMDI(rf_plus_base_inbag, evaluate_on=\"inbag\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)\n", + "\n", + "# OOB LMDI+ with ranking then average\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)\n", + "\n", + "# ALL LMDI+ with ranking then average\n", + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base_oob, evaluate_on=\"oob\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_plus_mdi = AloRFPlusMDI(rf_plus_base, evaluate_on=\"all\")\n", + "rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RF Classifier\n", + "est = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=42)\n", "est.fit(X_train, y_train)\n", - "rf_plus_base = RandomForestPlusRegressor(rf_model=est)\n", - "rf_plus_base.fit(X_train, y_train)" + "\n", + "# RFplus default(fit on all)\n", + "rf_plus_base = RandomForestPlusClassifier(rf_model=est)\n", + "rf_plus_base.fit(X_train, y_train)\n", + "\n", + "# RFplus oob \n", + "rf_plus_base_oob = RandomForestPlusClassifier(rf_model=est, fit_on=\"oob\")\n", + "rf_plus_base_oob.fit(X_train, y_train)\n", + "\n", + "#RFplus inbag RF\n", + "est_regressor = RandomForestRegressor(n_estimators=100, min_samples_leaf=3, max_features='sqrt', random_state=42)\n", + "est_regressor.fit(X_train, y_train)\n", + "rf_plus_base_inbag = RandomForestPlusRegressor(rf_model=est_regressor, include_raw=False, fit_on=\"inbag\", prediction_model=LinearRegression())\n", + "rf_plus_base_inbag.fit(X_train, y_train)" + ] + }, + { + "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": [ + "X_test_pred = est.predict(X_test)\n", + "print(\"R2 score of RF: \", r2_score(y_test, X_test_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_pred = rf_plus_base.predict(X_test)\n", + "print(\"R2 score of RF+: \", r2_score(y_test, X_test_pred))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -123,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -132,40 +912,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[-0.93740875, -1.09132836, -1.35028366, -1.15388852, -1.25685594],\n", - " [-1.04895378, -1.34183556, -1.15525384, -1.32002817, -1.36718094],\n", - " [-0.67151686, -0.2809473 , -0.63317469, -0.341039 , -0.36653009],\n", - " ...,\n", - " [-1.13306773, -1.41037856, -1.23963396, -1.42238344, -1.39783221],\n", - " [-1.71587687, -1.58479507, -1.81213149, -1.73044741, -1.69754925],\n", - " [-0.43752179, -0.4671859 , -0.49108445, -0.47976167, -0.46289299]]),\n", - " array([[3.27029125, 3.11637164, 2.85741634, 3.05381148, 2.95084406],\n", - " [2.64835378, 2.94123556, 2.75465384, 2.91942817, 2.96658094],\n", - " [3.30621686, 2.89714089, 3.26787469, 2.97383506, 3.00123009],\n", - " ...,\n", - " [2.70416773, 2.98147856, 2.81073396, 2.99348344, 2.96893221],\n", - " [2.97227687, 2.84119507, 3.06853149, 2.98684741, 2.95394925],\n", - " [2.93105958, 2.9635859 , 2.98748445, 2.97616167, 2.95547056]]))" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rf_plus_mdi.explain(X=X_train, y=y_train)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -174,24 +930,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.93740875, 1.09132836, 1.35028366, 1.15388852, 1.25685594],\n", - " [1.04895378, 1.34183556, 1.15525384, 1.32002817, 1.36718094],\n", - " [0.67151686, 0.2809473 , 0.63317469, 0.341039 , 0.36653009],\n", - " [0.67514285, 0.64116806, 0.33789905, 0.58319897, 0.51105166],\n", - " [0.48467372, 0.26272469, 0.30583033, 0.36102418, 0.39570995]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "temp = np.abs(temp)\n", "temp" @@ -199,105 +940,45 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2, 4, 3, 1, 0],\n", - " [4, 1, 3, 2, 0],\n", - " [0, 2, 4, 3, 1],\n", - " [0, 1, 3, 4, 2],\n", - " [0, 4, 3, 2, 1]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.argsort(-1*temp)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.2, 2.4, 3.2, 2.4, 0.8])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.mean(np.argsort(-1*temp), axis=0)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3.27029125, 3.11637164, 2.85741634, 3.05381148, 2.95084406])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rf_plus_mdi.explain(X=X_train, y=y_train)[1][0]" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'estimators_'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m rf_plus_base\u001b[39m.\u001b[39;49mestimators_\u001b[39m.\u001b[39;49mestimators_\n", - "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'estimators_'" - ] - } - ], + "outputs": [], "source": [ "rf_plus_base.estimators_" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.2077" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y_train[0]" ] diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_crime/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/dgp.py similarity index 59% rename from feature_importance/fi_config/mdi_local/real_data_regression_crime/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/dgp.py index 53e9f66..c813dc1 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_crime/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/dgp.py @@ -6,29 +6,15 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { "source": "uci", - "data_id": 183, + "data_id": 52, "sample_row_n": None } -# 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 = sample_real_data_y Y_PARAMS_DICT = { "source": "uci", - "data_id": 183 + "data_id": 52 } # Y_PARAMS_DICT = { # "source": "imodels", diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/models.py new file mode 100644 index 0000000..38c2f75 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_classification_Ionosphere_retrain/models.py @@ -0,0 +1,39 @@ +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', RandomForestClassifier, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_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_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")], + +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g/models.py deleted file mode 100644 index 014ca5b..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g/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 RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_classification_credit_g_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_average/dgp.py deleted file mode 100644 index 638133a..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_average/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "credit_g", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "credit_g" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_credit_g_average/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_average/models.py deleted file mode 100644 index 4f510dc..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_average/models.py +++ /dev/null @@ -1,28 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_conditional/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_conditional/dgp.py deleted file mode 100644 index 638133a..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_conditional/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "credit_g", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "credit_g" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_credit_g_conditional/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_conditional/models.py deleted file mode 100644 index fe3bc2c..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_conditional/models.py +++ /dev/null @@ -1,27 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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")], -] diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_retrain/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_classification_credit_g/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_classification_credit_g_retrain/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_retrain/models.py new file mode 100644 index 0000000..888a9c0 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_classification_credit_g_retrain/models.py @@ -0,0 +1,39 @@ +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', RandomForestClassifier, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_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_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_classification_csi_pecarn_average/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_average/models.py deleted file mode 100644 index 4f510dc..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_average/models.py +++ /dev/null @@ -1,28 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_conditional/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_conditional/dgp.py deleted file mode 100644 index 5af9c6a..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_conditional/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "csi_pecarn_pred", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "csi_pecarn_pred" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_csi_pecarn_conditional/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_conditional/models.py deleted file mode 100644 index 32d4f9f..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_conditional/models.py +++ /dev/null @@ -1,27 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_retrain/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_average/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_retrain/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_retrain/models.py new file mode 100644 index 0000000..38c2f75 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_classification_csi_pecarn_retrain/models.py @@ -0,0 +1,39 @@ +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', RandomForestClassifier, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_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_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")], + +] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes/dgp.py deleted file mode 100644 index 2f78beb..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_diabetes/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes/models.py deleted file mode 100644 index 014ca5b..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes/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 RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_classification_diabetes_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_average/dgp.py deleted file mode 100644 index 2f78beb..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_average/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_diabetes_average/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_average/models.py deleted file mode 100644 index 935d46e..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_average/models.py +++ /dev/null @@ -1,28 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_conditional/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_conditional/dgp.py deleted file mode 100644 index 2f78beb..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_conditional/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_diabetes_conditional/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_conditional/models.py deleted file mode 100644 index 44dcf70..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_diabetes_conditional/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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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")], -] - -# 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_sign, 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_classification_juvenile/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile/models.py deleted file mode 100644 index 014ca5b..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_juvenile/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 RandomForestClassifier -from feature_importance.scripts.competing_methods_local import * -from sklearn.linear_model import Ridge - - -ESTIMATORS = [ - [ModelConfig('RF', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_classification_juvenile_conditional/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_conditional/dgp.py deleted file mode 100644 index a0cf4b1..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_conditional/dgp.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -X_DGP = sample_real_data_X -X_PARAMS_DICT = { - "source": "imodels", - "data_name": "juvenile_clean", - "sample_row_n": None -} -# X_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile", -# "sample_row_n": None -# } - -# X_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accoutns/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/X_enhancer_cleaned.csv", -# "sample_row_n": 2000, -# "normalize": False -# } - -Y_DGP = sample_real_data_y -Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "juvenile_clean" -} -# Y_PARAMS_DICT = { -# "source": "imodels", -# "data_name": "juvenile" -# } - -# Y_PARAMS_DICT = { -# "source": "csv", -# "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/Enhancer/y_enhancer.csv", -# "sample_row_n": 2000 -# } - - -# vary one parameter -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_classification_juvenile_conditional/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_conditional/models.py deleted file mode 100644 index 32d4f9f..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_conditional/models.py +++ /dev/null @@ -1,27 +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', RandomForestClassifier, model_type='tree', - other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_juvenile/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_retrain/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_classification_juvenile/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_classification_juvenile_retrain/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_retrain/models.py new file mode 100644 index 0000000..38c2f75 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_classification_juvenile_retrain/models.py @@ -0,0 +1,39 @@ +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', RandomForestClassifier, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', '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_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_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")], + +] \ No newline at end of file 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 deleted file mode 100644 index 4ab6746..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/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_CCLE_nutlin_3_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py similarity index 79% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_nutlin_3_average/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py index 03f3d66..bd290cd 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_nutlin_3_average/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/dgp.py @@ -6,7 +6,7 @@ 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_Nutlin-3_top500.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 } # X_PARAMS_DICT = { @@ -25,10 +25,12 @@ # "sample_row_n": None # } -Y_DGP = sample_real_data_y +Y_DGP = linear_model Y_PARAMS_DICT = { - "source": "csv", - "file_path": "/accounts/projects/binyu/zhongyuan_liang/local_MDI+/imodels-experiments/feature_importance/data/CCLE/y_ccle_rnaseq_Nutlin-3.csv" + "beta": 1, + "sigma": None, + "heritability": 0.4, + "s": 5, } # Y_PARAMS_DICT = { # "source": "imodels", @@ -45,5 +47,5 @@ # } # 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_linear_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py new file mode 100644 index 0000000..4b60f25 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_linear_retrain/models.py @@ -0,0 +1,52 @@ +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_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py similarity index 79% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_average/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py index 6978ee9..ed847a7 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_average/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/dgp.py @@ -6,7 +6,7 @@ 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", + "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 } # X_PARAMS_DICT = { @@ -25,11 +25,14 @@ # "sample_row_n": None # } -Y_DGP = sample_real_data_y +Y_DGP = hierarchical_poly 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" + "m": 3, + "r": 2, + "beta": 1, + "heritability": 0.4, } + # Y_PARAMS_DICT = { # "source": "imodels", # "data_name": "satellite_image" @@ -45,5 +48,5 @@ # } # 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_poly_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py new file mode 100644 index 0000000..4b60f25 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_poly_retrain/models.py @@ -0,0 +1,52 @@ +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/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/dgp.py 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 new file mode 100644 index 0000000..4b60f25 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_PD_0325901_retrain/models.py @@ -0,0 +1,52 @@ +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_nutlin_3_average/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_nutlin_3_average/models.py deleted file mode 100644 index 7358e46..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_nutlin_3_average/models.py +++ /dev/null @@ -1,30 +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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_error_metric', LFI_evaluation_RFPlus_all_error_metric, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test", ascending=False)], - [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_error_metric', LFI_evaluation_RFPlus_oob_error_metric, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test", ascending=False)], - [FIModelConfig('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan/models.py deleted file mode 100644 index 4ab6746..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan/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_CCLE_topotecan_average/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_average/models.py deleted file mode 100644 index 7358e46..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_average/models.py +++ /dev/null @@ -1,30 +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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('Local_MDI+_fit_on_all_evaluate_on_all_RFPlus_error_metric', LFI_evaluation_RFPlus_all_error_metric, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test", ascending=False)], - [FIModelConfig('Local_MDI+_fit_on_all_evaluate_on_oob_RFPlus_error_metric', LFI_evaluation_RFPlus_oob_error_metric, model_type='tree', base_model="RFPlus_default", splitting_strategy = "train-test", ascending=False)], - [FIModelConfig('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/dgp.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/dgp.py diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py similarity index 100% rename from feature_importance/fi_config/mdi_local/real_data_regression_retrain/models.py rename to feature_importance/fi_config/mdi_local/real_data_regression_CCLE_topotecan_retrain/models.py diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_crime/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_crime/models.py deleted file mode 100644 index 5b4e6af..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_crime/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_crime_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_crime_retrain/dgp.py new file mode 100644 index 0000000..2597a09 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_crime_retrain/dgp.py @@ -0,0 +1,26 @@ +import sys +sys.path.append("../..") +from feature_importance.scripts.simulations_util import * + + +### Update start for local MDI+ +X_DGP = sample_real_data_X +X_PARAMS_DICT = { + "source": "uci", + "data_id": 183, + "sample_row_n": None +} +Y_DGP = sample_real_data_y +Y_PARAMS_DICT = { + "source": "uci", + "data_id": 183 +} +### Update for local MDI+ done + +# # vary one parameter +# VARY_PARAM_NAME = "sample_row_n" +# VARY_PARAM_VALS = {"100": 100, "250": 250, "500": 500, "1000": 1000} + +# vary two parameters in a grid +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_crime_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_crime_retrain/models.py new file mode 100644 index 0000000..a82071a --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_crime_retrain/models.py @@ -0,0 +1,29 @@ +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_diabetes/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_diabetes/models.py deleted file mode 100644 index b5c76eb..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_diabetes/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_diabetes_average/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_diabetes_average/models.py deleted file mode 100644 index 39cd484..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_diabetes_average/models.py +++ /dev/null @@ -1,28 +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_OOB_RFPlus_l2_norm', LFI_evaluation_RFPlus_oob_l2_norm_sign, 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_sign, 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_sign, model_type='tree', base_model="RFPlus_default", 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('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('LIME_RF', lime_evaluation_RF, model_type='tree', base_model="RF", splitting_strategy = "train-test")], - [FIModelConfig('Random', random, model_type='tree', base_model="None", splitting_strategy = "train-test")], -] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression_diabetes_average/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/dgp.py similarity index 54% rename from feature_importance/fi_config/mdi_local/real_data_regression_diabetes_average/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/dgp.py index 6521345..2e4347b 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_diabetes_average/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/dgp.py @@ -5,30 +5,16 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes_regr", + "source": "uci", + "data_id": 189, "sample_row_n": None } -# 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 = sample_real_data_y Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes_regr" + "source": "uci", + "data_id": 189 } # Y_PARAMS_DICT = { # "source": "imodels", 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 new file mode 100644 index 0000000..4b60f25 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_parkinsons_retrain/models.py @@ -0,0 +1,52 @@ +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_diabetes/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/dgp.py similarity index 54% rename from feature_importance/fi_config/mdi_local/real_data_regression_diabetes/dgp.py rename to feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/dgp.py index 6521345..1141615 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression_diabetes/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/dgp.py @@ -5,30 +5,16 @@ X_DGP = sample_real_data_X X_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes_regr", + "source": "uci", + "data_id": 320, "sample_row_n": None } -# 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 = sample_real_data_y Y_PARAMS_DICT = { - "source": "imodels", - "data_name": "diabetes_regr" + "source": "uci", + "data_id": 320 } # Y_PARAMS_DICT = { # "source": "imodels", 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 new file mode 100644 index 0000000..4b60f25 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_performance_retrain/models.py @@ -0,0 +1,52 @@ +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_retrain/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression_retrain/dgp.py deleted file mode 100644 index 57a1d27..0000000 --- a/feature_importance/fi_config/mdi_local/real_data_regression_retrain/dgp.py +++ /dev/null @@ -1,37 +0,0 @@ -import sys -sys.path.append("../..") -from feature_importance.scripts.simulations_util import * - - -### Update start for local MDI+ -X_DGP = sample_normal_X -X_PARAMS_DICT = { - "n_train": 250, - "n_test": 100, - "d": 20, -} -Y_DGP = linear_model -Y_PARAMS_DICT = { - "beta": 1, - "sigma": None, - "heritability": 0.4, - "s": 10, -} -### Update for local MDI+ done - -# # vary one parameter -# VARY_PARAM_NAME = "sample_row_n" -# VARY_PARAM_VALS = {"100": 100, "250": 250, "500": 500, "1000": 1000} - -# vary two parameters in a grid -# VARY_PARAM_NAME = ["heritability", "n_train"] -# VARY_PARAM_VALS = {"heritability": {"0.1": 0.1, "0.2": 0.2, "0.4": 0.4, "0.8": 0.8}, -# "n_train": {"100": 100, "250": 250, "750": 750}} - -VARY_PARAM_NAME = ["heritability", "n_train"] -VARY_PARAM_VALS = {"heritability": {"0.8": 0.8}, - "n_train": {"250": 250}} - -# # vary over n_estimators in RF model in models.py -# VARY_PARAM_NAME = "n_estimators" -# VARY_PARAM_VALS = {"placeholder": 0} \ 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_temperature_retrain/dgp.py new file mode 100644 index 0000000..f27c705 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/dgp.py @@ -0,0 +1,35 @@ +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 +} +# 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 = "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_retrain/models.py b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py new file mode 100644 index 0000000..d1ddbd7 --- /dev/null +++ b/feature_importance/fi_config/mdi_local/real_data_regression_temperature_retrain/models.py @@ -0,0 +1,53 @@ +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 347e1b8..e3ea4f9 100644 --- a/feature_importance/scripts/competing_methods_local.py +++ b/feature_importance/scripts/competing_methods_local.py @@ -31,13 +31,15 @@ # return masked_feature_importance -def random_retrain(X_train, y_train, fit=None, mode="absolute"): +##############################################################################################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) + return np.abs(local_fi_score_train), np.abs(local_fi_score_test) -def tree_shap_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): +def tree_shap_evaluation_RF_retrain(X_train, y_train, X_test, fit=None, mode="absolute"): """ Compute average treeshap value across observations. Larger absolute values indicate more important features. @@ -48,113 +50,329 @@ def tree_shap_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute") """ explainer = shap.TreeExplainer(fit) local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) + local_fi_score_test = explainer.shap_values(X_test, check_additivity=False) if sklearn.base.is_classifier(fit): if mode == "absolute": - return np.abs(local_fi_score_train[:,:,1]) + return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_test[:,:,1]) if mode == "absolute": - return np.abs(local_fi_score_train) + 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])) +# 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 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])) + test_result = np.zeros((X_test.shape[0], X_test.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] + 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]): + if task == "classification": + exp = explainer.explain_instance(X_train[i, :], fit.predict_proba, num_features=num_features) + elif task == "regression": + 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): + train_result[i, j] = sorted_feature_importance[j][1] + for i in range(X_test.shape[0]): + if task == "classification": + exp = explainer.explain_instance(X_test[i, :], fit.predict_proba, num_features=num_features) + elif task == "regression": + exp = explainer.explain_instance(X_test[i, :], fit.predict, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) + for j in range(num_features): + test_result[i, j] = sorted_feature_importance[j][1] if mode == "absolute": - lime_values = np.abs(result) - return lime_values + train_lime_values = np.abs(train_result) + test_lime_values = np.abs(test_result) + else: + train_lime_values = train_result + test_lime_values = test_result + return train_lime_values, test_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") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) - if mode == "absolute": - return np.abs(local_fi_score_train) +# 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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_oob_retrain(X_train, y_train, fit=None, mode="absolute"): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) - if mode == "absolute": - return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_all_retrain(X_train, y_train, 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) - if mode == "absolute": - return np.abs(local_fi_score_train) - +# def LFI_evaluation_RFPlus_oob_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_inbag_l2_norm_sign_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") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) - if mode == "absolute": - return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_all_retrain(X_train, y_train, 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) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_oob_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) - if mode == "absolute": - return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_inbag_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_all_l2_norm_sign_retrain(X_train, y_train, 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=True) - if mode == "absolute": - return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_oob_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_inbag_l2_norm_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") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) - if mode == "absolute": - return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_all_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) +# if mode == "absolute": +# return np.abs(local_fi_score_train) -def LFI_evaluation_RFPlus_oob_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): - assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) - rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") - local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) - if mode == "absolute": - return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_inbag_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + -def LFI_evaluation_RFPlus_all_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): +# def LFI_evaluation_RFPlus_oob_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_inbag_average_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_average_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_average_retrain(X_train, y_train, 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, leaf_average = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_inbag_ranking_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# 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, l2norm=True, sign=False) + 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) + 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) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_ranking_ridge_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="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_ranking_ridge_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="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_inbag_l2_norm_sign_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) +# def LFI_evaluation_RFPlus_all_l2_norm_sign_retrain(X_train, y_train, 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=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_inbag_l2_norm_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_l2_norm_retrain(X_train, y_train, 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) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_inbag_l2_norm_average_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_oob_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_l2_norm_average_retrain(X_train, y_train, 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, leaf_average= True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_inbag_l2_norm_ranking_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") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) +# 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) @@ -164,6 +382,7 @@ def LFI_evaluation_RFPlus_all_l2_norm_retrain(X_train, y_train, fit=None, mode=" +############################################################################################################# diff --git a/feature_importance/scripts/simulations_util.py b/feature_importance/scripts/simulations_util.py index 2f163c6..a08a398 100644 --- a/feature_importance/scripts/simulations_util.py +++ b/feature_importance/scripts/simulations_util.py @@ -7,6 +7,51 @@ import imodels import openml from ucimlrepo import fetch_ucirepo +from sklearn.preprocessing import OneHotEncoder +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import LabelEncoder + +def preprocessing_data_X(X): + categorical_cols = X.select_dtypes(include=["object", "category"]).columns + numerical_cols = X.select_dtypes(include=["number"]).columns + if X[numerical_cols].isnull().any().any(): + num_imputer = SimpleImputer(strategy="mean") + X[numerical_cols] = num_imputer.fit_transform(X[numerical_cols]) + if len(categorical_cols) > 0 and X[categorical_cols].isnull().any().any(): + # Convert categorical columns to string to ensure consistent types + X[categorical_cols] = X[categorical_cols].astype(str) + cat_imputer = SimpleImputer(strategy="most_frequent") + X[categorical_cols] = cat_imputer.fit_transform(X[categorical_cols]) + if len(categorical_cols) > 0: + encoder = OneHotEncoder(handle_unknown="ignore", sparse_output=False) + X_categorical = encoder.fit_transform(X[categorical_cols]) + X_categorical_df = pd.DataFrame( + X_categorical, + columns=encoder.get_feature_names_out(categorical_cols), + index=X.index + ) + X = pd.concat([X[numerical_cols], X_categorical_df], axis=1) + else: + X = X[numerical_cols] + X = X.to_numpy() + if X.shape[0]>2000: + X = X[:2000,:] + return X + +def preprocessing_data_y(y): + if y.to_numpy().shape[1] > 1: + y = y.iloc[:, 0].to_numpy().flatten() + else: + y = y.to_numpy().flatten() + if y.shape[0]>2000: + y = y[:2000] + + if np.all(np.vectorize(isinstance)(y, str)): + encoder = LabelEncoder() + y = encoder.fit_transform(y) + return y + + def sample_real_data_X(source=None, data_name=None, file_path=None, data_id=None, seed=4307, normalize=False, sample_row_n=None): if source == "imodels": @@ -20,10 +65,7 @@ def sample_real_data_X(source=None, data_name=None, file_path=None, data_id=None X, _, _, _ = dataset.get_data(target=dataset.default_target_attribute, dataset_format="array") elif source == "uci": dataset = fetch_ucirepo(id=data_id) - temp = dataset.data.features.replace('?', np.nan) - temp= temp.dropna(axis=1) - temp = temp.drop(columns=['communityname']) - X = temp.to_numpy() + X = preprocessing_data_X(dataset.data.features) elif source == "csv": X = pd.read_csv(file_path).to_numpy() if normalize: @@ -48,10 +90,7 @@ def sample_real_data_y(X=None, source=None, data_name=None, file_path=None, data _, y, _, _ = dataset.get_data(target=dataset.default_target_attribute, dataset_format="array") elif source == "uci": dataset = fetch_ucirepo(id=data_id) - if dataset.data.targets.to_numpy().shape[1] > 1: - y = dataset.data.targets.iloc[:, 0].to_numpy().flatten() - else: - y = dataset.data.targets.to_numpy().flatten() + y = preprocessing_data_y(dataset.data.targets) elif source == "csv": y = pd.read_csv(file_path).to_numpy().flatten() if sample_row_n is not None: @@ -307,94 +346,94 @@ def create_y(x, s, beta): return y_train -def linear_model_two_groups(X, sigma, s, beta, group_intercept=0.5, heritability=None, snr=None, error_fun=None, - frac_corrupt=None, corrupt_how='permute', corrupt_size=None, - corrupt_mean=None, return_support=False, seed=None): - """ - This method is used to crete responses for two groups from a linear model with hard sparsity - Parameters: - X: X matrix - s: sparsity - beta: coefficient vector. If beta not a vector, then assumed a constant - sigma: s.d. of added noise - Returns: - numpy array of shape (n) - """ - n, p = X.shape - - ### Update start for local MDI+ - def create_y(x, s, beta, group_index): - assert group_index in [0, 1] - linear_term = 0 - start = group_index * s - for j in range(s): - linear_term += x[start+j] * beta[j] - return linear_term +# def linear_model_two_groups(X, sigma, s, beta, group_intercept=0.5, heritability=None, snr=None, error_fun=None, +# frac_corrupt=None, corrupt_how='permute', corrupt_size=None, +# corrupt_mean=None, return_support=False, seed=None): +# """ +# This method is used to crete responses for two groups from a linear model with hard sparsity +# Parameters: +# X: X matrix +# s: sparsity +# beta: coefficient vector. If beta not a vector, then assumed a constant +# sigma: s.d. of added noise +# Returns: +# numpy array of shape (n) +# """ +# n, p = X.shape + +# ### Update start for local MDI+ +# def create_y(x, s, beta, group_index): +# assert group_index in [0, 1] +# linear_term = 0 +# start = group_index * s +# for j in range(s): +# linear_term += x[start+j] * beta[j] +# return linear_term - if seed is not None: - np.random.seed(seed) - - # Generate two coefficient vectors for each subgroup - beta = generate_coef(beta, s) - # Generate two response vectors for each subgroup5 - y_train_group1 = np.array([create_y(X[i, :], s, beta, group_index=0) for i in range(n//2)]) - group_intercept - y_train_group2 = np.array([create_y(X[i, :], s, beta, group_index=1) for i in range(n//2, n)]) + group_intercept - y_train = np.concatenate((y_train_group1, y_train_group2)) - ### Update for local MDI+ done - if heritability is not None: - sigma_group1 = (np.var(y_train_group1) * ((1.0 - heritability) / heritability)) ** 0.5 - sigma_group2 = (np.var(y_train_group2) * ((1.0 - heritability) / heritability)) ** 0.5 - sigma = (sigma_group1 + sigma_group2) / 2 - if snr is not None: - assert False - sigma = (np.var(y_train) / snr) ** 0.5 - if error_fun is None: - error_fun = np.random.randn - if frac_corrupt is None and corrupt_size is None: - y_train = y_train + sigma * error_fun(n) - else: - assert False - if frac_corrupt is None: - frac_corrupt = 0 - num_corrupt = int(np.floor(frac_corrupt*len(y_train))) - corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) - if corrupt_how == 'permute': - corrupt_array = y_train[corrupt_indices] - corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) - for i,index in enumerate(corrupt_indices): - y_train[index] = corrupt_array[i] - y_train = y_train + sigma * error_fun(n) - elif corrupt_how == 'cauchy': - for i in range(len(y_train)): - if i in corrupt_indices: - y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() - else: - y_train[i] = y_train[i] + sigma*error_fun() - elif corrupt_how == "leverage_constant": - if isinstance(corrupt_size, int): - corrupt_quantile = corrupt_size / n - else: - corrupt_quantile = corrupt_size - y_train = y_train + sigma * error_fun(n) - corrupt_idx = np.random.choice(range(s, p), size=1) - y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") - elif corrupt_how == "leverage_normal": - if isinstance(corrupt_size, int): - corrupt_quantile = corrupt_size / n - else: - corrupt_quantile = corrupt_size - y_train = y_train + sigma * error_fun(n) - corrupt_idx = np.random.choice(range(s, p), size=1) - y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") - ### Update start for local MDI+ - if return_support: - support_group1 = np.concatenate((np.ones(s), np.zeros(X.shape[1] - s))) - support_group2 = np.concatenate((np.zeros(s), np.ones(s), np.zeros(X.shape[1] - 2*s))) - support_groups = (support_group1, support_group2) - return y_train, support_groups, beta - else: - return y_train - ### Update for local MDI+ done +# if seed is not None: +# np.random.seed(seed) + +# # Generate two coefficient vectors for each subgroup +# beta = generate_coef(beta, s) +# # Generate two response vectors for each subgroup5 +# y_train_group1 = np.array([create_y(X[i, :], s, beta, group_index=0) for i in range(n//2)]) - group_intercept +# y_train_group2 = np.array([create_y(X[i, :], s, beta, group_index=1) for i in range(n//2, n)]) + group_intercept +# y_train = np.concatenate((y_train_group1, y_train_group2)) +# ### Update for local MDI+ done +# if heritability is not None: +# sigma_group1 = (np.var(y_train_group1) * ((1.0 - heritability) / heritability)) ** 0.5 +# sigma_group2 = (np.var(y_train_group2) * ((1.0 - heritability) / heritability)) ** 0.5 +# sigma = (sigma_group1 + sigma_group2) / 2 +# if snr is not None: +# assert False +# sigma = (np.var(y_train) / snr) ** 0.5 +# if error_fun is None: +# error_fun = np.random.randn +# if frac_corrupt is None and corrupt_size is None: +# y_train = y_train + sigma * error_fun(n) +# else: +# assert False +# if frac_corrupt is None: +# frac_corrupt = 0 +# num_corrupt = int(np.floor(frac_corrupt*len(y_train))) +# corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) +# if corrupt_how == 'permute': +# corrupt_array = y_train[corrupt_indices] +# corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) +# for i,index in enumerate(corrupt_indices): +# y_train[index] = corrupt_array[i] +# y_train = y_train + sigma * error_fun(n) +# elif corrupt_how == 'cauchy': +# for i in range(len(y_train)): +# if i in corrupt_indices: +# y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() +# else: +# y_train[i] = y_train[i] + sigma*error_fun() +# elif corrupt_how == "leverage_constant": +# if isinstance(corrupt_size, int): +# corrupt_quantile = corrupt_size / n +# else: +# corrupt_quantile = corrupt_size +# y_train = y_train + sigma * error_fun(n) +# corrupt_idx = np.random.choice(range(s, p), size=1) +# y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") +# elif corrupt_how == "leverage_normal": +# if isinstance(corrupt_size, int): +# corrupt_quantile = corrupt_size / n +# else: +# corrupt_quantile = corrupt_size +# y_train = y_train + sigma * error_fun(n) +# corrupt_idx = np.random.choice(range(s, p), size=1) +# y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") +# ### Update start for local MDI+ +# if return_support: +# support_group1 = np.concatenate((np.ones(s), np.zeros(X.shape[1] - s))) +# support_group2 = np.concatenate((np.zeros(s), np.ones(s), np.zeros(X.shape[1] - 2*s))) +# support_groups = (support_group1, support_group2) +# return y_train, support_groups, beta +# else: +# return y_train +# ### Update for local MDI+ done def lss_model(X, sigma, m, r, tau, beta, heritability=None, snr=None, error_fun=None, min_active=None, frac_corrupt=None, corrupt_how='permute', corrupt_size=None, corrupt_mean=None, @@ -510,130 +549,130 @@ def lss_vector_fun(x, beta): else: return y_train -def lss_model_two_groups(X, sigma, m, r, tau, beta, group_intercept=0.5, heritability=None, snr=None, error_fun=None, min_active=None, - frac_corrupt=None, corrupt_how='permute', corrupt_size=None, corrupt_mean=None, - return_support=False, seed=None): - """ - This method creates response from an LSS model - - X: data matrix - m: number of interaction terms - r: max order of interaction - tau: threshold - sigma: standard deviation of noise - beta: coefficient vector. If beta not a vector, then assumed a constant - - :return - y_train: numpy array of shape (n) - """ - n, p = X.shape - assert p >= m * r # Cannot have more interactions * size than the dimension - if seed is not None: - np.random.seed(seed) - - def lss_func_two_group(x, beta, group_index): - assert group_index in [0, 1] - start = group_index * m * r - x_bool = (x - tau) > 0 - y = 0 - for j in range(m): - lss_term_components = x_bool[start+(j*r):start+(j*r)+r] - lss_term = int(all(lss_term_components)) - y += lss_term * beta[j] - return y - - # def lss_vector_fun(x, beta): - # x_bool = (x - tau) > 0 - # y = 0 - # max_iter = 100 - # features = np.arange(p) - # support_idx = [] - # for j in range(m): - # cnt = 0 - # while True: - # int_features = np.random.choice(features, size=r, replace=False) - # lss_term_components = x_bool[:, int_features] - # lss_term = np.apply_along_axis(all, 1, lss_term_components) - # cnt += 1 - # if np.mean(lss_term) >= min_active or cnt > max_iter: - # y += lss_term * beta[j] - # features = list(set(features).difference(set(int_features))) - # support_idx.append(int_features) - # if cnt > max_iter: - # warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) - # break - # support_idx = np.stack(support_idx).ravel() - # support = np.zeros(p) - # for j in support_idx: - # support[j] = 1 - # return y, support - - beta = generate_coef(beta, m) - if tau == 'median': - tau = np.median(X,axis = 0) +# def lss_model_two_groups(X, sigma, m, r, tau, beta, group_intercept=0.5, heritability=None, snr=None, error_fun=None, min_active=None, +# frac_corrupt=None, corrupt_how='permute', corrupt_size=None, corrupt_mean=None, +# return_support=False, seed=None): +# """ +# This method creates response from an LSS model + +# X: data matrix +# m: number of interaction terms +# r: max order of interaction +# tau: threshold +# sigma: standard deviation of noise +# beta: coefficient vector. If beta not a vector, then assumed a constant + +# :return +# y_train: numpy array of shape (n) +# """ +# n, p = X.shape +# assert p >= m * r # Cannot have more interactions * size than the dimension +# if seed is not None: +# np.random.seed(seed) + +# def lss_func_two_group(x, beta, group_index): +# assert group_index in [0, 1] +# start = group_index * m * r +# x_bool = (x - tau) > 0 +# y = 0 +# for j in range(m): +# lss_term_components = x_bool[start+(j*r):start+(j*r)+r] +# lss_term = int(all(lss_term_components)) +# y += lss_term * beta[j] +# return y + +# # def lss_vector_fun(x, beta): +# # x_bool = (x - tau) > 0 +# # y = 0 +# # max_iter = 100 +# # features = np.arange(p) +# # support_idx = [] +# # for j in range(m): +# # cnt = 0 +# # while True: +# # int_features = np.random.choice(features, size=r, replace=False) +# # lss_term_components = x_bool[:, int_features] +# # lss_term = np.apply_along_axis(all, 1, lss_term_components) +# # cnt += 1 +# # if np.mean(lss_term) >= min_active or cnt > max_iter: +# # y += lss_term * beta[j] +# # features = list(set(features).difference(set(int_features))) +# # support_idx.append(int_features) +# # if cnt > max_iter: +# # warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) +# # break +# # support_idx = np.stack(support_idx).ravel() +# # support = np.zeros(p) +# # for j in support_idx: +# # support[j] = 1 +# # return y, support + +# beta = generate_coef(beta, m) +# if tau == 'median': +# tau = np.median(X,axis = 0) - if min_active is None: - y_train_group1 = np.array([lss_func_two_group(X[i, :], beta, group_index=0) for i in range(n//2)]) - group_intercept - y_train_group2 = np.array([lss_func_two_group(X[i, :], beta, group_index=1) for i in range(n//2, n)]) + group_intercept - y_train = np.concatenate((y_train_group1, y_train_group2)) - support_group1 = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) - support_group2 = np.concatenate((np.zeros(m * r), np.ones(m * r), np.zeros(X.shape[1] - 2*(m * r)))) - support_groups = (support_group1, support_group2) - else: - assert False - y_train, support = lss_vector_fun(X, beta) - - if heritability is not None: - sigma_group1 = (np.var(y_train_group1) * ((1.0 - heritability) / heritability)) ** 0.5 - sigma_group2 = (np.var(y_train_group2) * ((1.0 - heritability) / heritability)) ** 0.5 - sigma = (sigma_group1 + sigma_group2) / 2 - if snr is not None: - assert False - sigma = (np.var(y_train) / snr) ** 0.5 - if error_fun is None: - error_fun = np.random.randn - - if frac_corrupt is None and corrupt_size is None: - y_train = y_train + sigma * error_fun(n) - else: - assert False - if frac_corrupt is None: - frac_corrupt = 0 - num_corrupt = int(np.floor(frac_corrupt*len(y_train))) - corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) - if corrupt_how == 'permute': - corrupt_array = y_train[corrupt_indices] - corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) - for i,index in enumerate(corrupt_indices): - y_train[index] = corrupt_array[i] - y_train = y_train + sigma * error_fun(n) - elif corrupt_how == 'cauchy': - for i in range(len(y_train)): - if i in corrupt_indices: - y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() - else: - y_train[i] = y_train[i] + sigma*error_fun() - elif corrupt_how == "leverage_constant": - if isinstance(corrupt_size, int): - corrupt_quantile = corrupt_size / n - else: - corrupt_quantile = corrupt_size - y_train = y_train + sigma * error_fun(n) - corrupt_idx = np.random.choice(range(m*r, p), size=1) - y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") - elif corrupt_how == "leverage_normal": - if isinstance(corrupt_size, int): - corrupt_quantile = corrupt_size / n - else: - corrupt_quantile = corrupt_size - y_train = y_train + sigma * error_fun(n) - corrupt_idx = np.random.choice(range(m*r, p), size=1) - y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") +# if min_active is None: +# y_train_group1 = np.array([lss_func_two_group(X[i, :], beta, group_index=0) for i in range(n//2)]) - group_intercept +# y_train_group2 = np.array([lss_func_two_group(X[i, :], beta, group_index=1) for i in range(n//2, n)]) + group_intercept +# y_train = np.concatenate((y_train_group1, y_train_group2)) +# support_group1 = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) +# support_group2 = np.concatenate((np.zeros(m * r), np.ones(m * r), np.zeros(X.shape[1] - 2*(m * r)))) +# support_groups = (support_group1, support_group2) +# else: +# assert False +# y_train, support = lss_vector_fun(X, beta) + +# if heritability is not None: +# sigma_group1 = (np.var(y_train_group1) * ((1.0 - heritability) / heritability)) ** 0.5 +# sigma_group2 = (np.var(y_train_group2) * ((1.0 - heritability) / heritability)) ** 0.5 +# sigma = (sigma_group1 + sigma_group2) / 2 +# if snr is not None: +# assert False +# sigma = (np.var(y_train) / snr) ** 0.5 +# if error_fun is None: +# error_fun = np.random.randn + +# if frac_corrupt is None and corrupt_size is None: +# y_train = y_train + sigma * error_fun(n) +# else: +# assert False +# if frac_corrupt is None: +# frac_corrupt = 0 +# num_corrupt = int(np.floor(frac_corrupt*len(y_train))) +# corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) +# if corrupt_how == 'permute': +# corrupt_array = y_train[corrupt_indices] +# corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) +# for i,index in enumerate(corrupt_indices): +# y_train[index] = corrupt_array[i] +# y_train = y_train + sigma * error_fun(n) +# elif corrupt_how == 'cauchy': +# for i in range(len(y_train)): +# if i in corrupt_indices: +# y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() +# else: +# y_train[i] = y_train[i] + sigma*error_fun() +# elif corrupt_how == "leverage_constant": +# if isinstance(corrupt_size, int): +# corrupt_quantile = corrupt_size / n +# else: +# corrupt_quantile = corrupt_size +# y_train = y_train + sigma * error_fun(n) +# corrupt_idx = np.random.choice(range(m*r, p), size=1) +# y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") +# elif corrupt_how == "leverage_normal": +# if isinstance(corrupt_size, int): +# corrupt_quantile = corrupt_size / n +# else: +# corrupt_quantile = corrupt_size +# y_train = y_train + sigma * error_fun(n) +# corrupt_idx = np.random.choice(range(m*r, p), size=1) +# y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") - if return_support: - return y_train, support_groups, beta - else: - return y_train +# if return_support: +# return y_train, support_groups, beta +# else: +# return y_train def partial_linear_lss_model(X, sigma, s, m, r, tau, beta, heritability=None, snr=None, error_fun=None, min_active=None, frac_corrupt=None, corrupt_how='permute', corrupt_size=None, diff --git a/feature_importance/util.py b/feature_importance/util.py index 73e23da..a6af8d4 100644 --- a/feature_importance/util.py +++ b/feature_importance/util.py @@ -72,7 +72,7 @@ def __init__(self, 'train-test', 'train-tune-test', 'train-test-lowdata', 'train-tune-test-lowdata', 'train-test-prediction', None, 'test-300' } - assert base_model in ["None", "RF", "RFPlus_default", "RFPlus_inbag", "RFPlus_oob"] + # assert base_model in ["None", "RF", "RFPlus_default", "RFPlus_inbag", "RFPlus_oob"] self.name = name self.cls = cls