From a7070d26511ac764bd92062e81e49ba55bea0aaf Mon Sep 17 00:00:00 2001 From: zyliang2001 Date: Mon, 13 May 2024 16:23:03 -0700 Subject: [PATCH] make code cleaner --- .../01_ablation_regression_script.sh | 2 +- feature_importance/01_ablation_script.sh | 4 +- .../01_run_ablation_classification.py | 128 +- .../01_run_ablation_regression.py | 117 +- .../294_satellite_image.tsv.gz | Bin 0 -> 108154 bytes .../data/pmlb_data/diabetes/diabetes.tsv.gz | Bin 0 -> 9363 bytes .../diabetes_classification_test.png | Bin 385416 -> 393779 bytes .../diabetes_classification_test_subset_1.png | Bin 504379 -> 569023 bytes .../diabetes_classification_test_subset_2.png | Bin 533494 -> 540116 bytes .../diabetes_classification_train.png | Bin 513030 -> 481283 bytes .../real_data_classification/models.py | 8 +- .../mdi_local/real_data_regression/dgp.py | 19 + .../mdi_local/real_data_regression/models.py | 8 +- .../real_data_ablation_visulization_new.ipynb | 72 +- ..._data_ablation_visulization_version3.ipynb | 57675 +++------------- .../scripts/competing_methods_local.py | 24 + 16 files changed, 11092 insertions(+), 46965 deletions(-) create mode 100644 feature_importance/data/pmlb_data/294_satellite_image/294_satellite_image.tsv.gz create mode 100644 feature_importance/data/pmlb_data/diabetes/diabetes.tsv.gz diff --git a/feature_importance/01_ablation_regression_script.sh b/feature_importance/01_ablation_regression_script.sh index 7132c54..c7e493f 100755 --- a/feature_importance/01_ablation_regression_script.sh +++ b/feature_importance/01_ablation_regression_script.sh @@ -4,7 +4,7 @@ #SBATCH --partition=yugroup source activate mdi -command="01_run_ablation_regression.py --nreps 1 --config mdi_local.real_data_regression --split_seed ${1} --ignore_cache --create_rmd --result_name diabetes_regr" +command="01_run_ablation_regression.py --nreps 1 --config mdi_local.real_data_regression --split_seed ${1} --ignore_cache --create_rmd --result_name diabetes_regr_new" # Execute the command python $command \ No newline at end of file diff --git a/feature_importance/01_ablation_script.sh b/feature_importance/01_ablation_script.sh index b297966..511bc3a 100755 --- a/feature_importance/01_ablation_script.sh +++ b/feature_importance/01_ablation_script.sh @@ -1,8 +1,8 @@ #!/bin/bash -slurm_script="01_ablation_regression_script.sh" +slurm_script="01_ablation_classification_script.sh" -for rep in {1..2} +for rep in {1..5} do sbatch $slurm_script $rep # Submit SLURM job using the specified script done \ No newline at end of file diff --git a/feature_importance/01_run_ablation_classification.py b/feature_importance/01_run_ablation_classification.py index 673556e..989f5cc 100644 --- a/feature_importance/01_run_ablation_classification.py +++ b/feature_importance/01_run_ablation_classification.py @@ -62,7 +62,7 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): """ - Replace the top num_features max feature importance data with random shuffle for each sample + Replace the top num_features max feature importance data with mean value for each sample """ train_mean = np.mean(train, axis=0) assert mode in ["max", "min"] @@ -77,9 +77,9 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): data_copy[i, indices[i,j]] = train_mean[indices[i,j]] return data_copy -def ablation_by_addition(data, feature_importance, mode, num_features): +def ablation_by_addition(train, data, feature_importance, mode, num_features): """ - Initialize the data with zeros and add the top num_features max feature importance data for each sample + Initialize the data with mean values and add the top num_features max feature importance data for each sample """ assert mode in ["max", "min"] fi = feature_importance @@ -87,7 +87,8 @@ def ablation_by_addition(data, feature_importance, mode, num_features): indices = np.argsort(-fi) else: indices = np.argsort(fi) - data_copy = np.zeros(data.shape) + row_values = np.mean(train, axis=0).tolist() + data_copy = np.array([row_values] * data.shape[0]) for i in range(data.shape[0]): for j in range(num_features): data_copy[i, indices[i,j]] = data[i, indices[i,j]] @@ -144,8 +145,10 @@ def compare_estimators(estimators: List[ModelConfig], test_all_f1_rf = f1_score(y_test, est.predict_proba(X_test)[:, 1] > 0.5) # fit RF_plus model + start = time.time() rf_plus_base = RandomForestPlusClassifier(rf_model=est) rf_plus_base.fit(X_train, y_train) + end = time.time() 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) @@ -158,16 +161,14 @@ def compare_estimators(estimators: List[ModelConfig], X_test_subset = X_test[indices_test] y_test_subset = y_test[indices_test] - # loop over fi estimators - rng = np.random.RandomState() - number_of_ablations = 1 - seeds = rng.randint(0, 10000, number_of_ablations) 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, 'test_all_auc_rf': test_all_auc_rf, @@ -176,12 +177,13 @@ def compare_estimators(estimators: List[ModelConfig], 'test_all_auc_rf_plus': test_all_auc_rf_plus, 'test_all_auprc_rf_plus': test_all_auprc_rf_plus, 'test_all_f1_rf_plus': test_all_f1_rf_plus, + 'rf_plus_fit_time': end - start, } for i in range(100): metric_results[f'sample_train_{i}'] = indices_train[i] metric_results[f'sample_test_{i}'] = indices_test[i] - for i in range(len(seeds)): - metric_results[f'ablation_seed_{i}'] = seeds[i] + + print("Compute feature importance") start = time.time() if fi_est.name == "LFI_evaluate_on_all_RF_plus" or fi_est.name == "LFI_evaluate_on_oob_RF_plus": local_fi_score_train, local_parital_pred_train, local_fi_score_test, local_partial_pred_test, local_fi_score_test_subset, local_partial_pred_test_subset = fi_est.cls(X_train=X_train, y_train=y_train, @@ -218,6 +220,13 @@ def compare_estimators(estimators: List[ModelConfig], "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: + if a_model != "RF_Plus_Classifier": + ablation_models[a_model].fit(X_train, y_train) + end = time.time() + metric_results['ablation_model_fit_time'] = end - start + print("start ablation") # Subset Train data ablation for all FI methods start = time.time() @@ -235,26 +244,21 @@ def compare_estimators(estimators: List[ModelConfig], ablation_results_auroc_list = [0] * X_train_subset.shape[1] ablation_results_auprc_list = [0] * X_train_subset.shape[1] ablation_results_f1_list = [0] * X_train_subset.shape[1] - for seed in seeds: - for i in range(X_train_subset.shape[1]): - if fi_est.ascending: - ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1) - else: - ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1) - ablation_results_auroc_list[i] += roc_auc_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1]) - ablation_results_auprc_list[i] += average_precision_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1]) - ablation_results_f1_list[i] += f1_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1] > 0.5) - ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list] - ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list] - ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list] + for i in range(X_train_subset.shape[1]): + if fi_est.ascending: + ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1) + else: + ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1) + ablation_results_auroc_list[i] += roc_auc_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1]) + ablation_results_auprc_list[i] += average_precision_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1]) + ablation_results_f1_list[i] += f1_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1] > 0.5) for i in range(X_train_subset.shape[1]): metric_results[f'{a_model}_train_subset_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i] metric_results[f'{a_model}_train_subset_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i] metric_results[f'{a_model}_train_subset_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i] - end = time.time() - print(f"done with ablation {a_model} train subset {end - start}") end = time.time() - metric_results['train_subset_data_ablation_time'] = end - start + print(f"done with ablation train subset {end - start}") + metric_results['train_subset_ablation_time'] = end - start # Test data ablation @@ -274,26 +278,21 @@ def compare_estimators(estimators: List[ModelConfig], ablation_results_auroc_list = [0] * X_test_subset.shape[1] ablation_results_auprc_list = [0] * X_test_subset.shape[1] ablation_results_f1_list = [0] * X_test_subset.shape[1] - for seed in seeds: - for i in range(X_test_subset.shape[1]): - if fi_est.ascending: - ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1) - else: - ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1) - ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1]) - ablation_results_auprc_list[i] += average_precision_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1]) - ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1] > 0.5) - ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list] - ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list] - ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list] + for i in range(X_test_subset.shape[1]): + if fi_est.ascending: + ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1) + else: + ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1) + ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1]) + ablation_results_auprc_list[i] += average_precision_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1]) + ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1] > 0.5) for i in range(X_test_subset.shape[1]): metric_results[f'{a_model}_test_subset_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i] metric_results[f'{a_model}_test_subset_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i] metric_results[f'{a_model}_test_subset_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i] - end = time.time() - print(f"done with ablation {a_model} test subset {end - start}") end = time.time() - metric_results['test_subset_ablation_time'] = end - start + print(f"done with ablation 1 test subset {end - start}") + metric_results['test_subset_ablation_1_time'] = end - start # Subset test data ablation for all FI methods - addition @@ -311,26 +310,21 @@ def compare_estimators(estimators: List[ModelConfig], ablation_results_auroc_list = [0] * X_test_subset.shape[1] ablation_results_auprc_list = [0] * X_test_subset.shape[1] ablation_results_f1_list = [0] * X_test_subset.shape[1] - for seed in seeds: - for i in range(X_test_subset.shape[1]): - if fi_est.ascending: - ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "max", i+1) - else: - ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "min", i+1) - ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1]) - ablation_results_auprc_list[i] += average_precision_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1]) - ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1] > 0.5) - ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list] - ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list] - ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list] + for i in range(X_test_subset.shape[1]): + if fi_est.ascending: + ablation_X_test_subset_blank = ablation_by_addition(X_train, X_test_subset, imp_vals, "max", i+1) + else: + ablation_X_test_subset_blank = ablation_by_addition(X_train, X_test_subset, imp_vals, "min", i+1) + ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1]) + ablation_results_auprc_list[i] += average_precision_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1]) + ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1] > 0.5) for i in range(X_test_subset.shape[1]): metric_results[f'{a_model}_test_subset_AUROC_after_ablation_{i+1}_blank'] = ablation_results_auroc_list[i] metric_results[f'{a_model}_test_subset_AUPRC_after_ablation_{i+1}_blank'] = ablation_results_auprc_list[i] metric_results[f'{a_model}_test_subset_F1_after_ablation_{i+1}_blank'] = ablation_results_f1_list[i] - end = time.time() - print(f"done with ablation {a_model} test subset blank {end - start}") end = time.time() - metric_results['test_subset_blank_ablation_time'] = end - start + print(f"done with ablation 2 test subset {end - start}") + metric_results['test_subset_ablation_2_time'] = end - start # Whole test data ablation for all FI methods except for KernelSHAP and LIME if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]: @@ -349,24 +343,21 @@ def compare_estimators(estimators: List[ModelConfig], ablation_results_auroc_list = [0] * X_test.shape[1] ablation_results_auprc_list = [0] * X_test.shape[1] ablation_results_f1_list = [0] * X_test.shape[1] - for seed in seeds: - for i in range(X_test.shape[1]): - if fi_est.ascending: - ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1) - else: - ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1) - ablation_results_auroc_list[i] += roc_auc_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1]) - ablation_results_auprc_list[i] += average_precision_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1]) - ablation_results_f1_list[i] += f1_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1] > 0.5) - ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list] - ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list] - ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list] + for i in range(X_test.shape[1]): + if fi_est.ascending: + ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1) + else: + ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1) + ablation_results_auroc_list[i] += roc_auc_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1]) + ablation_results_auprc_list[i] += average_precision_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1]) + ablation_results_f1_list[i] += f1_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1] > 0.5) for i in range(X_test.shape[1]): metric_results[f'{a_model}_test_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i] metric_results[f'{a_model}_test_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i] metric_results[f'{a_model}_test_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i] end = time.time() metric_results['test_data_ablation_time'] = end - start + print(f"done with ablation test {end - start}") else: for a_model in ablation_models: metric_results[a_model+'_test_AUROC_before_ablation'] = None @@ -377,7 +368,8 @@ def compare_estimators(estimators: List[ModelConfig], metric_results[f'{a_model}_test_AUPRC_after_ablation_{i+1}'] = None metric_results[f'{a_model}_test_F1_after_ablation_{i+1}'] = None metric_results["test_data_ablation_time"] = None - print(f"fi: {fi_est.name} ablation done with time: {end - start}") + + print(f"fi: {fi_est.name} all ablation done") # initialize results with metadata and metric results kwargs: dict = model.kwargs # dict diff --git a/feature_importance/01_run_ablation_regression.py b/feature_importance/01_run_ablation_regression.py index 0806102..207f7bd 100644 --- a/feature_importance/01_run_ablation_regression.py +++ b/feature_importance/01_run_ablation_regression.py @@ -61,7 +61,7 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): """ - Replace the top num_features max feature importance data with random shuffle for each sample + Replace the top num_features max feature importance data with mean value for each sample """ train_mean = np.mean(train, axis=0) assert mode in ["max", "min"] @@ -76,9 +76,9 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): data_copy[i, indices[i,j]] = train_mean[indices[i,j]] return data_copy -def ablation_by_addition(data, feature_importance, mode, num_features): +def ablation_by_addition(train, data, feature_importance, mode, num_features): """ - Initialize the data with zeros and add the top num_features max feature importance data for each sample + Initialize the data with mean values and add the top num_features max feature importance data for each sample """ assert mode in ["max", "min"] fi = feature_importance @@ -86,7 +86,8 @@ def ablation_by_addition(data, feature_importance, mode, num_features): indices = np.argsort(-fi) else: indices = np.argsort(fi) - data_copy = np.zeros(data.shape) + row_values = np.mean(train, axis=0).tolist() + data_copy = np.array([row_values] * data.shape[0]) for i in range(data.shape[0]): for j in range(num_features): data_copy[i, indices[i,j]] = data[i, indices[i,j]] @@ -141,8 +142,10 @@ def compare_estimators(estimators: List[ModelConfig], test_all_r2_rf = r2_score(y_test, est.predict(X_test)) # fit RF_plus model + start = time.time() rf_plus_base = RandomForestPlusRegressor(rf_model=est) rf_plus_base.fit(X_train, y_train) + end = time.time() 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)) @@ -153,37 +156,35 @@ def compare_estimators(estimators: List[ModelConfig], y_train_subset = y_train[indices_train] X_test_subset = X_test[indices_test] y_test_subset = y_test[indices_test] - print(X_train.shape) # loop over fi estimators - rng = np.random.RandomState() - number_of_ablations = 1 - seeds = rng.randint(0, 10000, number_of_ablations) 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, 'test_all_mse_rf': test_all_mse_rf, 'test_all_r2_rf': test_all_r2_rf, 'test_all_mse_rf_plus': test_all_mse_rf_plus, 'test_all_r2_rf_plus': test_all_r2_rf_plus, + 'rf_plus_fit_time': end - start, } for i in range(100): metric_results[f'sample_train_{i}'] = indices_train[i] metric_results[f'sample_test_{i}'] = indices_test[i] - for i in range(len(seeds)): - metric_results[f'ablation_seed_{i}'] = seeds[i] + + print("Compute feature importance") start = time.time() if fi_est.name == "LFI_evaluate_on_all_RF_plus" or fi_est.name == "LFI_evaluate_on_oob_RF_plus": local_fi_score_train, local_parital_pred_train, local_fi_score_test, local_partial_pred_test, local_fi_score_test_subset, local_partial_pred_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_subset=X_test_subset, X_test=X_test, fit=rf_plus_base, **fi_est.kwargs) - print(local_fi_score_train.shape) local_fi_score_train_subset = local_fi_score_train[indices_train] local_partial_pred_train_subset = local_parital_pred_train[indices_train] elif fi_est.name == "LFI_fit_on_inbag_RF" or fi_est.name == "LFI_fit_on_inbag_RF": @@ -191,7 +192,6 @@ def compare_estimators(estimators: List[ModelConfig], X_train_subset = X_train_subset, y_train_subset=y_train_subset, X_test_subset=X_test_subset, X_test=X_test, fit=copy.deepcopy(est), **fi_est.kwargs) - print(local_fi_score_train.shape) local_fi_score_train_subset = local_fi_score_train[indices_train] local_partial_pred_train_subset = local_parital_pred_train[indices_train] elif fi_est.name == "TreeSHAP_RF": @@ -214,13 +214,18 @@ def compare_estimators(estimators: List[ModelConfig], "Linear": LinearRegression(), "XGB_Regressor": xgb.XGBRegressor(random_state=42), "RF_Plus_Regressor": rf_plus_base} + start = time.time() + for a_model in ablation_models: + if a_model != "RF_Plus_Regressor": + ablation_models[a_model].fit(X_train, y_train) + end = time.time() + metric_results['ablation_model_fit_time'] = end - start + print("start ablation") # Subset Train data ablation for all FI methods start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - if a_model != "RF_Plus_Regressor": - ablation_est.fit(X_train, y_train) y_pred_subset = ablation_est.predict(X_train_subset) metric_results[a_model + '_train_subset_MSE_before_ablation'] = mean_squared_error(y_train_subset, y_pred_subset) metric_results[a_model + '_train_subset_R_2_before_ablation'] = r2_score(y_train_subset, y_pred_subset) @@ -229,20 +234,18 @@ def compare_estimators(estimators: List[ModelConfig], imp_vals[imp_vals == float("inf")] = sys.maxsize - 1 ablation_results_list = [0] * X_train_subset.shape[1] ablation_results_list_r2 = [0] * X_train_subset.shape[1] - for seed in seeds: - for i in range(X_train_subset.shape[1]): - if fi_est.ascending: - ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1) - else: - ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1) - ablation_results_list[i] += mean_squared_error(y_train_subset, ablation_est.predict(ablation_X_train_subset)) - ablation_results_list_r2[i] += r2_score(y_train_subset, ablation_est.predict(ablation_X_train_subset)) - ablation_results_list = [x / len(seeds) for x in ablation_results_list] - ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2] + for i in range(X_train_subset.shape[1]): + if fi_est.ascending: + ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1) + else: + ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1) + ablation_results_list[i] += mean_squared_error(y_train_subset, ablation_est.predict(ablation_X_train_subset)) + ablation_results_list_r2[i] += r2_score(y_train_subset, ablation_est.predict(ablation_X_train_subset)) for i in range(X_train.shape[1]): metric_results[f'{a_model}_train_subset_MSE_after_ablation_{i+1}'] = ablation_results_list[i] metric_results[f'{a_model}_train_subset_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i] end = time.time() + print(f"done with ablation train subset {end - start}") metric_results['train_subset_ablation_time'] = end - start # Test data ablation @@ -250,8 +253,6 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - if a_model != "RF_Plus_Regressor": - ablation_est.fit(X_train, y_train) y_pred_subset = ablation_est.predict(X_test_subset) metric_results[a_model + '_test_subset_MSE_before_ablation'] = mean_squared_error(y_test_subset, y_pred_subset) metric_results[a_model + '_test_subset_R_2_before_ablation'] = r2_score(y_test_subset, y_pred_subset) @@ -260,29 +261,25 @@ def compare_estimators(estimators: List[ModelConfig], imp_vals[imp_vals == float("inf")] = sys.maxsize - 1 ablation_results_list = [0] * X_test_subset.shape[1] ablation_results_list_r2 = [0] * X_test_subset.shape[1] - for seed in seeds: - for i in range(X_test_subset.shape[1]): - if fi_est.ascending: - ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1) - else: - ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1) - ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset)) - ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset)) - ablation_results_list = [x / len(seeds) for x in ablation_results_list] - ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2] + for i in range(X_test_subset.shape[1]): + if fi_est.ascending: + ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1) + else: + ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1) + ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset)) + ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset)) for i in range(X_test_subset.shape[1]): metric_results[f'{a_model}_test_subset_MSE_after_ablation_{i+1}'] = ablation_results_list[i] metric_results[f'{a_model}_test_subset_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i] end = time.time() - metric_results['test_subset_ablation_time'] = end - start + print(f"done with ablation 1 test subset {end - start}") + metric_results['test_subset_ablation_1_time'] = end - start # Subset test data ablation for all FI methods - addition start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - if a_model != "RF_Plus_Regressor": - ablation_est.fit(X_train, y_train) metric_results[a_model + '_test_subset_MSE_before_ablation_blank'] = mean_squared_error(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape))) metric_results[a_model + '_test_subset_R_2_before_ablation_blank'] = r2_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape))) imp_vals = copy.deepcopy(local_fi_score_test_subset) @@ -290,29 +287,25 @@ def compare_estimators(estimators: List[ModelConfig], imp_vals[imp_vals == float("inf")] = sys.maxsize - 1 ablation_results_list = [0] * X_test_subset.shape[1] ablation_results_list_r2 = [0] * X_test_subset.shape[1] - for seed in seeds: - for i in range(X_test_subset.shape[1]): - if fi_est.ascending: - ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "max", i+1) - else: - ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "min", i+1) - ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank)) - ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank)) - ablation_results_list = [x / len(seeds) for x in ablation_results_list] - ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2] + for i in range(X_test_subset.shape[1]): + if fi_est.ascending: + ablation_X_test_subset_blank = ablation_by_addition(X_train, X_test_subset, imp_vals, "max", i+1) + else: + ablation_X_test_subset_blank = ablation_by_addition(X_train, X_test_subset, imp_vals, "min", i+1) + ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank)) + ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank)) for i in range(X_test_subset.shape[1]): metric_results[f'{a_model}_test_subset_MSE_after_ablation_{i+1}_blank'] = ablation_results_list[i] metric_results[f'{a_model}_test_subset_R_2_after_ablation_{i+1}_blank'] = ablation_results_list_r2[i] end = time.time() - metric_results['test_subset_blank_ablation_time'] = end - start + print(f"done with ablation 2 test subset {end - start}") + metric_results['test_subset_ablation_2_time'] = end - start # Whole test data ablation for all FI methods except for KernelSHAP and LIME if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]: start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - if a_model != "RF_Plus_Regressor": - ablation_est.fit(X_train, y_train) y_pred = ablation_est.predict(X_test) metric_results[a_model + '_test_MSE_before_ablation'] = mean_squared_error(y_test, y_pred) metric_results[a_model + '_test_R_2_before_ablation'] = r2_score(y_test, y_pred) @@ -321,21 +314,19 @@ def compare_estimators(estimators: List[ModelConfig], imp_vals[imp_vals == float("inf")] = sys.maxsize - 1 ablation_results_list = [0] * X_test.shape[1] ablation_results_list_r2 = [0] * X_test.shape[1] - for seed in seeds: - for i in range(X_test.shape[1]): - if fi_est.ascending: - ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1) - else: - ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1) - ablation_results_list[i] += mean_squared_error(y_test, ablation_est.predict(ablation_X_test)) - ablation_results_list_r2[i] += r2_score(y_test, ablation_est.predict(ablation_X_test)) - ablation_results_list = [x / len(seeds) for x in ablation_results_list] - ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2] + for i in range(X_test.shape[1]): + if fi_est.ascending: + ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1) + else: + ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1) + ablation_results_list[i] += mean_squared_error(y_test, ablation_est.predict(ablation_X_test)) + ablation_results_list_r2[i] += r2_score(y_test, ablation_est.predict(ablation_X_test)) for i in range(X_test.shape[1]): metric_results[f'{a_model}_test_MSE_after_ablation_{i+1}'] = ablation_results_list[i] metric_results[f'{a_model}_test_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i] end = time.time() metric_results['test_data_ablation_time'] = end - start + print(f"done with ablation test {end - start}") else: for a_model in ablation_models: metric_results[a_model + '_test_MSE_before_ablation'] = None @@ -345,7 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