From f6e8bb54002f22a15a1b861ead6861585540a5f7 Mon Sep 17 00:00:00 2001 From: zyliang2001 Date: Tue, 7 May 2024 14:30:19 -0700 Subject: [PATCH] Add changes --- .../01_ablation_classification_script.sh | 2 +- .../01_ablation_regression_script.sh | 2 +- .../01_run_ablation_classification.py | 90 +- .../01_run_ablation_regression.py | 77 +- .../diabetes_classification_test.png | Bin 0 -> 385416 bytes .../diabetes_classification_test_subset_1.png | Bin 0 -> 504379 bytes .../diabetes_classification_test_subset_2.png | Bin 0 -> 533494 bytes .../diabetes_classification_train.png | Bin 0 -> 513030 bytes .../real_data_classification/models.py | 14 +- .../mdi_local/real_data_regression/dgp.py | 26 +- .../mdi_local/real_data_regression/models.py | 13 +- ..._data_ablation_visulization_version3.ipynb | 56638 +++++++++++++--- .../satellite_image_regression_test.png | Bin 0 -> 385147 bytes ...tellite_image_regression_test_subset_1.png | Bin 0 -> 510158 bytes ...tellite_image_regression_test_subset_2.png | Bin 0 -> 534752 bytes .../satellite_image_regression_train.png | Bin 0 -> 499765 bytes .../scripts/competing_methods_local.py | 200 +- 17 files changed, 46145 insertions(+), 10917 deletions(-) create mode 100644 feature_importance/diabetes_classification_test.png create mode 100644 feature_importance/diabetes_classification_test_subset_1.png create mode 100644 feature_importance/diabetes_classification_test_subset_2.png create mode 100644 feature_importance/diabetes_classification_train.png create mode 100644 feature_importance/satellite_image_regression_test.png create mode 100644 feature_importance/satellite_image_regression_test_subset_1.png create mode 100644 feature_importance/satellite_image_regression_test_subset_2.png create mode 100644 feature_importance/satellite_image_regression_train.png diff --git a/feature_importance/01_ablation_classification_script.sh b/feature_importance/01_ablation_classification_script.sh index 3865dab..6df78b2 100755 --- a/feature_importance/01_ablation_classification_script.sh +++ b/feature_importance/01_ablation_classification_script.sh @@ -4,7 +4,7 @@ #SBATCH --partition=yugroup source activate mdi -command="01_run_ablation_classification.py --nreps 1 --config mdi_local.real_data_classification --split_seed ${1} --ignore_cache --create_rmd --result_name fico" +command="01_run_ablation_classification.py --nreps 1 --config mdi_local.real_data_classification --split_seed 1 --ignore_cache --create_rmd --result_name diabetes_classification" # Execute the command python $command \ No newline at end of file diff --git a/feature_importance/01_ablation_regression_script.sh b/feature_importance/01_ablation_regression_script.sh index 6686f85..f66cb31 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 satellite_image" +command="01_run_ablation_regression.py --nreps 1 --config mdi_local.real_data_regression --split_seed ${1} --ignore_cache --create_rmd --result_name ca_housing_new" # Execute the command python $command \ 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 adf1199..69a00e8 100644 --- a/feature_importance/01_run_ablation_classification.py +++ b/feature_importance/01_run_ablation_classification.py @@ -137,21 +137,28 @@ def compare_estimators(estimators: List[ModelConfig], y_tune = y y_test = y - normalizer = preprocessing.Normalizer() - if splitting_strategy == "train-test": - X_train = normalizer.fit_transform(X_train) - X_test = normalizer.transform(X_test) - else: - X = normalizer.fit_transform(X) - X_train = normalizer.transform(X_train) - X_test = normalizer.transform(X_test) + # normalizer = preprocessing.Normalizer() + # if splitting_strategy == "train-test": + # X_train = normalizer.fit_transform(X_train) + # X_test = normalizer.transform(X_test) + # else: + # X = normalizer.fit_transform(X) + # X_train = normalizer.transform(X_train) + # X_test = normalizer.transform(X_test) - # fit model + # fit RF model est.fit(X_train, y_train) - test_all_auc = roc_auc_score(y_test, est.predict_proba(X_test)[:, 1]) - test_all_auprc = average_precision_score(y_test, est.predict_proba(X_test)[:, 1]) - test_all_f1 = f1_score(y_test, est.predict_proba(X_test)[:, 1] > 0.5) + 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) + + # fit RF_plus model + rf_plus_base = RandomForestPlusClassifier(rf_model=est) + rf_plus_base.fit(X_train, y_train) + 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) np.random.seed(42) indices_train = np.random.choice(X_train.shape[0], 100, replace=False) @@ -173,9 +180,12 @@ def compare_estimators(estimators: List[ModelConfig], 'test_size': X_test.shape[0], 'num_features': X_train.shape[1], 'data_split_seed': args.split_seed, - 'test_all_auc': test_all_auc, - 'test_all_auprc': test_all_auprc, - 'test_all_f1': test_all_f1 + 'test_all_auc_rf': test_all_auc_rf, + 'test_all_auprc_rf': test_all_auprc_rf, + 'test_all_f1_rf': test_all_f1_rf, + '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, } for i in range(100): metric_results[f'sample_train_{i}'] = indices_train[i] @@ -183,24 +193,21 @@ def compare_estimators(estimators: List[ModelConfig], for i in range(len(seeds)): metric_results[f'ablation_seed_{i}'] = seeds[i] start = time.time() - local_fi_score_train_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, - fit=copy.deepcopy(est), data_fit_on="train_subset", **fi_est.kwargs) - if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]: - local_fi_score_test = 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, - fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs) + if fi_est.name.split("_")[-1] == "plus": + 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_subset=X_test_subset, y_test_subset=y_test_subset, + X_test=X_test, y_test=y_test, + fit=rf_plus_base, **fi_est.kwargs) else: - local_fi_score_test = None - 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_subset, y_test=y_test_subset, - fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs) + 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_subset=X_test_subset, y_test_subset=y_test_subset, + X_test=X_test, y_test=y_test, + fit=copy.deepcopy(est), **fi_est.kwargs) end = time.time() metric_results['fi_time'] = end - start - # feature_importance_list.append(local_fi_score_train_subset) + feature_importance_list.append(local_fi_score_train_subset) feature_importance_list.append(local_fi_score_test) feature_importance_list.append(local_fi_score_test_subset) @@ -208,13 +215,14 @@ def compare_estimators(estimators: List[ModelConfig], "LogisticCV": LogisticRegressionCV(random_state=42), "SVM": SVC(random_state=42, probability=True), "XGBoost_Classifier": xgb.XGBClassifier(random_state=42), - "RF_Plus_Classifier": RandomForestPlusClassifier(rf_model=RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42))} - + "RF_Plus_Classifier": rf_plus_base} + 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] - ablation_est.fit(X_train, y_train) + if a_model != "RF_Plus_Classifier": + ablation_est.fit(X_train, y_train) y_pred = ablation_est.predict_proba(X_train_subset)[:, 1] metric_results[a_model+'_train_subset_AUROC_before_ablation'] = roc_auc_score(y_train_subset, y_pred) metric_results[a_model+'_train_subset_AUPRC_before_ablation'] = average_precision_score(y_train_subset, y_pred) @@ -241,15 +249,19 @@ def compare_estimators(estimators: List[ModelConfig], 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 + # Test data ablation # Subset test data ablation for all FI methods - removal start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + if a_model != "RF_Plus_Classifier": + ablation_est.fit(X_train, y_train) y_pred_subset = ablation_est.predict_proba(X_test_subset)[:, 1] metric_results[a_model+'_test_subset_AUROC_before_ablation'] = roc_auc_score(y_test_subset, y_pred_subset) metric_results[a_model+'_test_subset_AUPRC_before_ablation'] = average_precision_score(y_test_subset, y_pred_subset) @@ -276,6 +288,8 @@ def compare_estimators(estimators: List[ModelConfig], 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 @@ -284,7 +298,8 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + if a_model != "RF_Plus_Classifier": + ablation_est.fit(X_train, y_train) metric_results[a_model+'_test_subset_AUROC_before_ablation_blank'] = roc_auc_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape))) metric_results[a_model+'_test_subset_AUPRC_before_ablation_blank'] = average_precision_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape))) metric_results[a_model+'_test_subset_F1_before_ablation_blank'] = f1_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)) > 0.5) @@ -310,6 +325,8 @@ def compare_estimators(estimators: List[ModelConfig], 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 @@ -318,7 +335,8 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + if a_model != "RF_Plus_Classifier": + ablation_est.fit(X_train, y_train) y_pred = ablation_est.predict_proba(X_test)[:, 1] metric_results[a_model+'_test_AUROC_before_ablation'] = roc_auc_score(y_test, y_pred) metric_results[a_model+'_test_AUPRC_before_ablation'] = average_precision_score(y_test, y_pred) diff --git a/feature_importance/01_run_ablation_regression.py b/feature_importance/01_run_ablation_regression.py index 85e3163..17f2495 100644 --- a/feature_importance/01_run_ablation_regression.py +++ b/feature_importance/01_run_ablation_regression.py @@ -135,19 +135,25 @@ def compare_estimators(estimators: List[ModelConfig], y_train = y y_test = y - normalizer = preprocessing.Normalizer() - if splitting_strategy == "train-test": - X_train = normalizer.fit_transform(X_train) - X_test = normalizer.transform(X_test) - else: - X = normalizer.fit_transform(X) - X_train = normalizer.transform(X_train) - X_test = normalizer.transform(X_test) - - # fit model + # normalizer = preprocessing.Normalizer() + # if splitting_strategy == "train-test": + # X_train = normalizer.fit_transform(X_train) + # X_test = normalizer.transform(X_test) + # else: + # X = normalizer.fit_transform(X) + # X_train = normalizer.transform(X_train) + # X_test = normalizer.transform(X_test) + + # fit RF model est.fit(X_train, y_train) - test_all_mse = mean_squared_error(y_test, est.predict(X_test)) - test_all_r2 = r2_score(y_test, est.predict(X_test)) + 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)) + + # fit RF_plus model + rf_plus_base = RandomForestPlusRegressor(rf_model=est) + rf_plus_base.fit(X_train, y_train) + 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)) np.random.seed(42) indices_train = np.random.choice(X_train.shape[0], 100, replace=False) @@ -169,8 +175,10 @@ def compare_estimators(estimators: List[ModelConfig], 'test_size': X_test.shape[0], 'num_features': X_train.shape[1], 'data_split_seed': args.split_seed, - 'test_all_mse': test_all_mse, - 'test_all_r2': test_all_r2 + '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, } for i in range(100): metric_results[f'sample_train_{i}'] = indices_train[i] @@ -178,37 +186,35 @@ def compare_estimators(estimators: List[ModelConfig], for i in range(len(seeds)): metric_results[f'ablation_seed_{i}'] = seeds[i] start = time.time() - local_fi_score_train_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, - fit=copy.deepcopy(est), data_fit_on="train_subset", **fi_est.kwargs) - if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]: - local_fi_score_test = 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, - fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs) + if fi_est.name.split("_")[-1] == "plus": + 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_subset=X_test_subset, y_test_subset=y_test_subset, + X_test=X_test, y_test=y_test, + fit=rf_plus_base, **fi_est.kwargs) else: - local_fi_score_test = None - 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_subset, y_test=y_test_subset, - fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs) + 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_subset=X_test_subset, y_test_subset=y_test_subset, + X_test=X_test, y_test=y_test, + fit=copy.deepcopy(est), **fi_est.kwargs) end = time.time() metric_results['fi_time'] = end - start - # feature_importance_list.append(local_fi_score_train_subset) + feature_importance_list.append(local_fi_score_train_subset) feature_importance_list.append(local_fi_score_test) feature_importance_list.append(local_fi_score_test_subset) ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=42), "Linear": LinearRegression(), "XGB_Regressor": xgb.XGBRegressor(random_state=42), - "RF_Plus_Regressor":RandomForestPlusRegressor(rf_model=RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=42))} + "RF_Plus_Regressor": rf_plus_base} # Subset Train data ablation for all FI methods start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + 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) @@ -238,7 +244,8 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + 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) @@ -268,7 +275,8 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + 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) @@ -297,7 +305,8 @@ def compare_estimators(estimators: List[ModelConfig], start = time.time() for a_model in ablation_models: ablation_est = ablation_models[a_model] - ablation_est.fit(X_train, y_train) + if a_model != "RF_Plus_Regressor": + 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