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"stream", - "text": [ - "fetching fico from imodels\n" - ] - } - ], - "source": [ - "X, y, feature_names = imodels.get_clean_dataset(\"fico\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10459,)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# Save X to csv file where column name is feature name\n", - "import pandas as pd\n", - "df = pd.DataFrame(X, columns=feature_names)\n", - "df.to_csv(\"X_fico.csv\", index=False)\n", - "\n", - "# Save y to csv file\n", - "df = pd.DataFrame(y, columns=[\"target\"])\n", - "df.to_csv(\"y_fico.csv\", index=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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...........................
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" - ], - "text/plain": [ - " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", - "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", - "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", - "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", - "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", - "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", - "... ... ... ... ... ... ... ... \n", - "20635 1.5603 25.0 5.045455 1.133333 845.0 2.560606 39.48 \n", - "20636 2.5568 18.0 6.114035 1.315789 356.0 3.122807 39.49 \n", - "20637 1.7000 17.0 5.205543 1.120092 1007.0 2.325635 39.43 \n", - "20638 1.8672 18.0 5.329513 1.171920 741.0 2.123209 39.43 \n", - "20639 2.3886 16.0 5.254717 1.162264 1387.0 2.616981 39.37 \n", - "\n", - " Longitude \n", - "0 -122.23 \n", - "1 -122.22 \n", - "2 -122.24 \n", - "3 -122.25 \n", - "4 -122.25 \n", - "... ... \n", - "20635 -121.09 \n", - "20636 -121.21 \n", - "20637 -121.22 \n", - "20638 -121.32 \n", - "20639 -121.24 \n", - "\n", - "[20640 rows x 8 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "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": "3.11.4" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/regression_data/CA_housing/X_california_housing.csv b/data/regression_data/CA_housing/X_california_housing.csv deleted file mode 100644 index 74fd85b..0000000 --- a/data/regression_data/CA_housing/X_california_housing.csv +++ /dev/null @@ -1,20641 +0,0 @@ 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-3.9403,2.3048,2.4621,4.8064,0.981,2.4694,0.6315,1.3679,2.28,1.9258,1.6994,6.3027,3.2376,1.5978,1.3292,3.138,5.6824,4.4884,3.6745,1.4692,3.2014,1.7524,2.8592,2.2778 -2.8308,0.5376,1.6499,0.8278,0.07841,1.8817,0.5167,0.5146,0,0.4998,0.1102,0.8524,0.672,0.5306,0.0179,0.241,5.5701,4.1169,NA,0.2007,0.7847,0.6228,1.3881,0.4073 -4.4719,1.2285,1.1688,0.7315,1.1717,NA,0.8005,1.0596,0.4156,NA,1.013,1.5942,NA,0.5818,1.1176,0.5641,4.4743,4.6473,0.5897,0.8454,0.6762,0.8432,3.5125,1.1891 -2.3161,0.1118,0,0.04301,0.03998,1.6931,0,0.2425,3.0902,0.3953,0.08222,0,0.06974,0.04209,0.2342,0.02117,2.1929,4.2563,1.5955,0.7984,0.3207,1.1268,0.02054,0.4616 -2.8089,1.6792,0.6744,0.3814,0.2241,2.7471,0.6825,0.3027,0.8415,0.881,0.1827,0.6933,0.8982,0.7483,0.3406,0.2444,4.7665,3.9046,1.9941,1.0101,1.6411,1.28,2.4799,1.4304 -2.0498,0.5949,0.6355,0.1949,0.258,NA,0.2596,0.3845,0,0.1649,0.01026,0.9822,0,0.2841,0,0.1145,3.7749,3.29,1.4015,0.09018,1.1179,0.4967,1.1671,0.7451 -3.4983,0.394,0.4455,0.4868,0.3731,3.2084,0,0.3257,0.4652,0.1259,0.3219,0.4013,0.2306,0.164,0.04508,0.1927,6.3818,3.9949,1.2236,0.1094,0.01591,0.5697,3.3175,1.3101 diff --git a/feature_importance/01_ablation_regression_script.sh b/feature_importance/01_ablation_regression_script.sh index f66cb31..7132c54 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 ca_housing_new" +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" # 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 c89702e..b297966 100755 --- a/feature_importance/01_ablation_script.sh +++ b/feature_importance/01_ablation_script.sh @@ -2,7 +2,7 @@ slurm_script="01_ablation_regression_script.sh" -for rep in {1..10} +for rep in {1..2} 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 69a00e8..673556e 100644 --- a/feature_importance/01_run_ablation_classification.py +++ b/feature_importance/01_run_ablation_classification.py @@ -22,7 +22,7 @@ from sklearn.linear_model import LogisticRegressionCV from sklearn.svm import SVC import xgboost as xgb -from imodels.importance import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -66,7 +66,7 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): """ train_mean = np.mean(train, axis=0) assert mode in ["max", "min"] - fi = feature_importance.to_numpy() + fi = feature_importance if mode == "max": indices = np.argsort(-fi) else: @@ -82,7 +82,7 @@ def ablation_by_addition(data, feature_importance, mode, num_features): Initialize the data with zeros and add the top num_features max feature importance data for each sample """ assert mode in ["max", "min"] - fi = feature_importance.to_numpy() + fi = feature_importance if mode == "max": indices = np.argsort(-fi) else: @@ -137,16 +137,6 @@ 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) - - # fit RF model est.fit(X_train, y_train) test_all_auc_rf = roc_auc_score(y_test, est.predict_proba(X_test)[:, 1]) @@ -193,18 +183,30 @@ def compare_estimators(estimators: List[ModelConfig], for i in range(len(seeds)): metric_results[f'ablation_seed_{i}'] = seeds[i] start = time.time() - 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, + 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, y_test_subset=y_test_subset, - X_test=X_test, y_test=y_test, + X_test_subset=X_test_subset, X_test=X_test, fit=rf_plus_base, **fi_est.kwargs) - else: + 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": + 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=copy.deepcopy(est), **fi_est.kwargs) + 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": 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, + X_test_subset=X_test_subset, X_test=X_test, fit=copy.deepcopy(est), **fi_est.kwargs) + elif fi_est.name == "Kernel_SHAP_RF_plus" or fi_est.name == "LIME_RF_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, X_test=X_test, + fit=rf_plus_base, **fi_est.kwargs) end = time.time() metric_results['fi_time'] = end - start feature_importance_list.append(local_fi_score_train_subset) diff --git a/feature_importance/01_run_ablation_regression.py b/feature_importance/01_run_ablation_regression.py index 17f2495..0806102 100644 --- a/feature_importance/01_run_ablation_regression.py +++ b/feature_importance/01_run_ablation_regression.py @@ -21,7 +21,7 @@ from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression import xgboost as xgb -from imodels.importance import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier sys.path.append(".") sys.path.append("..") sys.path.append("../..") @@ -65,7 +65,7 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features): """ train_mean = np.mean(train, axis=0) assert mode in ["max", "min"] - fi = feature_importance.to_numpy() + fi = feature_importance if mode == "max": indices = np.argsort(-fi) else: @@ -81,7 +81,7 @@ def ablation_by_addition(data, feature_importance, mode, num_features): Initialize the data with zeros and add the top num_features max feature importance data for each sample """ assert mode in ["max", "min"] - fi = feature_importance.to_numpy() + fi = feature_importance if mode == "max": indices = np.argsort(-fi) else: @@ -135,15 +135,6 @@ 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 RF model est.fit(X_train, y_train) test_all_mse_rf = mean_squared_error(y_test, est.predict(X_test)) @@ -162,6 +153,7 @@ 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() @@ -186,18 +178,32 @@ def compare_estimators(estimators: List[ModelConfig], for i in range(len(seeds)): metric_results[f'ablation_seed_{i}'] = seeds[i] start = time.time() - 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, + 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, y_test_subset=y_test_subset, - X_test=X_test, y_test=y_test, + X_test_subset=X_test_subset, X_test=X_test, fit=rf_plus_base, **fi_est.kwargs) - else: + 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": + 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=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": 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, + X_test_subset=X_test_subset, X_test=X_test, fit=copy.deepcopy(est), **fi_est.kwargs) + elif fi_est.name == "Kernel_SHAP_RF_plus" or fi_est.name == "LIME_RF_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, X_test=X_test, + fit=rf_plus_base, **fi_est.kwargs) end = time.time() metric_results['fi_time'] = end - start feature_importance_list.append(local_fi_score_train_subset) diff --git a/feature_importance/fi_config/mdi_local/real_data_classification/dgp.py b/feature_importance/fi_config/mdi_local/real_data_classification/dgp.py index 5833778..a4c7686 100644 --- a/feature_importance/fi_config/mdi_local/real_data_classification/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_classification/dgp.py @@ -3,35 +3,18 @@ from feature_importance.scripts.simulations_util import * -X_DGP = sample_real_data -# X_PARAMS_DICT = { -# "X_fpath": "../data/classification_data/Diabetes/X_diabetes.csv", -# "sample_row_n": None, -# "return_data": "X" -# } +X_DGP = sample_real_data_X X_PARAMS_DICT = { - "X_fpath": "../data/classification_data/Fico/X_fico.csv", - "sample_row_n": None, - "return_data": "X" + "source": "imodels", + "data_name": "diabetes", + "sample_row_n": None } -# X_PARAMS_DICT = { -# "X_fpath": "../data/classification_data/Juvenile/X_juvenile.csv", -# "sample_row_n": None, -# "return_data": "X" -# } -Y_DGP = sample_real_data -# Y_PARAMS_DICT = { -# "y_fpath": "../data/classification_data/Diabetes/y_diabetes.csv", -# "return_data": "y" -# } + +Y_DGP = sample_real_data_y Y_PARAMS_DICT = { - "y_fpath": "../data/classification_data/Fico/y_fico.csv", - "return_data": "y" + "source": "imodels", + "data_name": "diabetes" } -# Y_PARAMS_DICT = { -# "y_fpath": "../data/classification_data/Juvenile/y_juvenile.csv", -# "return_data": "y" -# } # vary one parameter VARY_PARAM_NAME = "sample_row_n" diff --git a/feature_importance/fi_config/mdi_local/real_data_classification/models.py b/feature_importance/fi_config/mdi_local/real_data_classification/models.py index fef07db..075ba9c 100644 --- a/feature_importance/fi_config/mdi_local/real_data_classification/models.py +++ b/feature_importance/fi_config/mdi_local/real_data_classification/models.py @@ -4,24 +4,21 @@ # from sklearn.utils.extmath import softmax from feature_importance.util import ModelConfig, FIModelConfig from sklearn.ensemble import RandomForestClassifier -from imodels.importance.rf_plus import RandomForestPlusClassifier from feature_importance.scripts.competing_methods_local import * - +from sklearn.linear_model import Ridge ESTIMATORS = [ - [ModelConfig('RF', RandomForestClassifier, model_type='', - other_params={'n_estimators': 100, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'random_state': 42})], - # [ModelConfig('RF_plus', RandomForestPlusClassifier, model_type='t_plus', - # other_params={'rf_model': RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42)})] + [ModelConfig('RF', RandomForestClassifier, model_type='tree', + other_params={'n_estimators': 100, 'min_samples_leaf': 3, 'max_features': 'sqrt', 'random_state': 42})], ] FI_ESTIMATORS = [ - # [FIModelConfig('LFI_with_raw_RF', LFI_evaluation_RF, model_type='tree', splitting_strategy = "train-test")], - [FIModelConfig('MDI_RF', LFI_evaluation_RF, model_type='tree', splitting_strategy = "train-test", other_params={"include_raw": False, "cv_ridge": 0, "calc_loo_coef":False, "sample_split":"inbag"})], - [FIModelConfig('LFI_with_raw_OOB_RF', LFI_evaluation_RF, model_type='tree', splitting_strategy = "train-test", other_params={"sample_split":"oob", "fit_on":"test", "calc_loo_coef":False})], [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF, model_type='tree', splitting_strategy = "train-test")], - [FIModelConfig('LFI_with_raw_RF_plus', LFI_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], + [FIModelConfig('LFI_fit_on_inbag_RF', LFI_evaluation_RF_MDI, model_type='tree', splitting_strategy = "train-test", other_params={"include_raw":False, "fit_on":"inbag", "prediction_model": Ridge(alpha=1e-6)})], + [FIModelConfig('LFI_fit_on_OOB_RF', LFI_evaluation_RF_OOB, model_type='tree', splitting_strategy = "train-test", other_params={"fit_on":"oob"})], + [FIModelConfig('LFI_evaluate_on_all_RF_plus', LFI_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], + [FIModelConfig('LFI_evaluate_on_oob_RF_plus', LFI_evaluation_RF_plus_OOB, model_type='tree', splitting_strategy = "train-test")], [FIModelConfig('Kernel_SHAP_RF_plus', kernel_shap_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], [FIModelConfig('LIME_RF_plus', lime_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], ] \ No newline at end of file diff --git a/feature_importance/fi_config/mdi_local/real_data_regression/dgp.py b/feature_importance/fi_config/mdi_local/real_data_regression/dgp.py index d593004..2e334f0 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression/dgp.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression/dgp.py @@ -3,35 +3,18 @@ from feature_importance.scripts.simulations_util import * -X_DGP = sample_real_data +X_DGP = sample_real_data_X X_PARAMS_DICT = { - "X_fpath": "../data/regression_data/CA_housing/X_california_housing.csv", - "sample_row_n": None, - "return_data": "X" + "source": "imodels", + "data_name": "diabetes_regr", + "sample_row_n": None } -# X_PARAMS_DICT = { -# "X_fpath": "../data/regression_data/Diabetes_regression/X_diabetes_regression.csv", -# "sample_row_n": None, -# "return_data": "X" -# } -# X_PARAMS_DICT = { -# "X_fpath": "../data/regression_data/Satellite_image/X_satellite_image.csv", -# "sample_row_n": None, -# "return_data": "X" -# } -Y_DGP = sample_real_data + +Y_DGP = sample_real_data_y Y_PARAMS_DICT = { - "y_fpath": "../data/regression_data/CA_housing/y_california_housing.csv", - "return_data": "y" + "source": "imodels", + "data_name": "diabetes_regr" } -# Y_PARAMS_DICT = { -# "y_fpath": "../data/regression_data/Diabetes_regression/y_diabetes_regression.csv", -# "return_data": "y" -# } -# Y_PARAMS_DICT = { -# "y_fpath": "../data/regression_data/Satellite_image/y_satellite_image.csv", -# "return_data": "y" -# } # 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/models.py b/feature_importance/fi_config/mdi_local/real_data_regression/models.py index 758423c..8dd425a 100644 --- a/feature_importance/fi_config/mdi_local/real_data_regression/models.py +++ b/feature_importance/fi_config/mdi_local/real_data_regression/models.py @@ -4,24 +4,21 @@ # from sklearn.utils.extmath import softmax from feature_importance.util import ModelConfig, FIModelConfig from sklearn.ensemble import RandomForestRegressor -from imodels.importance.rf_plus import RandomForestPlusRegressor 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})], - # [ModelConfig('RF_plus', RandomForestPlusRegressor, model_type='t_plus', - # other_params={'rf_model': RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)})], + other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})] ] FI_ESTIMATORS = [ - #[FIModelConfig('LFI_with_raw_CV_RF', LFI_test_evaluation_RF, model_type='tree', splitting_strategy = "train-test", other_params={"cv_ridge": 5, "calc_loo_coef":False})], - [FIModelConfig('MDI_RF', LFI_evaluation_RF, model_type='tree', splitting_strategy = "train-test", other_params={"include_raw": False, "cv_ridge": 0, "calc_loo_coef":False, "sample_split":"inbag"})], - [FIModelConfig('LFI_with_raw_OOB_RF', LFI_evaluation_RF, model_type='tree', splitting_strategy = "train-test", other_params={"sample_split":"oob", "fit_on":"test", "calc_loo_coef":False})], [FIModelConfig('TreeSHAP_RF', tree_shap_evaluation_RF, model_type='tree', splitting_strategy = "train-test")], - [FIModelConfig('LFI_with_raw_RF_plus', LFI_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], + [FIModelConfig('LFI_fit_on_inbag_RF', LFI_evaluation_RF_MDI, model_type='tree', splitting_strategy = "train-test", other_params={"include_raw":False, "fit_on":"inbag", "prediction_model": Ridge(alpha=1e-6)})], + [FIModelConfig('LFI_fit_on_OOB_RF', LFI_evaluation_RF_OOB, model_type='tree', splitting_strategy = "train-test", other_params={"fit_on":"oob"})], + [FIModelConfig('LFI_evaluate_on_all_RF_plus', LFI_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], + [FIModelConfig('LFI_evaluate_on_oob_RF_plus', LFI_evaluation_RF_plus_OOB, model_type='tree', splitting_strategy = "train-test")], [FIModelConfig('Kernel_SHAP_RF_plus', kernel_shap_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], [FIModelConfig('LIME_RF_plus', lime_evaluation_RF_plus, model_type='tree', splitting_strategy = "train-test")], ] \ No newline at end of file diff --git a/feature_importance/scripts/competing_methods_local.py b/feature_importance/scripts/competing_methods_local.py deleted file mode 100644 index 7b815d3..0000000 --- a/feature_importance/scripts/competing_methods_local.py +++ /dev/null @@ -1,530 +0,0 @@ -import os -import sys -import pandas as pd -import numpy as np -import sklearn.base -from sklearn.base import RegressorMixin, ClassifierMixin -from sklearn.metrics import mean_squared_error -from functools import reduce - -import shap -import lime -import lime.lime_tabular -from imodels.importance.rf_plus import RandomForestPlusRegressor, RandomForestPlusClassifier -from imodels.importance.rf_plus import _fast_r2_score -from sklearn.metrics import r2_score, mean_absolute_error, accuracy_score, roc_auc_score, mean_squared_error - - -# def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# """ -# Compute local MDI importance for each feature and sample. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :return: dataframe of shape: (n_samples, n_features) - -# """ -# num_samples, num_features = X.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X, y) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "sub", lfi=False)["local"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# """ -# Wrapper around MDI+ object to get feature importance scores - -# :param X: ndarray of shape (n_samples, n_features) -# The covariate matrix. If a pd.DataFrame object is supplied, then -# the column names are used in the output -# :param y: ndarray of shape (n_samples, n_targets) -# The observed responses. -# :param rf_model: scikit-learn random forest object or None -# The RF model to be used for interpretation. If None, then a new -# RandomForestRegressor or RandomForestClassifier is instantiated. -# :param kwargs: additional arguments to pass to -# RandomForestPlusRegressor or RandomForestPlusClassifier class. -# :return: dataframe - [Var, Importance] -# Var: variable name -# Importance: MDI+ score -# """ -# num_samples, num_features = X.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X, y) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "all", lfi=False)["local"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - - -# def LFI_absolute_sum(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# num_samples, num_features = X.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X, y) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="outside")["lfi"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def lime_local(X, y, fit): -# """ -# Compute LIME local importance for each feature and sample. -# Larger values indicate more important features. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :return: dataframe of shape: (n_samples, n_features) - -# """ - -# np.random.seed(1) -# num_samples, num_features = X.shape -# result = np.zeros((num_samples, num_features)) -# explainer = lime.lime_tabular.LimeTabularExplainer(X, verbose=False, mode='regression') -# for i in range(num_samples): -# exp = explainer.explain_instance(X[i], fit.predict, num_features=num_features) -# original_feature_importance = exp.as_map()[1] -# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) -# for j in range(num_features): -# result[i,j] = abs(sorted_feature_importance[j][1]) -# # Convert the array to a DataFrame -# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def tree_shap_local(X, y, fit): -# """ -# Compute average treeshap value across observations. -# Larger absolute values indicate more important features. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :return: dataframe of shape: (n_samples, n_features) -# """ -# explainer = shap.TreeExplainer(fit) -# shap_values = explainer.shap_values(X, check_additivity=False) -# if sklearn.base.is_classifier(fit): -# # Shape values are returned as a list of arrays, one for each class -# def add_abs(a, b): -# return abs(a) + abs(b) -# results = np.sum(np.abs(shap_values),axis=-1) -# else: -# results = abs(shap_values) -# result_table = pd.DataFrame(results, columns=[f'Feature_{i}' for i in range(X.shape[1])]) - -# return result_table - -# def permutation_local(X, y, fit, num_permutations=100): -# """ -# Compute local permutation importance for each feature and sample. -# Larger values indicate more important features. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :num_permutations: Number of permutations for each feature (default is 100) -# :return: dataframe of shape: (n_samples, n_features) -# """ - -# # Get the number of samples and features -# num_samples, num_features = X.shape - -# # Initialize array to store local permutation importance -# lpi = np.zeros((num_samples, num_features)) - -# # For each feature -# for k in range(num_features): -# # Permute X_k num_permutations times -# for b in range(num_permutations): -# X_permuted = X.copy() -# X_permuted[:, k] = np.random.permutation(X[:, k]) - -# # Feed permuted data through the fitted model -# y_pred_permuted = fit.predict(X_permuted) - -# # Calculate MSE for each sample -# for i in range(num_samples): -# lpi[i, k] += (y[i]-y_pred_permuted[i])**2 - -# lpi /= num_permutations - -# # Convert the array to a DataFrame -# result_table = pd.DataFrame(lpi, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -def LFI_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): - if isinstance(fit, RegressorMixin): - RFPlus = RandomForestPlusRegressor - elif isinstance(fit, ClassifierMixin): - RFPlus = RandomForestPlusClassifier - else: - raise ValueError("Unknown task.") - - rf_plus_model = RFPlus(rf_model=fit, **kwargs) - rf_plus_model.fit(X_train, y_train) - - subsets = [(X_train_subset, y_train_subset), (X_test, y_test), (X_test_subset, y_test_subset)] - result_tables = [] - - for X, y in subsets: - num_samples, num_features = X.shape - abs_lfi_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="none", sample_split=None, train_or_test="test")["lfi"].values - abs_lfi_scores = np.abs(abs_lfi_scores) - result_table = pd.DataFrame(abs_lfi_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - result_tables.append(result_table) - return tuple(result_tables) - -def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit): - """ - Compute LIME local importance for each feature and sample. - Larger values indicate more important features. - :param X: design matrix - :param y: response - :param fit: fitted model of interest (tree-based) - :return: dataframe of shape: (n_samples, n_features) - - """ - np.random.seed(1) - if isinstance(fit, RegressorMixin): - mode = 'regression' - elif isinstance(fit, ClassifierMixin): - mode = 'classification' - - def compute_result_table(data, fit, mode, num_features): - result = np.zeros((len(data), num_features)) - explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose=False, mode=mode) - - if mode == 'classification' and not hasattr(fit, 'predict_proba'): - raise ValueError("Classifier model must have predict_proba method") - - for i, sample in enumerate(data): - if mode == 'classification': - predict_fn = fit.predict_proba - else: - predict_fn = fit.predict - exp = explainer.explain_instance(sample, predict_fn, num_features=num_features) - original_feature_importance = exp.as_map()[1] - sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) - for j in range(num_features): - result[i, j] = abs(sorted_feature_importance[j][1]) - return pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) - - result_table_train_subset = compute_result_table(X_train_subset, fit, mode, X_train_subset.shape[1]) - result_table_test = compute_result_table(X_test, fit, mode, X_test.shape[1]) - result_table_test_subset = compute_result_table(X_test_subset, fit, mode, X_test_subset.shape[1]) - - return result_table_train_subset, result_table_test, result_table_test_subset - -def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit): - """ - Compute average treeshap value across observations. - Larger absolute values indicate more important features. - :param X: design matrix - :param y: response - :param fit: fitted model of interest (tree-based) - :return: dataframe of shape: (n_samples, n_features) - """ - def add_abs(a, b): - return abs(a) + abs(b) - - subsets = [(X_train_subset, y_train_subset), (X_test, y_test), (X_test_subset, y_test_subset)] - result_tables = [] - - explainer = shap.TreeExplainer(fit) - - for X, y in subsets: - shap_values = explainer.shap_values(X, check_additivity=False) - if sklearn.base.is_classifier(fit): - # Shape values are returned as a list of arrays, one for each class - results = np.sum(np.abs(shap_values), axis=-1) - else: - results = np.abs(shap_values) - - result_table = pd.DataFrame(results, columns=[f'Feature_{i}' for i in range(X.shape[1])]) - result_tables.append(result_table) - - return tuple(result_tables) - -def lime_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit): - subsets = [(X_train_subset, y_train_subset), (X_test_subset, y_test_subset)] - result_tables = [] - - for X, y in subsets: - num_samples, num_features = X.shape - lime_scores = fit.get_lime_scores(X_train, X_test_subset).values - result_table = pd.DataFrame(lime_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - result_tables.append(result_table) - - result_table_train_subset, result_table_test_subset = result_tables - - return result_table_train_subset, None, result_table_test_subset - - -def kernel_shap_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit): - subsets = [(X_train_subset, y_train_subset), (X_test_subset, y_test_subset)] - result_tables = [] - - for X, y in subsets: - num_samples, num_features = X.shape - kernel_shap_scores = fit.get_kernel_shap_scores(X_train, X) - result_table = pd.DataFrame(kernel_shap_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - result_tables.append(result_table) - - result_table_train_subset, result_table_test_subset = result_tables - - return result_table_train_subset, None, result_table_test_subset - - -def LFI_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit): - subsets = [(X_train_subset, y_train_subset), (X_test, y_test), (X_test_subset, y_test_subset)] - result_tables = [] - - for X, y in subsets: - num_samples, num_features = X.shape - abs_lfi_scores = fit.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="none", sample_split=None, train_or_test="test")["lfi"].values - abs_lfi_scores = np.abs(abs_lfi_scores) - result_table = pd.DataFrame(abs_lfi_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - result_tables.append(result_table) - - return tuple(result_tables) - - - -# def MDI_local_sub_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# """ -# Compute local MDI importance for each feature and sample. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :return: dataframe of shape: (n_samples, n_features) - -# """ -# num_samples, num_features = X_test.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X_train, y_train) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "sub", lfi=False, sample_split=None)["local"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def lime_local(X_train, y_train, X_test, y_test, fit): -# """ -# Compute LIME local importance for each feature and sample. -# Larger values indicate more important features. -# :param X: design matrix -# :param y: response -# :param fit: fitted model of interest (tree-based) -# :return: dataframe of shape: (n_samples, n_features) - -# """ -# if isinstance(fit, RegressorMixin): -# mode='regression' -# elif isinstance(fit, ClassifierMixin): -# mode='classification' -# np.random.seed(1) -# num_samples, num_features = X_test.shape -# result = np.zeros((num_samples, num_features)) -# explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose=False, mode=mode) -# for i in range(num_samples): -# exp = explainer.explain_instance(X_test[i], fit.predict, num_features=num_features) -# original_feature_importance = exp.as_map()[1] -# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) -# for j in range(num_features): -# result[i,j] = abs(sorted_feature_importance[j][1]) -# # Convert the array to a DataFrame -# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def MDI_local_all_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# """ -# Wrapper around MDI+ object to get feature importance scores - -# :param X: ndarray of shape (n_samples, n_features) -# The covariate matrix. If a pd.DataFrame object is supplied, then -# the column names are used in the output -# :param y: ndarray of shape (n_samples, n_targets) -# The observed responses. -# :param rf_model: scikit-learn random forest object or None -# The RF model to be used for interpretation. If None, then a new -# RandomForestRegressor or RandomForestClassifier is instantiated. -# :param kwargs: additional arguments to pass to -# RandomForestPlusRegressor or RandomForestPlusClassifier class. -# :return: dataframe - [Var, Importance] -# Var: variable name -# Importance: MDI+ score -# """ -# num_samples, num_features = X_test.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X_train, y_train) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "all", lfi=False, sample_split=None)["local"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def LFI_ablation_test_evaluation(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# num_samples, num_features = X_test.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X_train, y_train) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="none", sample_split=None, train_or_test = "test")["lfi"].values -# mdi_plus_scores = np.abs(mdi_plus_scores) -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -######################## Considering not using these methods -# def LFI_sum_absolute_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# num_samples, num_features = X_test.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X_train, y_train) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="inside", sample_split=None)["lfi"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table - -# def LFI_sum_absolute(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): -# num_samples, num_features = X.shape -# if isinstance(fit, RegressorMixin): -# RFPlus = RandomForestPlusRegressor -# elif isinstance(fit, ClassifierMixin): -# RFPlus = RandomForestPlusClassifier -# else: -# raise ValueError("Unknown task.") -# rf_plus_model = RFPlus(rf_model=fit, **kwargs) -# rf_plus_model.fit(X, y) - -# try: -# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="inside")["lfi"].values -# if return_stability_scores: -# raise NotImplementedError -# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) -# except ValueError as e: -# if str(e) == 'Transformer representation was empty for all trees.': -# mdi_plus_scores = np.zeros((num_samples, num_features)) -# stability_scores = None -# else: -# raise -# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) - -# return result_table \ No newline at end of file diff --git a/feature_importance/scripts/simulations_util.py b/feature_importance/scripts/simulations_util.py index 4b1380d..f772923 100644 --- a/feature_importance/scripts/simulations_util.py +++ b/feature_importance/scripts/simulations_util.py @@ -4,33 +4,42 @@ from scipy.linalg import toeplitz import warnings import math +import imodels +import openml - -def sample_real_data(X_fpath=None, y_fpath=None, seed=4307, normalize=False, - sample_row_n=None, sample_col_n=None, return_data=None, - return_support=True): - - assert return_data in ["X", "y"] +def sample_real_data_X(source=None, data_name=None, task_id=None, seed=4307, normalize=False, sample_row_n=None): np.random.seed(seed) - if return_data == "X": - X = pd.read_csv(X_fpath) - if normalize: - X = (X - X.mean()) / X.std() - if sample_row_n is not None: - keep_idx = np.random.choice(X.shape[0], sample_row_n, replace=False) - X = X.iloc[keep_idx, :] - if sample_col_n is not None: - X = X.sample(n=sample_col_n, replace=False, axis=1) - return X.to_numpy() - else: - y = pd.read_csv(y_fpath) - if sample_row_n is not None: - keep_idx = np.random.choice(y.shape[0], sample_row_n, replace=False) - y = y.iloc[keep_idx, :] - if return_support: - return y.to_numpy().flatten(), np.ones(y.shape[1]), None - return y.to_numpy().flatten() + if source == "imodels": + X, _, _ = imodels.get_clean_dataset(data_name) + elif source == "openml": + task = openml.tasks.get_task(task_id) + dataset_id = task.dataset_id + dataset = openml.datasets.get_dataset(dataset_id) + X, _, _, _ = dataset.get_data(target=dataset.default_target_attribute,dataset_format="array") + if normalize: + X = (X - X.mean()) / X.std() + if sample_row_n is not None: + keep_idx = np.random.choice(X.shape[0], sample_row_n, replace=False) + X = X.iloc[keep_idx, :] + return X + +def sample_real_data_y(X=None, source=None, data_name=None, task_id=None, + seed=4307, sample_row_n=None, return_support=True): + np.random.seed(seed) + if source == "imodels": + _, y, _ = imodels.get_clean_dataset(data_name) + elif source == "openml": + task = openml.tasks.get_task(task_id) + dataset_id = task.dataset_id + dataset = openml.datasets.get_dataset(dataset_id) + _, y, _, _ = dataset.get_data(target=dataset.default_target_attribute,dataset_format="array") + if sample_row_n is not None: + keep_idx = np.random.choice(y.shape[0], sample_row_n, replace=False) + y = y.iloc[keep_idx, :] + if return_support: + return y, np.ones(y.shape), None + return y def sample_real_X(fpath=None, X=None, seed=None, normalize=True, sample_row_n=None, sample_col_n=None, permute_col=True, diff --git a/feature_importance/util.py b/feature_importance/util.py index cb95e03..8b0b97e 100644 --- a/feature_importance/util.py +++ b/feature_importance/util.py @@ -13,7 +13,7 @@ from sklearn.preprocessing import label_binarize from sklearn.utils._encode import _unique from sklearn import metrics -from imodels.importance.ppms import huber_loss +# from imodels.importance.ppms import huber_loss DATASET_PATH = oj(dirname(os.path.realpath(__file__)), 'data') @@ -144,8 +144,8 @@ def neg_mean_absolute_error(y_true, y_pred, **kwargs): return -mean_absolute_error(y_true, y_pred, **kwargs) -def neg_huber_loss(y_true, y_pred, **kwargs): - return -huber_loss(y_true, y_pred, **kwargs) +# def neg_huber_loss(y_true, y_pred, **kwargs): +# return -huber_loss(y_true, y_pred, **kwargs) def restricted_roc_auc_score(y_true, y_score, ignored_indices=[]):