diff --git a/feature_importance/test.ipynb b/feature_importance/test.ipynb
new file mode 100644
index 0000000..c5e67e5
--- /dev/null
+++ b/feature_importance/test.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fetching diabetes from sklearn\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
+      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    3.5s\n",
+      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    9.3s finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scripts.simulations_util import *\n",
+    "from scripts.competing_methods_local import *\n",
+    "from util import apply_splitting_strategy\n",
+    "from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score, average_precision_score\n",
+    "X = sample_real_data_X(source = \"imodels\", data_name = \"diabetes_regr\", sample_row_n = 400)\n",
+    "y = linear_model(X, beta = 1, sigma = None, heritability = 0.8, s = 5)\n",
+    "X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, \"train-test\", 1)\n",
+    "np.random.seed(42)\n",
+    "indices_train = np.random.choice(X_train.shape[0], int(X_train.shape[0]*.25), replace=False)\n",
+    "indices_test = np.random.choice(X_test.shape[0], int(X_test.shape[0]*.25), replace=False)\n",
+    "X_train_subset = X_train[indices_train]\n",
+    "y_train_subset = y_train[indices_train]\n",
+    "X_test_subset = X_test[indices_test]\n",
+    "y_test_subset = y_test[indices_test]\n",
+    "# fit RF model\n",
+    "est = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 5, max_features = 0.33, random_state = 42)\n",
+    "\n",
+    "est.fit(X_train, y_train)\n",
+    "\n",
+    "# fit RF_plus model\n",
+    "rf_plus_base = RandomForestPlusRegressor(rf_model=est)\n",
+    "rf_plus_base.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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 = LFI_evaluation_RF_plus(X_train=X_train, y_train=y_train,\n",
+    "                       X_train_subset = X_train_subset, y_train_subset=y_train_subset,\n",
+    "                       X_test_subset=X_test_subset, X_test=X_test,\n",
+    "                       fit=rf_plus_base)\n",
+    "local_fi_score_train_subset = local_fi_score_train[indices_train]\n",
+    "local_partial_pred_train_subset = local_parital_pred_train[indices_train]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.6399999999999999, 1.0, 0.44000000000000006, 0.76, 0.8400000000000001, 0.4, 0.7600000000000001, 0.6399999999999999, 0.88, 0.92, 1.0, 0.28, 0.24000000000000002, 0.6400000000000001, 0.72, 0.8400000000000001, 1.0, 0.8, 0.6799999999999999, 0.4, 0.6, 0.6799999999999999, 0.7600000000000001, 1.0, 0.88, 0.52, 0.7200000000000002, 0.6799999999999999, 0.7600000000000001, 0.92, 0.28, 0.8, 0.48]\n",
+      "[0.8, 1.0, 0.6746031746031746, 0.8211111111111111, 0.911111111111111, 0.5088888888888888, 0.8599999999999999, 0.8, 0.925, 0.9428571428571428, 1.0, 0.45460317460317456, 0.5305555555555554, 0.7642857142857142, 0.8333333333333333, 0.911111111111111, 1.0, 0.8999999999999999, 0.8111111111111111, 0.6638888888888889, 0.7888888888888888, 0.7833333333333332, 0.8599999999999999, 1.0, 0.925, 0.7088888888888889, 0.81, 0.8111111111111111, 0.8599999999999999, 0.9428571428571428, 0.5412698412698412, 0.8999999999999999, 0.6888888888888889]\n",
+      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+      "0.6957575757575759\n",
+      "0.8100817700817701\n",
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "local_fi_score_test_subset = pd.DataFrame(local_fi_score_test_subset)\n",
+    "auroc = []\n",
+    "auprc = []\n",
+    "f1 = []\n",
+    "support = [1,1,1,1,1,0,0,0,0,0]\n",
+    "for rownum in range(local_fi_score_test_subset.shape[0]):\n",
+    "    auroc.append(roc_auc_score(support, local_fi_score_test_subset.iloc[rownum,:]))\n",
+    "    auprc.append(average_precision_score(support, local_fi_score_test_subset.iloc[rownum,:]))\n",
+    "    f1.append(f1_score(support, local_fi_score_test_subset.iloc[rownum,:] > 0.5))\n",
+    "print(auroc)\n",
+    "print(auprc)\n",
+    "print(f1)\n",
+    "print(np.array(auroc).mean())\n",
+    "print(np.array(auprc).mean())\n",
+    "print(np.array(f1).mean())\n",
+    "                    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}