local_diffi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretation of individual predictions with Local-DIFFI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook are reproduced the experimental results on the Local-DIFFI method described in our paper [Interpretable Anomaly Detection with DIFFI: Depth-based Feature Importance for the Isolation Forest](https://arxiv.org/pdf/2007.11117.pdf).\n",
    "\n",
    "The present code represents the very first release of the DIFFI algorithm: the code in this version is not yet optimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pickle as pkl \n",
    "import time\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.utils import shuffle\n",
    "import shap\n",
    "import interpretability_module as interp\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Glass\" dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(os.getcwd(), 'data', 'local', 'glass.pkl'), 'rb') as f:\n",
    "    data = pkl.load(f)\n",
    "# training data (inliers and outliers)\n",
    "X_tr = np.concatenate((data['X_in'], data['X_out_5'], data['X_out_6']))\n",
    "y_tr = np.concatenate((data['y_in'], data['y_out_5'], data['y_out_6']))\n",
    "X_tr, y_tr = shuffle(X_tr, y_tr, random_state=0)\n",
    "# test outliers\n",
    "X_te = data['X_out_7'] \n",
    "y_te = data['y_out_7']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "F1 score (on training data): 0.552\n"
     ]
    }
   ],
   "source": [
    "iforest = IsolationForest(n_estimators= 100, max_samples=64, contamination='auto', random_state=0, bootstrap=False)\n",
    "iforest.fit(X_tr)\n",
    "y_tr_pred = np.array(iforest.decision_function(X_tr) < 0).astype('int')\n",
    "f1 = f1_score(y_tr, y_tr_pred)\n",
    "print('\\nF1 score (on training data): {}'.format(round(f1, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get predictions and interpretations for test outliers\n",
    "We consider data points in the \"headlamps glass\" class (class 7 in the original dataset) as test outliers. We compare feature importance scores provided by SHAP with those obtained with Local-DIFFI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected anomalies (test, class 7): 28 out of 29\n"
     ]
    }
   ],
   "source": [
    "y_te_pred = np.array(iforest.decision_function(X_te) < 0).astype('int')\n",
    "print('Detected anomalies (test, class 7): {} out of {}'.format(int(sum(y_te_pred)), len(y_te)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local-DIFFI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time Local-DIFFI: 0.045\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Local-DIFFI\n",
    "diffi_te, ord_idx_diffi_te, exec_time_diffi_te = local_diffi_batch(iforest, X_te[np.where(y_te_pred == 1)])\n",
    "plot_ranking_glass(ord_idx_diffi_te, title = 'Feature ranking glass dataset class 7 outliers - Local DIFFI')\n",
    "print('Average computational time Local-DIFFI: {}'.format(round(np.mean(exec_time_diffi_te),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "The sklearn.ensemble.iforest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time SHAP: 0.318\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SHAP\n",
    "shap_te, ord_idx_shap_te, exec_time_shap_te = local_shap_batch(iforest, X_te[np.where(y_te_pred == 1)])\n",
    "plot_ranking_glass(ord_idx_shap_te, title = 'Feature ranking glass dataset class 7 outliers - SHAP')\n",
    "print('Average computational time SHAP: {}'.format(round(np.mean(exec_time_shap_te),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic dataset\n",
    "We consider data points represented by a 6-dimensional feature vector. Features $f_1$ and $f_2$ are the coordinates of the points on the 2-D plane, features $f_3$, $f_4$, $f_5$, $f_6$ are white noise samples. Inliers are concentrated near the origin, while outliers are far away from the origin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(os.getcwd(), 'data', 'local', 'syn_train.pkl'), 'rb') as f:\n",
    "    data_tr = pkl.load(f)\n",
    "with open(os.path.join(os.getcwd(), 'data', 'local', 'syn_test.pkl'), 'rb') as f:\n",
    "    data_te = pkl.load(f)\n",
    "# training data (inliers and outliers)\n",
    "X_tr = data_tr['X']\n",
    "y_tr = data_tr['y']\n",
    "# test outliers\n",
    "X_xaxis = data_te['X_xaxis']\n",
    "X_yaxis = data_te['X_yaxis']\n",
    "X_bisec = data_te['X_bisec']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "F1 score (on training data): 0.76\n"
     ]
    }
   ],
   "source": [
    "iforest = IsolationForest(n_estimators= 100, max_samples=256, contamination='auto', random_state=0, bootstrap=False)\n",
    "iforest.fit(X_tr)\n",
    "y_tr_pred = np.array(iforest.decision_function(X_tr) < 0).astype('int')\n",
    "f1 = f1_score(y_tr, y_tr_pred)\n",
    "print('\\nF1 score (on training data): {}'.format(round(f1, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get predictions and interpretations for test outliers\n",
    "We consider as test outliers the points displayed in the figure below (projected on the subspace of the relevant features, i.e. $f_1$ and $f_2$). We have:\n",
    "\n",
    "- outliers on the x-axis (blue points)\n",
    "- outliers on the y-axis (orange points)\n",
    "- outliers on the bisector (green points).\n",
    "\n",
    "We compare feature importance scores provided by SHAP with those obtained with Local-DIFFI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o8FR/xhElo8TUQeMF1w9NHtxuQzN8+CzyOp0OJ06cwIQJE6BUKjF69GhkZmYiPj4ezz77LAwGA3Q6HcaObZ+dFUTHQZ2SDnNlhVOnqi9QiYPAw5cZwyfA3qQ3sqz0SEE4wLAyO2MK17Q9crULCJ1t+q3P++eJq9TQDJ8dkiwbud5Pf+2yh2BcPfTOqihMvm2ck9gvO7Ta6zx2OdaKb4twDY14JQj4GGphbK8PE50QMoEPZ4RCMM3mFrca7+N6j0WEIsKr9sOhVrwUaGYogoBNqL0SekYJzf3/TsLehoh55vYyA3Yv3DXfXepIVtd2whESeYIAoOgxSHpMXtMZmvTpJPBtjCehdl3n2mG68MAKtFh4Brl5aCfcoHANQQCwXjwhfWNDM4xHd9AAqDbG0whTT2UGpI7Raa/lCqRCIk8Q8D4mby9pQELfNtizasTw9CNgn6dVjAhFRLstVyAVEnmCgI/pj9dKEROBxbU0ga8IeegKMGCurZ+WOims4/EAiTxBABCeIcoTNAAq8Gw/WyRpYJMUT9814yZCEYEZ/R/G3x/egJfuXRr2Ag9QxytBALheRdJ4dAdXRljRYxAsFYc8joTVb32eSg4HiLLK45I6SwH+DlPXwVP3JN+Nk3VnPA6mCmdI5AniGuqUdDehNiancMIvBJUcFoZvxCoAwVGs3sy+pACDp79a5NSu4+CpK4YGfFtZ3iFCMmKQyBOEA8aKUidvXj1kEqKnvQKtNgYXCt+A5cwBWwliVxymCiRsHLxQ5ia6H53eDpZlYYWVW7b1jK1fY2jyYK/i8FawTm1EMCq3ME9HyIP3BIk8QVzDWFHqVKjM0UNvqoq0hW5EqhxSfN6Zj0+4x9b5Js12FGKh3Hg1E4HO6s64YmiAAgwn8I5tmMAfxw/3PHhPkMgTHR5H792Nax76FQXjMTZPBcquU1Z5HLUt0uv024VYaDo+FuBqvLsKvCfCPQ/eEyTyRIfG1Xvng9XXwQzPA2vUQ6isNnA9BdIb7EJsD6t8+uMupzx3E2uCySyecdNZFQWj1eT0A9ER8uA9QSJPdGiMR3d4riPPKKCKiYe5sVZ4G01nisdfQ6y2u5JROsXk7TQam/D0V4sA2MTa2+K4EYoITL5tHHf8jpxN4wqJPNGhkRRHZ63oMnI6av6xgf8HQaWGJn164I1rp4jFwB/pNwU/NZzHwUvfOi13jNVLGanqiKuYd3RRd4VEnujQSKk+yUQnIGbgCNT/eIK3iJky5V7y4h3g6xi1Lx+aPNirNEkxumji8NK9SwPSVjjjl8i/8cYbKC4uBmCbKWrRokUoLS1Ffn4+DAYDMjIykJeXFxBDCaIt8Dj1n0rNxdqFiph5VdysHeKY624X8C6aOAxMSOUdaCTUMeqY8ugvFGuXjs8iX1pain/+85/47LPPwDAMHn/8cezevRsFBQXYvHkzunXrhjlz5uDAgQPQ6XSBtJkgAobrSFdoOgNgAIOey5O3byPk8Ydz6qTr7EyOQu0YcnHMdxdKg7R3rkqt9e5IlDISDMOg2dxCsXYv8VnktVotFi9eDLXaVu+jd+/eOH/+PHr27IkePXoAALKzs1FSUkIiT8gax5GuXDqlQe+2nWBoh1HAWFEaliEbbybItue786VBOnre43qPxUent/PmzPNBYRn/8LlAWUpKCu68804AwPnz51FcXAyGYaDVarltEhMTUVVV5b+VBBEE7OmUdiF3LScsWMSMtYZt2WFvPe4rhgYMTR6MaamT0DUqHoB7tcehyYOhUUgrBkdhGf/xu+O1oqICc+bMwaJFi6BUKnH+/HluHcuykgv32xGbkFYIrTbG632ChVxtk6tdQOhs+2VboXts3myEpbwQSB+DG9PHoCk2EjW7Xncf+Wo2wvTtx7gxfUzwDHagra5Z16h4rwY1dY2Kh1Ybg0ytDpm3C3/Bt4oUIesaFY+6lnokRMVj6qDxGN5zqFc2S0Wu70Cg7fJL5MvLyzFv3jwsXboUmZmZKCsrQ01NDbe+pqYGiYmJXrVZV6eH1So9R1auM9UD8rVNrnYBobVNKA/evvy30i9sefUCpQ2srU34rfSLoIdt2vKaZd4ymncEKh8Righk3jKas0XMrjiRuP3KexY7LWuLc5PrO+CLXQoFI+oc+xyuuXz5Mp5++mkUFBQgMzMTAHDHHXfg559/xoULF2CxWLB7926MGDHC10MQRHDRCL8o5/86G4b973rsZA3HSUQimOu+IHNt5G8XTRyGd7/HqTPVm2qPQrXeKTQTeHz25N99910YDAasWbOGW5abm4s1a9bg2WefhcFggE6nw9ixdNMI+WOsKAWMwiEEa6s076o9ZNrwlf/lE2fXzBoAUClUksS8rPI4/vHtl6htqec9hmNpYRqd2rb4LPLLli3DsmXLeNft2rXLZ4MIIhTYwjDSsj3EkHuRMlfhdi316whfZo1Y6V7HHw9HhI4xNHkwiXoQoOn/CAIB8sAdBk7JFTHhdkUos0ZoRiaxeVmFjkG0PSTyRIfHWFEKMD68CoySi+Mz0QnQDJ8t+1x5b4RbqEQv33Ip+fQdva57qKDaNUSHhis1LDIZCB+uo2HbC55GozriaVCTI1IEvKPXdQ8VJPJEh0ZSqWFHVOp24bELIUW4yyqPY/vZIm5CbQYM2Gv1aoQ6Rz2VKqDMmdBBIk90aKTH4hkw0fHt0nt3zaa5J/lu3sJi9m03n/rEqd47CxZKRomBCanYda4EH5za5rbfuN5j8cGpbbzHV4Dp8JNphxISeaJDI7XU8K3zN/IOUuGb+DuUPwKOgh6ljIQFFhgs179Urhga8G1luaDo7jpX4jahB2Cr9y5UkMyeJSMk8lawJPAhhDpeiQ6NesgkWweqECIZM55q3QQb1wyXFkurk8DbEct08aZz1LUdbzpqieBBIk90aNQp6YA6kn8loxCNv/PG869N/B0KvKkYKSTm3gqyYzs0ilWekMgTBE9ZYQAAa+UVeGNFKfRbn5ddfXlvvHAhMR/XeywUXsiCYzv26pP2ZV2j4ikWLwMoJk90eLypE8+lXIpk5IRq1KvUyTjEvOuhyYN552AFbJNwO9aA52vHcRSrXIuAdTRI5IkOj+AUgNfqxAMAtLYSwh5TLkM46pUvPdKVKGUkpvQd7+Rdu2bfGMwG3n01CjU0Kg3VmmlnkMgTHR67p27Y/zfeOvHGozuAa3XixUIxoc6ucS36JWXKPL5aNkK0WFrxF93KtjsBok0gkScI2ITe8PVG3nVOws4oBEfHhjp9EvC+6Jc3nbWUJdM+oY5XgriGUCzdablI+YP2OAWg1M5aypJpv5DIE8Q1eOdwdYmxi3aqhjB90leEvPPOqiifJwQh5AWFawjiGvZQi9gIVsFO2mu0h0lDHBmYkMqbSTM4cRByU3NCYBERaPwWeb1ej9zcXLz11lu46aabUFpaivz8fBgMBmRkZCAvLy8QdhJEUFCnpIvG1UU7aSHPSUNcs2cGJqRytWsU16bzc+Vk3ZkgW0m0FX6J/L/+9S8sW7YM58+fBwBcvXoVS5cuxebNm9GtWzfMmTMHBw4cgE4nPGs7QcgZe22aJn29W4EyN48+yOmTdvFuMDQgzovsGUfP3QqWt22q/R4++BWT/+STT7BixQokJiYCAE6cOIGePXuiR48eUKlUyM7ORkkJzQZDtE+ca9OwTrVp1Cnp0AyfzXnuwZ40xLFODYvrBcPKKo87bedN9owjlEkTPvjlya9atcrp7+rqami1Wu7vxMREVFVV+XMIgggZYrVp7GGdUKVMSp1/1RePnDJpwouAdrxarVYwzPUYH8uyTn9LISEh2uvjarUxXu8TLORqm1ztAuRjW5O+nnc5q68PuY0NAuLdYGhwsq1rVDxqW/jPwxEFowDLWpEQFY+pg8ZjeM+hAbEz1NdJDLnaFmi7AiryycnJqKmp4f6uqanhQjlSqavTw2rljxPyIef6GHK1Ta52AfKyjYmO582WYaLjQ25jnECdmjhNnJNtmbeM9ljqIEIR4ZYiGYjzk9O9dEWutvlil0LBiDrHAc2Tv+OOO/Dzzz/jwoULsFgs2L17N0aMGBHIQxBE0JCSNx8qpJb1da0M2UUTh+Hd76Ec+A5EQD15jUaDNWvW4Nlnn4XBYIBOp8PYsRTbI9onznnz7tk1ocSxTo1Ydo19WxLxjgvDsqz02EgQoHBN2yNXuwD52iZXuwD52iZXuwD52ib7cA1BEAQhL0jkCYIgwhgSeYIgiDCGRJ4gCCKMIZEnCIIIY0jkCYIgwhgSeYIgiDCGRJ4gCCKMIZEnCIIIY0jkCYIgwhgSeYIgiDCGRJ4gCCKMCWgVSoIQ4vAPlSg8cA51jQZ07qQEwzDQt5qREKtBjq43hg1IDrWJHRbHe+PL/fB3f6JtCcsqlI4PnYIBrCza5OHz9HC350p3gXpxD/9QiY/3/gh9q9njtgmxGszOGoABN8tvflG53kvA8zsgdv8O/1CJD4rPwGi2csvUKgVmZaRKut98+3N2dYnEhPtu9fq5CfSPBl974+5PkeX9bIsqlGEn8mIPnTcPryekvBz+vHxtiacHyd8XX6wdT2gilJg5tq/sPMH2JPJ8113JAJGdVNC3mp0cH4PJwvsDnBCrwV/+dK/HYy/870OoazQIrlerFLj39mScOFcn+My7fuUZTFaYLaxTG76+t0LP8rN/vDNsnAlPIh924ZrCA+cERcVotqLwwLmAfIryHcex/cM/VGLnPw+j5kortx8ApweurtGA9/ecxtb/OYvmq5aQiD7f+Xk6N6nt2cXEGwwmi9f3iHCG7/5ZWHBibr8nYuIsts7O4R8qPW5nNFvx9XeXnNr9oPgMAHDvyXu7T8Gu6c1XLbxtFB44BwBeO0hCz/IrW4979b4F8ss22E5em4j8559/jg0bNsBsNmPWrFmYPn16WxyGF08PXV2jAY+t+UrSBXb1AhwfUKHj1DUaePd7f89pWCwsXDXPbGFhtli47d75/BTe+fyUx0/sQD1wfOcn9CPp6dq6tuetwEs9DiFOIK5fQqxGdL39XvuCo8Ow9X/OwiLhOXF9Nl1/LPjss78jntoUasPejmu40XE/QNoPj1A77+0+xS1vq3BlwEW+qqoK69atQ2FhIdRqNXJzc/GHP/wBffr0CfShOHzxHqXcYDGPNiFWw/sAJcRqePczS3mSJdi3+Yszop6RNwidn9A19PTib/2fs16FZoTwdBzChv25r280IN5BYISeTamoVQruy1PomP7+kNj35/Pc+VAw4H1WP977o9tz7/p1IIbYF6pYuNFotmLr/5yFycx6/OERa8fxC6uu0YA3tv8r4OHKgIt8aWkp7rnnHsTF2X6NxowZg5KSEjzzzDOBPhQAYH/5RZ+9R08hCDFv/Yns/ryxvhxdb7zz+SnpRniw793dtrbsn7aOAu+4ndB5bP7iDA58fwlW1vai6O7sjuceGcKdBx9W1nYufOcGuH9JDOqdgLLTVZJfWDE0EUpBgSGuI/aVmaPr7VVfSOdOSnRSq3zqpPUVb37IXZ9FR/StZhz+odLJXqlfB3aE3gOx0C8gHlpytMdTO44YTBbeHy5/CLjIV1dXQ6vVcn8nJibixIkTgT4Mx4fFp0UvoCfPXswjEfPW7TfBVezsscNAYWXBvbxibfPZ6er1W1ng6+8uIbLT95is6y16fvbYvOuLzycufD88UuA7jlyza+SG2FemvcNUrDPTjlqlwLQHpXmOUsRKrWJgtrA2p0LBoG+PG3Dut0ZBhyE6UsXb8cswAMu6PyN8fFB8Gu/uPsU5Mt6GCRUM/3Jfv1Zc9/O2Hb4fLn8IuMhbrVYwzPWrxrKs09+eEOsl5qP2SqvgOm2XSLy3bDQA4LGXvkQNz7baLpHQamN495+dNQBvbP8XDKbrv9hKBQOj2Yp/X/MVunaJxOysAQCAjTv/12ex84TRbMXOf/6MepGHhe88DvzrMu+2JUd+wVOT7+Q9P02EErOzBuD+u3tg3P0pbvvu/OfhgHhygO3ht5+XtkskZmb0w/139whI222B0HMSCoSehfpGA7TaGIy7P8bp/u0vv4gPi087vQMKBYMHhvDfZ8d9aq+0omuXSI9ipVQwsLLXRdZqZfHTpSY8MKQHjp6p5tqx3+HSvNsAACAASURBVOf95Rd5tUGhYJCXe5fTsxAb0wmvbD3Oe1yj+bqq+9IPZGX57622SySvZkjhP94+zJ2nL+3s/OfPgvfFWwIu8snJyTh27Bj3d01NDRITEyXv720KZVeRC1hzpZVLR5pw36284ZUJ990qmuYYHamCSqlE81UL5xE1tZi49l/9+3dgrazo52F0pAqtV81efULynYuQ1wMALa1GZD9fxH1RnDhXJ3gdrVYWs1eWcF5ehErlNDBpwM1xgmlcvj70Qtjbq7nSitc/+R4AZOnJyy2FMl7gKyw+VsNr54Cb49zeAauVxd6jF9F61eSW4gg4Z4JJue+RGqXb82kwWXDkh0q3dMxd+ysEQz8My6Kx6arTeQy4OQ6dOykDEhJ0JSFWg137K9y+XPk0AwAYwC2BwhX789zYdFWwHU/7S33ePKVQBrysQXp6Og4fPoz6+nq0trbiyy+/xIgRIwJ9GI6ZGf0E1znG/YYNSMasjFRuWUKshjf31h6OsL9A+lYzTGYWT2T3Rye1yu2T12wRF3gAeG3+CDyW1d/p2CPv6i75HO37iA1psD/89vCJlCwj+35GkxVPZPfHX/50r8dsI6FP20BgNFvxYfHptjtAGJGj6w21yvn1FeswBYRDPI7Piz22720nuoKBoAPC9yyKhX4sLH9octqDfd3OORAkdol0eucd+zdmZaQiOvK6L9y5k9KjwNtxjM+7as8T2f1F9w1k8kHAPfmkpCTk5eVh5syZMJlMmDx5MgYNGhTow3Dcf3cPHD/t3iHJ98APG5Dsc16trxkF9pvFd2yp4Z1Ad+i6IiUH3v7j52tapCNi2R+B/lIIVxz7hFyza4SQ+vwazVYYPQ9QdsI+uEqoj8dbW/jW288t0O/B2V8a3J5r+zuRo+sNo+m6Hnj7JWE/D773X0xTApl80CZ58tnZ2cjOzm6LpnmZMSYVfW6KC0juuFhGjS+paWI3S6yTyH4s1wFY/qSuiR3PU7tCnpeCAfreHIfTFxo8Hr9zJyVe/386AMDja7/itUXRlp8KYYZdOKSGkvxNrfTUNl9Wj9DXhacOUiFPNhDvgSti74Q3mTF8iHnkQllQI+/qLu/smlAhxUuXgqeME9ebolIyojnwYjbp7uzO682PvKs7ZoxJdVvubWqcI/Zh6kLD0D19HoqlWy6cOhiHf6jEhyWnYTDxXwt7FofjfrztBeJTgeDFn+fHEaH0WteMM7HaNWK32VPYyZvzUDIAoxB/R8XGhbTVeAMAvBl6bZFdpnzxxRdfDGiLftLaaoQ31XQ6d9agpcUYsOPHRKlx8qc6WBzuulqlwNQHbrMNNLmhEy5UNqLVYCtDMO3B2/DjxSuCBZoeTBPOFrmjT1c0NhvwS1UTWNgetvsFBB4AeiRGux3/nv5JuFTb7GSvK3b7eyRGIyZKjR9+ruc9vx6Jwp03/zxxCa0G90/VhFgNRg+5GT0So5GVfisSu0Ry9ikYWwdVQqyGu36e2vN0zUJFoJ+zQCLVNqnPj1qlQCe1Aiaz+zNlv5eObTje2x6J0Rg95GaMv+9WTMvoj/jOal5bhO6/ggEezewn6hzxnccjo/vimZzbnZ6/hFgNpo3ui7tStNwyV9QqBYbf0Y33GjieJ991eGR0X7dr2dRi5L0uYudiv16jh9yMAX20Xj9nDMMgKor/OgNhWKAsEHhbNiDURZD4BieJFYT64ZcGbNr9g1ehrUAVLfPUXjgVjgoW/trG97wD8Pt+i9kV6OdJKkKjhIXe+WDbSVUoeZDLyxfu5UwBKgErR7uAtrPN3/stpdppqCqyenPNgmkniTwPHfHl8xe52gXI1za52gXI1za52gXI17a2EHma/o8gCCKMIZEnCIIIY0jkCYIgwhgSeYIgiDAmbAZDEe2Tssrj2HWuBFcMDeiiicMjd01EapRwPSKCILyDRJ4ICWWVx/Hpj7vQbG7hll0xNODto1swtW8OhiYPDqF1BBE+ULiGCDpllcex9cwOJ4G3Y7QYsetcSQisIojwhDx5Img4hmbE8LSeIAjpkMgTQcHuvZusJo/bdtHIr6wBQbRXKFxDBIVd50okCbxaqca43mODYBFBdAzIkyeCgpQQTJQyEv8+JJeyawgigPgt8uvXr4dSqcSzzz4LAGhsbMSCBQtw8eJFxMfHY/369dBqtX4bSrRfyir5J2C200UTh3G9x2Jo8mDZ1hQhiPaKz+GapqYmLF26FO+//77T8vXr1yMtLQ3FxcWYMmUKVq1a5beRRPtGLFtmVv9cvHTvUkqZJIg2wmdPft++fbjlllvw6KOPOi3fv38/tmzZAgDIysrCf/7nf8JkMiEiIsI/S4l2i1ioRg7ibqwohfHoDrD6OoBRAKwVTHQC1EMmQZ2SLmk/KdsTRCjwWeQnTJgAAHj99dedlldXV3PhGZVKhejoaNTX1yMpKckPM4n2iD1lUgg5ZNEYK0phOLgJMF+bjYe1TQ7B6utsywFe4Xbdz9P2BBEqPIp8cXEx8vPznZb16tULmzZtknQAlmWhUEiPConVRRZCq43xep9gIVfb2tqugxfK8PHZQhgtwlOZmVkTzrScxvCeQ4NqmyO/bCu8LvCumI2wlBdCmz7GzS7e/Vy2DyYd9TnzB7naFmi7PIp8RkYGMjIyJDeYmJiI2tpaJCcnw2w2o7m5GXFx0j02mjSk7QmGXR9995mowANAk7EZb5V9hMbGVi5sE+xrZm6s9bi+pqbJzS6h/ezbB5OO/Jz5ilxtaxeThuh0OuzcuRMAsGfPHqSlpVE8vgMiddSqyWoKaRkDJjrB43pjRSl+eX0OmjbOhn7r8zBWlNpi9/x7BN5IgvCDgOfJz58/H4sXL0ZmZiZiYmJQUFAQ6EMQIcS1aqQ99dGVLpo4yUIfyjIG6iGTnGPyjqjUUPQYxB97Z63u29u2gLGilOLyhGygOV7bELna5qtdfKUJIhQRmJY6yU3ot50pxMFL30pqVwEGVrAhKzXMl10DTTQAFjA0e90eE52A6GmvBN5QAcLtOQsGcrWtXYRriPCFrzQBX7ilrPI4vq0sl9yuFbYfdXupYU+DpwKNOiUd0dNegWbkk2A6d7EtNOh9EnjA5u0ThFwgkSckIxRWcV0utU6Ngid+HapSw/aUyEAItKc4P0EEExJ5QjJCee2uy6XE2O0hGj5CEaM3Ht0hnErpDSo11EMm+d8OQQQIEnlCMuN6j0WEwjlTKkIR4VY1srMqymNb9hg8H6EYJBWQEIumMzTDZ1OnKyErqAolIYprNs09yXfjZN0ZweyassrjaDVf9dhuF00cBiak8nbODkxIDeg5SIGJTvBL6DUjnyRxJ2QJiTwhiGs2zRVDA76tLOfNppE665OdgQmpOFl3hned0PK2RD1kEgxfb/RpXyY6gQSekC0UriEE8SabZuuZHV7F0u1fA3yEIiavTkmHst9I73ekGDwhc8iTJwQJdDaNaxtCA6aCHZN3zpNXAqxF0n5UeZJoD5DIE4JIFWFfPG97PN91cFWwp/9zr0JpARgGEBojyCiguf9xEnai3UDhGkIQqdk03nre9jaGJg/GtNRJ3P5dNHGYM2R6UGvM86ZOig0CZ60k8ES7gjx5QhC72HqqVcPnkQsRpYzElL7juTaGJg92ai/Yw829zaixD3SiCUOI9gKJPCGKqwgLbQM4/xgMTEhFedW/0GJpBWDLnZ982zhZzATliGDqpKYzYDE5e/nXOllpwhCiPUEiTwQEvh+D3NScEFkjHd4qlCo1NOnTAYDz1lWxXaG8OwfqlHTotz7PO2GI8egOEnlCdpDIE34htfSwXLGLslDoxf6vYxhJKMRDhckIOUIiTwDwTaz5BkttPbMDgDwm6JaKOiVdsgfOTRjCU0+eCpMRcoSyawiUVR7HR6e3c6mQVwwN+Oj0do8lf6UOlgoXuFg834QhNCiKkCk+e/Ll5eXIz8+HyWRCXFwcVq9ejRtvvBGNjY1YsGABLl68iPj4eKxfvx5arTaQNhMB5tMfd8HiMgDIwlrw6Y+7RD1yOY1Y9QepmTKClSoZBRUmI2SLz578woUL8dJLL6GoqAjZ2dl46aWXAADr169HWloaiouLMWXKFKxatSpgxhK+UVZ5HMsOrcbTXy3CskOrcfBCmdP6ZnML735Cy+3IqYqkr7jWkbdnyhgrSt22FYy5U+48IWN8Enmj0Yj58+cjNdVWLbBv3764fPkyAGD//v3Izs4GAGRlZeGbb76ByeTdkHcicGw7U4gPTm1zCsUEavYlqYOl5Ayvd34tU8YVoZg7xeIJOeNTuEatVmP8+PEAAKvVijfeeAMPPPAAAKC6upoLz6hUKkRHR6O+vh5JSUkBMpmQSlnlcd5SvvbZl+yhmChlJJfP7srTXy0S7IiVOlhKzohlyrQc/ACWMwcA1oomRgGmW1/gahNv7jxByBWPIl9cXIz8/HynZb169cKmTZtgNBqxePFimM1mzJkzh3d/lmWhUEj/YBCbkFYIrTbG632CRVvadvBCGT4+UYS6lnokRMVj6qDxGN5zKLf+H99+Kbhvg6GBs+3fh+Tiv4986BaXt3PF0ICPzxYiNjbSqX0AyNTqkHm7LgBnc51g3s/W2K4wN9a6r4jQwHL66+t/s1awl05D0/N2WK5chrmxDqrYBHQZOR0xA0cEzV4h5PoOyNUuQL62BdouhmXFCnUI09zcjKeeegpxcXEoKCiAWq0GAIwaNQpbt25FcnIyzGYzhg4diiNHjiAiIsJDizbq6vSwWqWbJNdZ14G2sU1K3Xa7R/3BqW2i27x071LedoWm5nPdpy0I9v10K1DmCUaBmCfea1ObvEWu74Bc7QLka5svdikUjKhz7FfHa8+ePbF+/XpO4AFAp9Nh586dAIA9e/YgLS1NssAT4kit227PVxebhs81bj40eTBeuncp3hz1sqzmXm1r1CnpUKbcK30HvvRJgpAxPsXkT506hX379qFPnz6YOHEiACAxMRHvvPMO5s+fj8WLFyMzMxMxMTEoKCgIqMEdGW/qtpusJkQwKkQoItz2Gd17BBc35xsEJZc678HCevGE9I0ZGlpCtC98Evn+/fvj7NmzvOvi4uLw1ltv+WUUwY+3nnSLpRWz+ufi0x93cemQaiYCpb+U48tz3yBKGQmD1cjF4u1fAPck341vK8udfhzaW9aMN3hTjkCZGtj+B4Joa8gtaUd460lHKSMBAEYHsTayJuhNzQBsPwKuna0mqwkn68641Xnnm9c1bNAIxDMZ5XXPnVFA2W8koobPCp5dBBEAqHaNTBCqHeO4XM1417dhsBqx/WyRT1PzSSkxHD4IdPSrOyFm1pswVpTCUl4I8+mvob94gmrHE+0KEnkZIFTo66eG805hEyPrnVhbWItg/rsY4Rp7F8TQLLjcliv/DTfvK6uvg2H/uwCodjzRPqBwjQwQKvR16NIRr71wfwnn2LsQYiNWLae/dp/Ym7XAULqlja0iiMBAIi8DhDpUhVIZvaGzKsqt9IAjCii4VMuwj70LoB4yCVCpPW/oiJD3TxAyg8I1MkAoZVFoUJJUIhQRmHzbOADXSw90VkWBYRjoTc3tsgxBW2APuxi+3hhiSwgi8JDIywC+ibAjFBG8qYxScRXwUE6W3R5Qp6Rz5YYlIZSRQxAyg0Q+BPBl0kxLncSbXdMr7hYnL5xlWbRYWkW9/GCUHwhHeOd75UOhgiZ9WlBsIgh/IZEPMkKZNNNSJ/EKs1Aq49NfLRI8RjiWHwgGXNhm/9/EyxdEaIJkEUH4D3W8BplATZknlubY4VIgA42n+jSGZhi+3oimD57hnVyEIOQEefJBRsqUeVIm1R7Xeyw2n/oEVjgLkpJRdrgUyEBhrCiF4YAXFSYNelt4B5QzT8gX8uSDjKcp81wrTdrDOa4zOQ1NHowZ/f/IlS4AbOmSj/Sb0uGzZXzFULoVsJq920lgFimCkAvkyQcZoUwau/ctFs7hm5mJBD2AGPQ+7eZNgTOCCDYk8j7CN8mGlLxzT1PmSQnnEPLDWFFKIRtClpDI+4Brhow9ldEeWgHgUej51otNrk2dqUFA09nnkazGoztI5AlZQjF5HxCbvMOXTBnHdoWgztS2R5M+3VZe2AcoZEPIFZ89+WPHjmH16tUwmUy48cYbsXbtWtxwww1obGzEggULcPHiRcTHx2P9+vXQarWBtDnkSJl+TypS5mwFxL8MiMBg98TtI1+Z6AQoegxyntBbALEiZwQRSnz25JcsWYKXX34Zn3/+Ofr06YN337WVX12/fj3S0tJQXFyMKVOmYNWqVQEzVi5ICZ0sO7QaBy+UiW4jdc5Wxwwaom1Rp6QjetoriHlyE6KnvWKbJETTWXwnldpW5IwgZIjPIr9nzx706dMHJpMJVVVViI2NBQDs378f2dnZAICsrCx88803MJmCWy63rRnXe6xoZUfA5s2/fXSLaJxd6pytDMN4bSPhO8aKUui3Po+mjbOh3/o8lL2GCm/MKKAZPpvi8YRs8TlcExERgbNnz+LRRx+FSqXCc889BwCorq7mwjMqlQrR0dGor69HUlJSYCyWAa4ZMkJ1ZIwWI2/qox2pYR37/KxE22CsKHUoTsbAcaYoVl8HS8Uh4Z1ZKwk8IWs8inxxcTHy8/OdlvXq1QubNm1C3759UVpaim3btiEvLw/btm1z259lWSgU0j8YEhK8r+6n1cZ4vY+/ZGp1yLz9+qTOD//9Kd5yYQ2GBkH7ukbFo7al3uOxukbFB/wcQ3HNpBJM25pOfgP9wQ/Amg3XlvDcRbPRNtcrT7kDVWxXWVxLOdjAh1ztAuRrW6Dt8ijyGRkZyMjIcFpmMBiwd+9ePPDAAwCAcePGYe3atQCAxMRE1NbWIjk5GWazGc3NzYiLk57+V1enh9UqvYa6XMrmxgnUhI/TxAnal3nLaLeBUa5EKCKQecvogJ6jXK4ZH8G2Tb/vIweBF4G12iYWcalQaW6sxc+vPhnSeV/lej/lahcgX9t8sUuhYESdY59i8iqVCitXrsTJkycB2Lz9wYNtIQmdToedO3cCsMXt09LSEBHh3QTU7RG+OL1aqXZKfSyrPI5lh1bj6a8WYdmh1QCAaamTuI7cLpo4DO9+j9PfHXGmpmAiNfVRFdsVmuGzebNoWH0dDAc3UbEyQpb4FJNXKpVYt24dli9fDovFgqSkJC6LZv78+Vi8eDEyMzMRExODgoKCgBocSsQKh/GNZH3krolIjerH7etNiWEiODDRCZ6FXqVGl5HTcTXpLqhT0qHf+rz7Ptdq2FB8npAbPne8pqWlobCw0G15XFwc3nrrLb+MkiN8Iv3BqW34qeE8clNzALiPZHX89PKmJg0RPDxPFMJAM3w2YgaOwNVr91LoR4EGRBFyhEa8SkQo3fHgpW9F0yTtUE0aeaJOSYdm+GyRLVgn79xYUWrrhOWBBkQRcoREXiJiYiyljIGnEsNE6FCnpAsKtONyY0Wpzevnm1SEBkQRMoVEXiJiYizFG+frmHUsMUyEDmNFKVjTVfcVLsJtPLqDP6xDA6IIGUMiLxExMZbijQ9NHuyWSUOZM6GH885dq09qot2EWzDmTgOiCBlDpYYlMjR5MH5qOI+Dl751Wu6NN06TfMgPIe+cidC4CbdQJg7F4gk5Q568F+Sm5mBW/1zyxsMIbzJl1EMm2QZEOUKxeELmkCfvJeSNhxfeeOd8pYhDOdKVIKRAIk90aHjz5EW8c3VKOok60a4gkSc6NOSdE+EOiTzR4fHknRsrSvHLtkKYG2vpR4Bod5DIE4QIXIrltXCOvRgZABJ6ol1AIk8QIvCmWJqNMJRuoRAP0S4gkScIEQQHQBmawV4bQEXePSFnKE+eIESQPNDpmndPEHKDRJ4gBLDVtJEwa5QdQzNNHELIDhJ5guDhek0bvcsaBlBqhPc7uqNN7SIIb/Fb5E+dOoWBAwdyfxuNRixcuBAZGRmYOHEizp075+8hCCLoCFacBAuwFsH9aOIQQm74JfKtra34r//6L5hM1yfT2Lx5MyIjI1FcXIylS5diyZIlfhtJEMFGVKytZgAM7yoqVkbIDb9Efs2aNZg1a5bTsv3792PcuHEAgCFDhqC+vh6XLl3y5zAEEXQ8izVLxcqIdoHPIr9v3z5cvXoVY8c6l9mtrq6GVqvl/tZqtaisrPTdQoIIAbwVJx1gohOgGT6b+zGw/00plITc8JgnX1xcjPz8fKdlvXr1gl6vx6ZNm9y2Z1kWDMM4/a1QSP8tSUiIlrytHa02xut9goVcbZOrXYBMbNOOQVNsJGq/eBfsVefOV0alQdd/ewQxA0cA6WNCZKAzsrhmPMjVLkC+tgXaLo8in5GRgYyMDKdl27dvx9tvv43p06dzy8aPH48tW7YgKSkJ1dXVuPnmmwEAtbW1SExMlGxQXZ0eVisreXutNgY1NU2Stw8mcrVNrnYBMrMt6S5Ez3wDxopSWMqda9dcTboLV2Vip6yumQNytQuQr22+2KVQMKLOsU8jXqdMmYIpU6Zwf/ft2xdFRUUAAJ1Oh6KiIqSlpeHYsWPQaDTo3r27L4chCFmgTkmHNn2MLEWBIDwR8Dz5GTNmwGg0IjMzE6tWrcLLL78c6EMQBEEQEglI7ZqzZ89y/9doNFi7dm0gmiUIgiD8hEa8EgRBhDEk8gRBEGGM7EoNKxT8IwkDvU+wkKttcrULkK9tcrULkK9tcrULkK9t3trlaXuGZVnp+YoEQRBEu4LCNQRBEGEMiTxBEEQYQyJPEAQRxpDIEwRBhDEk8gRBEGEMiTxBEEQYQyJPEAQRxpDIEwRBhDEk8gRBEGFMuxb5U6dOYeDAgdzfRqMRCxcuREZGBiZOnIhz584F3aZjx44hJycH2dnZmDt3Ln7//XcAQGNjI5588klkZGRg+vTpqKmpCbpt5eXlmDx5MsaPH49Zs2bht99+k41tALB+/Xq8/vrr3N9ysevzzz/HQw89hNGjR2PLli0hscERvV6PrKws/PrrrwCA0tJSZGdnY/To0Vi3bl3I7HrjjTeQmZmJzMxMrsS4HGx79dVX8dBDDyEzMxPvv/++bOyys3btWixevBgAcPr0aeTk5GDMmDH485//DLPZ7P8B2HZKS0sLm5uby952223csr/97W/sCy+8wLIsy5aVlbFTpkwJul0PPPAAW1FRwbIsy/7lL39hX3nlFZZlWXblypXs22+/zbIsy3722Wfs/Pnzg27byJEj2dOnT7Msy7Lbt29n586dKwvbGhsb2SVLlrCDBg1iX3vtNW55qO1iWZatrKxkR44cyV65coVtbm5ms7OzufsbCr7//ns2KyuLHTBgAHvx4kW2tbWV1el07C+//MKaTCb2scceY/fv3x90uw4dOsQ+/PDDrMFgYI1GIztz5kz2888/D7ltR44cYXNzc1mTycS2trZy70Co7bJTWlrK/uEPf2D/4z/+g2VZls3MzGS/++47lmVZdsmSJeyWLVv8Pka79eTXrFmDWbNmOS3bv38/xo0bBwAYMmQI6uvrcenSpaDatWfPHvTp0wcmkwlVVVWIjY3lbMvOzgYAZGVl4ZtvvoHJZAqaXUajEfPnz0dqaioA22xely9floVt+/btwy233IJHH33UaXmo7QJsHt8999yDuLg4REVFYcyYMSgpKQmqDY588sknWLFiBTel5okTJ9CzZ0/06NEDKpUK2dnZIbFPq9Vi8eLFUKvViIiIQO/evXH+/PmQ2zZ06FB8+OGHUKlUqKurg8ViQWNjY8jtAoCGhgasW7cOc+fOBQD89ttvuHr1Ku68804AQE5OTkDsapciv2/fPly9ehVjx451Wl5dXQ2tVsv9rdVqUVlZGVTbIiIicPbsWeh0Ohw5cgSZmZlutqlUKkRHR6O+vj5odqnVaowfPx4AYLVa8cYbb+CBBx6QhW0TJkzAk08+CaVS6bQ81Ha52gAAiYmJqKqqCqoNjqxatQppaWnc33KxLyUlhROn8+fPo7i4GAzDyMK2iIgIvPbaa8jMzMSwYcNkc82WL1+OvLw8zhHk069A2CW7UsOOFBcXIz8/32lZr169oNfrsWnTJrftWZYFwzBOfysUbfM7JmTbpk2b0LdvX5SWlmLbtm3Iy8vDtm3beG0NhW1GoxGLFy+G2WzGnDlzePdvK9vE7JJCW14zIaxWq9sz5fh3qJGbfRUVFZgzZw4WLVoEpVKJ8+fPy8K2efPm4YknnsDcuXNx/vz5kF+z7du3o1u3bhg2bBgKCwsBtN29lLXIZ2RkICMjw2nZ9u3b8fbbb2P69OncsvHjx2PLli1ISkpCdXU1br75ZgBAbW0t91kbDNsMBgP27t3Lecjjxo3jpkJMTExEbW0tkpOTYTab0dzcjLi4uKDZBgDNzc146qmnEBcXhw0bNiAiIiKotgnZJUQwr5kQycnJOHbsGPd3TU1Nmz1TvpCcnOzUIR1K+8rLyzFv3jwsXboUmZmZKCsrC7lt586dg9FoRL9+/RAZGYnRo0ejpKTE6asxFHbt2bMHNTU1GD9+PH7//Xe0tLSAYRin6xUo/Wp34ZopU6Zg7969KCoqQlFREQCgqKgI0dHR0Ol03LJjx45Bo9Gge/fuQbNNpVJh5cqVOHnyJACb5zp48GAAgE6nw86dOwHYbnBaWhonssFi4cKF6NmzJ9avXw+1Ws0tl4NtfMjBrvT0dBw+fBj19fVobW3Fl19+iREjRgTVBjHuuOMO/Pzzz7hw4QIsFgt2794dEvsuX76Mp59+GgUFBVyIUg62/frrr1i2bBmMRiOMRiP27duH3NzckNv1/vvvY/fu3SgqKsK8efMwatQo5OfnQ6PRoLy8HIBN1wJhl6w9eW+ZMWMGli9fjszMTKjVai6NK1golUqsW7cOy5cvh8ViQVJSElatWgUAmD9/PhYvXozMzEzExMSgoKAgqLadOnUK+/btQ58+fTBx4kQANk/5nXfeCbltQsjBrqSkJOTl8KEiMwAAALBJREFU5WHmzJkwmUyYPHkyBg0aFHQ7hNBoNFizZg2effZZGAwG6HQ6t76qYPDuu+/CYDBgzZo13LLc3NyQ26bT6XDixAlMmDABSqUSo0ePRmZmJuLj40N+zfgoKCjAsmXLoNfrMWDAAMycOdPvNmlmKIIgiDCm3YVrCIIgCOmQyBMEQYQxJPIEQRBhDIk8QRBEGEMiTxAEEcaQyBMEQYQxJPIEQRBhDIk8QRBEGPP/Adqhy6c2TMEkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_new_outliers_syn(X_xaxis, X_yaxis, X_bisec, 'Synthetic outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions - Outliers on the x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected anomalies: 64 out of 100\n"
     ]
    }
   ],
   "source": [
    "y_xaxis_pred = np.array(iforest.decision_function(X_xaxis) < 0).astype('int')\n",
    "print('Detected anomalies: {} out of {}'.format(int(sum(y_xaxis_pred)), len(y_xaxis_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local-DIFFI - Outliers on the x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time Local-DIFFI: 0.067\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffi_xaxis, ord_idx_diffi_xaxis, exec_time_diffi_xaxis = local_diffi_batch(iforest, X_xaxis[np.where(y_xaxis_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_diffi_xaxis, 'Feature ranking syn dataset, ' + r'$f_1$'+ ' outliers - Local DIFFI')\n",
    "print('Average computational time Local-DIFFI: {}'.format(round(np.mean(exec_time_diffi_xaxis),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP - Outliers on the x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time SHAP: 0.705\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_xaxis, ord_idx_shap_xaxis, exec_time_shap_xaxis = local_shap_batch(iforest, X_xaxis[np.where(y_xaxis_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_shap_xaxis, 'Feature ranking syn dataset, ' + r'$f_1$'+ ' outliers - SHAP')\n",
    "print('Average computational time SHAP: {}'.format(round(np.mean(exec_time_shap_xaxis),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions - Outliers on the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected anomalies: 34 out of 100\n"
     ]
    }
   ],
   "source": [
    "y_yaxis_pred = np.array(iforest.decision_function(X_yaxis) < 0).astype('int')\n",
    "print('Detected anomalies: {} out of {}'.format(int(sum(y_yaxis_pred)), len(y_yaxis_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local-DIFFI - Outliers on the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time Local-DIFFI: 0.068\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffi_yaxis, ord_idx_diffi_yaxis, exec_time_diffi_yaxis = local_diffi_batch(iforest, X_yaxis[np.where(y_yaxis_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_diffi_yaxis, 'Feature ranking syn dataset, ' + r'$f_2$'+ ' outliers - Local DIFFI')\n",
    "print('Average computational time Local-DIFFI: {}'.format(round(np.mean(exec_time_diffi_yaxis),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP - Outliers on the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time SHAP: 0.708\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_yaxis, ord_idx_shap_yaxis, exec_time_shap_yaxis = local_shap_batch(iforest, X_yaxis[np.where(y_yaxis_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_shap_yaxis, 'Feature ranking syn dataset, ' + r'$f_2$'+ ' outliers - SHAP')\n",
    "print('Average computational time SHAP: {}'.format(round(np.mean(exec_time_shap_yaxis),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions - Outliers on the bisector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected anomalies: 94 out of 100\n"
     ]
    }
   ],
   "source": [
    "y_bisec_pred = np.array(iforest.decision_function(X_bisec) < 0).astype('int')\n",
    "print('Detected anomalies: {} out of {}'.format(int(sum(y_bisec_pred)), len(y_bisec_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local-DIFFI - Outliers on the bisector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time Local-DIFFI: 0.068\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Local-DIFFI\n",
    "diffi_bisec, ord_idx_diffi_bisec, exec_time_diffi_bisec = local_diffi_batch(iforest, X_bisec[np.where(y_bisec_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_diffi_bisec, 'Feature ranking syn dataset, ' + r'$f_1$'+ ' and ' +r'$f_2$'+ ' outliers - Local DIFFI')\n",
    "print('Average computational time Local-DIFFI: {}'.format(round(np.mean(exec_time_diffi_bisec),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP - Outliers on the bisector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average computational time SHAP: 0.667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_bisec, ord_idx_shap_bisec, exec_time_shap_bisec = local_shap_batch(iforest, X_bisec[np.where(y_bisec_pred == 1)])\n",
    "plot_ranking_syn(ord_idx_shap_bisec, 'Feature ranking syn dataset, ' + r'$f_1$'+ ' and ' +r'$f_2$'+ ' outliers - SHAP')\n",
    "print('Average computational time SHAP: {}'.format(round(np.mean(exec_time_shap_bisec),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}