hyperparameter_sensitivity_shap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c30661e9-6256-4345-813c-869e5aeb97ce",
   "metadata": {},
   "source": [
    "# Fooling LIME and SHAP\n",
    "## Hyperparameter Sensitivity (10)\n",
    "\n",
    "Analyze the impact of the hyperparameters of LIME and SHAP (e.g., hyperparameters of the local model and of the pertubation algorithms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50e4479b-2610-4ac4-be4a-b5197590f0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from fooling_LIME_SHAP_Interface.adversarial_model_toolbox import AdversarialModelToolbox\n",
    "from fooling_LIME_SHAP_Interface.perturbator import Perturbator\n",
    "from fooling_LIME_SHAP_Interface.util.explainer_type import ExplainerType\n",
    "from fooling_LIME_SHAP_Interface.util.ml_type import MLType\n",
    "from fooling_LIME_SHAP_Interface.util.pertubation_method import PerturbationMethod"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a77f8f-a355-4d0c-bd3d-85e43b946d1d",
   "metadata": {},
   "source": [
    "First we define all the neccessary variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e68705fa-c888-4d2a-88b9-22d290655ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_TEST_SPLIT = 0.2\n",
    "SEED = 666\n",
    "\n",
    "dataset_name = 'HousingData'\n",
    "input_ids = [5, 7, 11, 12]\n",
    "categorical_input_ids = []\n",
    "output_id = 13\n",
    "biased_id = 11  # B\n",
    "\n",
    "categorical_input_ids = [input_ids.index(cat_id) for cat_id in categorical_input_ids]\n",
    "biased_id = input_ids.index(biased_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5bce39-34e7-45de-82f8-4327d94a0de8",
   "metadata": {},
   "source": [
    "Loading the dataset and doing some preprocessing. There are two datasets. One Dataset is with the biased feature and the other dataset is unbiased because all rows from the biased feature are set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5dadf37-027e-4086-967e-d7bc75e7cafc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "house_df = pd.read_csv(os.path.join(\"datasets\", dataset_name + \".csv\"))\n",
    "house_df = house_df.fillna(0)\n",
    "\n",
    "# Split input and output\n",
    "y_df = house_df.iloc[:, output_id]\n",
    "x_df = house_df.iloc[:, input_ids]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(x_df.to_numpy())\n",
    "x = scaler.transform(x_df.to_numpy())\n",
    "\n",
    "# Create train and test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y_df.to_numpy(), test_size=TRAIN_TEST_SPLIT, random_state=SEED)\n",
    "\n",
    "# Create training data without biased column\n",
    "ux_train = x_train.copy()\n",
    "ux_train[:, biased_id] = 0\n",
    "ux_test = x_test.copy()\n",
    "ux_test[:, biased_id] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7dd816-7c40-415a-b9ad-b07ebbafd69e",
   "metadata": {},
   "source": [
    "Train the biased and unbiased ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b41862d9-3705-4712-ad32-993960dd9afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of biased model: 0.77\n"
     ]
    }
   ],
   "source": [
    "biased_ml = RandomForestRegressor(random_state=SEED)\n",
    "biased_ml.fit(x_train, y_train)\n",
    "print(\"Accuracy of biased model: {0:3.2}\".format(biased_ml.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23270d54-e32e-45cf-9e68-14c6efbb67a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of unbiased model: 0.96\n"
     ]
    }
   ],
   "source": [
    "unbiased_ml = RandomForestRegressor(random_state=SEED)\n",
    "unbiased_ml.fit(ux_test, y_test)\n",
    "print(\"Accuracy of unbiased model: {0:3.2}\".format(unbiased_ml.score(ux_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ce0bcc-2813-40d8-8973-a60157cae461",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Hyperparameter sensitivity local model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c4846aa-5488-4857-87c3-1271a8182bb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================== rf_estimators: 1 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb09e922f0524bda9c179ecb06b9184e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f125a106d16148629d01d3cbe5835e75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.51551444 0.65701949 0.27406681 3.58884525]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.62\n",
      "============================== rf_estimators: 10 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58a9dfc939324e11915dfd9b816833c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24e1281bf41e4abaa6a959e47c4c80b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.44125408 0.7012343  0.40862313 3.67252657]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.7\n",
      "============================== rf_estimators: 100 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "520312a3d46b481aabd580b0b6832112",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0f41c097e1f4883a41937d22fab814e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.47427619 0.68355037 0.40235418 3.70673358]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.68\n",
      "============================== rf_estimators: 200 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9b031123f4b4bf3be4ef150f491e4b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba1e14b74b5240e2b89c15513317ea70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.46287566 0.67439962 0.39449872 3.70173428]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAADYCAYAAAADBiHAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbV0lEQVR4nO3deZglVX3w8e8PBlQYAWURFYaBgLigshzXoBk3NOpE1PAiiogIAtE3efXFmCDovJEtinGJGgUFBBUDJiKgoKDiglFzFBdUJCAzIDBhmyEsCgzW+8c5zdRc+nbf6enpO7f6+3mefvrWfk6dU/WrOnVu3WiaBkmS1E3rDTsBkiRp7THQS5LUYQZ6SZI6zEAvSVKHGeglSeqwOcNOwNpw3nnnNQsXLhx2MiRJminRb4J39JIkdZiBXpKkDjPQS5LUYQZ6SZI6zEAvSVKHGeglSeowA70kSR1moJckqcMM9JIkdZiBXpKkDjPQS5LUYQZ6SZI6zEAvSVKHGeglSeowA70kSR1moJckqcMM9JIkdZiBXpKkDjPQS5LUYQZ6SZI6zEAvSVKHRdM0w07DtIsTV3QvU5KkTmiOmLM2Vhv9JnhHL0lShxnoJUnqMAO9JEkdZqCXJKnDDPSSJHWYgV6SpA4z0EuS1GEGekmSOmzSb+2nlC4BLs45H9MzfkvgBOAlwKbAHcBPgYOANwJH1lkD2Ai4Gxh7kc1xOefjUkobATcAtwI75pyblNIngP3rfOsBDwPuam360Jzz51Yvm5IkzU5r8nqezwK3A7vlnG9KKW1FCfpNzvk44DiAlNI2wHXAk3LOi3vW8Zr6fzvghcBFOefDgMPqsnsC3805z12DdEqSNGutSaB/NrBvzvkmgPr/9NVcx6GUC4bt6ueL1iA9kiSpx5oE+u8A76937P8J/DznfP+gC6eUngo8HTicEuj/NaW0dc556RqkSZIktaxJZ7x9KXfjbwS+D9yaUvpQSumhAy5/KPCznPNPgPOBZXVdkiRpmkw50Oec78w5H59zfhalM94BrNoJr6+U0sbA64BT6rruozT7H5JS6vsLPJIkafVMy2/l5ZzvBc5NKV0M7DrAIvsBmwDvSSmNXRg8BNgMeBHw9elIlyRJs92ggX7OOE3yxwFnAr8A7gWeCzwPOH6A9b0Z+BxwRM/4MyhN+gZ6SZKmwaCB/j31r+2TwKnAPMr3468HTgQ+MNGKUkq7Ak8DDu7teJdSej/wlZTSo3PONw6YNkmS1Ec0TTP5XCMmTlzRvUxJkjqhOWJanpr36tu/zVfgSpLUYQZ6SZI6zEAvSVKHGeglSeowA70kSR22Vrr+Ddu5O1/AwoULh50MSZKGzjt6SZI6zEAvSVKHGeglSeowA70kSR1moJckqcMM9JIkdZiBXpKkDjPQS5LUYQZ6SZI6zN+jl6QpWEu/KS5Nlb9HL0nSbGSglySpwwz0kiR1mIFekqQOM9BLktRhBnpJkjrMQC9JUocZ6CVJ6rAZfeNDSukS4FnAfcD9wDXAsTnns1vT/wzYN+d8Vmu5ZwA/AJbknOfPZJolSRplw7ijf2/OeS6wOXAa8PmU0o6t6b8GDulZ5pA6XpIkrYahNd3nnFcAJ1NaFXZtTfp3YLeU0g4AKaWHA68GTp3pNEqSNOqGFuhTShsCh9fBK1uT/gB8DnhTHd4P+DZw48ylTpKkbhhGoH9XSmk58HvgGODgnPPPe+Y5GXhjSmkO8OY6LEmSVtMwAv2xOefNgC2ArwLP750h53w5sAQ4GngUcOFMJlCSpK4Y5jP6ZcDBwEtTSq8YZ5aTKIH+0znn+2c0cZIkdcRQv0efc74N+CfguJRSb1rOBPYCPjzjCZMkqSNm9Hv0fXwYeBtwQHtkzvkPwMVDSZEkSR0RTdMMOw3TLk5c0b1MSVqnNEesC/dJ0gOi3wRfgStJUocZ6CVJ6jADvSRJHWaglySpwzrZm+TcnS9g4cKFw06GJElD5x29JEkdZqCXJKnDDPSSJHWYgV6SpA4z0EuS1GEGekmSOsxAL0lShxnoJUnqMAO9JEkdZqCXJKnD/D16Af62tiSNOH+PXpKk2chAL0lShxnoJUnqMAO9JEkdZqCXJKnDDPSSJHWYgV6SpA6bkS9Pp5QuAZ4F3Av8EbgVuBT4UM75x615Ls45H1OHnw8sAp5MuSBZCnwx5/yumUizJEldMJN39O/NOT8857wp8DxgCfCDlNIre2dMKW0PnA+cDGwFbA68CrhiBtMrSdLIG8rr0HLOS4CjUkqPBv45pXROzyy7A3fknM9ojftl/ZMkSQMa9ntPvwAcBOzcMz4Dc1NKZwD/Bvw453zdTCdOkqRRN+zOeL+r/zdvj6x3/M8A7gFOBJaklK5IKe09s8mTJGm0DTvQb1P/39o7Ied8ec754JzzjsDWwIXA2Smlx81kAiVJGmXDDvT7AtcDv5loppzzTcDRlEcNu8xAuiRJ6oShPKNPKW0LHAwcCOybc25SSu3pzwF2A86hNO9vDLwT+D3l+b0kSRrATAb6o1NK7wQaSlP994Fn55x/NM68y4AFwN8Cm1EC/M+Al+acr52R1EqS1AHRNM2w0zDt4sQV3cvUWtYcMewvYEiS1kD0mzDsZ/SSJGktMtBLktRhBnpJkjrMQC9JUocZ6CVJ6rBOdrU+d+cLWLhw4bCTIUnS0HlHL0lShxnoJUnqMAO9JEkdZqCXJKnDDPSSJHWYgV6SpA4z0EuS1GEGekmSOsxAL0lSh/l79OsIfw9ekrQG/D16SZJmIwO9JEkdZqCXJKnDDPSSJHWYgV6SpA4z0EuS1GEGekmSOsxAL0lSh62zb2lJKV0CPAu4r45aCnw05/yhYaVJkqRRs67f0b835zw35zwX2B84NqW017ATJUnSqFjXA/0Dcs4/AH4F7DLstEiSNCrW2ab7tpRSAM8GHg/8x5CTI0nSyFjX7+jflVJaDtwFfA/4HPCjoaZIkqQRsq4H+mNzzpvlnDcCtgWeCJwy5DRJkjQy1vVA/4Cc8++As4BXDTstkiSNipEJ9CmlrYF9gJ8NOy2SJI2Kdb0z3tEppb+rn+8Cvg0cMcT0SJI0UtbZQJ9zXjDsNEiSNOpGpulekiStPgO9JEkdZqCXJKnDDPSSJHWYgV6SpA5bZ3vdr4lzd76AhQsXDjsZkiQNnXf0kiR1mIFekqQOM9BLktRhBnpJkjrMQC9JUocZ6CVJ6jADvSRJHWaglySpwwz0kiR1mIFekqQOM9BLktRhBnpJkjrMQC9JUocZ6CVJ6jADvSRJHWaglySpwwz0kiR1mIFekqQOM9BLktRhBnpJkjrMQC9JUodF0zTDTsO0e8hDHnL5vffe+4dhp2NtmjNnzhYrVqy4ZdjpWJtmQx5hduRzNuQRZkc+Z0MeYSTzeUvTNC8Zd0rTNJ3722OPPfKw02AezaP5nF15nC35nA157Fo+bbqXJKnDDPSSJHVYVwP9ScNOwAwwj90xG/I5G/IIsyOfsyGP0KF8drIzniRJKrp6Ry9JkjDQS5LUaXOGnYCpSik9DvgMsDlwK3BAzvm/euZZH/gI8BKgAU7IOX9qptM6VQPmcRHwV8ANddSlOee3zGQ610RK6UTg1cB84Mk558vHmWfUy3GQPC5itMtxc+AM4E+Ae4CrgENzzjf3zDfqZTloPhcx2uV5DrA98EfgTuB/55x/2jPPSJclDJzPRYxwWcIIB3rgE8DHcs6fTSntD3wSeH7PPK8DdgR2ogTLy1JKF+ecF89oSqdukDwCnJ5zPmJmkzZtzgE+DHx3gnlGvRzPYfI8wmiXYwO8L+d8CUBK6f3ACcCbeuYb9bIcNJ8w2uX5hpzz7QAppVcApwC798wz6mUJg+UTRrssR7PpPqW0FaUwzqyjzgR2Tylt2TPrvsDJOec/1ivuc4B9Ziyha2A18jjScs7fyzlfN8lsI1uOMHAeR1rO+bax4Ff9ANhunFlHvSwHzedIGwt+1aaUO95eI12WMHA+R96o3tFvC1yfc74fIOd8f0rphjq+3YQ2D1jSGr62zjMKBs0jwGtSSnsBS4H35Jz/Y2aTutaNcjmujk6UY0ppPeBw4NxxJnemLCfJJ4x4eaaUPgXsBQSleb5XJ8pygHzCiJflSN7RaxWfALbPOT8FeD/w5focUaOlS+X4z5TnnR8ddkLWsonyOfLlmXM+OOc8DziSkodOGiCfI1+WoxrorwMeWzuDjHUKeUwd33YtqzarzRtnnnXVQHnMOS/NOd9XP19Up+8yw2ld20a5HAfSlXKsHQ93AvbNOY/XDNqJspwsn10pT4Cc8xnA88YJbp0oyzH98tmFshzJQJ9zvgn4KbBfHbUfcFlvz1fgbOCQlNJ69dn23sC/zVQ618SgeUwpPbb1eVdKz+7fzEgiZ87IluOgulCOKaVjgT2AvXPO9/SZbeTLcpB8jnJ5ppTmppS2bQ0vBG6rf20jXZaD5nOUy3LMqD6jBzgM+ExK6d3AMuAAgJTSV4F355wz5WswzwDGvpL2Dznn3w4jsVM0SB6PSyntAdwP3Au8Pue8dFgJXl0ppY8ArwK2Bi5OKd2ac35Sl8pxwDyOejk+idL0eSXw/ZQSwDU551d2rCwHzecol+fGwNkppY0p6b8NWJhzbrpUlgyez1EuS8BX4EqS1Gkj2XQvSZIGY6CXJKnDDPSSJHWYgV6SpA4z0EuS1GEG+nVARLw4Ir7bGl4QEYuHmKQZExGnRcS0/eJVRMyPiKY1vGVELImILQZY9rCIOGO60jIKIuI5EbF82OmYjSJi/9U5zqf7WNHE1taxMYVy/8eIeO+abNNAP2QREcAHgfdMMt/hEXF5RPxPRCyLiBwR+7amL46I/cdZ7kHjo7iyrmtuz7QFEdFExJ3174aIODUiHrlmOR2OpmluBj7P5Pt3Y+AfgEUzkKx1RtM0322aZrNhp6OfiFgUERcPOx2zwdra1xFxSUQcNd3rXdt6j40h1sUTgLdExGMnnbMPA/3w7QVsCHyr3wwRsR8lUL2J8gtLjwHeRnmJzlQ8D9iB8ktN+40z/f6maeY2TTMX2BN4FvChKW5rXXAK8MaI2GSCefYHftE0zdUzlKZVRMT6EeHxKGkVTdMsAy4ADp3qOmbViaXe3R4VEd+qd6u/iIinRMR+EXFVRNweEZ+KiDmtZeZFxBcj4sb6d1JEPLw1/biI+G1d39UR8X9a0+bXu+PXR8SvIuKOiPh6RDy6lay9gYubid9c9GzgO03T/LApfl+vNr8+xV1xKHAh5c1WE1aepml+C5wP7NY7LSLm1H3yip7xn4mIU+rnF0TED2srxM0R8YWI2Krf9ur+2rM1vCAiVvRs88jaIrE8Ii6NiD0mycN/AbcAL5xgtr2Bi3rS8jcRcUUtt2sj4viIWL9OOzEivtQz//PqvBvX4V0i4msRcUtr+Q3qtLG68aaI+BVwN7BVRLwmIn5WW1tujIhPjq2vLrd1RJxX6+qVdfkmIua35jmktv7cHhGXRcRe/TI9zv49LSLOiIhT6v69vh4fu0bEf9b8fSsiHtNaZnFEvDsivlePgxwRT2tNn7AORMQGtUx/U9d/dUS8OkqL1ZHAgljZwrRDn3z8Wd3G7bXMDm1NWxARKyJi37ru2yPirPZxPM76pnKueEpEfLPm87d1+fVb059e982dEfE9ysV2e5sb1Xp1TUTcFhEXRsSO/dI4Tpo3j4jTa71ZGuU4fGRr+iqte606uE2/fR0RB9b8vrOu96aI+MA49Xib1noPjIir6uePAs8Bjq7rHPfVsVHulr8RpZn65oi4NSLeHhHb1X16R0T8OCKe0FpmjY6VWFnXT46Vdf1B9aZ+nnD/9ORllUcs01TuF1HOUVPTNM2s+QMWU17X+ARgA+CzwNXASZTXIc4DbgJeW+d/KHAVpUn3YcAjgK8Cp7TWuT/lDjuA5wO/B15cp80HGkqg3ALYBLgUOLm1/A+Bv+5J5wJgcWt4H+APwDHAC4DN+uRt/8nGA1sC91BeybprTd8ePdte0RrekfJe51P67NP3Aee0hudSftHrOXV4T+BplNctbw18BzizNf9pwKdaww2w5wTpOa7usx2A9SmtHLcAj2jv83HSeR5wzAR147+Bv+gZ92pg+1q2u9V5Dq3Tnkh5HeaWrfk/A3y6ft4KuJVyIbUh8FggA+/uqRvfqPtlw5qfPweeRLkI3xH4FXB8axvfoLxPfJO6jUvqeubX6W+m1Nmn1nW8tJbHjn3y3bt/T6PU4ZfV5Q+ry58LbANsBHwTOKmnjt1Aef/7hsDfUX5KeZMB68A/1nw+pe7rbYCn1GmLKBfCEx3X29c0v7Fu45mU15nu08pjA3yaUj8fRTkPvGsazxWb1vpxNPCQutxvgXe0pt9a982GdX8sZdXj/POUc8Wj6jz/D7gC2GC8Y2WcNF9IqeePqH9fAb4ywblgft0v2/Tb18CBwH3AxyjnwD+hvP7378dbR2uZq1rDlwBHTVKGi+p2DmblcXA/cHFPGXy9tcyaHiunUerNX9R1vKqmYbs+x0a//XNVz7gHymk6yr3OswelBXbDifZj3/07lYVG9a9W9He0hl9aC759sj4L+GD9/JfA1T3r2IMSKNfvs40vAu/rOQie1pr+FuCy1vCVwIE961jQrgh13MuBf6ecTO6nNPXv0pO3u4DlPX9/ZNWD+28pJ6ixk8dPgE/2bLupyy4DrqH8TONmffL7BErA26oOHwRcOUEZvBy4abyDog73DfSUIHAH8Nyedf5iLI/0D/SfAz4+QbruBRZMUn9OBM5qDf8QeFv9/HBKQPzTOnwE8M2e5V9NPSm06sZzJ9nmW4Ef1c/b1GV2aE1/AauevC4HDuhZx3n0OdEyfqBvB4eN6vr3aY37K1atw4uB97aGg/LLZq+drA7Uee8EXtZn3kVMHuiPBC7tGXc88LWeOt0+zt8PfGmCdS5m9c4Vr6X8qlm0ph8K/KZ+fl3dJ+3px1KPc8qNQAPMa01fD7idejwwQaCn3Gw0wE6tcTvXcY9u5Wkqgf4eYKPWuIOpx3jvOlrLTCXQ/7Jn3E3jlMGyaTxWTqNV1+u4m4FX9Dk2+u2fiQL9Gpd7HbdTnW+rifZjv79R/lGbqbqx9fluyvPom3vGjTXpbQ/Miwf3vGwodybXR8RfA4dQKlZQrno/P8E272qtH0ownejZcdlg05xPueojIh4PfBw4PyK2b2pNoNxtfra9XLR6d0ZE1LR+tmma++roTwMnRMT/bZrmzjru/mbADlpN0/w6In5Cadn4J8pd1amtbe5BuQt/KiVoBOWuaiq2qMueF62e9ZSr/W3GX+QBm1AuWvp5UDlE6RvxdkrrwRzK1fYPWrOcSgl6HwT+F3B90zSX1mnbA3/aU3eCcrfStrhnmy8C3g08nnJnuD7lhAelVQDKiWPMkp71bQ98LCI+0ho3B/gdg3ugvjZNc3epNg86bnqbvRe3lmki4lpqmUxSB7ak3CFfuRrp67Ut5e657WrgFa3h3uO89zgcz+qcK7alnLzb9fLqOh7KvljSM71dH7ev/39e9/eYDVrrmMjYPO11Xt2adiNTd1PTNHe3hhcz+fE2Fb1pvJsJ6t00HCvjbXOQerE6pqvcN2HlDdhqm1XP6KdgCeXKdbOev4c2TXN9RPwppdnxUGCLGhzPo5zIBnUZpRl4YE3TXEEJLttRmugG9QJKE9dB9RneUkoz0VzKHclUnQocWJ8rPRM4vTXtC5RWg8c1TbMJ43f+a7uLcuIf85jW51vq9Bf2lMfGTdOcMMl6d6Hs635WKYeI2JbSVHgM5Y5oU0rzZbtsvwDsFBG7U67sT21NW0K5+m+nc9OmdHBse+C3zCNiQ+Ccut55dX+9s7XN6+v/ea3l25/HtntQz3bnNk1z+AR5nw7zxz7UC8p5rLy4mKgO3Ewp0536rHe837TvdR0rT5hjdmBmfxv9OmC7WPVs3U7D9eNMb6d5LAjt1FN2GzVNc+aA24dWObDyWfDYtDvpf2xB/329VURs1Bqez8qyHbs5mMp6p2yajpXVNV4+evcprJr/6Sr3XSgtHvdOJeEG+omdD4x1FHp4FI+NiFfW6ZtQmtFvBpqIeBnludHqOIcSgPuKiIMiYp+o3wWvHV8OA37VNE3vb0RP5M2U56OPpzyf35VSgU5lDXp0Ug62HYGPABc1TXN9a9omlGaoOyJiHuVZ1UQy8IaI2LB2mnn72IR6Vfxh4MSI2AkgIuZGeQ9B78nlAfUCZEvK875+zmHVznpzKcfHzcB9EfFM4PXtBZqmWQ58iXIx0HuBczqQatk9NCLWq513XjJBGjak9AtZ1jTN7yPiiZTmyLHt/Y7SDHpCrY9bAb1fW/ogsChK57mIiIdFxJ61FWhtOigido/SSesdlDv3r9RpfetALdN/Ad4XpfPi2DH25DrLUkqr2oYTbPtMYI+IOCBKZ82nU+rzp6c1hxP7CqXsjqx1d2dK4BlLw/mUOvWOKJ0Pd6c85gKgaZqbKC2BH4/6NaqI2CwiXhk9X4EdT9M0NwBfBz5Ql3sE8AHggqZpxu5aM7BfPWa2pPQnaOu3r9ej1LmHRekMeQSlPwpN09xCvbiM8s2RJ1NaDXvXO3CnwgFNx7GyusbbP5dRLoReXo/xVwLPbU2frnJ/EeUcNSUG+gnU5qoXUO70rqCcrL5BCZAAX6P0XP8R5W7zLykn/tXxNWBFRCyYYJ5llCbiX0fEXZRnw8spzzoHUiv63sCJTdMsbf9RWiV2i4i0mmkHoGma2yn5/nPKV9na3kx5pncHpY/B2ZOs7q2Uk8JtlGegp/VMfw/wZeDLEfE/lA5ThzFxXT4IOK2ms58zgKfWExlN0/y6ta3llOA03p3VqZR8f62ebKnLL6V8jXFvSlPnMso+GrfXeF3mTuBwStC7k9KC0PsY6LWUIPo74Hus3J/31HWcTOkgeWrd5rWUE/oGE+R9OpxEudBbBuxLeeY+tr8nqwPvopT1OXWeb7PyDv9syh3p0ig9o3vv3Gma5hrK89u3Ujo+nUHp9HjWdGVuMjWve1EuFv+bclyfTnmcNXZR+DLKvllG2Vf/0rOaQygdXy+JiDsofU/2oTTZDmJ/yv67ov4tBw5oTT+KcmNyIyUIfqFn+X77egnlzvQayrnnQkodG/MGyrno9prf3gusD1IuepdHxC8HzMuEpuNYmYIH7Z+mfB33byj1/zbgJZQOgGPpXM4alntEbEap35+YYrr9Pfp1Qb3LO7JpmufW4QWUwDR/iMkaSbUV4JqmaaIObwH8GEg9z1fHW/YwSme6108037okIl5MuRh5WDOkgzlKP5CjevuHaPRFxIGUsp3uO/IZty4cK1MREcdT+odMuUViNnbGW+c0TXMh5SpZ06w2LW434LyfYA2ummdCRDyVcqX/C8qzvmOAfx2lE5c0E7pyrDRN8/drug6b7tdNixntN9EN03JKB8OueiSl+ftOSnPkzylNh5JW5bFS2XQvSVKHeUcvSVKHGeglSeowA70kSR1moJckqcMM9JIkddj/B6B38QZeTxsnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.67\n"
     ]
    }
   ],
   "source": [
    "for hp in [1, 10, 100, 200]:\n",
    "    print(\"============================== rf_estimators: {} ==============================\".format(hp))\n",
    "    adv = AdversarialModelToolbox(biased_model=biased_ml, \n",
    "                                  x_train=x_train, \n",
    "                                  y_train=y_train, \n",
    "                                  x_test=x_test, \n",
    "                                  y_test=y_test,\n",
    "                                  input_feature_names=house_df.columns[input_ids].tolist(),\n",
    "                                  categorical_feature_indices=categorical_input_ids,\n",
    "                                  unbiased_model=unbiased_ml,\n",
    "                                  biased_id=biased_id, \n",
    "                                  fool_explainer_type=ExplainerType.SHAP,\n",
    "                                  ml_type=MLType.REGRESSION, \n",
    "                                  seed=SEED)\n",
    "    adv.train(rf_estimators=hp, perturbator=Perturbator(PerturbationMethod.SUBSTITUTIONS, perturbation_multiplier=10, n_kmeans=10, n_samples=2e4, seed=SEED))\n",
    "    adv.get_explanations(explanation_sample_number=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0069c98-d242-4510-88f5-adf66dee9bce",
   "metadata": {},
   "source": [
    "## Hyperparameter sensitivity pertubation multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20194c2e-b758-423a-b9b9-5d74e55d8f34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================== perturbation_multiplier: 1 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f56d23548a747598429247871309a44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac486913b49b4b519e03a79f035296d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.66193905 0.78464377 0.67227747 3.49042738]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.85\n",
      "============================== perturbation_multiplier: 5 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8cb7b1bace8496a8dd2f784e7a73094",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70076071f4eb4951885045a06e2fd147",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.4802277  0.72935114 0.48685641 3.7106943 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.76\n",
      "============================== perturbation_multiplier: 10 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d90d5b71ddc442caa03ae892566f268",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbe4778c0f7749e28e3b619d4eec3915",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.47427619 0.68355037 0.40235418 3.70673358]\n"
     ]
    },
    {
     "data": {
      "image/png": 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VpccAb6O8RGcqngfsQPm1pv3GmX5/0zRzm6aZC+wJPAv40BS3NRucArwxIjaZYJ79gV80TXP1DKVpFRGxfkR4PEpaRdM0y4ALgEOnuo516sRS726Piohv1bvVX0TEUyJiv4i4KiJuj4hPRcSc1jLzIuKLEXFj/TspIh7emn5cRPy2ru/qiPg/rWnz693x6yPiVxFxR0R8PSIe3UrW3sDFzcRvLno28J2maX7YFL+vV5tfn+KuOBS4kPJmqwkrT9M0vwXOB3brnRYRc+o+eUXP+M9ExCn18wsi4oe1FeLmiPhCRGzVb3t1f+3ZGl4QESt6tnlkbZFYHhGXRsQeq8nDfwG3AC+cYLa9gYt60vI3EXFFLbdrI+L4iFi/TjsxIr7UM//z6rwb1+FdIuJrEXFLa/kN6rSxuvGmiPgVcDewVUS8JiJ+VltbboyIT46try63dUScV+vqlXX5JiLmt+Y5pLb+3B4Rl0XEXv0yPc7+PS0izoiIU+r+vb4eH7tGxH/W/H0rIh7TWmZxRLw7Ir5Xj4McEU9rTZ+wDkTEBrVMf1PXf3VEvDpKi9WRwIJY2cK0Q598/Fndxu21zA5tTVsQESsiYt+67tsj4qz2cTzO+qZyrnhKRHyz5vO3dfn1W9OfXvfNnRHxPcrFdnubG9V6dU1E3BYRF0bEjv3SOE6aN4+I02u9WRrlOHxka/oqrXutOrhNv30dEQfW/L6zrvemiPjAOPV4m9Z6D4yIq+rnjwLPAY6u6xz39bFR7pa/EaWZ+uaIuDUi3h4R29V9ekdE/DgintBaZo2OlVhZ10+OlXX9QfWmfp5w//TkZZVHLNNU7hdRzlFT0zTNOvMHLKa8rvEJwAbAZ4GrgZMor0ScB9wEvLbO/1DgKkqT7sOARwBfBU5prXN/yh12AM8Hfg+8uE6bDzSUQLkFsAlwKXBya/kfAn/dk84FwOLW8D7AH4BjgBcAm/XJ2/6rGw9sCdxDeSXrrjV9e/Rse0VreEfKu51P6bNP3wec0xqeS/lVr+fU4T2Bp1Fet7w18B3gzNb8pwGfag03wJ4TpOe4us92ANantHLcAjyivc/HSed5wDET1I3/Bv6iZ9yrge1r2e5W5zm0Tnsi5ZWYW7bm/wzw6fp5K+BWyoXUhsBjgQy8u6dufKPulw1rfv4ceBLlInxH4FfA8a1tfIPyPvFN6jYuqeuZX6e/mVJnn1rX8dJaHjv2yXfv/j2NUodfVpc/rC5/LrANsBHwTeCknjp2A+Ud8BsCf0f5KeVNBqwD/1jz+ZS6r7cBnlKnLaJcCE90XG9f0/zGuo1nUl5puk8rjw3waUr9fBTlPPCuaTxXbFrrx9HAQ+pyvwXe0Zp+a903G9b9sZRVj/PPU84Vj6rz/D/gCmCD8Y6VcdJ8IaWeP6L+fQX4ygTngvl1v2zTb18DBwL3AR+jnAP/hPIK4L8fbx2tZa5qDV8CHLWaMlxUt3MwK4+D+4GLe8rg661l1vRYOY1Sb/6iruNVNQ3b9Tk2+u2fq3rGPVBO01HudZ49KC2wG060H/vu36ksNKp/taK/ozX80lrw7ZP1WcAH6+e/BK7uWccelEC5fp9tfBF4X89B8LTW9LcAl7WGrwQO7FnHgnZFqONeDvw75WRyP6Wpf5eevN0FLO/5+yOrHtx/SzlBjZ08fgJ8smfbTV12GXAN5acaN+uT3ydQAt5Wdfgg4MoJyuDlwE3jHRR1uG+gpwSBO4Dn9qzzF2N5pH+g/xzw8QnSdS+wYDX150TgrNbwD4G31c8PpwTEP63DRwDf7Fn+1dSTQqtuPHc123wr8KP6eZu6zA6t6S9g1ZPX5cABPes4jz4nWsYP9O3gsFFd/z6tcX/FqnV4MfDe1nBQftnstaurA3XeO4GX9Zl3EasP9EcCl/aMOx74Wk+dbh/n7we+NME6FzO5c8VrKb9sFq3phwK/qZ9fV/dJe/qx1OOcciPQAPNa09cDbqceD0wQ6Ck3Gw2wU2vcznXco1t5mkqgvwfYqDXuYOox3ruO1jJTCfS/7Bl30zhlsGwaj5XTaNX1Ou5m4BV9jo1++2eiQL/G5V7H7VTn22qi/djvb5R/1Gaqbmx9vpvyPPrmnnFjTXrbA/PiwT0vG8qdyfUR8dfAIZSKFZSr3s9PsM27WuuHEkwnenZcNtg051Ou+oiIxwMfB86PiO2bWhMod5ufbS8Xrd6dERE1rZ9tmua+OvrTwAkR8X+bprmzjru/GbCDVtM0v46In1BaNv6Jcld1amube1Duwp9KCRpBuauaii3qsudFq2c95Wp/m/EXecAmlIuWfh5UDlH6Rryd0nowh3K1/YPWLKdSgt4Hgf8FXN80zaV12vbAn/bUnaDcrbQt7tnmi4B3A4+n3BmuTznhQWkVgHLiGLOkZ33bAx+LiI+0xs0BfsfgHqivTdPcXarNg46b3mbvxa1lmoi4llomq6kDW1LukK+cRPp6bUu5e267GnhFa7j3OO89DsczmXPFtpSTd7teXl3HQ9kXS3qmt+vj9vX/z+v+HrNBax0TGZunvc6rW9NuZOpuaprm7tbwYlZ/vE1FbxrvZoJ6Nw3HynjbHKReTMZ0lfsmrLwBm7R16hn9FCyhXLlu1vP30KZpro+IP6U0Ox4KbFGD43mUE9mgLqM0Aw+saZorKMFlO0oT3aBeQGniOqg+w1tKaSaaS7kjmapTgQPrc6VnAqe3pn2B0mrwuKZpNmH8zn9td1FO/GMe0/p8S53+wp7y2LhpmhNWs95dKPu6n1XKISK2pTQVHkO5I9qU0nzZLtsvADtFxO6UK/tTW9OWUK7+2+nctCkdHNse+D3ziNgQOKeud17dX+9sbfP6+n9ea/n257HtHtSz3blN0xw+Qd6nw/yxD/WCch4rLy4mqgM3U8p0pz7rHe837Xtdx8oT5pgdmNnfRr8O2C5WPVu303D9ONPbaR4LQjv1lN1GTdOcOeD2oVUOrHwWPDbtTvofW9B/X28VERu1huezsmzHbg6mst4pm6ZjZbLGy0fvPoVV8z9d5b4LpcXj3qkk3EA/sfOBsY5CD4/isRHxyjp9E0oz+s1AExEvozw3moxzKAG4r4g4KCL2ifpd8Nrx5TDgV03T9P5G9ETeTHk++njK8/ldKRXoVNagRyflYNsR+AhwUdM017embUJphrojIuZRnlVNJANviIgNa6eZt49NqFfFHwZOjIidACJibpT3EPSeXB5QL0C2pDzv6+ccVu2sN5dyfNwM3BcRzwRe316gaZrlwJcoFwO9FzinA6mW3UMjYr3aeeclE6RhQ0q/kGVN0/w+Ip5IaY4c297vKM2gJ9T6uBXQ+7WlDwKLonSei4h4WETsWVuB1qaDImL3KJ203kG5c/9Knda3DtQy/RfgfVE6L44dY0+usyyltKptOMG2zwT2iIgDonTWfDqlPn96WnM4sa9Qyu7IWnd3pgSesTScT6lT74jS+XB3ymMuAJqmuYnSEvjxqF+jiojNIuKV0fMV2PE0TXMD8HXgA3W5RwAfAC5ommbsrjUD+9VjZktKf4K2fvt6PUqde1iUzpBHUPqj0DTNLdSLyyjfHHkypdWwd70Ddyoc0HQcK5M13v65jHIh9PJ6jL8SeG5r+nSV+4so56gpMdBPoDZXvYByp3cF5WT1DUqABPgapef6jyh3m39JOfFPxteAFRGxYIJ5llGaiH8dEXdRng0vpzzrHEit6HsDJzZNs7T9R2mV2C0i0iTTDkDTNLdT8v3nlK+ytb2Z8kzvDkofg7NXs7q3Uk4Kt1GegZ7WM/09wJeBL0fE/1A6TB3GxHX5IOC0ms5+zgCeWk9kNE3z69a2llOC03h3VqdS8v21erKlLr+U8jXGvSlNncso+2jcXuN1mTuBwylB705KC0LvY6DXUoLo74DvsXJ/3lPXcTKlg+SpdZvXUk7oG0yQ9+lwEuVCbxmwL+WZ+9j+Xl0deBelrM+p83yblXf4Z1PuSJdG6Rnde+dO0zTXUJ7fvpXS8ekMSqfHs6Yrc6tT87oX5WLxvynH9emUx1ljF4Uvo+ybZZR99S89qzmE0vH1koi4g9L3ZB9Kk+0g9qfsvyvq33LggNb0oyg3JjdSguAXepbvt6+XUO5Mr6Gcey6k1LExb6Cci26v+e29wPog5aJ3eUT8csC8TGg6jpUpeND+acrXcf+GUv9vA15C6QA4ls7lrGG5R8RmlPr9iSmm29+jnw3qXd6RTdM8tw4voASm+UNM1kiqrQDXNE0TdXgL4MdA6nm+Ot6yh1E6071+ovlmk4h4MeVi5GHNkA7mKP1AjurtH6LRFxEHUsp2uu/IZ9xsOFamIiKOp/QPmXKLxLrYGW/WaZrmQspVsqZZbVrcbsB5P8EaXDXPhIh4KuVK/xeUZ33HAP86SicuaSZ05Vhpmubv13QdNt3PTosZ7TfRDdNySgfDrnokpfn7Tkpz5M8pTYeSVuWxUtl0L0lSh3lHL0lShxnoJUnqMAO9JEkdZqCXJKnDDPSSJHXY/wdRqPEGDvuokAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.68\n",
      "============================== perturbation_multiplier: 20 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "861bff9d3dd74950ba0931736e498d9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fde1c4c98bf54d8d9b5428614f5288b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.43223407 0.58714166 0.28645797 3.76982543]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.57\n"
     ]
    }
   ],
   "source": [
    "for hp in [1, 5, 10, 20]:\n",
    "    print(\"============================== perturbation_multiplier: {} ==============================\".format(hp))\n",
    "    adv = AdversarialModelToolbox(biased_model=biased_ml, \n",
    "                                  x_train=x_train, \n",
    "                                  y_train=y_train, \n",
    "                                  x_test=x_test, \n",
    "                                  y_test=y_test,\n",
    "                                  input_feature_names=house_df.columns[input_ids].tolist(),\n",
    "                                  categorical_feature_indices=categorical_input_ids,\n",
    "                                  unbiased_model=unbiased_ml,\n",
    "                                  biased_id=biased_id, \n",
    "                                  fool_explainer_type=ExplainerType.SHAP,\n",
    "                                  ml_type=MLType.REGRESSION, \n",
    "                                  seed=SEED)\n",
    "    adv.train(rf_estimators=100, perturbator=Perturbator(PerturbationMethod.SUBSTITUTIONS, perturbation_multiplier=hp, n_kmeans=10, n_samples=2e4, seed=SEED))\n",
    "    adv.get_explanations(explanation_sample_number=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8a0185-6b9a-45ee-80ba-35523c9cc5e9",
   "metadata": {},
   "source": [
    "## Hyperparameter sensitivity n_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f39f06f8-3e58-43de-8537-ca933abdd5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================== n_kmeans: 1 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7690384965f641fc85fe3fa0a07ada74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c46f98f599c44d6e87b16414dfa9407e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[3.49191419 0.92987912 0.34424898 2.53781161]\n"
     ]
    },
    {
     "data": {
      "image/png": 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bdd1cYDfgWcCHprit1cFJwBsjYoMJ5tkH+EXXdVfOUJqWERFrR4THo6RldF23BDgPOGiq61ijTiz17vbwiPhWvVv9RUQ8JSL2jogrIuLWiPhURMzpLTMvIr4YEdfXvxMi4uG96cdExG/r+q6MiP/Tm7Z1vTt+fUT8KiJui4ivR8Sje8naA7iwm/jNRc8GvtN13Q+74vf1avPrU9wVBwHnU95CNWHl6brut8C5wM6D0yJiTt0nrxgY/5mIOKl+fkFE/LC2QtwYEV+IiM2Gba/ur916w/Mj4t6BbR5WWySWRsTFEbHrcvLwX8BNwAsnmG0P4IKBtPxNRFxWy+3qiDg2Itau046PiC8NzP+8Ou/6dXjHiPhaRNzUW36dOm2sbrwpIn4F3AlsFhGviYif1daW6yPik2Prq8ttHhHn1Lp6eV2+i4ite/McWFt/bo2ISyJi92GZHmf/nhIRp0XESXX/XluPj50i4j9r/r4VEY/pLbMwIt4dEd+rx0GOiKf1pk9YByJinVqmv6nrvzIiXh2lxeowYH480MK07ZB8/Fndxq21zA7qTZsfEfdGxF513bdGxBn943ic9U3lXPGUiPhmzedv6/Jr96Y/ve6b2yPie5SL7f4216v16qqIuCUizo+I7YalcZw0bxwRp9Z6szjKcfjI3vRlWvd6dXCLYfs6Ivar+X1nXe8NEfGBcerxFr317hcRV9TPHwWeAxxR1znuq12j3C1/I0oz9Y0RcXNEvD0itqr79LaI+HFEPKG3zEodK/FAXT8xHqjrD6o39fOE+2cgL8s8Ypmmcr+Aco6amq7r1pg/YCHl1YpPANYBPgtcCZxAeV3hPOAG4LV1/ocCV1CadB8GPAL4KnBSb537UO6wA3g+8HvgxXXa1kBHCZSbABsAFwMn9pb/IfDXA+mcDyzsDe8J/AE4CngBsNGQvO2zvPHApsBdlNes7lTTt+vAtu/tDW9Hee/ySUP26fuAs3rDcym/xvWcOrwb8DTK65Y3B74DnN6b/xTgU73hDthtgvQcU/fZtsDalFaOm4BH9Pf5OOk8Bzhqgrrx38BfDIx7NbBNLdud6zwH1WlPpLyuctPe/J8BPl0/bwbcTLmQWhd4LJCBdw/UjW/U/bJuzc+fA0+iXIRvB/wKOLa3jW9Q3v29Qd3GRXU9W9fpb6bU2afWdby0lsd2Q/I9uH9PodThl9XlD67Lnw1sAawHfBM4YaCOXUd5d/u6wN9Rfjp5g0nWgX+s+XxK3ddbAE+p046kXAhPdFxvU9P8xrqNZ1JeN7pnL48d8GlK/XwU5Tzwrmk8V2xY68cRwEPqcr8F3tGbfnPdN+vW/bGYZY/zz1POFY+q8/w/4DJgnfGOlXHSfD6lnj+i/n0F+MoE54Kt637ZYti+BvYD7gE+RjkH/gnl1b1/P946estc0Ru+CDh8OWV4ZN3OATxwHNwHXDhQBl/vLbOyx8oplHrzF3Udr6pp2GrIsTFs/1wxMO7+cpqOcq/z7EppgV13ov04dP9OZaHZ+lcr+jt6wy+tBd8/WZ8BfLB+/kvgyoF17EoJlGsP2cYXgfcNHARP601/C3BJb/hyYL+BdczvV4Q67uXAv1NOJvdRmvp3HMjbHcDSgb8/suzB/beUE9TYyeMnwCcHtt3VZZcAV1F+RnGjIfl9AiXgbVaH9wcun6AMXg7cMN5BUYeHBnpKELgNeO7AOn8xlkeGB/rPAR+fIF13A/OXU3+OB87oDf8QeFv9/HBKQPzTOnwo8M2B5V9NPSn06sZzl7PNtwI/qp+3qMts25v+ApY9eV0K7DuwjnMYcqJl/EDfDw7r1fXv2Rv3VyxbhxcC7+0NB+VXyF67vDpQ570deNmQeY9k+YH+MODigXHHAl8bqNP94/z9wJcmWOdCVuxc8VrKr45Fb/pBwG/q59fVfdKffjT1OKfcCHTAvN70tYBbqccDEwR6ys1GB2zfG7dDHffoXp6mEujvAtbrjTuAeowPrqO3zFQC/S8Hxt0wThksmcZj5RR6db2OuxF4xZBjY9j+mSjQr3S513Hb1/k2m2g/DvubzT9qM1XX9z7fSXkefePAuLEmvW2AefHgnpcd5c7k2oj4a+BASsUKylXv5yfY5h299UMJphM9Oy4b7LpzKVd9RMTjgY8D50bENl2tCZS7zc/2l4te786IiJrWz3Zdd08d/WnguIj4v13X3V7H3ddNsoNW13W/joifUFo2/olyV3Vyb5u7Uu7Cn0oJGkG5q5qKTeqy50SvZz3lan+L8Re53waUi5ZhHlQOUfpGvJ3SejCHcrX9g94sJ1OC3geB/wVc23XdxXXaNsCfDtSdoNyt9C0c2OaLgHcDj6fcGa5NOeFBaRWAcuIYs2hgfdsAH4uIj/TGzQF+x+TdX1+7rruzVJsHHTeDzd4Le8t0EXE1tUyWUwc2pdwhX74C6Ru0JeXuue9K4BW94cHjfPA4HM+KnCu2pJy8+/Xyyjoeyr5YNDC9Xx+3qf9/Xvf3mHV665jI2Dz9dV7Zm3Y9U3dD13V39oYXsvzjbSoG03gnE9S7aThWxtvmZOrFipiuct+AB27AVtga9Yx+ChZRrlw3Gvh7aNd110bEn1KaHQ8CNqnB8RzKiWyyLqE0A09a13WXUYLLVpQmusl6AaWJa//6DG8xpZloLuWOZKpOBvarz5WeCZzam/YFSqvB47qu24DxO//13UE58Y95TO/zTXX6CwfKY/2u645bznp3pOzrYZYph4jYktJUeBTljmhDSvNlv2y/AGwfEbtQruxP7k1bRLn676dzw650cOy7/3fII2Jd4Ky63nl1f72zt81r6/95veX7n8e2u//Adud2XXfIBHmfDluPfagXlPN44OJiojpwI6VMtx+y3vF+m37QNTxwwhyzLTP7O+bXAFvFsmfrfhquHWd6P81jQWj7gbJbr+u60ye5feiVAw88Cx6bdjvDjy0Yvq83i4j1esNb80DZjt0cTGW9UzZNx8qKGi8fg/sUls3/dJX7jpQWj7unknAD/cTOBcY6Cj08isdGxCvr9A0ozeg3Al1EvIzy3GhFnEUJwENFxP4RsWfU74LXji8HA7/qum7w95wn8mbK89HHU57P70SpQCezEj06KQfbdsBHgAu6rru2N20DSjPUbRExj/KsaiIZeENErFs7zbx9bEK9Kv4wcHxEbA8QEXOjvIdg8ORyv3oBsinled8wZ7FsZ725lOPjRuCeiHgm8Pr+Al3XLQW+RLkYGLzAORVIteweGhFr1c47L5kgDetS+oUs6bru9xHxREpz5Nj2fkdpBj2u1sfNgMGvLX0QODJK57mIiIdFxG61FWhV2j8idonSSesdlDv3r9RpQ+tALdN/Ad4XpfPi2DH25DrLYkqr2roTbPt0YNeI2DdKZ82nU+rzp6c1hxP7CqXsDqt1dwdK4BlLw7mUOvWOKJ0Pd6E85gKg67obKC2BH4/6NaqI2CgiXhkDX4EdT9d11wFfBz5Ql3sE8AHgvK7rxu5aM7B3PWY2pfQn6Bu2r9ei1LmHRekMeSilPwpd191EvbiM8s2RJ1NaDQfXO+lOhZM0HcfKihpv/1xCuRB6eT3GXwk8tzd9usr9RZRz1JQY6CdQm6teQLnTu4xysvoGJUACfI3Sc/1HlLvNv6Sc+FfE14B7I2L+BPMsoTQR/zoi7qA8G15KedY5KbWi7wEc33Xd4v4fpVVi54hIK5h2ALquu5WS7z+nfJWt782UZ3q3UfoYnLmc1b2VclK4hfIM9JSB6e8Bvgx8OSL+h9Jh6mAmrsv7A6fUdA5zGvDUeiKj67pf97a1lBKcxruzOpmS76/Vky11+cWUrzHuQWnqXELZR+P2Gq/L3A4cQgl6t1NaEAYfA72WEkR/B3yPB/bnXXUdJ1I6SJ5ct3k15YS+zgR5nw4nUC70lgB7UZ65j+3v5dWBd1HK+qw6z7d54A7/TMod6eIoPaMH79zpuu4qyvPbt1I6Pp1G6fR4xnRlbnlqXnenXCz+N+W4PpXyOGvsovBllH2zhLKv/mVgNQdSOr5eFBG3Ufqe7Elpsp2MfSj777L6txTYtzf9cMqNyfWUIPiFgeWH7etFlDvTqyjnnvMpdWzMGyjnoltrfgcvsD5IuehdGhG/nGReJjQdx8oUPGj/dOXruH9Dqf+3AC+hdAAcS+dSVrLcI2IjSv3+xBTT7e/Rrw7qXd5hXdc9tw7PpwSmrUeYrFmptgJc1XVd1OFNgB8DaeD56njLHkzpTPf6ieZbnUTEiykXIw/rRnQwR+kHcvhg/xDNfhGxH6Vsp/uOfMatDsfKVETEsZT+IVNukVgTO+OtdrquO59ylaxpVpsWt5rkvJ9gJa6aZ0JEPJVypf8LyrO+o4B/nU0nLmkmtHKsdF339yu7DpvuV08Lmd1vohulpZQOhq16JKX5+3ZKc+TPKU2HkpblsVLZdC9JUsO8o5ckqWEGekmSGmaglySpYQZ6SZIaZqCXJKlh/x9aTPEGpymwVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.97\n",
      "============================== n_kmeans: 5 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d92e634aaea74d2dbb6c930ffac825ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41cfbd203cbe46e5bb57158bd1898b10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.64863621 0.69307539 0.44594336 3.65549276]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.74\n",
      "============================== n_kmeans: 10 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "757f5324d0d344d7ad0d2a0fe6e2bf5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66f4d7ed9d31451da286ff2eeb683f8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.47427619 0.68355037 0.40235418 3.70673358]\n"
     ]
    },
    {
     "data": {
      "image/png": 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VpccAb6O8RGcqngfsQPm1pv3GmX5/0zRzm6aZC+wJPAv40BS3NRucArwxIjaZYJ79gV80TXP1DKVpFRGxfkR4PEpaRdM0y4ALgEOnuo516sRS726Piohv1bvVX0TEUyJiv4i4KiJuj4hPRcSc1jLzIuKLEXFj/TspIh7emn5cRPy2ru/qiPg/rWnz693x6yPiVxFxR0R8PSIe3UrW3sDFzcRvLno28J2maX7YFL+vV5tfn+KuOBS4kPJmqwkrT9M0vwXOB3brnRYRc+o+eUXP+M9ExCn18wsi4oe1FeLmiPhCRGzVb3t1f+3ZGl4QESt6tnlkbZFYHhGXRsQeq8nDfwG3AC+cYLa9gYt60vI3EXFFLbdrI+L4iFi/TjsxIr7UM//z6rwb1+FdIuJrEXFLa/kN6rSxuvGmiPgVcDewVUS8JiJ+VltbboyIT46try63dUScV+vqlXX5JiLmt+Y5pLb+3B4Rl0XEXv0yPc7+PS0izoiIU+r+vb4eH7tGxH/W/H0rIh7TWmZxRLw7Ir5Xj4McEU9rTZ+wDkTEBrVMf1PXf3VEvDpKi9WRwIJY2cK0Q598/Fndxu21zA5tTVsQESsiYt+67tsj4qz2cTzO+qZyrnhKRHyz5vO3dfn1W9OfXvfNnRHxPcrFdnubG9V6dU1E3BYRF0bEjv3SOE6aN4+I02u9WRrlOHxka/oqrXutOrhNv30dEQfW/L6zrvemiPjAOPV4m9Z6D4yIq+rnjwLPAY6u6xz39bFR7pa/EaWZ+uaIuDUi3h4R29V9ekdE/DgintBaZo2OlVhZ10+OlXX9QfWmfp5w//TkZZVHLNNU7hdRzlFT0zTNOvMHLKa8rvEJwAbAZ4GrgZMor0ScB9wEvLbO/1DgKkqT7sOARwBfBU5prXN/yh12AM8Hfg+8uE6bDzSUQLkFsAlwKXBya/kfAn/dk84FwOLW8D7AH4BjgBcAm/XJ2/6rGw9sCdxDeSXrrjV9e/Rse0VreEfKu51P6bNP3wec0xqeS/lVr+fU4T2Bp1Fet7w18B3gzNb8pwGfag03wJ4TpOe4us92ANantHLcAjyivc/HSed5wDET1I3/Bv6iZ9yrge1r2e5W5zm0Tnsi5ZWYW7bm/wzw6fp5K+BWyoXUhsBjgQy8u6dufKPulw1rfv4ceBLlInxH4FfA8a1tfIPyPvFN6jYuqeuZX6e/mVJnn1rX8dJaHjv2yXfv/j2NUodfVpc/rC5/LrANsBHwTeCknjp2A+Ud8BsCf0f5KeVNBqwD/1jz+ZS6r7cBnlKnLaJcCE90XG9f0/zGuo1nUl5puk8rjw3waUr9fBTlPPCuaTxXbFrrx9HAQ+pyvwXe0Zp+a903G9b9sZRVj/PPU84Vj6rz/D/gCmCD8Y6VcdJ8IaWeP6L+fQX4ygTngvl1v2zTb18DBwL3AR+jnAP/hPIK4L8fbx2tZa5qDV8CHLWaMlxUt3MwK4+D+4GLe8rg661l1vRYOY1Sb/6iruNVNQ3b9Tk2+u2fq3rGPVBO01HudZ49KC2wG060H/vu36ksNKp/taK/ozX80lrw7ZP1WcAH6+e/BK7uWccelEC5fp9tfBF4X89B8LTW9LcAl7WGrwQO7FnHgnZFqONeDvw75WRyP6Wpf5eevN0FLO/5+yOrHtx/SzlBjZ08fgJ8smfbTV12GXAN5acaN+uT3ydQAt5Wdfgg4MoJyuDlwE3jHRR1uG+gpwSBO4Dn9qzzF2N5pH+g/xzw8QnSdS+wYDX150TgrNbwD4G31c8PpwTEP63DRwDf7Fn+1dSTQqtuPHc123wr8KP6eZu6zA6t6S9g1ZPX5cABPes4jz4nWsYP9O3gsFFd/z6tcX/FqnV4MfDe1nBQftnstaurA3XeO4GX9Zl3EasP9EcCl/aMOx74Wk+dbh/n7we+NME6FzO5c8VrKb9sFq3phwK/qZ9fV/dJe/qx1OOcciPQAPNa09cDbqceD0wQ6Ck3Gw2wU2vcznXco1t5mkqgvwfYqDXuYOox3ruO1jJTCfS/7Bl30zhlsGwaj5XTaNX1Ou5m4BV9jo1++2eiQL/G5V7H7VTn22qi/djvb5R/1Gaqbmx9vpvyPPrmnnFjTXrbA/PiwT0vG8qdyfUR8dfAIZSKFZSr3s9PsM27WuuHEkwnenZcNtg051Ou+oiIxwMfB86PiO2bWhMod5ufbS8Xrd6dERE1rZ9tmua+OvrTwAkR8X+bprmzjru/GbCDVtM0v46In1BaNv6Jcld1amube1Duwp9KCRpBuauaii3qsudFq2c95Wp/m/EXecAmlIuWfh5UDlH6Rryd0nowh3K1/YPWLKdSgt4Hgf8FXN80zaV12vbAn/bUnaDcrbQt7tnmi4B3A4+n3BmuTznhQWkVgHLiGLOkZ33bAx+LiI+0xs0BfsfgHqivTdPcXarNg46b3mbvxa1lmoi4llomq6kDW1LukK+cRPp6bUu5e267GnhFa7j3OO89DsczmXPFtpSTd7teXl3HQ9kXS3qmt+vj9vX/z+v+HrNBax0TGZunvc6rW9NuZOpuaprm7tbwYlZ/vE1FbxrvZoJ6Nw3HynjbHKReTMZ0lfsmrLwBm7R16hn9FCyhXLlu1vP30KZpro+IP6U0Ox4KbFGD43mUE9mgLqM0Aw+saZorKMFlO0oT3aBeQGniOqg+w1tKaSaaS7kjmapTgQPrc6VnAqe3pn2B0mrwuKZpNmH8zn9td1FO/GMe0/p8S53+wp7y2LhpmhNWs95dKPu6n1XKISK2pTQVHkO5I9qU0nzZLtsvADtFxO6UK/tTW9OWUK7+2+nctCkdHNse+D3ziNgQOKeud17dX+9sbfP6+n9ea/n257HtHtSz3blN0xw+Qd6nw/yxD/WCch4rLy4mqgM3U8p0pz7rHe837Xtdx8oT5pgdmNnfRr8O2C5WPVu303D9ONPbaR4LQjv1lN1GTdOcOeD2oVUOrHwWPDbtTvofW9B/X28VERu1huezsmzHbg6mst4pm6ZjZbLGy0fvPoVV8z9d5b4LpcXj3qkk3EA/sfOBsY5CD4/isRHxyjp9E0oz+s1AExEvozw3moxzKAG4r4g4KCL2ifpd8Nrx5TDgV03T9P5G9ETeTHk++njK8/ldKRXoVNagRyflYNsR+AhwUdM017embUJphrojIuZRnlVNJANviIgNa6eZt49NqFfFHwZOjIidACJibpT3EPSeXB5QL0C2pDzv6+ccVu2sN5dyfNwM3BcRzwRe316gaZrlwJcoFwO9FzinA6mW3UMjYr3aeeclE6RhQ0q/kGVN0/w+Ip5IaY4c297vKM2gJ9T6uBXQ+7WlDwKLonSei4h4WETsWVuB1qaDImL3KJ203kG5c/9Knda3DtQy/RfgfVE6L44dY0+usyyltKptOMG2zwT2iIgDonTWfDqlPn96WnM4sa9Qyu7IWnd3pgSesTScT6lT74jS+XB3ymMuAJqmuYnSEvjxqF+jiojNIuKV0fMV2PE0TXMD8HXgA3W5RwAfAC5ommbsrjUD+9VjZktKf4K2fvt6PUqde1iUzpBHUPqj0DTNLdSLyyjfHHkypdWwd70Ddyoc0HQcK5M13v65jHIh9PJ6jL8SeG5r+nSV+4so56gpMdBPoDZXvYByp3cF5WT1DUqABPgapef6jyh3m39JOfFPxteAFRGxYIJ5llGaiH8dEXdRng0vpzzrHEit6HsDJzZNs7T9R2mV2C0i0iTTDkDTNLdT8v3nlK+ytb2Z8kzvDkofg7NXs7q3Uk4Kt1GegZ7WM/09wJeBL0fE/1A6TB3GxHX5IOC0ms5+zgCeWk9kNE3z69a2llOC03h3VqdS8v21erKlLr+U8jXGvSlNncso+2jcXuN1mTuBwylB705KC0LvY6DXUoLo74DvsXJ/3lPXcTKlg+SpdZvXUk7oG0yQ9+lwEuVCbxmwL+WZ+9j+Xl0deBelrM+p83yblXf4Z1PuSJdG6Rnde+dO0zTXUJ7fvpXS8ekMSqfHs6Yrc6tT87oX5WLxvynH9emUx1ljF4Uvo+ybZZR99S89qzmE0vH1koi4g9L3ZB9Kk+0g9qfsvyvq33LggNb0oyg3JjdSguAXepbvt6+XUO5Mr6Gcey6k1LExb6Cci26v+e29wPog5aJ3eUT8csC8TGg6jpUpeND+acrXcf+GUv9vA15C6QA4ls7lrGG5R8RmlPr9iSmm29+jnw3qXd6RTdM8tw4voASm+UNM1kiqrQDXNE0TdXgL4MdA6nm+Ot6yh1E6071+ovlmk4h4MeVi5GHNkA7mKP1AjurtH6LRFxEHUsp2uu/IZ9xsOFamIiKOp/QPmXKLxLrYGW/WaZrmQspVsqZZbVrcbsB5P8EaXDXPhIh4KuVK/xeUZ33HAP86SicuaSZ05Vhpmubv13QdNt3PTosZ7TfRDdNySgfDrnokpfn7Tkpz5M8pTYeSVuWxUtl0L0lSh3lHL0lShxnoJUnqMAO9JEkdZqCXJKnDDPSSJHXY/wdRqPEGDvuokAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.68\n",
      "============================== n_kmeans: 20 ==============================\n",
      "Calculating Shap explanations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95c227e29e514ba19cd2f47b256ac0a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shap explanation:\n",
      "[3.49940184 0.93627934 0.33441141 2.50455891]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5b952a4fd024be1a1b360b26cb62be7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/101 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Shap explanation:\n",
      "[2.3991273  0.69733306 0.44198388 3.74857586]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction fidelity between original and adversarial model: 0.52\n"
     ]
    }
   ],
   "source": [
    "for hp in [1, 5, 10, 20]:\n",
    "    print(\"============================== n_kmeans: {} ==============================\".format(hp))\n",
    "    adv = AdversarialModelToolbox(biased_model=biased_ml, \n",
    "                                  x_train=x_train, \n",
    "                                  y_train=y_train, \n",
    "                                  x_test=x_test, \n",
    "                                  y_test=y_test,\n",
    "                                  input_feature_names=house_df.columns[input_ids].tolist(),\n",
    "                                  categorical_feature_indices=categorical_input_ids,\n",
    "                                  unbiased_model=unbiased_ml,\n",
    "                                  biased_id=biased_id, \n",
    "                                  fool_explainer_type=ExplainerType.SHAP,\n",
    "                                  ml_type=MLType.REGRESSION, \n",
    "                                  seed=SEED)\n",
    "    adv.train(rf_estimators=100, perturbator=Perturbator(PerturbationMethod.SUBSTITUTIONS, perturbation_multiplier=10, n_kmeans=hp, n_samples=2e4, seed=SEED))\n",
    "    adv.get_explanations(explanation_sample_number=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd67d0d-a249-49f6-a6ce-d181f22e79e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}