main.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're going to demonstrate the usage of each XAI method on some example dataset.\n",
    "\n",
    "Methods will be\n",
    "- LIME\n",
    "- SHAP\n",
    "- Dice"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SpaceShip Titanic Dataset\n",
    "To download the dataset proceed the following link https://www.kaggle.com/competitions/spaceship-titanic. And extract the downloaded dataset into `datasets/spaceship-titanic`."
   ]
  },
  {
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   "execution_count": 1,
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    }
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shap\n",
    "import lime\n",
    "import xgboost as xgb\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('datasets/spaceship-titanic/train.csv')\n",
    "df_train.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "HomePlanet      201\n",
       "CryoSleep       217\n",
       "Cabin           199\n",
       "Destination     182\n",
       "Age             179\n",
       "VIP             203\n",
       "RoomService     181\n",
       "FoodCourt       183\n",
       "ShoppingMall    208\n",
       "Spa             183\n",
       "VRDeck          188\n",
       "Name            200\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum()"
   ]
  },
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   "cell_type": "code",
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    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      object\n",
       "HomePlanet       object\n",
       "CryoSleep        object\n",
       "Cabin            object\n",
       "Destination      object\n",
       "Age             float64\n",
       "VIP              object\n",
       "RoomService     float64\n",
       "FoodCourt       float64\n",
       "ShoppingMall    float64\n",
       "Spa             float64\n",
       "VRDeck          float64\n",
       "Name             object\n",
       "Transported        bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Earth     4602\n",
       "Europa    2131\n",
       "Mars      1759\n",
       "Name: HomePlanet, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.HomePlanet.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRAPPIST-1e      5915\n",
       "55 Cancri e      1800\n",
       "PSO J318.5-22     796\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Destination.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "execution": {
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    {
     "data": {
      "text/plain": [
       "(array([ 591.,  649., 2089., 1976., 1350.,  893.,  536.,  294.,  106.,\n",
       "          30.]),\n",
       " array([ 0. ,  7.9, 15.8, 23.7, 31.6, 39.5, 47.4, 55.3, 63.2, 71.1, 79. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 7,
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    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_train.Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:18:38.004881Z",
     "iopub.status.busy": "2022-05-05T06:18:38.004173Z",
     "iopub.status.idle": "2022-05-05T06:18:38.024092Z",
     "shell.execute_reply": "2022-05-05T06:18:38.023190Z",
     "shell.execute_reply.started": "2022-05-05T06:18:38.004814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples shape with NA values: (8693, 14)\n",
      "Training samples shape without NA values: (6606, 14)\n"
     ]
    }
   ],
   "source": [
    "print(f'Training samples shape with NA values: {df_train.shape}')\n",
    "print(f'Training samples shape without NA values: {df_train.dropna().shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:26:04.680839Z",
     "iopub.status.busy": "2022-05-05T06:26:04.680285Z",
     "iopub.status.idle": "2022-05-05T06:26:04.715862Z",
     "shell.execute_reply": "2022-05-05T06:26:04.714824Z",
     "shell.execute_reply.started": "2022-05-05T06:26:04.680806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_without_na = df_train.dropna()\n",
    "df_train_without_na.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:52:28.589205Z",
     "iopub.status.busy": "2022-05-05T06:52:28.588848Z",
     "iopub.status.idle": "2022-05-05T06:52:28.612097Z",
     "shell.execute_reply": "2022-05-05T06:52:28.611030Z",
     "shell.execute_reply.started": "2022-05-05T06:52:28.589170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074783</td>\n",
       "      <td>0.135844</td>\n",
       "      <td>0.042314</td>\n",
       "      <td>0.123820</td>\n",
       "      <td>0.105031</td>\n",
       "      <td>-0.082553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoomService</th>\n",
       "      <td>0.074783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013614</td>\n",
       "      <td>0.060478</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>-0.026002</td>\n",
       "      <td>-0.247291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FoodCourt</th>\n",
       "      <td>0.135844</td>\n",
       "      <td>-0.013614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012320</td>\n",
       "      <td>0.215995</td>\n",
       "      <td>0.216997</td>\n",
       "      <td>0.055025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShoppingMall</th>\n",
       "      <td>0.042314</td>\n",
       "      <td>0.060478</td>\n",
       "      <td>-0.012320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022168</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.011602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spa</th>\n",
       "      <td>0.123820</td>\n",
       "      <td>0.012472</td>\n",
       "      <td>0.215995</td>\n",
       "      <td>0.022168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.149447</td>\n",
       "      <td>-0.219854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VRDeck</th>\n",
       "      <td>0.105031</td>\n",
       "      <td>-0.026002</td>\n",
       "      <td>0.216997</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.149447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.207950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transported</th>\n",
       "      <td>-0.082553</td>\n",
       "      <td>-0.247291</td>\n",
       "      <td>0.055025</td>\n",
       "      <td>0.011602</td>\n",
       "      <td>-0.219854</td>\n",
       "      <td>-0.207950</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age  RoomService  FoodCourt  ShoppingMall       Spa  \\\n",
       "Age           1.000000     0.074783   0.135844      0.042314  0.123820   \n",
       "RoomService   0.074783     1.000000  -0.013614      0.060478  0.012472   \n",
       "FoodCourt     0.135844    -0.013614   1.000000     -0.012320  0.215995   \n",
       "ShoppingMall  0.042314     0.060478  -0.012320      1.000000  0.022168   \n",
       "Spa           0.123820     0.012472   0.215995      0.022168  1.000000   \n",
       "VRDeck        0.105031    -0.026002   0.216997      0.000383  0.149447   \n",
       "Transported  -0.082553    -0.247291   0.055025      0.011602 -0.219854   \n",
       "\n",
       "                VRDeck  Transported  \n",
       "Age           0.105031    -0.082553  \n",
       "RoomService  -0.026002    -0.247291  \n",
       "FoodCourt     0.216997     0.055025  \n",
       "ShoppingMall  0.000383     0.011602  \n",
       "Spa           0.149447    -0.219854  \n",
       "VRDeck        1.000000    -0.207950  \n",
       "Transported  -0.207950     1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_without_na.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the correlation results shows that `HomePlanet`, `CryoSleep`, `VIP`, `Destination` has the highest correlation with `Transported`. So we're going to use them in prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:55:37.392892Z",
     "iopub.status.busy": "2022-05-05T06:55:37.392155Z",
     "iopub.status.idle": "2022-05-05T06:55:37.401505Z",
     "shell.execute_reply": "2022-05-05T06:55:37.400389Z",
     "shell.execute_reply.started": "2022-05-05T06:55:37.392865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0       4109\n",
       "1.0        113\n",
       "2.0         80\n",
       "5.0         44\n",
       "3.0         44\n",
       "          ... \n",
       "1792.0       1\n",
       "330.0        1\n",
       "1924.0       1\n",
       "3440.0       1\n",
       "1643.0       1\n",
       "Name: Spa, Length: 1143, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_without_na.Spa.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:08:24.178001Z",
     "iopub.status.busy": "2022-05-05T07:08:24.177619Z",
     "iopub.status.idle": "2022-05-05T07:08:24.187325Z",
     "shell.execute_reply": "2022-05-05T07:08:24.186576Z",
     "shell.execute_reply.started": "2022-05-05T07:08:24.177976Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train_without_na.VIP = df_train_without_na.VIP.astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:45:14.401695Z",
     "iopub.status.busy": "2022-05-05T06:45:14.401273Z",
     "iopub.status.idle": "2022-05-05T06:45:14.407413Z",
     "shell.execute_reply": "2022-05-05T06:45:14.405914Z",
     "shell.execute_reply.started": "2022-05-05T06:45:14.401664Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = ['HomePlanet', 'CryoSleep', 'VIP', 'Destination' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T06:38:37.336975Z",
     "iopub.status.busy": "2022-05-05T06:38:37.336612Z",
     "iopub.status.idle": "2022-05-05T06:38:37.345293Z",
     "shell.execute_reply": "2022-05-05T06:38:37.344690Z",
     "shell.execute_reply.started": "2022-05-05T06:38:37.336949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRAPPIST-1e      4576\n",
       "55 Cancri e      1407\n",
       "PSO J318.5-22     623\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_without_na.Destination.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:04:09.017547Z",
     "iopub.status.busy": "2022-05-05T07:04:09.017067Z",
     "iopub.status.idle": "2022-05-05T07:04:09.020984Z",
     "shell.execute_reply": "2022-05-05T07:04:09.020432Z",
     "shell.execute_reply.started": "2022-05-05T07:04:09.017516Z"
    }
   },
   "outputs": [],
   "source": [
    "## set of columns that will be used in training\n",
    "cols_training = ['HomePlanet', 'CryoSleep', 'VIP', 'Destination', 'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:04:44.013981Z",
     "iopub.status.busy": "2022-05-05T07:04:44.012977Z",
     "iopub.status.idle": "2022-05-05T07:04:44.025647Z",
     "shell.execute_reply": "2022-05-05T07:04:44.024817Z",
     "shell.execute_reply.started": "2022-05-05T07:04:44.013949Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train_X = pd.get_dummies(df_train_without_na[cols_training], columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## drop the ages with zero values\n",
    "df_train_X = df_train_X.drop(df_train_X[df_train_X.Age == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:04:49.894054Z",
     "iopub.status.busy": "2022-05-05T07:04:49.893358Z",
     "iopub.status.idle": "2022-05-05T07:04:49.905674Z",
     "shell.execute_reply": "2022-05-05T07:04:49.905071Z",
     "shell.execute_reply.started": "2022-05-05T07:04:49.893996Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>HomePlanet_Earth</th>\n",
       "      <th>HomePlanet_Europa</th>\n",
       "      <th>HomePlanet_Mars</th>\n",
       "      <th>CryoSleep_False</th>\n",
       "      <th>CryoSleep_True</th>\n",
       "      <th>VIP_False</th>\n",
       "      <th>VIP_True</th>\n",
       "      <th>Destination_55 Cancri e</th>\n",
       "      <th>Destination_PSO J318.5-22</th>\n",
       "      <th>Destination_TRAPPIST-1e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  HomePlanet_Earth  HomePlanet_Europa  HomePlanet_Mars  \\\n",
       "0  39.0                 0                  1                0   \n",
       "1  24.0                 1                  0                0   \n",
       "2  58.0                 0                  1                0   \n",
       "3  33.0                 0                  1                0   \n",
       "4  16.0                 1                  0                0   \n",
       "\n",
       "   CryoSleep_False  CryoSleep_True  VIP_False  VIP_True  \\\n",
       "0                1               0          1         0   \n",
       "1                1               0          1         0   \n",
       "2                1               0          0         1   \n",
       "3                1               0          1         0   \n",
       "4                1               0          1         0   \n",
       "\n",
       "   Destination_55 Cancri e  Destination_PSO J318.5-22  Destination_TRAPPIST-1e  \n",
       "0                        0                          0                        1  \n",
       "1                        0                          0                        1  \n",
       "2                        0                          0                        1  \n",
       "3                        0                          0                        1  \n",
       "4                        0                          0                        1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:04:54.349881Z",
     "iopub.status.busy": "2022-05-05T07:04:54.349449Z",
     "iopub.status.idle": "2022-05-05T07:04:54.353259Z",
     "shell.execute_reply": "2022-05-05T07:04:54.352309Z",
     "shell.execute_reply.started": "2022-05-05T07:04:54.349852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6466,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_Y = df_train_without_na[df_train_without_na.Age != 0].Transported\n",
    "df_train_Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:04:56.259879Z",
     "iopub.status.busy": "2022-05-05T07:04:56.259130Z",
     "iopub.status.idle": "2022-05-05T07:04:58.150484Z",
     "shell.execute_reply": "2022-05-05T07:04:58.149241Z",
     "shell.execute_reply.started": "2022-05-05T07:04:56.259846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=123)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=123)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model = SVC(random_state=123)\n",
    "svm_model.fit(df_train_X, df_train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:13:43.217163Z",
     "iopub.status.busy": "2022-05-05T07:13:43.216875Z",
     "iopub.status.idle": "2022-05-05T07:13:47.819336Z",
     "shell.execute_reply": "2022-05-05T07:13:47.818341Z",
     "shell.execute_reply.started": "2022-05-05T07:13:43.217137Z"
    }
   },
   "outputs": [],
   "source": [
    "train_pred = svm_model.predict(df_train_X)\n",
    "confusion_matrix_svm_trn = confusion_matrix(df_train_Y, train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:14:09.132352Z",
     "iopub.status.busy": "2022-05-05T07:14:09.131952Z",
     "iopub.status.idle": "2022-05-05T07:14:09.323437Z",
     "shell.execute_reply": "2022-05-05T07:14:09.322138Z",
     "shell.execute_reply.started": "2022-05-05T07:14:09.132313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x23773cc0880>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(confusion_matrix_svm_trn).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T07:15:25.624523Z",
     "iopub.status.busy": "2022-05-05T07:15:25.623946Z",
     "iopub.status.idle": "2022-05-05T07:15:25.630813Z",
     "shell.execute_reply": "2022-05-05T07:15:25.630105Z",
     "shell.execute_reply.started": "2022-05-05T07:15:25.624490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.0745437673987"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Accuracy\n",
    "acc = 0\n",
    "for i in range(0, 2):\n",
    "    acc += confusion_matrix_svm_trn[i, i]\n",
    "acc * 100 / len(train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training Accuracy is 73.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First convert data into Dmatrix form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(df_train_X, label=df_train_Y, feature_names=df_train_X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:53:43] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
      "Parameters: { \"silent\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_params = {\n",
    "'eta': 0.3,\n",
    "'max_depth': 6,\n",
    "'min_child_weight': 1,\n",
    "'objective': 'binary:logistic',\n",
    "'eval_metric': 'auc', #A\n",
    "'nthread': 8,\n",
    "'seed': 1,\n",
    "'silent': 1\n",
    "}\n",
    "xgb_model = xgb.train(xgb_params, dtrain=dtrain, num_boost_round=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_train_pred = xgb_model.predict(dtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_acc(confusion_matrix):\n",
    "    \"\"\"\n",
    "    Find the accuracy of a binary classification problem using confusion matrix\n",
    "\n",
    "    Parameters:\n",
    "    ------------\n",
    "    confusion_matrix : matrix_like\n",
    "        the confusion matrix of the binary classification problem\n",
    "\n",
    "    Returns:\n",
    "    ---------\n",
    "    acc : float\n",
    "        the accuracy value between 0 and 1\n",
    "    \"\"\"\n",
    "    ## Accuracy\n",
    "    acc = 0\n",
    "    for i in range(0, 2):\n",
    "        acc += confusion_matrix[i, i]\n",
    "    acc = acc * 100 / len(train_pred)\n",
    "\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold=0.00, Accuracy:49.659758738014226\n",
      "threshold=0.00, AUC score:0.5\n",
      "threshold=0.10, Accuracy:49.659758738014226\n",
      "threshold=0.10, AUC score:0.5\n",
      "threshold=0.20, Accuracy:52.93844726260439\n",
      "threshold=0.20, AUC score:0.5325189763873321\n",
      "threshold=0.30, Accuracy:65.1252706464584\n",
      "threshold=0.30, AUC score:0.652757346697532\n",
      "threshold=0.40, Accuracy:73.7086297556449\n",
      "threshold=0.40, AUC score:0.7368552130469331\n",
      "threshold=0.50, Accuracy:74.55923291060934\n",
      "threshold=0.50, AUC score:0.7447206965686789\n",
      "threshold=0.60, Accuracy:74.07980204144758\n",
      "threshold=0.60, AUC score:0.7395757000824259\n",
      "threshold=0.70, Accuracy:71.34240643365295\n",
      "threshold=0.70, AUC score:0.7116626745332504\n",
      "threshold=0.80, Accuracy:69.81132075471699\n",
      "threshold=0.80, AUC score:0.6961753496166451\n",
      "threshold=0.90, Accuracy:65.88308072997216\n",
      "threshold=0.90, AUC score:0.6565164581620113\n",
      "threshold=1.00, Accuracy:50.340241261985774\n",
      "threshold=1.00, AUC score:0.5\n"
     ]
    }
   ],
   "source": [
    "## find accuracy and auc score for different thresholds \n",
    "for threshold in np.linspace(0, 1, 11):\n",
    "    xgb_trn_confusion_matrix = confusion_matrix(df_train_Y,xgb_train_pred >= threshold)\n",
    "    acc = find_acc(xgb_trn_confusion_matrix)\n",
    "    auc_score = roc_auc_score(df_train_Y, xgb_train_pred >= threshold)\n",
    "    print(f'threshold={threshold:.2f}, Accuracy:{acc}')\n",
    "    print(f'threshold={threshold:.2f}, AUC score:{auc_score}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLP_model = MLPClassifier()\n",
    "\n",
    "MLP_model.fit(df_train_X, df_train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_train_results = MLP_model.predict(df_train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2377372fe50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_confusion_matrix = confusion_matrix(df_train_Y, mlp_train_results)\n",
    "ConfusionMatrixDisplay(mlp_confusion_matrix).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.75502629137024"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## mlp accuracy\n",
    "acc = 0\n",
    "for i in range(0, 2):\n",
    "    acc+= mlp_confusion_matrix[i,i]\n",
    "acc *100 / np.sum(mlp_confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we've achieved about 73 acc score for multilayer perceptron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIME explainer for xgboost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Age', 'HomePlanet_Earth', 'HomePlanet_Europa', 'HomePlanet_Mars',\n",
       "       'CryoSleep_False', 'CryoSleep_True', 'VIP_False', 'VIP_True',\n",
       "       'Destination_55 Cancri e', 'Destination_PSO J318.5-22',\n",
       "       'Destination_TRAPPIST-1e'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_X.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xgboost_classifier_fn(X):\n",
    "    data_cols = ['Age', 'HomePlanet_Earth', 'HomePlanet_Europa', 'HomePlanet_Mars',\n",
    "       'CryoSleep_False', 'CryoSleep_True', 'VIP_False', 'VIP_True',\n",
    "       'Destination_55 Cancri e', 'Destination_PSO J318.5-22',\n",
    "       'Destination_TRAPPIST-1e']\n",
    "    X_D = xgb.DMatrix(X,feature_names=data_cols)\n",
    "\n",
    "    prediction = xgb_model.predict(X_D)\n",
    "    return prediction.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer_lime = lime.lime_tabular.LimeTabularExplainer(\n",
    "    df_train_X.values, \n",
    "    feature_names=df_train_X.columns.values, \n",
    "    class_names=['False_Transport', 'True_Transport'], discretize_continuous=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_index = 5\n",
    "\n",
    "label = svm_model.predict(df_train_X.values[data_index].reshape((1, -1)))[0]\n",
    "\n",
    "exp = explainer_lime.explain_instance(df_train_X.iloc[data_index], xgboost_classifier_fn, num_features=11, labels=(0,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.as_pyplot_figure(label=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIME for MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = lime.lime_tabular.LimeTabularExplainer(df_train_X.values, feature_names=df_train_X.columns, \n",
    "                    class_names=['False_Transport', 'True_Transport'], discretize_continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIME is a local explainer, so we're using one random data in test set to interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 5\n",
    "exp = explainer.explain_instance(df_train_X.values[i], MLP_model.predict_proba, num_features=10, top_labels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.as_pyplot_figure(label=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP for xgboost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explain the model's predictions using SHAP\n",
    "explainer = shap.Explainer(xgb_model)\n",
    "shap_values = explainer(df_train_X)\n",
    "\n",
    "# visualize the first prediction's explanation\n",
    "shap.plots.waterfall(shap_values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SHAP for MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explain the model's predictions using SHAP\n",
    "explainer = shap.KernelExplainer(MLP_model.predict_proba, df_train_X[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "## local explanation for 5-th data in training set\n",
    "i = 5\n",
    "shap_values = explainer.shap_values(df_train_X.iloc[i, :] ,nsamples=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.5378140209596468, \"outValue\": 0.6899395790245835, \"link\": \"identity\", \"featureNames\": [\"Age\", \"HomePlanet_Earth\", \"HomePlanet_Europa\", \"HomePlanet_Mars\", \"CryoSleep_False\", \"CryoSleep_True\", \"VIP_False\", \"VIP_True\", \"Destination_55 Cancri e\", \"Destination_PSO J318.5-22\", \"Destination_TRAPPIST-1e\"], \"features\": {\"0\": {\"effect\": 0.03058609717040961, \"value\": 44.0}, \"1\": {\"effect\": 0.03030082542379653, \"value\": 1.0}, \"2\": {\"effect\": 0.024806751231211538, \"value\": 0.0}, \"3\": {\"effect\": -0.015659373260278893, \"value\": 0.0}, \"4\": {\"effect\": 0.0508730957984799, \"value\": 1.0}, \"5\": {\"effect\": 0.05345648692738093, \"value\": 0.0}, \"8\": {\"effect\": 0.013075049955855261, \"value\": 0.0}, \"9\": {\"effect\": -0.02827413240948365, \"value\": 1.0}, \"10\": {\"effect\": -0.007039242772434562, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iNEHJSSBTGPQ7Y0CD00MC')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x2377f388610>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shap.summary_plot(shap_values, df_train_X.iloc[5], feature_names=df_train_X.columns)\n",
    "## for class False\n",
    "shap.initjs()\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[0]  ,df_train_X.iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iC7Q7DDVVVXDP3XNBKN8E'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.5378140209596468, \"outValue\": 0.38568846289471004, \"link\": \"identity\", \"featureNames\": [\"Age\", \"HomePlanet_Earth\", \"HomePlanet_Europa\", \"HomePlanet_Mars\", \"CryoSleep_False\", \"CryoSleep_True\", \"VIP_False\", \"VIP_True\", \"Destination_55 Cancri e\", \"Destination_PSO J318.5-22\", \"Destination_TRAPPIST-1e\"], \"features\": {\"0\": {\"effect\": -0.030586097170409554, \"value\": 44.0}, \"1\": {\"effect\": -0.0303008254237965, \"value\": 1.0}, \"2\": {\"effect\": -0.024806751231211496, \"value\": 0.0}, \"3\": {\"effect\": 0.015659373260278983, \"value\": 0.0}, \"4\": {\"effect\": -0.05087309579847982, \"value\": 1.0}, \"5\": {\"effect\": -0.05345648692738111, \"value\": 0.0}, \"8\": {\"effect\": -0.013075049955855178, \"value\": 0.0}, \"9\": {\"effect\": 0.02827413240948376, \"value\": 1.0}, \"10\": {\"effect\": 0.0070392427724341455, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
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       "  );\n",
       "</script>"
      ],
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     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for class True\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[1]  ,df_train_X.iloc[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dice-ml Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dice_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "## prepare the whole dataset\n",
    "train_dataset = df_train_X.copy()\n",
    "train_dataset['Transported'] = df_train_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = dice_ml.Data(dataframe=train_dataset,\n",
    "                 continuous_features=[],\n",
    "                 outcome_name='Transported')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = dice_ml.Model(model=MLP_model,\n",
    "                  backend='sklearn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = dice_ml.Dice(d, m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████| 5/5 [00:00<00:00,  5.46it/s]\n"
     ]
    }
   ],
   "source": [
    "# Generate counterfactual examples\n",
    "query_instance = df_train_X[5:10].copy()\n",
    "dice_exp = exp.generate_counterfactuals(query_instance, total_CFs=4, desired_class=\"opposite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query instance (original outcome : 0)\n"
     ]
    },
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      "Diverse Counterfactual set (new outcome: 1.0)\n"
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    },
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       "3  45.0                0               1.0             0.0               0   \n",
       "\n",
       "  CryoSleep_True VIP_False VIP_True Destination_55 Cancri e  \\\n",
       "0              1       1.0        0                     1.0   \n",
       "1            0.0       1.0      0.0                     1.0   \n",
       "2            0.0       1.0      0.0                     1.0   \n",
       "3            0.0       1.0      0.0                     1.0   \n",
       "\n",
       "  Destination_PSO J318.5-22 Destination_TRAPPIST-1e  Transported  \n",
       "0                       0.0                     0.0            1  \n",
       "1                       0.0                     0.0            1  \n",
       "2                       0.0                     0.0            1  \n",
       "3                       0.0                     0.0            1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize counterfactual explanation\n",
    "dice_exp.visualize_as_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras NN model\n",
    "the model is from the toturial\n",
    "https://keras.io/examples/vision/mnist_convnet/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# Model / data parameters\n",
    "num_classes = 10\n",
    "input_shape = (28, 28, 1)\n",
    "\n",
    "# Load the data and split it between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "\n",
    "# Scale images to the [0, 1] range\n",
    "x_train = x_train.astype(\"float32\") / 255\n",
    "x_test = x_test.astype(\"float32\") / 255\n",
    "# Make sure images have shape (28, 28, 1)\n",
    "x_train = np.expand_dims(x_train, -1)\n",
    "x_test = np.expand_dims(x_test, -1)\n",
    "print(\"x_train shape:\", x_train.shape)\n",
    "print(x_train.shape[0], \"train samples\")\n",
    "print(x_test.shape[0], \"test samples\")\n",
    "\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                16010     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=input_shape),\n",
    "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Flatten(),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(num_classes, activation=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "422/422 [==============================] - 14s 33ms/step - loss: 0.3739 - accuracy: 0.8862 - val_loss: 0.0898 - val_accuracy: 0.9757\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 14s 34ms/step - loss: 0.1150 - accuracy: 0.9644 - val_loss: 0.0616 - val_accuracy: 0.9830\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 14s 34ms/step - loss: 0.0852 - accuracy: 0.9737 - val_loss: 0.0471 - val_accuracy: 0.9880\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 16s 39ms/step - loss: 0.0713 - accuracy: 0.9777 - val_loss: 0.0437 - val_accuracy: 0.9893\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 17s 40ms/step - loss: 0.0632 - accuracy: 0.9804 - val_loss: 0.0399 - val_accuracy: 0.9900\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 18s 43ms/step - loss: 0.0561 - accuracy: 0.9826 - val_loss: 0.0346 - val_accuracy: 0.9912\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 15s 37ms/step - loss: 0.0531 - accuracy: 0.9827 - val_loss: 0.0341 - val_accuracy: 0.9905\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 15s 35ms/step - loss: 0.0491 - accuracy: 0.9849 - val_loss: 0.0357 - val_accuracy: 0.9908\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 16s 38ms/step - loss: 0.0458 - accuracy: 0.9851 - val_loss: 0.0344 - val_accuracy: 0.9913\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 14s 33ms/step - loss: 0.0419 - accuracy: 0.9864 - val_loss: 0.0312 - val_accuracy: 0.9910\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 14s 33ms/step - loss: 0.0412 - accuracy: 0.9865 - val_loss: 0.0319 - val_accuracy: 0.9917\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 18s 43ms/step - loss: 0.0388 - accuracy: 0.9874 - val_loss: 0.0305 - val_accuracy: 0.9913\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 16s 37ms/step - loss: 0.0365 - accuracy: 0.9885 - val_loss: 0.0315 - val_accuracy: 0.9912\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 16s 37ms/step - loss: 0.0357 - accuracy: 0.9883 - val_loss: 0.0308 - val_accuracy: 0.9915\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 14s 33ms/step - loss: 0.0339 - accuracy: 0.9888 - val_loss: 0.0304 - val_accuracy: 0.9915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x237096d98a0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "epochs = 15\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.02235320210456848\n",
      "Test accuracy: 0.9926000237464905\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explainers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using iNNvestigate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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