demo-house-prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4038a496",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p data/house-prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c03a0ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data/house-prices-advanced-regression-techniques.zip\n",
      "  inflating: data/house-prices/data_description.txt  \n",
      "  inflating: data/house-prices/sample_submission.csv  \n",
      "  inflating: data/house-prices/test.csv  \n",
      "  inflating: data/house-prices/train.csv  \n"
     ]
    }
   ],
   "source": [
    "!unzip -o data/house-prices-advanced-regression-techniques.zip -d data/house-prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc09efca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "\n",
    "import advertion\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import RocCurveDisplay, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "seed = np.random.RandomState(31337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "522e9523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset = pd.read_csv(\"data/house-prices/train.csv\")\n",
    "\n",
    "trainset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bebed19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset = pd.read_csv(\"data/house-prices/test.csv\")\n",
    "\n",
    "testset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b56a640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd16506c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only numerical features\n",
    "X_train = trainset.select_dtypes(include=[\"number\"]).copy()\n",
    "X_test = testset.select_dtypes(include=[\"number\"]).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c4df49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop target variable from our training dataset\n",
    "X_train = X_train.drop([\"SalePrice\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edcdf55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"_adval_label_\"] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c67fd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[\"_adval_label_\"] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ff63ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 38)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine training & test datasets\n",
    "combined_dataset = pd.concat([X_train, X_test], axis=0, ignore_index=True)\n",
    "\n",
    "combined_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "267e3fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',\n",
       "       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',\n",
       "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd',\n",
       "       'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n",
       "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',\n",
       "       'MiscVal', 'MoSold', 'YrSold', '_adval_label_'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be49a3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle it\n",
    "combined_dataset_shuffled = combined_dataset.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7890c1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = combined_dataset_shuffled.drop([\"_adval_label_\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a37ffd50",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = combined_dataset_shuffled[\"_adval_label_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "acfaf59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 5\n",
    "cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)\n",
    "classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6feeb1aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c23df8faedff4630b9fbafc534649495",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "\n",
    "for fold, (train, test) in tqdm([*enumerate(cv.split(X, y))]):\n",
    "    classifier.fit(X.iloc[train], y.iloc[train])\n",
    "    viz = RocCurveDisplay.from_estimator(\n",
    "        classifier,\n",
    "        X.iloc[test],\n",
    "        y.iloc[test],\n",
    "        name=f\"ROC fold {fold}\",\n",
    "        alpha=0.3,\n",
    "        lw=1,\n",
    "        ax=ax,\n",
    "        plot_chance_level=(fold == n_splits - 1),\n",
    "    )\n",
    "    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
    "    interp_tpr[0] = 0.0\n",
    "    tprs.append(interp_tpr)\n",
    "    aucs.append(viz.roc_auc)\n",
    "\n",
    "mean_tpr = np.mean(tprs, axis=0)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "std_auc = np.std(aucs)\n",
    "ax.plot(\n",
    "    mean_fpr,\n",
    "    mean_tpr,\n",
    "    color=\"b\",\n",
    "    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
    "    lw=2,\n",
    "    alpha=0.8,\n",
    ")\n",
    "\n",
    "std_tpr = np.std(tprs, axis=0)\n",
    "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "ax.fill_between(\n",
    "    mean_fpr,\n",
    "    tprs_lower,\n",
    "    tprs_upper,\n",
    "    color=\"grey\",\n",
    "    alpha=0.2,\n",
    "    label=r\"$\\pm$ 1 std. dev.\",\n",
    ")\n",
    "\n",
    "ax.set(\n",
    "    xlim=[-0.05, 1.05],\n",
    "    ylim=[-0.05, 1.05],\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    ")\n",
    "ax.axis(\"square\")\n",
    "ax.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed79f8ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 0.9982876712328766, 1.0, 1.0, 1.0]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8053906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9996575342465753"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(aucs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a50ab2f8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier = xgb.XGBClassifier(eval_metric=\"logloss\")\n",
    "classifier.fit(X, y)\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "xgb.plot_importance(classifier, ax=ax)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdf4b769",
   "metadata": {
    "scrolled": false
   },
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33a0f29e",
   "metadata": {},
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0     1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1     1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2     1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3     1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4     1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...         120        0    NaN  MnPrv   \n",
       "1            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "3            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "4            HLS    AllPub  ...         144        0    NaN    NaN   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      6    2010        WD         Normal  \n",
       "1           Gar2   12500      6    2010        WD         Normal  \n",
       "2            NaN       0      3    2010        WD         Normal  \n",
       "3            NaN       0      6    2010        WD         Normal  \n",
       "4            NaN       0      1    2010        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1454         NaN       0      6    2006        WD         Normal  \n",
       "1455         NaN       0      4    2006        WD        Abnorml  \n",
       "1456         NaN       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458         NaN       0     11    2006        WD         Normal  \n",
       "\n",
       "[1459 rows x 80 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6eda061",
   "metadata": {},
   "source": [
    "It is apparent that a classifier can easily distinguish training from test instances via a very simple heuristic: `Id >= 1461`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cfbaa18",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop([\"Id\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dae3f1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 5\n",
    "cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)\n",
    "classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e68eee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "740e8aab727a4ca8b1e96683507f07c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "\n",
    "for fold, (train, test) in tqdm([*enumerate(cv.split(X, y))]):\n",
    "    classifier.fit(X.iloc[train], y.iloc[train])\n",
    "    viz = RocCurveDisplay.from_estimator(\n",
    "        classifier,\n",
    "        X.iloc[test],\n",
    "        y.iloc[test],\n",
    "        name=f\"ROC fold {fold}\",\n",
    "        alpha=0.3,\n",
    "        lw=1,\n",
    "        ax=ax,\n",
    "        plot_chance_level=(fold == n_splits - 1),\n",
    "    )\n",
    "    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
    "    interp_tpr[0] = 0.0\n",
    "    tprs.append(interp_tpr)\n",
    "    aucs.append(viz.roc_auc)\n",
    "\n",
    "mean_tpr = np.mean(tprs, axis=0)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "std_auc = np.std(aucs)\n",
    "ax.plot(\n",
    "    mean_fpr,\n",
    "    mean_tpr,\n",
    "    color=\"b\",\n",
    "    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
    "    lw=2,\n",
    "    alpha=0.8,\n",
    ")\n",
    "\n",
    "std_tpr = np.std(tprs, axis=0)\n",
    "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "ax.fill_between(\n",
    "    mean_fpr,\n",
    "    tprs_lower,\n",
    "    tprs_upper,\n",
    "    color=\"grey\",\n",
    "    alpha=0.2,\n",
    "    label=r\"$\\pm$ 1 std. dev.\",\n",
    ")\n",
    "\n",
    "ax.set(\n",
    "    xlim=[-0.05, 1.05],\n",
    "    ylim=[-0.05, 1.05],\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    ")\n",
    "ax.axis(\"square\")\n",
    "ax.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "825823cf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5219787952711578,\n",
       " 0.5326398010883844,\n",
       " 0.5170529179958716,\n",
       " 0.5292620566710452,\n",
       " 0.5295862166360683]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0d22e30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5261039575325055"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(aucs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118bd890",
   "metadata": {},
   "source": [
    "### using the advertion package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb13a891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Working only with available numerical features, categorical features are not yet supported.\n",
      "INFO: Will try to identify adversarial features (see: https://advertion.readthedocs.io/en/0.1.0-alpha/adversarial-features)\n",
      "INFO: Identified adversarial feature: [name: Id, statistic: 1.0, p-value: 0.0].\n",
      "INFO: training and test datasets follow the same underlying distribution [mean ROC AUC: 0.512].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'adversarial_features': ['Id'],\n",
       " 'datasets_follow_same_distribution': True,\n",
       " 'mean_roc_auc': 0.5120309454073187}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertion.validate(\n",
    "    trainset=trainset,\n",
    "    testset=testset,\n",
    "    target=\"SalePrice\",\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}