{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90f0e316",
   "metadata": {},
   "source": [
    "# Shapash in Jupyter - Debugging a Titanic Model with Explainability\n",
    "\n",
    "In this tutorial, we use explainability to detect a classic ML issue: target leakage.\n",
    "\n",
    "You will:\n",
    "- build a deliberately flawed model\n",
    "- identify the bug using Shapash plots\n",
    "- fix the pipeline and compare performance\n",
    "- leave with a functional and numerical validation checklist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "328d68b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from category_encoders import one_hot\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from shapash import SmartExplainer\n",
    "from shapash.data.data_loader import data_loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304f4126",
   "metadata": {},
   "source": [
    "## 1. Load Titanic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3eb13e99",
   "metadata": {},
   "outputs": [
    {
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        {
         "name": "PassengerId",
         "rawType": "int64",
         "type": "integer"
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         "name": "Pclass",
         "rawType": "int64",
         "type": "integer"
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         "name": "Age",
         "rawType": "float64",
         "type": "float"
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         "name": "Sex",
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         "type": "string"
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         "type": "integer"
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         "name": "Parch",
         "rawType": "int64",
         "type": "integer"
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         "name": "Fare",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "Embarked",
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         "type": "string"
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         "name": "Title",
         "rawType": "object",
         "type": "string"
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Survived</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>Southampton</td>\n",
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       "      <th>2</th>\n",
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       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.28</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>26.0</td>\n",
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       "      <td>Southampton</td>\n",
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       "      <th>4</th>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>53.10</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mr</td>\n",
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      "text/plain": [
       "             Pclass   Age     Sex  SibSp  Parch   Fare     Embarked Title  \\\n",
       "PassengerId                                                                 \n",
       "1                 3  22.0    male      1      0   7.25  Southampton    Mr   \n",
       "2                 1  38.0  female      1      0  71.28    Cherbourg   Mrs   \n",
       "3                 3  26.0  female      0      0   7.92  Southampton  Miss   \n",
       "4                 1  35.0  female      1      0  53.10  Southampton   Mrs   \n",
       "5                 3  35.0    male      0      0   8.05  Southampton    Mr   \n",
       "\n",
       "             Survived  \n",
       "PassengerId            \n",
       "1                   0  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  \n",
       "5                   0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df, _ = data_loading(\"titanic\")\n",
    "\n",
    "# Same mapping logic as shapash/webapp/webapp_launch.py\n",
    "titanic_df[\"Pclass\"] = titanic_df[\"Pclass\"].map({\"First class\": 1, \"Second class\": 2, \"Third class\": 3})\n",
    "\n",
    "# Some dataset versions do not include Title: rebuild it if needed.\n",
    "if \"Title\" not in titanic_df.columns:\n",
    "    titanic_df[\"Title\"] = titanic_df[\"Name\"].str.extract(r\",\\s*([^\\.]+)\\.\", expand=False).fillna(\"Unknown\")\n",
    "\n",
    "target_name = \"Survived\"\n",
    "titanic_df[[\"Pclass\", \"Age\", \"Sex\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"Title\", target_name]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c81ce8",
   "metadata": {},
   "source": [
    "## 2. Common ML mistakes to keep in mind\n",
    "\n",
    "These are exactly the kinds of mistakes explainability can uncover:\n",
    "- target leakage: a feature computed using the target (directly or indirectly)\n",
    "- business variables that are unavailable at prediction time\n",
    "- confusion between missing values and actual numeric values\n",
    "- unstable encoding between training and inference\n",
    "- metrics that are not suitable for imbalanced classes\n",
    "- feature engineering that depends on future information (time leakage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f88ece",
   "metadata": {},
   "source": [
    "## 3. Build a deliberately flawed model (with target leakage)\n",
    "\n",
    "We create a `leak_survival_rate_by_title` variable using the true `Survived` target over the full dataset.\n",
    "This is a practical way to show how a suspicious feature immediately stands out in Shapash."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e321ea96",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
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       "  --sklearn-color-border-box: white;\n",
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       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
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       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
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       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
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       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(learning_rate=0.05, max_depth=4, n_estimators=200, verbose=-1)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>LGBMClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('boosting_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">boosting_type</td>\n",
       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_leaves</td>\n",
       "            <td class=\"value\">31</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth</td>\n",
       "            <td class=\"value\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators</td>\n",
       "            <td class=\"value\">200</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_for_bin</td>\n",
       "            <td class=\"value\">200000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_split_gain',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_split_gain</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_samples</td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_freq',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_freq</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type</td>\n",
       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "LGBMClassifier(learning_rate=0.05, max_depth=4, n_estimators=200, verbose=-1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bug = titanic_df.copy()\n",
    "\n",
    "# Intentional bug: target leakage (mean Survived computed on all rows)\n",
    "df_bug[\"leak_survival_rate_by_title\"] = df_bug.groupby(\"Title\")[target_name].transform(\"mean\")\n",
    "\n",
    "features_bug = [\n",
    "    \"Pclass\", \"Age\", \"Sex\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"Title\",\n",
    "    \"leak_survival_rate_by_title\",\n",
    "]\n",
    "\n",
    "X_bug_raw = df_bug[features_bug]\n",
    "y = df_bug[[target_name]]\n",
    "\n",
    "X_bug_train_raw, X_bug_test_raw, y_bug_train, y_bug_test = train_test_split(\n",
    "    X_bug_raw,\n",
    "    y,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=y,\n",
    ")\n",
    "\n",
    "encoder_bug = one_hot.OneHotEncoder(cols=[\"Sex\", \"Embarked\", \"Title\"])\n",
    "X_bug_train = encoder_bug.fit_transform(X_bug_train_raw)\n",
    "X_bug_test = encoder_bug.transform(X_bug_test_raw)\n",
    "\n",
    "model_bug = LGBMClassifier(max_depth=4, n_estimators=200, learning_rate=0.05, verbose=-1)\n",
    "model_bug.fit(X_bug_train, y_bug_train.iloc[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3d550a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "accuracy",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "precision",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "recall",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "f1",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "332dd8aa-6835-40bd-89b7-1244697fa8c5",
       "rows": [
        [
         "train_bug",
         "0.9131736526946108",
         "0.9304347826086956",
         "0.8359375",
         "0.8806584362139918"
        ],
        [
         "test_bug",
         "0.8026905829596412",
         "0.7916666666666666",
         "0.6627906976744186",
         "0.7215189873417721"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 2
       }
      },
      "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_bug</th>\n",
       "      <td>0.913174</td>\n",
       "      <td>0.930435</td>\n",
       "      <td>0.835938</td>\n",
       "      <td>0.880658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_bug</th>\n",
       "      <td>0.802691</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.662791</td>\n",
       "      <td>0.721519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           accuracy  precision    recall        f1\n",
       "train_bug  0.913174   0.930435  0.835938  0.880658\n",
       "test_bug   0.802691   0.791667  0.662791  0.721519"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cls_metrics(y_true, y_pred):\n",
    "    return {\n",
    "        \"accuracy\": accuracy_score(y_true, y_pred),\n",
    "        \"precision\": precision_score(y_true, y_pred, zero_division=0),\n",
    "        \"recall\": recall_score(y_true, y_pred, zero_division=0),\n",
    "        \"f1\": f1_score(y_true, y_pred, zero_division=0),\n",
    "    }\n",
    "\n",
    "y_bug_pred_train = model_bug.predict(X_bug_train)\n",
    "y_bug_pred_test = model_bug.predict(X_bug_test)\n",
    "\n",
    "metrics_bug = pd.DataFrame(\n",
    "    [\n",
    "        cls_metrics(y_bug_train.iloc[:, 0], y_bug_pred_train),\n",
    "        cls_metrics(y_bug_test.iloc[:, 0], y_bug_pred_test),\n",
    "    ],\n",
    "    index=[\"train_bug\", \"test_bug\"],\n",
    ")\n",
    "metrics_bug"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e6ebfb",
   "metadata": {},
   "source": [
    "## 4. Explain the flawed model with Shapash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4ce8a35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Shap explainer type - <shap.explainers._tree.TreeExplainer object at 0x1228ec0e0>\n"
     ]
    }
   ],
   "source": [
    "feature_dict_bug = {\n",
    "    \"Pclass\": \"Ticket class\",\n",
    "    \"Age\": \"Age\",\n",
    "    \"Sex\": \"Sex\",\n",
    "    \"SibSp\": \"Number of siblings/spouses\",\n",
    "    \"Parch\": \"Number of parents/children\",\n",
    "    \"Fare\": \"Ticket fare\",\n",
    "    \"Embarked\": \"Port of embarkation\",\n",
    "    \"Title\": \"Title\",\n",
    "    \"leak_survival_rate_by_title\": \"SURVIVAL RATE BY TITLE (LEAK)\",\n",
    "}\n",
    "\n",
    "postprocess = {\n",
    "    \"Age\": {\"type\": \"suffix\", \"rule\": \" years\"},\n",
    "    \"Sex\": {\"type\": \"transcoding\", \"rule\": {\"male\": \"Man\", \"female\": \"Woman\"}},\n",
    "    \"Pclass\": {\"type\": \"transcoding\", \"rule\": {1: \"First\", 2: \"Second\", 3: \"Third\"}},\n",
    "}\n",
    "\n",
    "xpl_bug = SmartExplainer(\n",
    "    model=model_bug,\n",
    "    preprocessing=encoder_bug,\n",
    "    features_dict=feature_dict_bug,\n",
    "    label_dict={0: \"Deceased\", 1: \"Survived\"},\n",
    "    postprocessing=postprocess,\n",
    "    title_story=\"Titanic model debugging - buggy version\",\n",
    ")\n",
    "\n",
    "y_bug_pred_test_df = pd.DataFrame(\n",
    "    model_bug.predict(X_bug_test),\n",
    "    columns=[target_name],\n",
    "    index=X_bug_test.index,\n",
    ")\n",
    "\n",
    "xpl_bug.compile(\n",
    "    x=X_bug_test,\n",
    "    y_pred=y_bug_pred_test_df,\n",
    "    y_target=y_bug_test,\n",
    "    additional_data=df_bug.loc[X_bug_test.index, [\"Name\", \"Title\", \"Fare\", \"Embarked\"]],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef3a2280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl_bug.plot.features_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef9bfe99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl_bug.plot.contribution_plot(\"leak_survival_rate_by_title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcee49e6",
   "metadata": {},
   "source": [
    "If `SURVIVAL RATE BY TITLE (LEAK)` is a dominant feature, this is a red flag:\n",
    "- this variable is not truly available in production\n",
    "- it contains information derived from the target\n",
    "- observed performance is overly optimistic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978bd2d9",
   "metadata": {},
   "source": [
    "## 5. Fix the pipeline (without target leakage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a81b9149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "accuracy",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "precision",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "recall",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "f1",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "183184b3-3657-47af-a22e-521917b9f5da",
       "rows": [
        [
         "train_clean",
         "0.907185628742515",
         "0.9254385964912281",
         "0.82421875",
         "0.871900826446281"
        ],
        [
         "test_clean",
         "0.7982062780269058",
         "0.7887323943661971",
         "0.6511627906976745",
         "0.7133757961783439"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 2
       }
      },
      "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_clean</th>\n",
       "      <td>0.907186</td>\n",
       "      <td>0.925439</td>\n",
       "      <td>0.824219</td>\n",
       "      <td>0.871901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_clean</th>\n",
       "      <td>0.798206</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.651163</td>\n",
       "      <td>0.713376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             accuracy  precision    recall        f1\n",
       "train_clean  0.907186   0.925439  0.824219  0.871901\n",
       "test_clean   0.798206   0.788732  0.651163  0.713376"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_clean = [\"Pclass\", \"Age\", \"Sex\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"Title\"]\n",
    "\n",
    "X_clean_raw = titanic_df[features_clean]\n",
    "y_clean = titanic_df[[target_name]]\n",
    "\n",
    "X_clean_train_raw, X_clean_test_raw, y_clean_train, y_clean_test = train_test_split(\n",
    "    X_clean_raw,\n",
    "    y_clean,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=y_clean,\n",
    ")\n",
    "\n",
    "encoder_clean = one_hot.OneHotEncoder(cols=[\"Sex\", \"Embarked\", \"Title\"])\n",
    "X_clean_train = encoder_clean.fit_transform(X_clean_train_raw)\n",
    "X_clean_test = encoder_clean.transform(X_clean_test_raw)\n",
    "\n",
    "model_clean = LGBMClassifier(max_depth=4, n_estimators=200, learning_rate=0.05, verbose=-1)\n",
    "model_clean.fit(X_clean_train, y_clean_train.iloc[:, 0])\n",
    "\n",
    "y_clean_pred_train = model_clean.predict(X_clean_train)\n",
    "y_clean_pred_test = model_clean.predict(X_clean_test)\n",
    "\n",
    "metrics_clean = pd.DataFrame(\n",
    "    [\n",
    "        cls_metrics(y_clean_train.iloc[:, 0], y_clean_pred_train),\n",
    "        cls_metrics(y_clean_test.iloc[:, 0], y_clean_pred_test),\n",
    "    ],\n",
    "    index=[\"train_clean\", \"test_clean\"],\n",
    ")\n",
    "metrics_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4f55959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Shap explainer type - <shap.explainers._tree.TreeExplainer object at 0x122d1b2c0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl_clean = SmartExplainer(\n",
    "    model=model_clean,\n",
    "    preprocessing=encoder_clean,\n",
    "    features_dict={k: v for k, v in feature_dict_bug.items() if k in features_clean},\n",
    "    label_dict={0: \"Deceased\", 1: \"Survived\"},\n",
    "    postprocessing=postprocess,\n",
    "    title_story=\"Titanic model debugging - cleaned version\",\n",
    ")\n",
    "\n",
    "y_clean_pred_test_df = pd.DataFrame(\n",
    "    model_clean.predict(X_clean_test),\n",
    "    columns=[target_name],\n",
    "    index=X_clean_test.index,\n",
    ")\n",
    "\n",
    "xpl_clean.compile(\n",
    "    x=X_clean_test,\n",
    "    y_pred=y_clean_pred_test_df,\n",
    "    y_target=y_clean_test,\n",
    "    additional_data=titanic_df.loc[X_clean_test.index, [\"Name\", \"Title\", \"Fare\", \"Embarked\"]],\n",
    ")\n",
    "\n",
    "xpl_clean.plot.features_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db238df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "accuracy",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "precision",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "recall",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "f1",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "3168f5d6-3a36-4a30-9403-7638ffc9f7f6",
       "rows": [
        [
         "train_bug",
         "0.9131736526946108",
         "0.9304347826086956",
         "0.8359375",
         "0.8806584362139918"
        ],
        [
         "test_bug",
         "0.8026905829596412",
         "0.7916666666666666",
         "0.6627906976744186",
         "0.7215189873417721"
        ],
        [
         "train_clean",
         "0.907185628742515",
         "0.9254385964912281",
         "0.82421875",
         "0.871900826446281"
        ],
        [
         "test_clean",
         "0.7982062780269058",
         "0.7887323943661971",
         "0.6511627906976745",
         "0.7133757961783439"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 4
       }
      },
      "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_bug</th>\n",
       "      <td>0.913174</td>\n",
       "      <td>0.930435</td>\n",
       "      <td>0.835938</td>\n",
       "      <td>0.880658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_bug</th>\n",
       "      <td>0.802691</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.662791</td>\n",
       "      <td>0.721519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_clean</th>\n",
       "      <td>0.907186</td>\n",
       "      <td>0.925439</td>\n",
       "      <td>0.824219</td>\n",
       "      <td>0.871901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_clean</th>\n",
       "      <td>0.798206</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.651163</td>\n",
       "      <td>0.713376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             accuracy  precision    recall        f1\n",
       "train_bug    0.913174   0.930435  0.835938  0.880658\n",
       "test_bug     0.802691   0.791667  0.662791  0.721519\n",
       "train_clean  0.907186   0.925439  0.824219  0.871901\n",
       "test_clean   0.798206   0.788732  0.651163  0.713376"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.concat([metrics_bug, metrics_clean])\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813534b0",
   "metadata": {},
   "source": [
    "## 6. Functional and numerical validation checklist\n",
    "\n",
    "Apply this before production release:\n",
    "\n",
    "Functional validation:\n",
    "- are all features available at prediction time?\n",
    "- are business rules stable over time?\n",
    "- is the target definition consistent with the business objective?\n",
    "- are edge business cases covered (rare profiles, extreme values)?\n",
    "\n",
    "Data and numerical validation:\n",
    "- missing values: rate, imputation strategy, impact on predictions\n",
    "- outliers: plausible bounds, winsorization/capping if needed\n",
    "- train vs production distribution: drift on critical features\n",
    "- encoding: unknown categories, column order, expected numeric types\n",
    "- metrics: track precision/recall/f1, not only accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b00ea5",
   "metadata": {},
   "source": [
    "## 7. Launch the Shapash webapp (optional)\n",
    "\n",
    "As in `shapash/webapp/webapp_launch.py`, initialize the app and retrieve the Dash object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6675580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dash.dash.Dash at 0x122d396d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xpl_clean.init_app()\n",
    "app = xpl_clean.smartapp.app\n",
    "app\n",
    "# Explicit launch: app.run_server(debug=False, host=\"0.0.0.0\", port=8080)"
   ]
  }
 ],
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