{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dfb5a8e",
   "metadata": {},
   "source": [
    "# Shapash + TabICL for Regression\n",
    "\n",
    "This tutorial shows how to:\n",
    "- train a `TabICLRegressor` on a regression problem\n",
    "- compute SHAP values with `tabicl.shap`\n",
    "- convert these contributions into a format compatible with Shapash\n",
    "- explore global and local explainability in a notebook\n",
    "- optionally launch the Shapash WebApp\n",
    "\n",
    "The dataset used here is `house_prices`, already provided in Shapash."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ea251d",
   "metadata": {},
   "source": [
    "## 1. Prerequisites\n",
    "\n",
    "TabICL does not return Shapley values directly from `predict()`.\n",
    "However, the project does provide a `tabicl.shap` module that computes SHAP values by leveraging the native column-wise `NaN` masking.\n",
    "\n",
    "Typical installation:\n",
    "`pip install tabicl[shap]`\n",
    "\n",
    "On some macOS Intel environments, you may need to install PyTorch before `tabicl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d91fea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment if needed\n",
    "# !pip install tabicl[shap]\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "from shapash import SmartExplainer\n",
    "from shapash.data.data_loader import data_loading\n",
    "from tabicl import TabICLRegressor\n",
    "from tabicl.shap import get_shap_values, plot_shap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecd7895",
   "metadata": {},
   "source": [
    "## 2. Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8602c12",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Warranty Deed - Conventional</td>\n",
       "      <td>Normal Sale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>1-1/2 Story Finished All Ages</td>\n",
       "      <td>Residential Low Density</td>\n",
       "      <td>9060</td>\n",
       "      <td>Paved</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Near Flat/Level</td>\n",
       "      <td>All public Utilities (E,G,W,&amp; S)</td>\n",
       "      <td>Inside lot</td>\n",
       "      <td>Gentle slope</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>Warranty Deed - Conventional</td>\n",
       "      <td>Normal Sale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>1-Story 1946 &amp; Newer All Styles</td>\n",
       "      <td>Residential Low Density</td>\n",
       "      <td>12342</td>\n",
       "      <td>Paved</td>\n",
       "      <td>Slightly irregular</td>\n",
       "      <td>Near Flat/Level</td>\n",
       "      <td>All public Utilities (E,G,W,&amp; S)</td>\n",
       "      <td>Inside lot</td>\n",
       "      <td>Gentle slope</td>\n",
       "      <td>North Ames</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>Warranty Deed - Conventional</td>\n",
       "      <td>Normal Sale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1-Story 1946 &amp; Newer All Styles</td>\n",
       "      <td>Residential Low Density</td>\n",
       "      <td>9750</td>\n",
       "      <td>Paved</td>\n",
       "      <td>Regular</td>\n",
       "      <td>Near Flat/Level</td>\n",
       "      <td>All public Utilities (E,G,W,&amp; S)</td>\n",
       "      <td>Inside lot</td>\n",
       "      <td>Gentle slope</td>\n",
       "      <td>North Ames</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2007</td>\n",
       "      <td>Court Officer Deed/Estate</td>\n",
       "      <td>Normal Sale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             MSSubClass  \\\n",
       "Id                                                        \n",
       "1024  1-Story PUD (Planned Unit Development) - 1946 ...   \n",
       "811                     1-Story 1946 & Newer All Styles   \n",
       "1385                      1-1/2 Story Finished All Ages   \n",
       "627                     1-Story 1946 & Newer All Styles   \n",
       "814                     1-Story 1946 & Newer All Styles   \n",
       "\n",
       "                     MSZoning  LotArea Street            LotShape  \\\n",
       "Id                                                                  \n",
       "1024  Residential Low Density     3182  Paved             Regular   \n",
       "811   Residential Low Density    10140  Paved             Regular   \n",
       "1385  Residential Low Density     9060  Paved             Regular   \n",
       "627   Residential Low Density    12342  Paved  Slightly irregular   \n",
       "814   Residential Low Density     9750  Paved             Regular   \n",
       "\n",
       "          LandContour                         Utilities   LotConfig  \\\n",
       "Id                                                                    \n",
       "1024  Near Flat/Level  All public Utilities (E,G,W,& S)  Inside lot   \n",
       "811   Near Flat/Level  All public Utilities (E,G,W,& S)  Inside lot   \n",
       "1385  Near Flat/Level  All public Utilities (E,G,W,& S)  Inside lot   \n",
       "627   Near Flat/Level  All public Utilities (E,G,W,& S)  Inside lot   \n",
       "814   Near Flat/Level  All public Utilities (E,G,W,& S)  Inside lot   \n",
       "\n",
       "         LandSlope         Neighborhood  ... OpenPorchSF EnclosedPorch  \\\n",
       "Id                                       ...                             \n",
       "1024  Gentle slope  Bloomington Heights  ...          20             0   \n",
       "811   Gentle slope       Northwest Ames  ...           0             0   \n",
       "1385  Gentle slope              Edwards  ...           0             0   \n",
       "627   Gentle slope           North Ames  ...           0            36   \n",
       "814   Gentle slope           North Ames  ...           0           275   \n",
       "\n",
       "     3SsnPorch ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "Id                                                              \n",
       "1024         0           0         0        0       5    2008   \n",
       "811          0           0       648        0       1    2006   \n",
       "1385         0           0         0        0      10    2009   \n",
       "627          0           0         0      600       8    2007   \n",
       "814          0           0         0      500       4    2007   \n",
       "\n",
       "                          SaleType SaleCondition  \n",
       "Id                                                \n",
       "1024  Warranty Deed - Conventional   Normal Sale  \n",
       "811   Warranty Deed - Conventional   Normal Sale  \n",
       "1385  Warranty Deed - Conventional   Normal Sale  \n",
       "627   Warranty Deed - Conventional   Normal Sale  \n",
       "814      Court Officer Deed/Estate   Normal Sale  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df, house_dict = data_loading('house_prices')\n",
    "\n",
    "y = house_df['SalePrice']\n",
    "X = house_df.drop(columns=['SalePrice'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    train_size=0.75,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6ba9be",
   "metadata": {},
   "source": [
    "## 3. Train the TabICL Model\n",
    "\n",
    "In this version, the data is encoded numerically before `fit` (ordinal encoding of categorical variables), and missing values are imputed before training.\n",
    "\n",
    "The model is then trained on `X_train_num` and evaluated on `X_test_num`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00d8d853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\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",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\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",
       "}\n",
       "\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",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\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",
       "}\n",
       "\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",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\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",
       "}\n",
       "\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>TabICLRegressor(device=&#x27;cpu&#x27;, kv_cache=True, n_estimators=4)</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>TabICLRegressor</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=\"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\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('norm_methods',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">norm_methods</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('feat_shuffle_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">feat_shuffle_method</td>\n",
       "            <td class=\"value\">&#x27;latin&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('outlier_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">outlier_threshold</td>\n",
       "            <td class=\"value\">4.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('batch_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">batch_size</td>\n",
       "            <td class=\"value\">8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kv_cache',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kv_cache</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('model_path',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">model_path</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('allow_auto_download',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">allow_auto_download</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('checkpoint_version',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">checkpoint_version</td>\n",
       "            <td class=\"value\">&#x27;tabicl-regressor-v2-20260212.ckpt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('device',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">device</td>\n",
       "            <td class=\"value\">&#x27;cpu&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('use_amp',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">use_amp</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('use_fa3',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">use_fa3</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('offload_mode',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">offload_mode</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('disk_offload_dir',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">disk_offload_dir</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('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state</td>\n",
       "            <td class=\"value\">42</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('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inference_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">inference_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "TabICLRegressor(device='cpu', kv_cache=True, n_estimators=4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values then encode categoricals to numeric before fit\n",
    "num_cols = X_train.select_dtypes(include=['number', 'bool']).columns\n",
    "cat_cols = X_train.columns.difference(num_cols)\n",
    "\n",
    "X_train_filled = X_train.copy()\n",
    "X_test_filled = X_test.copy()\n",
    "\n",
    "num_fill_values = X_train_filled[num_cols].median()\n",
    "X_train_filled[num_cols] = X_train_filled[num_cols].fillna(num_fill_values)\n",
    "X_test_filled[num_cols] = X_test_filled[num_cols].fillna(num_fill_values)\n",
    "\n",
    "if len(cat_cols) > 0:\n",
    "    cat_fill_values = X_train_filled[cat_cols].mode(dropna=True).iloc[0]\n",
    "    X_train_filled[cat_cols] = X_train_filled[cat_cols].fillna(cat_fill_values)\n",
    "    X_test_filled[cat_cols] = X_test_filled[cat_cols].fillna(cat_fill_values)\n",
    "\n",
    "    ordinal_encoder = OrdinalEncoder(\n",
    "        handle_unknown='use_encoded_value',\n",
    "        unknown_value=-1,\n",
    "        encoded_missing_value=-1,\n",
    "    )\n",
    "    X_train_cat = pd.DataFrame(\n",
    "        ordinal_encoder.fit_transform(X_train_filled[cat_cols]),\n",
    "        index=X_train_filled.index,\n",
    "        columns=cat_cols,\n",
    "    )\n",
    "    X_test_cat = pd.DataFrame(\n",
    "        ordinal_encoder.transform(X_test_filled[cat_cols]),\n",
    "        index=X_test_filled.index,\n",
    "        columns=cat_cols,\n",
    "    )\n",
    "else:\n",
    "    X_train_cat = pd.DataFrame(index=X_train_filled.index)\n",
    "    X_test_cat = pd.DataFrame(index=X_test_filled.index)\n",
    "\n",
    "X_train_num = pd.concat([X_train_filled[num_cols].astype(float), X_train_cat.astype(float)], axis=1)\n",
    "X_test_num = pd.concat([X_test_filled[num_cols].astype(float), X_test_cat.astype(float)], axis=1)\n",
    "\n",
    "# Keep the original feature order\n",
    "X_train_num = X_train_num.reindex(columns=X_train.columns).fillna(0.0)\n",
    "X_test_num = X_test_num.reindex(columns=X_train.columns).fillna(0.0)\n",
    "\n",
    "regressor = TabICLRegressor(\n",
    "    n_estimators=4,\n",
    "    kv_cache=True,\n",
    "    device='cpu',\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "regressor.fit(X_train_num, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86ea31c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "0",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "7459c50d-3755-4b3a-9296-a361fa8786f6",
       "rows": [
        [
         "MAE",
         "13479.4658"
        ],
        [
         "RMSE",
         "21565.9094"
        ],
        [
         "R2",
         "0.9336"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 3
       }
      },
      "text/plain": [
       "MAE     13479.4658\n",
       "RMSE    21565.9094\n",
       "R2          0.9336\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = pd.DataFrame(regressor.predict(X_test_num), columns=['pred'], index=X_test_num.index)\n",
    "\n",
    "metrics = pd.Series({\n",
    "    'MAE': mean_absolute_error(y_test, y_pred['pred']),\n",
    "    'RMSE': np.sqrt(mean_squared_error(y_test, y_pred['pred'])),\n",
    "    'R2': r2_score(y_test, y_pred['pred']),\n",
    "})\n",
    "\n",
    "metrics.round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9073bbd0",
   "metadata": {},
   "source": [
    "## 4. Compute SHAP Values with TabICL\n",
    "\n",
    "We only explain a subset of the test set to keep the notebook responsive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "108de705",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PermutationExplainer explainer: 366it [07:53,  1.32s/it]                         \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "shap._explanation.Explanation"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_explain_columns = X_test_num.columns.tolist()\n",
    "\n",
    "# X_explain = X_explain.fillna(0.0).astype(float)\n",
    "\n",
    "shap_values = get_shap_values(\n",
    "    estimator=regressor,\n",
    "    X_test=X_test_num,\n",
    "    attribute_names=X_explain_columns,\n",
    ")\n",
    "\n",
    "type(shap_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be230da",
   "metadata": {},
   "source": [
    "## 5. Convert to a Shapash-Compatible Format\n",
    "\n",
    "Shapash accepts user-provided local contributions with `SmartExplainer.compile(..., contributions=...)`.\n",
    "For regression, you simply provide an `(n_samples, n_features)` matrix aligned with `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0dfbcbc1",
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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        {
         "name": "Id",
         "rawType": "int64",
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         "name": "MSSubClass",
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        },
        {
         "name": "MSZoning",
         "rawType": "float64",
         "type": "float"
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         "name": "LotArea",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "Street",
         "rawType": "float64",
         "type": "float"
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         "name": "LotShape",
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         "type": "float"
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         "name": "LandContour",
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        {
         "name": "Utilities",
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        {
         "name": "LotConfig",
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         "type": "float"
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         "name": "LandSlope",
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         "type": "float"
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         "name": "Neighborhood",
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         "type": "float"
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        {
         "name": "Condition1",
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         "type": "float"
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        {
         "name": "Condition2",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "BldgType",
         "rawType": "float64",
         "type": "float"
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         "name": "HouseStyle",
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        {
         "name": "OverallQual",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "OverallCond",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "YearBuilt",
         "rawType": "float64",
         "type": "float"
        },
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         "name": "YearRemodAdd",
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         "name": "RoofStyle",
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         "name": "RoofMatl",
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         "name": "Exterior1st",
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         "name": "Exterior2nd",
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         "name": "MasVnrType",
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         "name": "MasVnrArea",
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         "name": "ExterQual",
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         "name": "ExterCond",
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         "name": "Foundation",
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         "type": "float"
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         "name": "BsmtQual",
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         "name": "BsmtCond",
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         "name": "BsmtExposure",
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         "name": "BsmtFinType1",
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         "name": "BsmtFinSF1",
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         "name": "BsmtFinType2",
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        {
         "name": "BsmtFinSF2",
         "rawType": "float64",
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        {
         "name": "BsmtUnfSF",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "TotalBsmtSF",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "Heating",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "HeatingQC",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "CentralAir",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "Electrical",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "1stFlrSF",
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         "name": "2ndFlrSF",
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        {
         "name": "LowQualFinSF",
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         "name": "GrLivArea",
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        {
         "name": "BsmtFullBath",
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         "name": "BsmtHalfBath",
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        {
         "name": "FullBath",
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         "name": "HalfBath",
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        {
         "name": "BedroomAbvGr",
         "rawType": "float64",
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        {
         "name": "KitchenAbvGr",
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        {
         "name": "KitchenQual",
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        {
         "name": "TotRmsAbvGrd",
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        {
         "name": "Functional",
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         "name": "Fireplaces",
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>1134.341146</td>\n",
       "      <td>1165.591146</td>\n",
       "      <td>-4267.718750</td>\n",
       "      <td>-243.335938</td>\n",
       "      <td>-846.552083</td>\n",
       "      <td>-1180.197917</td>\n",
       "      <td>-129.291667</td>\n",
       "      <td>-722.570312</td>\n",
       "      <td>-959.122396</td>\n",
       "      <td>-718.528646</td>\n",
       "      <td>...</td>\n",
       "      <td>-2731.927083</td>\n",
       "      <td>-1290.799479</td>\n",
       "      <td>-2229.411458</td>\n",
       "      <td>384.627604</td>\n",
       "      <td>-3934.169271</td>\n",
       "      <td>324.828125</td>\n",
       "      <td>-1374.604167</td>\n",
       "      <td>-55.851562</td>\n",
       "      <td>-1159.580729</td>\n",
       "      <td>-3525.583333</td>\n",
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       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>2210.226562</td>\n",
       "      <td>1919.864583</td>\n",
       "      <td>4093.882812</td>\n",
       "      <td>-616.252604</td>\n",
       "      <td>54.406250</td>\n",
       "      <td>-2897.070312</td>\n",
       "      <td>-54.440104</td>\n",
       "      <td>-442.393229</td>\n",
       "      <td>-2222.575521</td>\n",
       "      <td>9676.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>-975.208333</td>\n",
       "      <td>-317.333333</td>\n",
       "      <td>-2853.255208</td>\n",
       "      <td>580.546875</td>\n",
       "      <td>-7612.604167</td>\n",
       "      <td>736.776042</td>\n",
       "      <td>-1093.338542</td>\n",
       "      <td>-518.557292</td>\n",
       "      <td>-1599.414062</td>\n",
       "      <td>-5408.106771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>-1800.076823</td>\n",
       "      <td>-1522.001302</td>\n",
       "      <td>-2821.118490</td>\n",
       "      <td>-107.744792</td>\n",
       "      <td>-535.144531</td>\n",
       "      <td>-1283.110677</td>\n",
       "      <td>-81.026042</td>\n",
       "      <td>-792.263021</td>\n",
       "      <td>-646.457031</td>\n",
       "      <td>-2638.473958</td>\n",
       "      <td>...</td>\n",
       "      <td>-2467.863281</td>\n",
       "      <td>333.020833</td>\n",
       "      <td>-2093.165365</td>\n",
       "      <td>191.466146</td>\n",
       "      <td>-3519.006510</td>\n",
       "      <td>585.395833</td>\n",
       "      <td>-988.803385</td>\n",
       "      <td>-290.632812</td>\n",
       "      <td>-970.368490</td>\n",
       "      <td>-3135.895833</td>\n",
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       "      <th>523</th>\n",
       "      <td>1554.544271</td>\n",
       "      <td>-2411.419271</td>\n",
       "      <td>-11190.130208</td>\n",
       "      <td>-137.018229</td>\n",
       "      <td>-1000.174479</td>\n",
       "      <td>-2037.104167</td>\n",
       "      <td>172.817708</td>\n",
       "      <td>95.820312</td>\n",
       "      <td>-1223.960938</td>\n",
       "      <td>4175.825521</td>\n",
       "      <td>...</td>\n",
       "      <td>-1240.661458</td>\n",
       "      <td>-105.760417</td>\n",
       "      <td>-2577.388021</td>\n",
       "      <td>366.585938</td>\n",
       "      <td>-5503.609375</td>\n",
       "      <td>846.067708</td>\n",
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       "      <td>-1523.947917</td>\n",
       "      <td>-4321.684896</td>\n",
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       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>801.145833</td>\n",
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       "      <td>4766.515625</td>\n",
       "      <td>-670.895833</td>\n",
       "      <td>-154.882812</td>\n",
       "      <td>3451.523438</td>\n",
       "      <td>-229.979167</td>\n",
       "      <td>-1795.559896</td>\n",
       "      <td>-2748.388021</td>\n",
       "      <td>5653.872396</td>\n",
       "      <td>...</td>\n",
       "      <td>-2727.916667</td>\n",
       "      <td>-969.276042</td>\n",
       "      <td>-3819.666667</td>\n",
       "      <td>271.013021</td>\n",
       "      <td>-9041.513021</td>\n",
       "      <td>94.385417</td>\n",
       "      <td>709.822917</td>\n",
       "      <td>-470.518229</td>\n",
       "      <td>-2645.000000</td>\n",
       "      <td>-6211.145833</td>\n",
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       "<p>5 rows × 72 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       MSSubClass     MSZoning       LotArea      Street     LotShape  \\\n",
       "Id                                                                      \n",
       "893   1134.341146  1165.591146  -4267.718750 -243.335938  -846.552083   \n",
       "1106  2210.226562  1919.864583   4093.882812 -616.252604    54.406250   \n",
       "414  -1800.076823 -1522.001302  -2821.118490 -107.744792  -535.144531   \n",
       "523   1554.544271 -2411.419271 -11190.130208 -137.018229 -1000.174479   \n",
       "1037   801.145833  1113.877604   4766.515625 -670.895833  -154.882812   \n",
       "\n",
       "      LandContour   Utilities    LotConfig    LandSlope  Neighborhood  ...  \\\n",
       "Id                                                                     ...   \n",
       "893  -1180.197917 -129.291667  -722.570312  -959.122396   -718.528646  ...   \n",
       "1106 -2897.070312  -54.440104  -442.393229 -2222.575521   9676.208333  ...   \n",
       "414  -1283.110677  -81.026042  -792.263021  -646.457031  -2638.473958  ...   \n",
       "523  -2037.104167  172.817708    95.820312 -1223.960938   4175.825521  ...   \n",
       "1037  3451.523438 -229.979167 -1795.559896 -2748.388021   5653.872396  ...   \n",
       "\n",
       "      OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch     PoolArea  \\\n",
       "Id                                                                        \n",
       "893  -2731.927083   -1290.799479 -2229.411458   384.627604 -3934.169271   \n",
       "1106  -975.208333    -317.333333 -2853.255208   580.546875 -7612.604167   \n",
       "414  -2467.863281     333.020833 -2093.165365   191.466146 -3519.006510   \n",
       "523  -1240.661458    -105.760417 -2577.388021   366.585938 -5503.609375   \n",
       "1037 -2727.916667    -969.276042 -3819.666667   271.013021 -9041.513021   \n",
       "\n",
       "         MiscVal       MoSold      YrSold     SaleType  SaleCondition  \n",
       "Id                                                                     \n",
       "893   324.828125 -1374.604167  -55.851562 -1159.580729   -3525.583333  \n",
       "1106  736.776042 -1093.338542 -518.557292 -1599.414062   -5408.106771  \n",
       "414   585.395833  -988.803385 -290.632812  -970.368490   -3135.895833  \n",
       "523   846.067708   301.609375  -58.408854 -1523.947917   -4321.684896  \n",
       "1037   94.385417   709.822917 -470.518229 -2645.000000   -6211.145833  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contributions = pd.DataFrame(\n",
    "    shap_values.values,\n",
    "    columns=X_explain_columns,\n",
    "    index=X_test_num.index,\n",
    ")\n",
    "\n",
    "contributions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6500f1b4",
   "metadata": {},
   "source": [
    "## 6. Compile SmartExplainer with TabICL Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "094062df",
   "metadata": {},
   "outputs": [],
   "source": [
    "xpl = SmartExplainer(\n",
    "    model=regressor,\n",
    "    features_dict=house_dict,\n",
    "    title_story='House Prices - TabICL Regressor',\n",
    ")\n",
    "\n",
    "xpl.compile(\n",
    "    x=X_test_num,\n",
    "    contributions=contributions,\n",
    "    y_pred=y_pred,\n",
    "    y_target=y_test,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882f1ee5",
   "metadata": {},
   "source": [
    "## 7. Explainability in the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f4414aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl.plot.features_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3117d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_index = X_test_num.index[0]\n",
    "xpl.plot.local_plot(index=example_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f695b1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "pred",
         "rawType": "float32",
         "type": "float"
        },
        {
         "name": "feature_1",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_1",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_1",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "feature_2",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_2",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_2",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "feature_3",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_3",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_3",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "feature_4",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_4",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_4",
         "rawType": "object",
         "type": "unknown"
        }
       ],
       "ref": "a9c2bf87-4b4d-4df6-ae7c-bb4bde5d9a55",
       "rows": [
        [
         "893",
         "145425.6",
         "Ground living area square feet",
         "1068.0",
         "-17761.609375",
         "Exterior materials' quality",
         "0.0",
         "-6394.3359375",
         "Number of fireplaces",
         "0.0",
         "-6384.5625",
         "Lot size square feet",
         "8414.0",
         "-4267.71875"
        ],
        [
         "1106",
         "338848.62",
         "Ground living area square feet",
         "2622.0",
         "63718.979166666664",
         "Overall material and finish of the house",
         "8.0",
         "30047.625",
         "Second floor square feet",
         "1122.0",
         "10486.434895833334",
         "Number of fireplaces",
         "2.0",
         "9939.911458333334"
        ],
        [
         "414",
         "106041.74",
         "Ground living area square feet",
         "1028.0",
         "-16405.161458333332",
         "Overall material and finish of the house",
         "5.0",
         "-11118.700520833334",
         "Proximity to various conditions",
         "2.0",
         "-10017.9609375",
         "Exterior materials' quality",
         "0.0",
         "-5917.255208333333"
        ],
        [
         "523",
         "157701.84",
         "Lot size square feet",
         "5000.0",
         "-11190.130208333334",
         "Proximity to various conditions",
         "3.0",
         "-10930.1953125",
         "Exterior materials' quality",
         "0.0",
         "-9621.018229166666",
         "Number of fireplaces",
         "2.0",
         "9555.598958333334"
        ],
        [
         "1037",
         "344184.75",
         "Overall material and finish of the house",
         "9.0",
         "62309.341145833336",
         "Original construction date",
         "2007.0",
         "15426.658854166666",
         "Kitchen quality",
         "0.0",
         "12653.299479166666",
         "Total square feet of basement area",
         "1620.0",
         "12174.908854166666"
        ]
       ],
       "shape": {
        "columns": 13,
        "rows": 5
       }
      },
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>value_1</th>\n",
       "      <th>contribution_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>value_2</th>\n",
       "      <th>contribution_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>value_3</th>\n",
       "      <th>contribution_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>value_4</th>\n",
       "      <th>contribution_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>145425.593750</td>\n",
       "      <td>Ground living area square feet</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>-17761.609375</td>\n",
       "      <td>Exterior materials' quality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6394.335938</td>\n",
       "      <td>Number of fireplaces</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6384.5625</td>\n",
       "      <td>Lot size square feet</td>\n",
       "      <td>8414.0</td>\n",
       "      <td>-4267.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>338848.625000</td>\n",
       "      <td>Ground living area square feet</td>\n",
       "      <td>2622.0</td>\n",
       "      <td>63718.979167</td>\n",
       "      <td>Overall material and finish of the house</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30047.625</td>\n",
       "      <td>Second floor square feet</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>10486.434896</td>\n",
       "      <td>Number of fireplaces</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9939.911458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>106041.742188</td>\n",
       "      <td>Ground living area square feet</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>-16405.161458</td>\n",
       "      <td>Overall material and finish of the house</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-11118.700521</td>\n",
       "      <td>Proximity to various conditions</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-10017.960938</td>\n",
       "      <td>Exterior materials' quality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5917.255208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>157701.843750</td>\n",
       "      <td>Lot size square feet</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>-11190.130208</td>\n",
       "      <td>Proximity to various conditions</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-10930.195312</td>\n",
       "      <td>Exterior materials' quality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9621.018229</td>\n",
       "      <td>Number of fireplaces</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9555.598958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>344184.750000</td>\n",
       "      <td>Overall material and finish of the house</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62309.341146</td>\n",
       "      <td>Original construction date</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>15426.658854</td>\n",
       "      <td>Kitchen quality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12653.299479</td>\n",
       "      <td>Total square feet of basement area</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>12174.908854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pred                                 feature_1 value_1  \\\n",
       "893   145425.593750            Ground living area square feet  1068.0   \n",
       "1106  338848.625000            Ground living area square feet  2622.0   \n",
       "414   106041.742188            Ground living area square feet  1028.0   \n",
       "523   157701.843750                      Lot size square feet  5000.0   \n",
       "1037  344184.750000  Overall material and finish of the house     9.0   \n",
       "\n",
       "     contribution_1                                 feature_2 value_2  \\\n",
       "893   -17761.609375               Exterior materials' quality     0.0   \n",
       "1106   63718.979167  Overall material and finish of the house     8.0   \n",
       "414   -16405.161458  Overall material and finish of the house     5.0   \n",
       "523   -11190.130208           Proximity to various conditions     3.0   \n",
       "1037   62309.341146                Original construction date  2007.0   \n",
       "\n",
       "     contribution_2                        feature_3 value_3 contribution_3  \\\n",
       "893    -6394.335938             Number of fireplaces     0.0     -6384.5625   \n",
       "1106      30047.625         Second floor square feet  1122.0   10486.434896   \n",
       "414   -11118.700521  Proximity to various conditions     2.0  -10017.960938   \n",
       "523   -10930.195312      Exterior materials' quality     0.0   -9621.018229   \n",
       "1037   15426.658854                  Kitchen quality     0.0   12653.299479   \n",
       "\n",
       "                               feature_4 value_4 contribution_4  \n",
       "893                 Lot size square feet  8414.0    -4267.71875  \n",
       "1106                Number of fireplaces     2.0    9939.911458  \n",
       "414          Exterior materials' quality     0.0   -5917.255208  \n",
       "523                 Number of fireplaces     2.0    9555.598958  \n",
       "1037  Total square feet of basement area  1620.0   12174.908854  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = xpl.to_pandas(max_contrib=4)\n",
    "summary_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043d52db",
   "metadata": {},
   "source": [
    "## 8. Optionally Launch the WebApp\n",
    "\n",
    "As in the other Shapash tutorials, you can launch the WebApp directly from the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99588ecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Your Shapash application run on http://PMP01087:8050/\n",
      "INFO:root:Use the method .kill() to down your app.\n"
     ]
    }
   ],
   "source": [
    "app = xpl.run_app(port=8050)\n",
    "# Then use: app.kill()"
   ]
  }
 ],
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