{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc074d5d",
   "metadata": {},
   "source": [
    "# Shapash + Keras in Jupyter: Titanic Survival Classification\n",
    "\n",
    "This tutorial shows how to:\n",
    "- train a tabular deep learning model (Keras)\n",
    "- predict Titanic passenger survival\n",
    "- explain predictions in a notebook with Shapash"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7d57dd",
   "metadata": {},
   "source": [
    "## 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d554ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import clear_output, display\n",
    "\n",
    "from category_encoders import one_hot\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import keras\n",
    "from keras import layers\n",
    "\n",
    "from shapash import SmartExplainer\n",
    "from shapash.data.data_loader import data_loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10183481",
   "metadata": {},
   "source": [
    "## 2. Loading the Titanic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e19f182e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "PassengerId",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Survived",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Pclass",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Name",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Sex",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Age",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "SibSp",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Parch",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Fare",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Embarked",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Title",
         "rawType": "object",
         "type": "string"
        }
       ],
       "ref": "ca3ebc4f-a0c9-4d54-a2b7-ba0b38f7bdb1",
       "rows": [
        [
         "1",
         "0",
         "Third class",
         "Braund Owen Harris",
         "male",
         "22.0",
         "1",
         "0",
         "7.25",
         "Southampton",
         "Mr"
        ],
        [
         "2",
         "1",
         "First class",
         "Cumings John Bradley (Florence Briggs Thayer)",
         "female",
         "38.0",
         "1",
         "0",
         "71.28",
         "Cherbourg",
         "Mrs"
        ],
        [
         "3",
         "1",
         "Third class",
         "Heikkinen Laina",
         "female",
         "26.0",
         "0",
         "0",
         "7.92",
         "Southampton",
         "Miss"
        ],
        [
         "4",
         "1",
         "First class",
         "Futrelle Jacques Heath (Lily May Peel)",
         "female",
         "35.0",
         "1",
         "0",
         "53.1",
         "Southampton",
         "Mrs"
        ],
        [
         "5",
         "0",
         "Third class",
         "Allen William Henry",
         "male",
         "35.0",
         "0",
         "0",
         "8.05",
         "Southampton",
         "Mr"
        ]
       ],
       "shape": {
        "columns": 10,
        "rows": 5
       }
      },
      "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Third class</td>\n",
       "      <td>Braund Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>First class</td>\n",
       "      <td>Cumings John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.28</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Third class</td>\n",
       "      <td>Heikkinen Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>First class</td>\n",
       "      <td>Futrelle Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.10</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>Third class</td>\n",
       "      <td>Allen William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived       Pclass  \\\n",
       "PassengerId                          \n",
       "1                   0  Third class   \n",
       "2                   1  First class   \n",
       "3                   1  Third class   \n",
       "4                   1  First class   \n",
       "5                   0  Third class   \n",
       "\n",
       "                                                      Name     Sex   Age  \\\n",
       "PassengerId                                                                \n",
       "1                                       Braund Owen Harris    male  22.0   \n",
       "2            Cumings John Bradley (Florence Briggs Thayer)  female  38.0   \n",
       "3                                          Heikkinen Laina  female  26.0   \n",
       "4                   Futrelle Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                      Allen William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch   Fare     Embarked Title  \n",
       "PassengerId                                          \n",
       "1                1      0   7.25  Southampton    Mr  \n",
       "2                1      0  71.28    Cherbourg   Mrs  \n",
       "3                0      0   7.92  Southampton  Miss  \n",
       "4                1      0  53.10  Southampton   Mrs  \n",
       "5                0      0   8.05  Southampton    Mr  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df, titanic_dict = data_loading('titanic')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f89d33e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 7), (891, 1))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "target = 'Survived'\n",
    "\n",
    "df = titanic_df[features + [target]].copy()\n",
    "\n",
    "# Convert Pclass labels to ordinal values\n",
    "pclass_map = {\n",
    "    'First class': 1,\n",
    "    'Second class': 2,\n",
    "    'Third class': 3,\n",
    "}\n",
    "df['Pclass'] = df['Pclass'].map(pclass_map).fillna(df['Pclass'])\n",
    "df['Pclass'] = pd.to_numeric(df['Pclass'], errors='coerce')\n",
    "\n",
    "# Clean minimal values for a tabular neural network\n",
    "df['Age'] = df['Age'].fillna(df['Age'].median())\n",
    "df['Fare'] = df['Fare'].fillna(df['Fare'].median())\n",
    "df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode()[0])\n",
    "df['Pclass'] = df['Pclass'].fillna(df['Pclass'].median())\n",
    "\n",
    "X = df[features]\n",
    "y = df[target].astype(int).to_frame()\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac2c2d9",
   "metadata": {},
   "source": [
    "## 3. Encoding categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be20dae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((668, 10), (223, 10))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = one_hot.OneHotEncoder(cols=['Sex', 'Embarked'], use_cat_names=True)\n",
    "X_enc = encoder.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_enc, y,\n",
    "    test_size=0.25,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fc0883",
   "metadata": {},
   "source": [
    "## 4. Deep learning model with Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "956f9245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7979 - loss: 0.4499 - val_accuracy: 0.7803 - val_loss: 0.5101\n",
      "Best val_accuracy: 0.7937\n"
     ]
    }
   ],
   "source": [
    "keras.utils.set_random_seed(42)\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Input(shape=(X_train.shape[1],)),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dropout(0.2),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['accuracy'],\n",
    ")\n",
    "\n",
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=15,\n",
    "    restore_best_weights=True\n",
    ")\n",
    "\n",
    "SHAPASH_YELLOW = '#f4c000'\n",
    "SHAPASH_GREY = '#343736'\n",
    "\n",
    "class LiveLossPlot(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs=None):\n",
    "        self.train_losses = []\n",
    "        self.val_losses = []\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        logs = logs or {}\n",
    "        self.train_losses.append(logs.get('loss'))\n",
    "        self.val_losses.append(logs.get('val_loss'))\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8, 4))\n",
    "        ax.plot(self.train_losses, label='Train loss', color=SHAPASH_YELLOW, linewidth=2.5)\n",
    "        ax.plot(self.val_losses, label='Validation loss', color=SHAPASH_GREY, linewidth=2.5)\n",
    "        ax.set_title(f'Learning curve (epoch {epoch + 1})')\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Binary crossentropy loss')\n",
    "        ax.grid(alpha=0.25)\n",
    "        ax.legend()\n",
    "        fig.tight_layout()\n",
    "\n",
    "        clear_output(wait=True)\n",
    "        display(fig)\n",
    "        plt.close(fig)\n",
    "\n",
    "live_plot = LiveLossPlot()\n",
    "\n",
    "history = model.fit(\n",
    "    X_train.values,\n",
    "    y_train.values,\n",
    "    validation_data=(X_test.values, y_test.values),\n",
    "    epochs=200,\n",
    "    batch_size=32,\n",
    "    callbacks=[early_stopping, live_plot],\n",
    "    # verbose=0\n",
    ")\n",
    "# load the best model weights from early stopping\n",
    "print(f\"Best val_accuracy: {max(history.history['val_accuracy']):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48dbe017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.7892\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.8082    0.8613    0.8339       137\n",
      "           1     0.7532    0.6744    0.7117        86\n",
      "\n",
      "    accuracy                         0.7892       223\n",
      "   macro avg     0.7807    0.7679    0.7728       223\n",
      "weighted avg     0.7870    0.7892    0.7868       223\n",
      "\n"
     ]
    }
   ],
   "source": [
    "proba_test = model.predict(X_test.values, verbose=0).reshape(-1)\n",
    "pred_test = (proba_test >= 0.5).astype(int)\n",
    "\n",
    "print(f\"Test accuracy: {accuracy_score(y_test.values.reshape(-1), pred_test):.4f}\")\n",
    "print(classification_report(y_test.values.reshape(-1), pred_test, digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8d2e26",
   "metadata": {},
   "source": [
    "## 5. Sklearn-like wrapper for Shapash\n",
    "\n",
    "Shapash detects classification with the `classes_` and `predict_proba` attributes.\n",
    "We add a lightweight wrapper around the Keras model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1ed5e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "class KerasBinaryClassifierWrapper:\n",
    "    def __init__(self, keras_model, threshold=0.5):\n",
    "        self.keras_model = keras_model\n",
    "        self.threshold = threshold\n",
    "        self.classes_ = np.array([0, 1])\n",
    "        self._classes = [0, 1]\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        p1 = self.keras_model.predict(np.asarray(X), verbose=0).reshape(-1)\n",
    "        p0 = 1.0 - p1\n",
    "        return np.column_stack([p0, p1])\n",
    "\n",
    "    def predict(self, X):\n",
    "        p1 = self.predict_proba(X)[:, 1]\n",
    "        return (p1 >= self.threshold).astype(int)\n",
    "\n",
    "wrapped_model = KerasBinaryClassifierWrapper(keras_model=model, threshold=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cad0392",
   "metadata": {},
   "source": [
    "## 6. Compiling SmartExplainer\n",
    "\n",
    "We use the `shap` backend here to keep computation time reasonable for a tabular deep learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64e48495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Shap explainer type - <shap.explainers._exact.ExactExplainer object at 0x12e970440>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ExactExplainer explainer: 201it [00:55,  3.10it/s]                         \n"
     ]
    }
   ],
   "source": [
    "# Keep explanation runtime low for a notebook demo\n",
    "n_explain = min(200, len(X_test))\n",
    "X_explain = X_test.sample(n=n_explain, random_state=42)\n",
    "y_target_explain = y_test.loc[X_explain.index]\n",
    "\n",
    "y_pred = pd.DataFrame(\n",
    "    wrapped_model.predict(X_explain),\n",
    "    index=X_explain.index,\n",
    "    columns=['Survived']\n",
    ")\n",
    "\n",
    "response_dict = {0: 'Deceased', 1: 'Survived'}\n",
    "\n",
    "xpl = SmartExplainer(\n",
    "    model=wrapped_model,\n",
    "    preprocessing=encoder,\n",
    "    features_dict=titanic_dict,\n",
    "    label_dict=response_dict,\n",
    "    backend='shap',\n",
    "    title_story='Titanic survival with a Keras'\n",
    ")\n",
    "\n",
    "xpl.compile(\n",
    "    x=X_explain,\n",
    "    y_pred=y_pred,\n",
    "    y_target=y_target_explain\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5576703c",
   "metadata": {},
   "source": [
    "## 7. Shapash plots in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7feb8a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl.plot.features_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1772f8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl.plot.contribution_plot('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b28e399a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_id = X_explain.index[0]\n",
    "xpl.plot.local_plot(index=row_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd4dfbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Survived",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "feature_1",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_1",
         "rawType": "object",
         "type": "string"
        },
        {
         "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"
        },
        {
         "name": "feature_5",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_5",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_5",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "feature_6",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_6",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_6",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "feature_7",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value_7",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "contribution_7",
         "rawType": "object",
         "type": "unknown"
        }
       ],
       "ref": "e43b2c23-b913-4d6f-8a2d-23cb5fceadbd",
       "rows": [
        [
         "735",
         "Deceased",
         "Sex",
         "male",
         "0.12768356938352665",
         "Passenger fare",
         "13.0",
         "0.050850900769470186",
         "Relatives such as brother or wife",
         "0",
         "-0.02853931147687961",
         "Port of embarkation",
         "Southampton",
         "0.014882390063433398",
         "Age",
         "23.0",
         "-0.01047218907849197",
         "Ticket class",
         "2",
         "-0.007397481506069031",
         "Relatives like children or parents",
         "0",
         "-0.001051311225172132"
        ],
        [
         "625",
         "Deceased",
         "Sex",
         "male",
         "0.11623569979128393",
         "Relatives such as brother or wife",
         "0",
         "-0.02884450550732155",
         "Ticket class",
         "3",
         "0.025976219465334044",
         "Passenger fare",
         "16.1",
         "0.021562631567319204",
         "Age",
         "21.0",
         "-0.017173285160745868",
         "Port of embarkation",
         "Southampton",
         "0.014301426739919732",
         "Relatives like children or parents",
         "0",
         "-0.0007638261389164612"
        ],
        [
         "104",
         "Deceased",
         "Sex",
         "male",
         "0.12561864300190448",
         "Passenger fare",
         "8.65",
         "0.08886070174002454",
         "Relatives such as brother or wife",
         "0",
         "-0.025192863642223343",
         "Ticket class",
         "3",
         "0.020885572554809752",
         "Port of embarkation",
         "Southampton",
         "0.013359412119502086",
         "Age",
         "33.0",
         "0.012454842173154335",
         "Relatives like children or parents",
         "0",
         "-0.0009605533762110314"
        ],
        [
         "388",
         "Survived",
         "Sex",
         "female",
         "0.2867591286528085",
         "Relatives such as brother or wife",
         "0",
         "0.032434958661300355",
         "Passenger fare",
         "13.0",
         "-0.02376918296270547",
         "Port of embarkation",
         "Southampton",
         "-0.014273609717332167",
         "Ticket class",
         "2",
         "0.011565080565621562",
         "Relatives like children or parents",
         "0",
         "0.006015533058652779",
         "Age",
         "36.0",
         "0.0029347756688318043"
        ],
        [
         "342",
         "Survived",
         "Sex",
         "female",
         "0.20664794136817383",
         "Relatives such as brother or wife",
         "3",
         "-0.12162249656002393",
         "Passenger fare",
         "263.0",
         "0.10648385981205731",
         "Ticket class",
         "1",
         "0.015444750174185038",
         "Port of embarkation",
         "Southampton",
         "-0.010941897314899262",
         "Age",
         "24.0",
         "-0.008412087957670924",
         "Relatives like children or parents",
         "2",
         "0.00530435015364654"
        ],
        [
         "352",
         "Deceased",
         "Sex",
         "male",
         "0.11563585361412651",
         "Passenger fare",
         "35.0",
         "-0.06491485111557296",
         "Ticket class",
         "1",
         "-0.04878907220491391",
         "Relatives such as brother or wife",
         "0",
         "-0.031078240090182868",
         "Port of embarkation",
         "Southampton",
         "0.011675984541575113",
         "Relatives like children or parents",
         "0",
         "-0.004565062805300661",
         "Age",
         "29.5",
         "0.0029194542843675748"
        ],
        [
         "367",
         "Survived",
         "Sex",
         "female",
         "0.2448757372348468",
         "Passenger fare",
         "75.25",
         "0.17098170187150613",
         "Ticket class",
         "1",
         "0.053929637451028384",
         "Port of embarkation",
         "Cherbourg",
         "0.03814150564317836",
         "Age",
         "60.0",
         "-0.015540232329725547",
         "Relatives such as brother or wife",
         "1",
         "-0.008887833301739301",
         "Relatives like children or parents",
         "0",
         "0.004334659198446891"
        ],
        [
         "296",
         "Deceased",
         "Sex",
         "male",
         "0.10904998140037075",
         "Ticket class",
         "1",
         "-0.045848325359679545",
         "Passenger fare",
         "27.72",
         "-0.03526612630557453",
         "Relatives such as brother or wife",
         "0",
         "-0.03066082699951675",
         "Port of embarkation",
         "Cherbourg",
         "-0.030228303191917306",
         "Relatives like children or parents",
         "0",
         "-0.004338781316365517",
         "Age",
         "29.5",
         "0.004128348478958663"
        ],
        [
         "428",
         "Survived",
         "Sex",
         "female",
         "0.2425088674715823",
         "Relatives such as brother or wife",
         "0",
         "0.04090312422415806",
         "Passenger fare",
         "26.0",
         "0.02634297095871866",
         "Age",
         "19.0",
         "0.0237517073231912",
         "Port of embarkation",
         "Southampton",
         "-0.011516873533408783",
         "Ticket class",
         "2",
         "0.011443181430536742",
         "Relatives like children or parents",
         "0",
         "0.0047544255805985255"
        ],
        [
         "824",
         "Survived",
         "Sex",
         "female",
         "0.26622851150264104",
         "Relatives such as brother or wife",
         "0",
         "0.0325298409582308",
         "Passenger fare",
         "12.48",
         "-0.03144897273975232",
         "Ticket class",
         "3",
         "-0.02546692440126659",
         "Port of embarkation",
         "Southampton",
         "-0.013691777416637947",
         "Relatives like children or parents",
         "1",
         "-0.00870717034518484",
         "Age",
         "27.0",
         "0.005659460434394243"
        ]
       ],
       "shape": {
        "columns": 22,
        "rows": 10
       }
      },
      "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>Survived</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>...</th>\n",
       "      <th>contribution_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>value_5</th>\n",
       "      <th>contribution_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>value_6</th>\n",
       "      <th>contribution_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>value_7</th>\n",
       "      <th>contribution_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>Deceased</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.127684</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.050851</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.028539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014882</td>\n",
       "      <td>Age</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.010472</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.007397</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Deceased</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.116236</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.028845</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>3</td>\n",
       "      <td>0.025976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021563</td>\n",
       "      <td>Age</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.017173</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>0.014301</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Deceased</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.125619</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>8.65</td>\n",
       "      <td>0.088861</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.025193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>0.013359</td>\n",
       "      <td>Age</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Survived</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.286759</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.023769</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011565</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006016</td>\n",
       "      <td>Age</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.002935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Survived</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.206648</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.121622</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.106484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015445</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>-0.010942</td>\n",
       "      <td>Age</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.008412</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Deceased</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.115636</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-0.064915</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.048789</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031078</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>0.011676</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.004565</td>\n",
       "      <td>Age</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.002919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Survived</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.244876</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>75.25</td>\n",
       "      <td>0.170982</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038142</td>\n",
       "      <td>Age</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-0.01554</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.008888</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Deceased</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.10905</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.045848</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>27.72</td>\n",
       "      <td>-0.035266</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030661</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>-0.030228</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.004339</td>\n",
       "      <td>Age</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.004128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Survived</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.242509</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040903</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.026343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023752</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>-0.011517</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011443</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>Survived</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.266229</td>\n",
       "      <td>Relatives such as brother or wife</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03253</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>12.48</td>\n",
       "      <td>-0.031449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025467</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>-0.013692</td>\n",
       "      <td>Relatives like children or parents</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.008707</td>\n",
       "      <td>Age</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.005659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived feature_1 value_1 contribution_1  \\\n",
       "735  Deceased       Sex    male       0.127684   \n",
       "625  Deceased       Sex    male       0.116236   \n",
       "104  Deceased       Sex    male       0.125619   \n",
       "388  Survived       Sex  female       0.286759   \n",
       "342  Survived       Sex  female       0.206648   \n",
       "352  Deceased       Sex    male       0.115636   \n",
       "367  Survived       Sex  female       0.244876   \n",
       "296  Deceased       Sex    male        0.10905   \n",
       "428  Survived       Sex  female       0.242509   \n",
       "824  Survived       Sex  female       0.266229   \n",
       "\n",
       "                             feature_2 value_2 contribution_2  \\\n",
       "735                     Passenger fare    13.0       0.050851   \n",
       "625  Relatives such as brother or wife       0      -0.028845   \n",
       "104                     Passenger fare    8.65       0.088861   \n",
       "388  Relatives such as brother or wife       0       0.032435   \n",
       "342  Relatives such as brother or wife       3      -0.121622   \n",
       "352                     Passenger fare    35.0      -0.064915   \n",
       "367                     Passenger fare   75.25       0.170982   \n",
       "296                       Ticket class       1      -0.045848   \n",
       "428  Relatives such as brother or wife       0       0.040903   \n",
       "824  Relatives such as brother or wife       0        0.03253   \n",
       "\n",
       "                             feature_3 value_3 contribution_3  ...  \\\n",
       "735  Relatives such as brother or wife       0      -0.028539  ...   \n",
       "625                       Ticket class       3       0.025976  ...   \n",
       "104  Relatives such as brother or wife       0      -0.025193  ...   \n",
       "388                     Passenger fare    13.0      -0.023769  ...   \n",
       "342                     Passenger fare   263.0       0.106484  ...   \n",
       "352                       Ticket class       1      -0.048789  ...   \n",
       "367                       Ticket class       1        0.05393  ...   \n",
       "296                     Passenger fare   27.72      -0.035266  ...   \n",
       "428                     Passenger fare    26.0       0.026343  ...   \n",
       "824                     Passenger fare   12.48      -0.031449  ...   \n",
       "\n",
       "    contribution_4            feature_5      value_5 contribution_5  \\\n",
       "735       0.014882                  Age         23.0      -0.010472   \n",
       "625       0.021563                  Age         21.0      -0.017173   \n",
       "104       0.020886  Port of embarkation  Southampton       0.013359   \n",
       "388      -0.014274         Ticket class            2       0.011565   \n",
       "342       0.015445  Port of embarkation  Southampton      -0.010942   \n",
       "352      -0.031078  Port of embarkation  Southampton       0.011676   \n",
       "367       0.038142                  Age         60.0       -0.01554   \n",
       "296      -0.030661  Port of embarkation    Cherbourg      -0.030228   \n",
       "428       0.023752  Port of embarkation  Southampton      -0.011517   \n",
       "824      -0.025467  Port of embarkation  Southampton      -0.013692   \n",
       "\n",
       "                              feature_6      value_6 contribution_6  \\\n",
       "735                        Ticket class            2      -0.007397   \n",
       "625                 Port of embarkation  Southampton       0.014301   \n",
       "104                                 Age         33.0       0.012455   \n",
       "388  Relatives like children or parents            0       0.006016   \n",
       "342                                 Age         24.0      -0.008412   \n",
       "352  Relatives like children or parents            0      -0.004565   \n",
       "367   Relatives such as brother or wife            1      -0.008888   \n",
       "296  Relatives like children or parents            0      -0.004339   \n",
       "428                        Ticket class            2       0.011443   \n",
       "824  Relatives like children or parents            1      -0.008707   \n",
       "\n",
       "                              feature_7 value_7 contribution_7  \n",
       "735  Relatives like children or parents       0      -0.001051  \n",
       "625  Relatives like children or parents       0      -0.000764  \n",
       "104  Relatives like children or parents       0      -0.000961  \n",
       "388                                 Age    36.0       0.002935  \n",
       "342  Relatives like children or parents       2       0.005304  \n",
       "352                                 Age    29.5       0.002919  \n",
       "367  Relatives like children or parents       0       0.004335  \n",
       "296                                 Age    29.5       0.004128  \n",
       "428  Relatives like children or parents       0       0.004754  \n",
       "824                                 Age    27.0       0.005659  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xpl.to_pandas(max_contrib=8).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e65df8",
   "metadata": {},
   "source": [
    "## 8. (Optional) Launch the Shapash WebApp\n",
    "\n",
    "As in `shapash/webapp/webapp_launch.py`, you can also launch the interactive application:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfe1b413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://localhost:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x12d4ac080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl.init_app()\n",
    "app = xpl.smartapp.app\n",
    "app.run_server(host='localhost', port=8050)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "survptf_312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}