tutorial/use_encoders/tuto-encoder01-using-category_encoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Category_encoder tutorial\n",
    "\n",
    "This tutorial shows how to use category_encoder encoders to reverse data preprocessing and display explicit labels.\n",
    "\n",
    "We used Kaggle's [Titanic](https://www.kaggle.com/c/titanic) dataset.\n",
    "\n",
    "This Tutorial:\n",
    "- Encode data with Category_encoder\n",
    "- Build a Binary Classifier (Random Forest)\n",
    "- Using Shapash\n",
    "- Show inversed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from category_encoders import OrdinalEncoder\n",
    "from category_encoders import OneHotEncoder\n",
    "from category_encoders import TargetEncoder\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load titanic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapash.data.data_loader import data_loading\n",
    "titan_df, titan_dict = data_loading('titanic')\n",
    "del titan_df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Third class</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>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>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>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>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     Sex   Age  SibSp  Parch   Fare  \\\n",
       "PassengerId                                                             \n",
       "1                   0  Third class    male  22.0      1      0   7.25   \n",
       "2                   1  First class  female  38.0      1      0  71.28   \n",
       "3                   1  Third class  female  26.0      0      0   7.92   \n",
       "4                   1  First class  female  35.0      1      0  53.10   \n",
       "5                   0  Third class    male  35.0      0      0   8.05   \n",
       "\n",
       "                Embarked Title  \n",
       "PassengerId                     \n",
       "1            Southampton    Mr  \n",
       "2              Cherbourg   Mrs  \n",
       "3            Southampton  Miss  \n",
       "4            Southampton   Mrs  \n",
       "5            Southampton    Mr  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titan_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for the model with Category Encoder\n",
    "\n",
    "Create Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = titan_df['Survived']\n",
    "X = titan_df.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train category encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train category encoder\n",
    "onehot = OneHotEncoder(cols=['Pclass']).fit(X)\n",
    "result_1 = onehot.transform(X)\n",
    "ordinal = OrdinalEncoder(cols=['Embarked','Title']).fit(result_1)\n",
    "result_2 = ordinal.transform(result_1)\n",
    "target = TargetEncoder(cols=['Sex']).fit(result_2,y)\n",
    "result_3 =target.transform(result_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = [onehot,ordinal,target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster=None, colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints=None,\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=2, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=200, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=None,\n",
       "              validate_parameters=False, verbosity=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(result_3, y, train_size=0.75, random_state=1)\n",
    "\n",
    "clf = XGBClassifier(n_estimators=200,min_child_weight=2).fit(Xtrain,ytrain)\n",
    "clf.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Shapash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapash import SmartExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpl = SmartExplainer(\n",
    "    model=clf,\n",
    "    preprocessing=encoder,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backend: Shap TreeExplainer\n"
     ]
    }
   ],
   "source": [
    "xpl.compile(x=Xtest,\n",
    "y_target=ytest, # Optional: allows to display True Values vs Predicted Values\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize data in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>48.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>25.93</td>\n",
       "      <td>Southampton</td>\n",
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       "      <td>29.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Second class</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.25</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Third class</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.85</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Second class</td>\n",
       "      <td>male</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>First class</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>35</th>\n",
       "      <td>First class</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82.17</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Third class</td>\n",
       "      <td>female</td>\n",
       "      <td>29.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                   Pclass     Sex   Age  SibSp  Parch   Fare     Embarked  \\\n",
       "PassengerId                                                                 \n",
       "863           First class  female  48.0      0      0  25.93  Southampton   \n",
       "224           Third class    male  29.5      0      0   7.90  Southampton   \n",
       "85           Second class  female  17.0      0      0  10.50  Southampton   \n",
       "681           Third class  female  29.5      0      0   8.14   Queenstown   \n",
       "536          Second class  female   7.0      0      2  26.25  Southampton   \n",
       "624           Third class    male  21.0      0      0   7.85  Southampton   \n",
       "149          Second class    male  36.5      0      2  26.00  Southampton   \n",
       "4             First class  female  35.0      1      0  53.10  Southampton   \n",
       "35            First class    male  28.0      1      0  82.17    Cherbourg   \n",
       "242           Third class  female  29.5      1      0  15.50   Queenstown   \n",
       "\n",
       "            Title  \n",
       "PassengerId        \n",
       "863           Mrs  \n",
       "224            Mr  \n",
       "85           Miss  \n",
       "681          Miss  \n",
       "536          Miss  \n",
       "624            Mr  \n",
       "149            Mr  \n",
       "4             Mrs  \n",
       "35             Mr  \n",
       "242          Miss  "
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     "metadata": {},
     "output_type": "execute_result"
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    "xpl.x_init"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>82.17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>29.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass_1  Pclass_2  Pclass_3       Sex   Age  SibSp  Parch  \\\n",
       "PassengerId                                                               \n",
       "863                 0         1         0  0.742038  48.0      0      0   \n",
       "224                 1         0         0  0.188908  29.5      0      0   \n",
       "85                  0         0         1  0.742038  17.0      0      0   \n",
       "681                 1         0         0  0.742038  29.5      0      0   \n",
       "536                 0         0         1  0.742038   7.0      0      2   \n",
       "624                 1         0         0  0.188908  21.0      0      0   \n",
       "149                 0         0         1  0.188908  36.5      0      2   \n",
       "4                   0         1         0  0.742038  35.0      1      0   \n",
       "35                  0         1         0  0.188908  28.0      1      0   \n",
       "242                 1         0         0  0.742038  29.5      1      0   \n",
       "\n",
       "              Fare  Embarked  Title  \n",
       "PassengerId                          \n",
       "863          25.93         1      2  \n",
       "224           7.90         1      1  \n",
       "85           10.50         1      3  \n",
       "681           8.14         3      3  \n",
       "536          26.25         1      3  \n",
       "624           7.85         1      1  \n",
       "149          26.00         1      1  \n",
       "4            53.10         1      2  \n",
       "35           82.17         2      1  \n",
       "242          15.50         3      3  "
      ]
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