tutorial/explainer/tuto-expl01-Shapash-Viz-using-Shap-contributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Contributions with Shap - Summarize Them With Shapash\n",
    "\n",
    "Shapash uses Shap backend to compute the Shapley contributions <br />\n",
    "in order to satisfy the most hurry users who wish to display <br />\n",
    "results with little lines of code.\n",
    "\n",
    "But we recommend you to refer to the excellent [Shap library](https://github.com/slundberg/shap).\n",
    "\n",
    "This tutorial shows how to use precalculated contributions with Shap in Shapash \n",
    "\n",
    "Contents:\n",
    "- Build a Binary Classifier\n",
    "- Use Shap KernelExplainer\n",
    "- Compile Shapash SmartExplainer\n",
    "- Display local_plot\n",
    "- to_pandas export\n",
    "\n",
    "We used Kaggle's [Titanic](https://www.kaggle.com/c/titanic) dataset"
   ]
  },
  {
   "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 sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapash.data.data_loader import data_loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "titan_df, titan_dict = data_loading('titanic')\n",
    "del titan_df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titan_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = titan_df['Survived']\n",
    "X = titan_df.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "varcat=['Pclass','Sex','Embarked','Title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categ_encoding = OrdinalEncoder(cols=varcat, \\\n",
    "                                handle_unknown='ignore', \\\n",
    "                                return_df=True).fit(X)\n",
    "X = categ_encoding.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Test split + Random Forest fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=3, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.75, random_state=1)\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100,min_samples_leaf=3)\n",
    "rf.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=pd.DataFrame(rf.predict(Xtest),columns=['pred'],index=Xtest.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Shapley Contributions with Shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 223 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4667a6becc4f4bf1bcdb5686a243f5d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=223), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "explainer = shap.KernelExplainer(rf.predict_proba, Xtest)\n",
    "shap_contrib = explainer.shap_values(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Shapash With Shapley Contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapash.explainer.smart_explainer import SmartExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpl = SmartExplainer(features_dict=titan_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use contributions parameter of compile method to declare Shapley contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpl.compile(contributions=shap_contrib, # Shap Contributions pd.DataFrame\n",
    "            y_pred=ypred,\n",
    "            x=Xtest,\n",
    "            model=rf,\n",
    "            preprocessing=categ_encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl.plot.local_plot(index=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>proba</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>1</td>\n",
       "      <td>0.785470</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.192354</td>\n",
       "      <td>Title of passenger</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.158498</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>First class</td>\n",
       "      <td>0.092583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0</td>\n",
       "      <td>0.972264</td>\n",
       "      <td>Title of passenger</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0.105669</td>\n",
       "      <td>Passenger fare</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.074578</td>\n",
       "      <td>Sex</td>\n",
       "      <td>male</td>\n",
       "      <td>0.0693082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>0.826169</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.183571</td>\n",
       "      <td>Title of passenger</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0.177265</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>Second class</td>\n",
       "      <td>0.083088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>1</td>\n",
       "      <td>0.686340</td>\n",
       "      <td>Title of passenger</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0.162849</td>\n",
       "      <td>Port of embarkation</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>0.146734</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.119779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>1</td>\n",
       "      <td>0.965928</td>\n",
       "      <td>Title of passenger</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0.199437</td>\n",
       "      <td>Ticket class</td>\n",
       "      <td>Second class</td>\n",
       "      <td>0.118737</td>\n",
       "      <td>Sex</td>\n",
       "      <td>female</td>\n",
       "      <td>0.110403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pred     proba           feature_1 value_1 contribution_1  \\\n",
       "863     1  0.785470                 Sex  female       0.192354   \n",
       "224     0  0.972264  Title of passenger      Mr       0.105669   \n",
       "85      1  0.826169                 Sex  female       0.183571   \n",
       "681     1  0.686340  Title of passenger    Miss       0.162849   \n",
       "536     1  0.965928  Title of passenger    Miss       0.199437   \n",
       "\n",
       "               feature_2       value_2 contribution_2     feature_3  \\\n",
       "863   Title of passenger           Mrs       0.158498  Ticket class   \n",
       "224       Passenger fare           7.9       0.074578           Sex   \n",
       "85    Title of passenger          Miss       0.177265  Ticket class   \n",
       "681  Port of embarkation    Queenstown       0.146734           Sex   \n",
       "536         Ticket class  Second class       0.118737           Sex   \n",
       "\n",
       "          value_3 contribution_3  \n",
       "863   First class       0.092583  \n",
       "224          male      0.0693082  \n",
       "85   Second class       0.083088  \n",
       "681        female       0.119779  \n",
       "536        female       0.110403  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = xpl.to_pandas(max_contrib=3,positive=True,proba=True)\n",
    "summary_df.head()"
   ]
  }
 ],
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