AutoML titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated ML (AutoML) with Azure ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Automated machine learning**, also referred to as automated ML or AutoML, is the **process of automating the time consuming, iterative tasks of machine learning model development**.<br><br>\n",
    "It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. **Automated ML is based on a breakthrough from our Microsoft Research division.**\n",
    "\n",
    "Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. **With automated machine learning, you'll accelerate the time it takes to get production-ready ML models with great ease and efficiency.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://docs.microsoft.com/ko-kr/azure/machine-learning/service/media/how-to-automated-ml/automl-concept-diagram2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoML Documentation:\n",
    "* https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml\n",
    "* https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://kwmp.ca/wp-content/uploads/2018/04/titanic-the-musical-1024x538.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook Python Azure ML pour la modélisation automatique (AUTOML) de la survie des passagers du Titanic.\n",
    "<br><br>\n",
    "https://www.kaggle.com/c/titanic\n",
    "<br><br>\n",
    "Données : \n",
    "https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Description des variables :\n",
    " - PassengerId: and id given to each traveler on the boat\n",
    " - Pclass: the passenger class. It has three possible values: 1,2,3 (first, second and third class)\n",
    " - The Name of the passeger\n",
    " - The Sex\n",
    " - The Age\n",
    " - SibSp: number of siblings and spouses traveling with the passenger\n",
    " - Parch: number of parents and children traveling with the passenger\n",
    " - The ticket number\n",
    " - The ticket Fare\n",
    " - The cabin number\n",
    " - The embarkation. This describe three possible areas of the Titanic from which the people embark. Three possible values S,C,Q\n",
    "\n",
    "> https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Paramétrage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \\n[GCC 7.3.0]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today = 2020-09-30 15:28:35.616077\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "now = datetime.datetime.now()\n",
    "print('Today =', now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade azureml-sdk\n",
    "#!pip install --upgrade azureml-sdk[automl,notebooks,explain,contrib,services,tensorboard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version Azure ML Service : 1.8.0\n"
     ]
    }
   ],
   "source": [
    "import azureml.core\n",
    "print(\"Version Azure ML Service :\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"TOBEREPLACED\")\n",
    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"titanicworkspaceRG\")\n",
    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"Titanicworkspace\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing interactive authentication. Please follow the instructions on the terminal.\n",
      "To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code CL5W2MFFD to authenticate.\n",
      "Interactive authentication successfully completed.\n",
      "Le workspace Azure ML service a été trouvé : OK\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "try:\n",
    "   ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
    "   ws.write_config()\n",
    "   print(\"Le workspace Azure ML service a été trouvé : OK\")\n",
    "except:\n",
    "   print(\"Le workspace Azure ML service n'a pas été trouvé\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas.io.sql as pd_sql\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "\n",
    "import logging\n",
    "import os\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Chargement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving data:\n",
      "https://raw.githubusercontent.com/retkowsky/titanic/master/train.csv\n"
     ]
    },
    {
     "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>PassengerId</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>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.25</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.05</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId Survived Pclass  \\\n",
       "0           1        0      3   \n",
       "1           2        1      1   \n",
       "2           3        1      3   \n",
       "3           4        1      1   \n",
       "4           5        0      3   \n",
       "\n",
       "                                                Name     Sex Age SibSp Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22     1     0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38     1     0   \n",
       "2                             Heikkinen, Miss. Laina  female  26     0     0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35     1     0   \n",
       "4                           Allen, Mr. William Henry    male  35     0     0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171     7.25  None        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282    7.925  None        S  \n",
       "3            113803     53.1  C123        S  \n",
       "4            373450     8.05  None        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "titanic_path = \"https://raw.githubusercontent.com/retkowsky/titanic/master/train.csv\"\n",
    "\n",
    "print(\"Retrieving data:\")\n",
    "print(titanic_path)\n",
    "\n",
    "titanic_df = dprep.read_csv(path=titanic_path, header=dprep.PromoteHeadersMode.GROUPED)\n",
    "\n",
    "display(titanic_df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId Survived Pclass     Sex Age SibSp     Fare Embarked\n",
       "0           1        0      3    male  22     1     7.25        S\n",
       "1           2        1      1  female  38     1  71.2833        C\n",
       "2           3        1      3  female  26     0    7.925        S\n",
       "3           4        1      1  female  35     1     53.1        S\n",
       "4           5        0      3    male  35     0     8.05        S"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_columns = dprep.ColumnSelector(term=\".*\", use_regex=True)\n",
    "\n",
    "drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]\n",
    "\n",
    "useful_columns = [\n",
    "    \"PassengerId\", \"Survived\", \"Pclass\", \"Sex\", \"Age\", \"SibSp\",\n",
    "    \"Fare\", \"Embarked\"\n",
    "]\n",
    "\n",
    "tmp_df = (titanic_df\n",
    "    .replace_na(columns=all_columns)\n",
    "    .drop_nulls(*drop_if_all_null)\n",
    "    .rename_columns(column_pairs={\n",
    "        \"PassengerId\": \"PassengerId\",\n",
    "        \"Survived\": \"Survived\",\n",
    "        \"Pclass\": \"Pclass\",\n",
    "        \"Sex\": \"Sex\",\n",
    "        \"Age\": \"Age\",\n",
    "        \"SibSp\": \"SibSp\",\n",
    "        \"Fare\": \"Fare\",\n",
    "        \"Embarked\": \"Embarked\",\n",
    "     })\n",
    "    .keep_columns(columns=useful_columns))\n",
    "tmp_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "decimal_type = dprep.TypeConverter(data_type=dprep.FieldType.DECIMAL)\n",
    "profile = tmp_df.set_column_types(type_conversions={\n",
    "    \"Age\": decimal_type,\n",
    "    \"Fare\": decimal_type\n",
    "}).get_profile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Count</th>\n",
       "      <th>Missing Count</th>\n",
       "      <th>Not Missing Count</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Error Count</th>\n",
       "      <th>Empty Count</th>\n",
       "      <th>Unique Values</th>\n",
       "      <th>0.1% Quantile (est.)</th>\n",
       "      <th>1% Quantile (est.)</th>\n",
       "      <th>5% Quantile (est.)</th>\n",
       "      <th>25% Quantile (est.)</th>\n",
       "      <th>50% Quantile (est.)</th>\n",
       "      <th>75% Quantile (est.)</th>\n",
       "      <th>95% Quantile (est.)</th>\n",
       "      <th>99% Quantile (est.)</th>\n",
       "      <th>99.9% Quantile (est.)</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>WhiskerTop</th>\n",
       "      <th>WhiskerBottom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>female</td>\n",
       "      <td>male</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>FieldType.DECIMAL</td>\n",
       "      <td>0.42</td>\n",
       "      <td>80</td>\n",
       "      <td>891.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>0.198653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89</td>\n",
       "      <td>0.4735</td>\n",
       "      <td>13.5111</td>\n",
       "      <td>12.6667</td>\n",
       "      <td>20.28</td>\n",
       "      <td>28</td>\n",
       "      <td>38.2</td>\n",
       "      <td>56.3067</td>\n",
       "      <td>67.44</td>\n",
       "      <td>78.716</td>\n",
       "      <td>29.6991</td>\n",
       "      <td>14.5265</td>\n",
       "      <td>211.019</td>\n",
       "      <td>0.387474</td>\n",
       "      <td>0.159767</td>\n",
       "      <td>65</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>FieldType.DECIMAL</td>\n",
       "      <td>0</td>\n",
       "      <td>512.329</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>7.54406</td>\n",
       "      <td>7.5156</td>\n",
       "      <td>7.90776</td>\n",
       "      <td>14.456</td>\n",
       "      <td>31.066</td>\n",
       "      <td>112.451</td>\n",
       "      <td>255.394</td>\n",
       "      <td>512.329</td>\n",
       "      <td>32.2042</td>\n",
       "      <td>49.6934</td>\n",
       "      <td>2469.44</td>\n",
       "      <td>4.77121</td>\n",
       "      <td>33.1231</td>\n",
       "      <td>63.3433</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>891.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>0.002245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "ColumnProfile:\n",
       "    column_name: PassengerId\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1\n",
       "    max: 99\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 891\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Survived\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 0\n",
       "    max: 1\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 2\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Pclass\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1\n",
       "    max: 3\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 3\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Sex\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: female\n",
       "    max: male\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 2\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Age\n",
       "    type: FieldType.DECIMAL\n",
       "\n",
       "    min: 0.42\n",
       "    max: 80.0\n",
       "    count: 891.0\n",
       "    missing_count: 177.0\n",
       "    not_missing_count: 714.0\n",
       "    percent_missing: 0.19865319865319866\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 89\n",
       "\n",
       "\n",
       "    Quantiles (est.):\n",
       "         0.1%: 0.47350000000000003\n",
       "           1%: 13.511111111111113\n",
       "           5%: 12.666666666666666\n",
       "          25%: 20.28\n",
       "          50%: 28.0\n",
       "          75%: 38.2\n",
       "          95%: 56.30666666666666\n",
       "          99%: 67.44000000000005\n",
       "        99.9%: 78.71599999999967\n",
       "\n",
       "    mean: 29.69911764705884\n",
       "    std: 14.526497332334035\n",
       "    variance: 211.01912474630785\n",
       "    skewness: 0.38747440217598533\n",
       "    kurtosis: 0.15976705504659172\n",
       "    whisker_top: 65.0\n",
       "    whisker_bottom: 0.42\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: SibSp\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 0\n",
       "    max: 8\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 7\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Fare\n",
       "    type: FieldType.DECIMAL\n",
       "\n",
       "    min: 0.0\n",
       "    max: 512.3292\n",
       "    count: 891.0\n",
       "    missing_count: 0.0\n",
       "    not_missing_count: 891.0\n",
       "    percent_missing: 0.0\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 248\n",
       "\n",
       "\n",
       "    Quantiles (est.):\n",
       "         0.1%: 0.0\n",
       "           1%: 7.544055\n",
       "           5%: 7.5156\n",
       "          25%: 7.90776388888889\n",
       "          50%: 14.456044897959186\n",
       "          75%: 31.065970833333328\n",
       "          95%: 112.45119222222219\n",
       "          99%: 255.39352600000015\n",
       "        99.9%: 512.3292\n",
       "\n",
       "    mean: 32.20420796857465\n",
       "    std: 49.69342859718089\n",
       "    variance: 2469.4368457431156\n",
       "    skewness: 4.771209669373588\n",
       "    kurtosis: 33.123068214278824\n",
       "    whisker_top: 63.34334\n",
       "    whisker_bottom: 0.0\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Embarked\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: C\n",
       "    max: S\n",
       "    count: 891.0\n",
       "    missing_count: 2.0\n",
       "    not_missing_count: 889.0\n",
       "    percent_missing: 0.002244668911335578\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "    unique_values: 4\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://raw.githubusercontent.com/retkowsky/titanic/master/train.csv\"\n",
    "df=pd.read_csv(url, index_col=None, na_values=['NA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppression valeurs manquantes\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nombre de col et de lignes\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppression de colonnes\n",
    "df = df.drop(['Cabin','Parch'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>PassengerId</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>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\n",
       "      <td>female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17572</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harris, Mr. Henry Birkhardt</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>36973</td>\n",
       "      <td>83.4750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nye, Mrs. (Elizabeth Ramell)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29395</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moen, Mr. Sigurd Hansen</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>348123</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mr. Herbert Fuller</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>White, Mr. Richard Frasar</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35281</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Porter, Mr. Walter Chamberlain</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110465</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "6              7         0       1   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "21            22         1       2   \n",
       "23            24         1       1   \n",
       "27            28         0       1   \n",
       "52            53         1       1   \n",
       "54            55         0       1   \n",
       "62            63         0       1   \n",
       "66            67         1       2   \n",
       "75            76         0       3   \n",
       "88            89         1       1   \n",
       "92            93         0       1   \n",
       "96            97         0       1   \n",
       "97            98         1       1   \n",
       "102          103         0       1   \n",
       "110          111         0       1   \n",
       "118          119         0       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "52            Harper, Mrs. Henry Sleeper (Myna Haxtun)  female  49.0      1   \n",
       "54                      Ostby, Mr. Engelhart Cornelius    male  65.0      0   \n",
       "62                         Harris, Mr. Henry Birkhardt    male  45.0      1   \n",
       "66                        Nye, Mrs. (Elizabeth Ramell)  female  29.0      0   \n",
       "75                             Moen, Mr. Sigurd Hansen    male  25.0      0   \n",
       "88                          Fortune, Miss. Mabel Helen  female  23.0      3   \n",
       "92                         Chaffee, Mr. Herbert Fuller    male  46.0      1   \n",
       "96                           Goldschmidt, Mr. George B    male  71.0      0   \n",
       "97                     Greenfield, Mr. William Bertram    male  23.0      0   \n",
       "102                          White, Mr. Richard Frasar    male  21.0      0   \n",
       "110                     Porter, Mr. Walter Chamberlain    male  47.0      0   \n",
       "118                           Baxter, Mr. Quigg Edmond    male  24.0      0   \n",
       "\n",
       "          Ticket      Fare Embarked  \n",
       "1       PC 17599   71.2833        C  \n",
       "3         113803   53.1000        S  \n",
       "6          17463   51.8625        S  \n",
       "10       PP 9549   16.7000        S  \n",
       "11        113783   26.5500        S  \n",
       "21        248698   13.0000        S  \n",
       "23        113788   35.5000        S  \n",
       "27         19950  263.0000        S  \n",
       "52      PC 17572   76.7292        C  \n",
       "54        113509   61.9792        C  \n",
       "62         36973   83.4750        S  \n",
       "66    C.A. 29395   10.5000        S  \n",
       "75        348123    7.6500        S  \n",
       "88         19950  263.0000        S  \n",
       "92   W.E.P. 5734   61.1750        S  \n",
       "96      PC 17754   34.6542        C  \n",
       "97      PC 17759   63.3583        C  \n",
       "102        35281   77.2875        S  \n",
       "110       110465   52.0000        S  \n",
       "118     PC 17558  247.5208        C  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Analyses exploratoires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>455.366120</td>\n",
       "      <td>0.672131</td>\n",
       "      <td>1.191257</td>\n",
       "      <td>35.674426</td>\n",
       "      <td>0.464481</td>\n",
       "      <td>78.682469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>247.052476</td>\n",
       "      <td>0.470725</td>\n",
       "      <td>0.515187</td>\n",
       "      <td>15.643866</td>\n",
       "      <td>0.644159</td>\n",
       "      <td>76.347843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>263.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>457.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>676.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp        Fare\n",
       "count   183.000000  183.000000  183.000000  183.000000  183.000000  183.000000\n",
       "mean    455.366120    0.672131    1.191257   35.674426    0.464481   78.682469\n",
       "std     247.052476    0.470725    0.515187   15.643866    0.644159   76.347843\n",
       "min       2.000000    0.000000    1.000000    0.920000    0.000000    0.000000\n",
       "25%     263.500000    0.000000    1.000000   24.000000    0.000000   29.700000\n",
       "50%     457.000000    1.000000    1.000000   36.000000    0.000000   57.000000\n",
       "75%     676.000000    1.000000    1.000000   47.500000    1.000000   90.000000\n",
       "max     890.000000    1.000000    3.000000   80.000000    3.000000  512.329200"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.148495</td>\n",
       "      <td>-0.089136</td>\n",
       "      <td>0.030933</td>\n",
       "      <td>-0.083488</td>\n",
       "      <td>0.029740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.148495</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034542</td>\n",
       "      <td>-0.254085</td>\n",
       "      <td>0.106346</td>\n",
       "      <td>0.134241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.089136</td>\n",
       "      <td>-0.034542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.306514</td>\n",
       "      <td>-0.103592</td>\n",
       "      <td>-0.315235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.030933</td>\n",
       "      <td>-0.254085</td>\n",
       "      <td>-0.306514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156162</td>\n",
       "      <td>-0.092424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.083488</td>\n",
       "      <td>0.106346</td>\n",
       "      <td>-0.103592</td>\n",
       "      <td>-0.156162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.134241</td>\n",
       "      <td>-0.315235</td>\n",
       "      <td>-0.092424</td>\n",
       "      <td>0.286433</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp      Fare\n",
       "PassengerId     1.000000  0.148495 -0.089136  0.030933 -0.083488  0.029740\n",
       "Survived        0.148495  1.000000 -0.034542 -0.254085  0.106346  0.134241\n",
       "Pclass         -0.089136 -0.034542  1.000000 -0.306514 -0.103592 -0.315235\n",
       "Age             0.030933 -0.254085 -0.306514  1.000000 -0.156162 -0.092424\n",
       "SibSp          -0.083488  0.106346 -0.103592 -0.156162  1.000000  0.286433\n",
       "Fare            0.029740  0.134241 -0.315235 -0.092424  0.286433  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Modélisation AutoML avec Azure ML service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_target=['Survived']\n",
    "col_train=['Age','Pclass','Sex','Fare','SibSp','Embarked']\n",
    "X=df[col_train]\n",
    "y=df[col_target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.674426</td>\n",
       "      <td>1.191257</td>\n",
       "      <td>78.682469</td>\n",
       "      <td>0.464481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.643866</td>\n",
       "      <td>0.515187</td>\n",
       "      <td>76.347843</td>\n",
       "      <td>0.644159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age      Pclass        Fare       SibSp\n",
       "count  183.000000  183.000000  183.000000  183.000000\n",
       "mean    35.674426    1.191257   78.682469    0.464481\n",
       "std     15.643866    0.515187   76.347843    0.644159\n",
       "min      0.920000    1.000000    0.000000    0.000000\n",
       "25%     24.000000    1.000000   29.700000    0.000000\n",
       "50%     36.000000    1.000000   57.000000    0.000000\n",
       "75%     47.500000    1.000000   90.000000    1.000000\n",
       "max     80.000000    3.000000  512.329200    3.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dic = {'male':0,'female':1}\n",
    "X['Sex']=X['Sex'].apply(lambda x:dic[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/plotting/_tools.py:308: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/plotting/_tools.py:308: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/plotting/_tools.py:314: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/plotting/_tools.py:314: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=10,figsize=(12,12),grid=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tableau (param1, param2):\n",
    "    df_slice = df[[param1, param2, 'PassengerId']]\n",
    "    slice_pivot = df_slice.pivot_table(index=[param1], columns=[param2],aggfunc=np.size, fill_value=0)\n",
    "    \n",
    "    p_chart = slice_pivot.plot.bar()\n",
    "    for p in p_chart.patches:\n",
    "        p_chart.annotate(str(p.get_height()), (p.get_x() * 1.05, p.get_height() * 1.01))\n",
    "    \n",
    "    return slice_pivot\n",
    "    return p_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PassengerId       \n",
       "Pclass             1   2  3\n",
       "Survived                   \n",
       "0                 52   3  5\n",
       "1                106  12  5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEDCAYAAADOc0QpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAfSklEQVR4nO3de3RU5fn28e9NQiAICpjEAikELMeGECBIAjrVRk7Fl0NaUKECBUWXUmm1Wmz7U7QiUq21LP3VoqBRUaiKgPhKsTQg8ioSJEEoUQLlEEUIKDQgCIHn/WOGKYecJ8mEzfVZy5WZfXruDOPF5pm97zHnHCIi4i31wl2AiIhUP4W7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4UGS4CwCIiYlxCQkJ4S5DxLO2b9/OwYMHiYyM5Pvf/z4AxcXFbNu2jWPHjhEVFUW7du2IjPRHQlFREbt27cI5R2RkJB07dgxn+VKKdevW7XPOxZa0rk6Ee0JCAtnZ2eEuQ8Sz3nvvPRo3bsyYMWOC/6/de++9NG/enClTpvDoo4/y9ddfM2PGDA4cOECfPn3Iy8ujdevW7N27l7i4uDD/BlISM9tR2jpNy4hcAHw+H82bNz9j2aJFixg7diwAY8eOZeHChQC88sorZGRk0Lp1awAF+3lK4S5ygdqzZw8tWrQAoEWLFuzduxeAzz77jK+//pqrr76anj178uKLL4azTKmiOjEtIyJ1R3FxMevWrWP58uUcOXKEtLQ0UlNT6dChQ7hLk0qos+F+/PhxCgoKOHr0aLhLkWrQsGFD4uPjqV+/frhLkYDLLruM3bt306JFC3bv3h2cfomPjycmJoaLLrqIiy66CJ/PR25ursL9PFNnw72goIAmTZqQkJCAmYW7HAmBc479+/dTUFBA27Ztw12OBAwZMoTMzEymTJlCZmYmQ4cOBWDo0KFMmjSJ4uJijh07xpo1a/jlL38Z5mqlsupsuB89elTB7hFmxqWXXkphYWG4S7lg3XjjjaxYsYJ9+/YRHx/Pgw8+yJQpUxg5ciSzZ8+mdevWvPbaawB07tyZgQMHkpSURL169bj55ptJTEwM828glWV1oeVvSkqKO/tSyM2bN9O5c+cwVSQ1QX+mItXLzNY551JKWqerZU5jZtx9993B548//jhTp06t0TETEhLo2rUr3bp1o3///nz55ZelbvvCCy8wadKkGq1HRLyhzk7LhEODBg1YsGAB9913HzExMbU2blZWFjExMfzmN7/hkUceYebMmbU2tnhDwpS3a3W87Y8OrtXxpPJ05n6ayMhIJk6cyJ/+9Kdz1u3YsYP09HSSkpJIT09n586dAIwbN44777yTPn360K5dO15//fXgPo899hi9evUiKSmJBx54oNzxfT4f+fn5ACxdupQePXrQrVs30tPTz9n2rbfeonfv3nTv3p1rr72WPXv2ALBy5UqSk5NJTk6me/fuFBUVsXv3bnw+H8nJySQmJrJq1aoqvT4icv5QuJ/ljjvuYO7cuRw8ePCM5ZMmTWLMmDFs2LCB0aNHc+eddwbX7d69m/fff58lS5YwZcoUAJYtW8aWLVv46KOPyMnJYd26dbz33ntljr1kyRK6du1KYWEht9xyC2+88Qa5ubnBD7pOd+WVV/Lhhx+yfv16brjhBv7whz8A/qmkp59+mpycHFatWkV0dDSvvPIKAwYMICcnh9zcXJKTk0N9mUSkjtO0zFkuvvhixowZw8yZM4mOjg4u/+CDD1iwYAEAN910E/fee29w3bBhw6hXrx5dunQJnkEvW7aMZcuW0b17dwAOHTrEli1b8Pl854x5zTXXEBERQVJSEg8//DCrVq3C5/MFLxs8+7Zx8F8qev3117N7926OHTsW3LZv377cddddjB49moyMDOLj4+nVqxfjx4/n+PHjDBs2TOEucgHQmXsJfvGLXzB79mwOHz5c6janX6LZoEGD4ONTVx8557jvvvvIyckhJyeH/Px8JkyYUOKxsrKyyMnJ4cUXX6Rp06Y458q9BPTnP/85kyZN4pNPPuGvf/1r8GavKVOm8Nxzz3HkyBFSU1PJy8vD5/Px3nvv0apVK2666SbdTi5yAVC4l6B58+bB639P6dOnD/PmzQNg7ty5XHnllWUeY8CAAcyZM4dDhw4B8Pnnnwd7d6Snp/P555+Xum9aWhorV67k3//+NwBfffXVOdscPHiQVq1aAZCZmRlcvnXrVrp27cqvf/1rUlJSyMvLY8eOHcTFxXHLLbcwYcIEPv7444q8DCJyHtO0TCnuvvtunnrqqeDzmTNnMn78eB577DFiY2N5/vnny9y/f//+bN68mbS0NAAaN27Myy+/TExMDPn5+SVOtZwSGxvLrFmzyMjI4OTJk8TFxfHuu++esc3UqVMZMWIErVq1IjU1NfgXwZNPPklWVhYRERF06dKFQYMGMW/ePB577DHq169P48aNdeYucgHQTUy1bOPGjcyZM4cnnngi3KXUOq/+mdYFuhTywhTSTUxmNsfM9prZxtOWNTezd81sS+Bns8ByM7OZZpZvZhvMrEf1/RrekJiYeEEGu4jUrorMub8ADDxr2RRguXOuPbA88BxgENA+8N9E4C/VU6aIiFRGueHunHsPOPsTvaHAqU/xMoFhpy1/0fl9CDQ1sxbVVayIiFRMVa+Wucw5txsg8PPU93C1Anadtl1BYJmIiNSi6r4UsqSLs0v8xNbMJppZtpllqxWsiEj1qmq47zk13RL4uTewvAD47mnbxQNflHQA59ws51yKcy4lNja2imWIiEhJqhrui4GxgcdjgUWnLR8TuGomFTh4avrmfHTkyBF+8IMfcOLECbZv3050dDTJycl06dKF2267jZMnT4a7xCpLSEhg37595yyfOnUqjz/+eJn75uXlkZaWRoMGDc7Y9tixY/h8PoqLi6u9XhGpnHJvYjKzV4GrgRgzKwAeAB4F/mZmE4CdwIjA5v8X+BGQD3wD/Ky6Cq3u63grcp3unDlzyMjIICIiAoDLL7+cnJwciouL+eEPf8jChQvJyMio1rpqmnOOUO9taN68OTNnzmThwoVnLI+KiiI9PZ358+czevTokMYQkdBU5GqZG51zLZxz9Z1z8c652c65/c65dOdc+8DPrwLbOufcHc65y51zXZ1z2eUdvy6bO3du8HslTxcZGUmfPn3Iz8/n2WefpVevXnTr1o0f//jHfPPNNwC89tprJCYm0q1bt2CzsE2bNnHFFVeQnJxMUlISW7ZsAeDll18OLr/11ls5ceIE4L+r9be//S3dunUjNTU12JRs69atpKam0qtXL+6//34aN24crK2kNsPbt2+nc+fO3H777fTo0YNdu07/zBumTZtGx44dufbaa/n000/LfV3i4uLo1atXiV92PWzYMObOnVvuMUSkZqm3TCmOHTvGtm3bSEhIOGfdN998w/Lly+natSsZGRmsXbuW3NxcOnfuHOxH89BDD/H3v/+d3NxcFi9eDMAzzzzD5MmTycnJITs7m/j4eDZv3sz8+fNZvXo1OTk5REREBMPx8OHDpKamkpubi8/n49lnnwVg8uTJTJ48mbVr19KyZctgXWW1Gf70008ZM2YM69evp02bNsF91q1bx7x581i/fj0LFixg7dq1Ib1uiYmJIR9DREKn3jKl2LdvH02bNj1j2datW0lOTsbMGDp0KIMGDWLlypX87ne/48CBAxw6dIgBAwYA/ta748aNY+TIkcGpm7S0NKZNm0ZBQQEZGRm0b9+e5cuXs27dOnr16gX45/nj4vxXlkZFRXHdddcB0LNnz2B/mQ8++CA4JTJq1Ch+9atfAaW3GW7dujVt2rQhNTX1nN9z1apVDB8+nEaNGgEwZMiQkF63iIgIoqKiKCoqokmTJiEdS0SqTuFeiujo6GAb3VNOzbmfbty4cSxcuJBu3brxwgsvsGLFCsB/lr5mzRrefvttkpOTycnJYdSoUfTu3Zu3336bAQMG8Nxzz+GcY+zYsUyfPv2cGurXrx9s/RsREVHuB5Wn2gzfeuutZyzfvn07F110Uan7lddeuLK+/fZbGjZsWK3HFJHK0bRMKZo1a8aJEyfOCfizFRUV0aJFC44fP37GXPPWrVvp3bs3Dz30EDExMezatYtt27bRrl077rzzToYMGcKGDRtIT0/n9ddfD7YD/uqrr9ixY0eZY6ampvLGG28ABNsQQ9lthkvj8/l48803OXLkCEVFRbz11lvBdU899dQZnTErYv/+/cTGxpY4Hy8itUdn7mXo378/77//Ptdee22p2/z+97+nd+/etGnThq5du1JUVATAPffcw5YtW3DOkZ6eTrdu3Xj00Ud5+eWXqV+/Pt/5zne4//77ad68OQ8//DD9+/fn5MmT1K9fn6effvqMefGzPfnkk/z0pz/lj3/8I4MHD+aSSy4J1ltSm+FTV/uUpEePHlx//fUkJyfTpk0brrrqquC6vLw8+vbte84+X375JSkpKfznP/+hXr16PPnkk/zrX//i4osvJisrix/96Edlv7AiUuPU8rcM69ev54knnuCll14Kax1n++abb4iOjsbMmDdvHq+++iqLFi0qf8dKuu6661iwYAFRUVEV3icjI4Pp06fTsWPHc9bVhT9Tr1LL3wtTWS1/deZehu7du3PNNddw4sSJMs9+a9u6deuYNGkSzjmaNm3KnDlzamScJUuWVGr7Y8eOMWzYsBKDXURql8K9HOPHjw93Cee46qqryM3NDXcZ54iKimLMmDHhLkNE0AeqIiKepHAXEfEghbuIiAcp3EVEPEjhXga1/C3Z3LlzSUpKIikpiT59+gQ/3FXLX5G64/y5WmbqJdV8vIPlbqKWvyVr27YtK1eupFmzZrzzzjtMnDiRNWvWqOWvSB2iM/cyqOVvyfr06UOzZs0AfyuEgoKC4Dq1/BWpGxTupVDL34qZPXs2gwYNCj5Xy1+RuuH8mZapZWr5W76srCxmz57N+++/H1ymlr8idYPCvRRq+Vu2DRs2cPPNN/POO+9w6aWXnrFOLX9Fwk/TMqVQy9/SW/7u3LmTjIwMXnrpJTp06HDGOrX8FakbdOZeBrX8Lbnl70MPPcT+/fu5/fbbAf8HzKe6eqrlr0jdoJa/ZVDLX7X8PV+o5e+FSS1/q0gtf9XyV+R8pXAvh1r+Vpxa/orUHfpAVUTEgxTuIiIepHAXEfEghbuIiAcp3Muglr8lW7RoEUlJSSQnJ5OSkhJsP1BYWMjAgQNrpF4RqZzz5mqZrpldq/V4n4z9pNxt1PK3ZOnp6QwZMgQzY8OGDYwcOZK8vDxiY2Np0aIFq1evLvHmJxGpPTpzL4Na/pascePGwX40hw8fPqM3jVr+itQNIYW7mf3SzDaZ2UYze9XMGppZWzNbY2ZbzGy+mVX89sY6RC1/y/bmm2/SqVMnBg8efMZNVCkpKaxataoSr7SI1IQqh7uZtQLuBFKcc4lABHADMAP4k3OuPfA1MKE6Cq1tZbX87du3L4MHD2bQoEFs3LiRq666iq5duzJ37lw2bdoE/Lfl77PPPhs8E09LS+ORRx5hxowZ7Nixg+jo6DNa/iYnJ7N8+XK2bdsGnNvyd/v27YC/5e+IESMAf8vfU05v+dujRw/y8vKC/zqoSMvfiy++uMItf4cPH05eXh4LFy7kf/7nf4LL4+Li+OKLLyp0DBGpOaHOuUcC0WZ2HGgE7AZ+CJxKnExgKvCXEMepdWr5WzE+n4+tW7eyb98+YmJiOHr0KNHR0VU+nohUjyqfuTvnPgceB3biD/WDwDrggHPuVAoVAK1CLTIc1PK39Ja/+fn5wQ9lP/74Y44dOxbs6f7ZZ5+RmJhY5pgiUvOqfOZuZs2AoUBb4ADwGjCohE1LvDTDzCYCEwFat25d1TJqlFr+ltzy94033uDFF1+kfv36REdHM3/+/ODZf1ZWFoMHq2OgSLhVueWvmY0ABjrnJgSejwHSgBHAd5xzxWaWBkx1zg0o61hq+Vs5dbnlr8/nY9GiRcEv0D5dXfgz9Sq1/L0w1VTL351Aqpk1Ao4A6UA2kAX8BJgHjAWqP3VqiVr+Vq7lb2FhIXfddVeJwS4itavK4e6cW2NmrwMfA8XAemAW8DYwz8weDiybXR2Fhota/lZcbGwsw4YNC3cZIkKIV8s45x4AHjhr8TbgilCOKyIioanTd6jWha8AlOqhP0uR2lVnw71hw4bs379foeABzjn2799Pw4YNw12KyAWjzjYOi4+Pp6CggMLCwnCXItWgYcOGxMfHh7sMkQtGnQ33+vXr07Zt23CXISJyXqqz0zIiIlJ1CncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAcp3EVEPEjhLiLiQQp3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ8KKdzNrKmZvW5meWa22czSzKy5mb1rZlsCP5tVV7EiIlIxoZ65/xlY6pzrBHQDNgNTgOXOufbA8sBzERGpRVUOdzO7GPABswGcc8eccweAoUBmYLNMYFioRYqISOWEcubeDigEnjez9Wb2nJldBFzmnNsNEPgZVw11iohIJYQS7pFAD+AvzrnuwGEqMQVjZhPNLNvMsgsLC0MoQ0REzhZKuBcABc65NYHnr+MP+z1m1gIg8HNvSTs752Y551KccymxsbEhlCEiImercrg7574EdplZx8CidOBfwGJgbGDZWGBRSBWKiEilRYa4/8+BuWYWBWwDfob/L4y/mdkEYCcwIsQxRESkkkIKd+dcDpBSwqr0UI4rIiKh0R2qIiIepHAXEfGgUOfcLygJCQk0adKEiIgIIiMjyc7O5p577uGtt94iKiqKyy+/nOeff56mTZuGu1QRucDpzL2SsrKyyMnJITs7G4B+/fqxceNGNmzYQIcOHZg+fXqYKxQRUbiHrH///kRG+v8BlJqaSkFBQZgrEhFRuFeKmdG/f3969uzJrFmzzlk/Z84cBg0aFIbKRETOpDn3Sli9ejUtW7Zk79699OvXj06dOuHz+QCYNm0akZGRjB49OsxViojozL1SWrZsCUBcXBzDhw/no48+AiAzM5MlS5Ywd+5czCycJYqIAAr3Cjt8+DBFRUXBx8uWLSMxMZGlS5cyY8YMFi9eTKNGjcJcpYiIn6ZlKmjPnj0MHz4cgOLiYkaNGsXAgQP53ve+x7fffku/fv0A/4eqzzzzTDhLFRFRuFdUu3btyM3NPWd5fn5+GKoRESmbwr0SEqa8XavjbX90cK2OJyLeoTl3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeFDI4W5mEWa23syWBJ63NbM1ZrbFzOabWVToZYqISGVUx5n7ZGDzac9nAH9yzrUHvgYmVMMYIiJSCSGFu5nFA4OB5wLPDfgh8Hpgk0xgWChjiIhI5YV65v4kcC9wMvD8UuCAc6448LwAaBXiGCIiUklVDnczuw7Y65xbd/riEjZ1pew/0cyyzSy7sLCwqmWIiEgJQjlz7wsMMbPtwDz80zFPAk3NLDKwTTzwRUk7O+dmOedSnHMpsbGxIZQhIiJnq3K4O+fuc87FO+cSgBuAfzrnRgNZwE8Cm40FFoVcpYiIVEpNXOf+a+AuM8vHPwc/uwbGEBGRMkSWv0n5nHMrgBWBx9uAK6rjuCIiUjW6Q1VExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAcp3EVEPEjhLiLiQQp3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD6pyuJvZd80sy8w2m9kmM5scWN7czN41sy2Bn82qr1wREamIUM7ci4G7nXOdgVTgDjPrAkwBljvn2gPLA89FRKQWVTncnXO7nXMfBx4XAZuBVsBQIDOwWSYwLNQiRUSkcqplzt3MEoDuwBrgMufcbvD/BQDElbLPRDPLNrPswsLC6ihDREQCQg53M2sMvAH8wjn3n4ru55yb5ZxLcc6lxMbGhlqGiIicJqRwN7P6+IN9rnNuQWDxHjNrEVjfAtgbWokiIlJZoVwtY8BsYLNz7onTVi0GxgYejwUWVb08ERGpisgQ9u0L3AR8YmY5gWW/AR4F/mZmE4CdwIjQShQRkcqqcrg7594HrJTV6VU9roiIhE53qIqIeJDCXUTEgxTuIlIjxo8fT1xcHImJicFl99xzD506dSIpKYnhw4dz4MCBMFbobQp3EakR48aNY+nSpWcs69evHxs3bmTDhg106NCB6dOnh6k671O4i0iN8Pl8NG/e/Ixl/fv3JzLSfx1HamoqBQUF4SjtgqBwF5GwmDNnDoMGDQp3GZ6lcBeRWjdt2jQiIyMZPXp0uEvxrFBuYhIRqbTMzEyWLFnC8uXL8d/oLjVB4S4itWbp0qXMmDGDlStX0qhRo3CX42malhGRGnHjjTeSlpbGp59+Snx8PLNnz2bSpEkUFRXRr18/kpOTue2228JdpmfpzF1EKm/qJeVu8mpHoCNAI6AIdt3FhJ+eWnuqO/i/YeqrFRjvYFWqvKDpzF1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxIV8vIORISEmjSpAkRERFERkaSnZ0d7pJEKkzvXz+Fu5QoKyuLmJiYcJchUiV6/2paRkTEkxTucg4zo3///vTs2ZNZs2aFuxyRStH710/TMnXY0aNH8fl8fPvttxQXF/OTn/yEBx98sMbHXb16NS1btmTv3r3069ePTp064fP5anxckeqg96+fztzrsAYNGvDPf/6T3NxccnJyWLp0KR9++GGNj9uyZUsA4uLiGD58OB999FGNjylSXfT+9VO412FmRuPGjQE4fvw4x48fr/EWqYcPH6aoqCj4eNmyZWd8B6ZIXab3739pWqaOO3HiBD179iQ/P5877riD3r171+h4e/bsYfjw4QAUFxczatQoBg4cWKNjilQXvX//S+Fex0VERJCTk8OBAwcYPnw4GzdurLEzka6ZXf0P7vL/qEc95jGPeZnzamS8T8Z+UiPHFe8Jvjcrohrev154b2pa5jzRtGlTrr766nO+TV5EpCQK9zqssLCQAwcOAHDkyBH+8Y9/0KlTpzBXJSLnA03L1GG7d+9m7NixnDhxgpMnTzJy5Eiuu+66cJclIucBhXsdlrTgKtYPPW3ByT/C1D/W3IBtW9fcsUWkVmlaRkTEg2ok3M1soJl9amb5ZjalJsYQEZHSVXu4m1kE8DQwCOgC3GhmXap7HBERKV1NnLlfAeQ757Y5544B84Ch5ewjIiLVqCY+UG0F7DrteQFwzm2VZjYRmBh4esjMPq2BWs5rITQaiAH2VX63jVUfsQpsXM22UpCao/dmndGmtBU1Ee4lvSrunAXOzQIu3H6cNcjMsp1zKeGuQ+Rsem/WnpqYlikAvnva83jgixoYR0RESlET4b4WaG9mbc0sCrgBWFwD44iISCmqfVrGOVdsZpOAvwMRwBzn3KbqHkfKpOkuqav03qwl5tw50+EiInKe0x2qIiIepHAXEfEghbuIiAepK+R5zsw64b8DuBX++wm+ABY75zaHtTARCSuduZ/HzOzX+Ns7GPAR/stQDXhVDdukLjOzn4W7Bq/T1TLnMTP7DPi+c+74WcujgE3OufbhqUykbGa20zmnLxCoQZqWOb+dBFoCO85a3iKwTiRszGxDaauAy2qzlguRwv389gtguZlt4b/N2loD3wMmha0qEb/LgAHA12ctN+D/1X45FxaF+3nMObfUzDrgb7PcCv//NAXAWufcibAWJwJLgMbOuZyzV5jZitov58KiOXcREQ/S1TIiIh6kcBcR8SCFu3iKmf3WzDaZ2QYzyzGzc74FrArHHFJd9w2Y2aHqOI5IeTTnLp5hZmnAE8DVzrlvzSwGiHLOlftlMWYW6ZwrroUaDznnGtf0OCI6cxcvaQHsc859C+Cc2+ec+8LMtgeCHjNLOXWlhplNNbNZZrYMeNHM1pjZ908dzMxWmFlPMxtnZk+Z2SWBY9ULrG9kZrvMrL6ZXW5mS81snZmtCrSFIPClNR+Y2Voz+30tvx5yAVO4i5csA75rZp+Z2f+a2Q8qsE9PYKhzbhT+Vg4jAcysBdDSObfu1IbOuYNALnDquP8H+HvgDuFZwM+dcz2BXwH/G9jmz8BfnHO9gC9D/g1FKkjhLp7hnDuEP6wnAoXAfDMbV85ui51zRwKP/waMCDweCbxWwvbzgesDj28IjNEY6AO8ZmY5wF/x/ysCoC/wauDxS5X6hURCoJuYxFMCN2+tAFaY2SfAWKCY/57INDxrl8On7fu5me03syT8AX5rCUMsBqabWXP8f5H8E7gIOOCcSy6trCr+OiJVpjN38Qwz62hmpzdLS8bfd2c7/iAG+HE5h5kH3Atc4pz75OyVgX8dfIR/umWJc+6Ec+4/wL/NbESgDjOzboFdVuM/wwcYXfnfSqRqFO7iJY2BTDP7V6BpVRdgKvAg8GczWwWU15bhdfxh/LcytpkP/DTw85TRwAQzywU24e+xDzAZuMPM1gKXVO7XEak6XQopIuJBOnMXEfEghbuIiAcp3EVEPEjhLiLiQQp3EREPUriLiHiQwl1ExIMU7iIiHvT/AfJr+awpR1+NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableau ('Survived','Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PassengerId     \n",
       "Sex           female male\n",
       "Survived                 \n",
       "0                  6   54\n",
       "1                 82   41"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableau ('Survived','Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PassengerId       \n",
       "Embarked           C  Q   S\n",
       "Survived                   \n",
       "0                 17  1  42\n",
       "1                 48  1  74"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tableau ('Survived','Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Partitionnement des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55, 6)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e8c092470>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Survived').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e8c078390>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sur le Training\n",
    "y_train.groupby('Survived').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e7f71cbe0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sur le test\n",
    "y_test.groupby('Survived').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Connexion workspace Azure ML service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "experiment_name = 'AutoMLTitanicSDK'\n",
    "\n",
    "project_folder = './sample_projects/automltitanicsdk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Version Azure ML service</th>\n",
       "      <td>1.8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Workspace Name</th>\n",
       "      <td>Titanicworkspace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resource Group</th>\n",
       "      <td>titanicworkspaceRG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>westeurope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project Directory</th>\n",
       "      <td>./sample_projects/automltitanicsdk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experiment Name</th>\n",
       "      <td>AutoMLTitanicSDK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            \n",
       "Version Azure ML service  1.8.0                             \n",
       "Workspace Name            Titanicworkspace                  \n",
       "Resource Group            titanicworkspaceRG                \n",
       "Location                  westeurope                        \n",
       "Project Directory         ./sample_projects/automltitanicsdk\n",
       "Experiment Name           AutoMLTitanicSDK                  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['Version Azure ML service'] = azureml.core.VERSION\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 AutoML avec Azure ML service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Les principes de l'autoML\n",
    "> https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml\n",
    "\n",
    " ### Aide en ligne AutoML avec Azure ML service\n",
    "> https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paramétrage AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 5,             # Time out per iteration\n",
    "    \"experiment_timeout_minutes\" : 20,           # Time out for the fullprocess\n",
    "    \"iterations\" : 20,                           # Max number of iterations\n",
    "    \"primary_metric\" : 'AUC_weighted',           # Metric\n",
    "    \"enable_early_stopping\" : True,              # Stopping criteria\n",
    "    \"verbosity\" : logging.INFO,                  # Logging\n",
    "    #\"enable_voting_ensemble\": False,            # No votin model\n",
    "    #\"enable_stack_ensemble\": False              # No ensemble model\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - The AutoMLConfig parameters, X and y, will soon be deprecated. Please refer to our documentation for the latest interface: https://aka.ms/AutoMLConfig\n"
     ]
    }
   ],
   "source": [
    "from azureml.train.automl import AutoMLConfig\n",
    "\n",
    "automl_config = AutoMLConfig(task='classification',\n",
    "                             debug_log = 'automated_ml_errors.log',\n",
    "                             path = project_folder,\n",
    "                             model_explainability=True,\n",
    "                             X = X_train.values,\n",
    "                             y = y_train.values.flatten(),\n",
    "                             X_valid = X_test.values,\n",
    "                             y_valid = y_test.values.flatten(),\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Liste des modèles disponibles : https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AUTOML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local machine\n",
      "Parent Run ID: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\n",
      "\n",
      "Current status: DatasetEvaluation. Gathering dataset statistics.\n",
      "Current status: FeaturesGeneration. Generating features for the dataset.\n",
      "Current status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\n",
      "Current status: DatasetFeaturizationCompleted. Completed fit featurizers and featurizing the dataset.\n",
      "\n",
      "****************************************************************************************************\n",
      "DATA GUARDRAILS: \n",
      "\n",
      "TYPE:         Class balancing detection\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  Your inputs were analyzed, and all classes are balanced in your training data.\n",
      "              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         Missing feature values imputation\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  No feature missing values were detected in the training data.\n",
      "              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         High cardinality feature detection\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n",
      "              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "Current status: ModelSelection. Beginning model selection.\n",
      "\n",
      "****************************************************************************************************\n",
      "ITERATION: The iteration being evaluated.\n",
      "PIPELINE: A summary description of the pipeline being evaluated.\n",
      "DURATION: Time taken for the current iteration.\n",
      "METRIC: The result of computing score on the fitted pipeline.\n",
      "BEST: The best observed score thus far.\n",
      "****************************************************************************************************\n",
      "\n",
      " ITERATION   PIPELINE                                       DURATION      METRIC      BEST\n",
      "         0   MaxAbsScaler LightGBM                          0:00:21       0.8596    0.8596\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:00:18       0.8874    0.8874\n",
      "         2   MaxAbsScaler RandomForest                      0:00:20       0.8341    0.8874\n",
      "         3   MaxAbsScaler SGD                               0:00:19       0.8694    0.8874\n",
      "         4   MaxAbsScaler SGD                               0:00:19       0.8649    0.8874\n",
      "         5   MaxAbsScaler ExtremeRandomTrees                0:00:19       0.8514    0.8874\n",
      "         6   MaxAbsScaler ExtremeRandomTrees                0:00:18       0.8881    0.8881\n",
      "         7   MaxAbsScaler SGD                               0:00:20       0.8649    0.8881\n",
      "         8   MaxAbsScaler RandomForest                      0:00:19       0.8251    0.8881\n",
      "         9   MaxAbsScaler SGD                               0:00:19       0.8709    0.8881\n",
      "        10   MaxAbsScaler RandomForest                      0:00:19       0.8018    0.8881\n",
      "        11   MaxAbsScaler SGD                               0:00:19       0.8649    0.8881\n",
      "        12   MaxAbsScaler SGD                               0:00:19       0.8844    0.8881\n",
      "        13   MaxAbsScaler RandomForest                      0:00:19       0.8566    0.8881\n",
      "        14   StandardScalerWrapper ExtremeRandomTrees       0:00:19       0.8506    0.8881\n",
      "        15   SparseNormalizer ExtremeRandomTrees            0:00:19       0.8866    0.8881\n",
      "        16   SparseNormalizer ExtremeRandomTrees            0:00:19       0.8213    0.8881\n",
      "        17   MaxAbsScaler SGD                               0:00:18       0.8784    0.8881\n",
      "        18   VotingEnsemble                                 0:00:27       0.9114    0.9114\n",
      "        19   StackEnsemble                                  0:00:27       0.8844    0.9114\n",
      "****************************************************************************************************\n",
      "Current status: BestRunExplainModel. Best run model explanations started\n",
      "Current status: ModelExplanationDataSetSetup. Model explanations data setup completed\n",
      "Current status: PickSurrogateModel. Choosing LightGBM as the surrogate model for explanations\n",
      "Current status: EngineeredFeatureExplanations. Computation of engineered features started\n",
      "Current status: EngineeredFeatureExplanations. Computation of engineered features completed\n",
      "Current status: BestRunExplainModel. Best run model explanations completed\n",
      "****************************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.experiment import Experiment\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation des itérations depuis Azure ML Studio :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>AutoMLTitanicSDK</td><td>AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b</td><td>automl</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/AutoMLTitanicSDK/runs/AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/titanicworkspaceRG/workspaces/Titanicworkspace\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Run(Experiment: AutoMLTitanicSDK,\n",
       "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b,\n",
       "Type: automl,\n",
       "Status: Completed)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Widget AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a93d29dca2248d1982acb9dfe4786cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\"run_metrics\": [{\"name\": \"experiment_status\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"series\": [{\"data\": [\"DatasetEvaluation\", \"FeaturesGeneration\", \"DatasetFeaturization\", \"DatasetFeaturizationCompleted\", \"ModelSelection\", \"BestRunExplainModel\", \"ModelExplanationDataSetSetup\", \"PickSurrogateModel\", \"EngineeredFeatureExplanations\", \"EngineeredFeatureExplanations\", \"BestRunExplainModel\"]}]}, {\"name\": \"experiment_status_description\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"series\": [{\"data\": [\"Gathering dataset statistics.\", \"Generating features for the dataset.\", \"Beginning to fit featurizers and featurize the dataset.\", \"Completed fit featurizers and featurizing the dataset.\", \"Beginning model selection.\", \"Best run model explanations started\", \"Model explanations data setup completed\", \"Choosing LightGBM as the surrogate model for explanations\", \"Computation of engineered features started\", \"Computation of engineered features completed\", \"Best run model explanations completed\"]}]}, {\"name\": \"average_precision_score_weighted\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.9225432403611944]}]}, {\"name\": \"average_precision_score_micro\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.9277863580457313]}]}, {\"name\": \"precision_score_micro\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8545454545454545]}]}, {\"name\": \"balanced_accuracy\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8205705705705706]}]}, {\"name\": \"average_precision_score_macro\", \"run_id\": 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\"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8812758417011222]}]}, {\"name\": \"f1_score_weighted\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8521812552772305]}]}, {\"name\": \"AUC_macro\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.9114114114114114]}]}, {\"name\": \"accuracy\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8545454545454545]}]}, {\"name\": \"precision_score_weighted\", \"run_id\": \"AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b\", \"categories\": [0], \"series\": [{\"data\": [0.8523892773892774]}]}], \"run_logs\": \"\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.8.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Liste des résultats autoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUC_macro</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC_micro</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC_weighted</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_macro</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_micro</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_weighted</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_macro</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_micro</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_weighted</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_loss</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matthews_correlation</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_macro_recall</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_macro</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_micro</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_weighted</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_score_macro</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_score_micro</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_score_weighted</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted_accuracy</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0    1    2    3    4    5    6    7    8  \\\n",
       "AUC_macro                        0.86 0.89 0.83 0.87 0.86 0.85 0.89 0.86 0.83   \n",
       "AUC_micro                        0.88 0.88 0.87 0.87 0.88 0.87 0.90 0.85 0.85   \n",
       "AUC_weighted                     0.86 0.89 0.83 0.87 0.86 0.85 0.89 0.86 0.83   \n",
       "accuracy                         0.75 0.76 0.78 0.76 0.76 0.75 0.78 0.78 0.67   \n",
       "average_precision_score_macro    0.85 0.85 0.83 0.85 0.82 0.84 0.88 0.83 0.81   \n",
       "average_precision_score_micro    0.89 0.89 0.87 0.88 0.89 0.89 0.91 0.87 0.86   \n",
       "average_precision_score_weighted 0.88 0.89 0.86 0.88 0.86 0.87 0.90 0.87 0.85   \n",
       "balanced_accuracy                0.67 0.67 0.67 0.81 0.78 0.74 0.80 0.68 0.60   \n",
       "f1_score_macro                   0.68 0.68 0.68 0.76 0.75 0.73 0.77 0.70 0.60   \n",
       "f1_score_micro                   0.75 0.76 0.78 0.76 0.76 0.75 0.78 0.78 0.67   \n",
       "f1_score_weighted                0.73 0.74 0.74 0.77 0.77 0.75 0.79 0.75 0.66   \n",
       "log_loss                         0.43 0.44 0.45 0.50 0.42 0.41 0.39 0.46 0.46   \n",
       "matthews_correlation             0.38 0.42 0.50 0.58 0.53 0.46 0.56 0.48 0.22   \n",
       "norm_macro_recall                0.34 0.33 0.33 0.62 0.56 0.48 0.59 0.36 0.20   \n",
       "precision_score_macro            0.72 0.77 0.88 0.77 0.75 0.72 0.76 0.82 0.62   \n",
       "precision_score_micro            0.75 0.76 0.78 0.76 0.76 0.75 0.78 0.78 0.67   \n",
       "precision_score_weighted         0.73 0.77 0.84 0.84 0.80 0.76 0.81 0.80 0.66   \n",
       "recall_score_macro               0.67 0.67 0.67 0.81 0.78 0.74 0.80 0.68 0.60   \n",
       "recall_score_micro               0.75 0.76 0.78 0.76 0.76 0.75 0.78 0.78 0.67   \n",
       "recall_score_weighted            0.75 0.76 0.78 0.76 0.76 0.75 0.78 0.78 0.67   \n",
       "weighted_accuracy                0.81 0.84 0.87 0.73 0.75 0.75 0.77 0.86 0.73   \n",
       "\n",
       "                                    9   10   11   12   13   14   15   16   17  \\\n",
       "AUC_macro                        0.87 0.80 0.86 0.88 0.86 0.85 0.89 0.82 0.88   \n",
       "AUC_micro                        0.88 0.83 0.87 0.87 0.88 0.87 0.89 0.86 0.89   \n",
       "AUC_weighted                     0.87 0.80 0.86 0.88 0.86 0.85 0.89 0.82 0.88   \n",
       "accuracy                         0.76 0.75 0.75 0.78 0.76 0.76 0.78 0.80 0.76   \n",
       "average_precision_score_macro    0.86 0.78 0.84 0.86 0.86 0.85 0.86 0.83 0.87   \n",
       "average_precision_score_micro    0.90 0.84 0.88 0.88 0.89 0.88 0.90 0.87 0.90   \n",
       "average_precision_score_weighted 0.89 0.83 0.87 0.89 0.88 0.88 0.89 0.86 0.89   \n",
       "balanced_accuracy                0.71 0.63 0.81 0.68 0.72 0.78 0.71 0.75 0.71   \n",
       "f1_score_macro                   0.72 0.63 0.74 0.70 0.73 0.75 0.72 0.76 0.72   \n",
       "f1_score_micro                   0.76 0.75 0.75 0.78 0.76 0.76 0.78 0.80 0.76   \n",
       "f1_score_weighted                0.76 0.70 0.75 0.75 0.76 0.77 0.77 0.79 0.76   \n",
       "log_loss                         0.41 0.50 0.48 0.46 0.51 0.43 0.41 0.53 0.40   \n",
       "matthews_correlation             0.44 0.38 0.59 0.48 0.46 0.53 0.48 0.53 0.44   \n",
       "norm_macro_recall                0.42 0.25 0.62 0.36 0.45 0.56 0.42 0.50 0.42   \n",
       "precision_score_macro            0.73 0.78 0.78 0.82 0.73 0.75 0.77 0.78 0.73   \n",
       "precision_score_micro            0.76 0.75 0.75 0.78 0.76 0.76 0.78 0.80 0.76   \n",
       "precision_score_weighted         0.76 0.77 0.86 0.80 0.76 0.80 0.78 0.79 0.76   \n",
       "recall_score_macro               0.71 0.63 0.81 0.68 0.72 0.78 0.71 0.75 0.71   \n",
       "recall_score_micro               0.76 0.75 0.75 0.78 0.76 0.76 0.78 0.80 0.76   \n",
       "recall_score_weighted            0.76 0.75 0.75 0.78 0.76 0.76 0.78 0.80 0.76   \n",
       "weighted_accuracy                0.81 0.84 0.69 0.86 0.79 0.75 0.84 0.84 0.81   \n",
       "\n",
       "                                   18   19  \n",
       "AUC_macro                        0.91 0.88  \n",
       "AUC_micro                        0.92 0.89  \n",
       "AUC_weighted                     0.91 0.88  \n",
       "accuracy                         0.85 0.76  \n",
       "average_precision_score_macro    0.90 0.87  \n",
       "average_precision_score_micro    0.93 0.90  \n",
       "average_precision_score_weighted 0.92 0.90  \n",
       "balanced_accuracy                0.82 0.77  \n",
       "f1_score_macro                   0.83 0.75  \n",
       "f1_score_micro                   0.85 0.76  \n",
       "f1_score_weighted                0.85 0.77  \n",
       "log_loss                         0.40 0.41  \n",
       "matthews_correlation             0.66 0.51  \n",
       "norm_macro_recall                0.64 0.53  \n",
       "precision_score_macro            0.84 0.74  \n",
       "precision_score_micro            0.85 0.76  \n",
       "precision_score_weighted         0.85 0.79  \n",
       "recall_score_macro               0.82 0.77  \n",
       "recall_score_micro               0.85 0.76  \n",
       "recall_score_weighted            0.85 0.76  \n",
       "weighted_accuracy                0.88 0.76  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Choix du meilleur modèle selon l'AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle selon l'AUC :\n",
      "\n",
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])\n"
     ]
    }
   ],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "\n",
    "print(\"Meilleur modèle selon l'AUC :\")\n",
    "print(\"\")\n",
    "print(best_run)\n",
    "print(\"\")\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Pipeline.get_params of Pipeline(memory=None,\n",
       "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
       "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
       "        featurization_config=None, force_text_dnn=None,\n",
       "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
       "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model.get_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
       "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
       "        featurization_config=None, force_text_dnn=None,\n",
       "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
       "        observer=None, task=None, working_dir=None)), ('prefittedsoftvotingclassifier', PreFittedSoftVotingClassifier(classification_labels=None,\n",
       "               estimators=[('6', Pipeline(memory=None,\n",
       "     steps=[('maxabsscaler', MaxAbsScaler(copy=True)), ('extratreesclassifier', ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features=0.9, max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, mi...e', max_iter=1000, n_jobs=1,\n",
       "           penalty='none', power_t=0, random_state=None, tol=0.01))]))],\n",
       "               flatten_transform=None,\n",
       "               weights=[0.35714285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))]"
      ],
      "text/plain": [
       "[('datatransformer',\n",
       "  DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
       "          feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
       "          featurization_config=None, force_text_dnn=None,\n",
       "          is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
       "          observer=None, task=None, working_dir=None)),\n",
       " ('prefittedsoftvotingclassifier',\n",
       "  PreFittedSoftVotingClassifier(classification_labels=None,\n",
       "                 estimators=[('6', Pipeline(memory=None,\n",
       "       steps=[('maxabsscaler', MaxAbsScaler(copy=True)), ('extratreesclassifier', ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "             max_depth=None, max_features=0.9, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, mi...e', max_iter=1000, n_jobs=1,\n",
       "             penalty='none', power_t=0, random_state=None, tol=0.01))]))],\n",
       "                 flatten_transform=None,\n",
       "                 weights=[0.35714285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Changement métrique de qualité\n",
    "\n",
    "Autres métriques disponibles :\n",
    "accuracy, AUC_weighted, average_precision_score_weighted, norm_macro_recall, precision_score_weighted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle selon le score F1 :\n",
      "\n",
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])\n"
     ]
    }
   ],
   "source": [
    "lookup_metric = \"f1_score_weighted\"\n",
    "\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "\n",
    "print(\"Meilleur modèle selon le score F1 :\")\n",
    "print(\"\")\n",
    "print(best_run)\n",
    "print(\"\")\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle selon accuracy :\n",
      "\n",
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])\n"
     ]
    }
   ],
   "source": [
    "lookup_metric = \"accuracy\"\n",
    "\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "\n",
    "print(\"Meilleur modèle selon accuracy :\")\n",
    "print(\"\")\n",
    "print(best_run)\n",
    "print(\"\")\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7 Choix d'une itération particulière"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "moniteration = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline 7ème itération :\n",
      "\n",
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_7,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...    n_jobs=1, penalty='none', power_t=0.3333333333333333,\n",
      "           random_state=None, tol=0.001))])\n"
     ]
    }
   ],
   "source": [
    "my_run, my_model = local_run.get_output(iteration = moniteration)\n",
    "\n",
    "print(\"Pipeline \"+ str(moniteration) +\"ème itération :\")\n",
    "print(\"\")\n",
    "print(my_run)\n",
    "print(\"\")\n",
    "print(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. On applique le modèle sur la base de Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 On utilise le meilleur modèle selon l'AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meilleur modèle selon la métrique initiale :\n",
      "\n",
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])\n"
     ]
    }
   ],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "\n",
    "print(\"Meilleur modèle selon la métrique initiale :\")\n",
    "print(\"\")\n",
    "print(best_run)\n",
    "print(\"\")\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run(Experiment: AutoMLTitanicSDK,\n",
      "Id: AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18,\n",
      "Type: None,\n",
      "Status: Completed)\n",
      "\n",
      "Pipeline(memory=None,\n",
      "     steps=[('datatransformer', DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
      "        feature_sweeping_config=None, feature_sweeping_timeout=None,\n",
      "        featurization_config=None, force_text_dnn=None,\n",
      "        is_cross_validation=None, is_onnx_compatible=None, logger=None,\n",
      "        obser...4285714285715, 0.07142857142857142, 0.2857142857142857, 0.21428571428571427, 0.07142857142857142]))])\n",
      "\n",
      "average_precision_score_weighted  =  0.9225432403611944\n",
      "average_precision_score_micro  =  0.9277863580457313\n",
      "precision_score_micro  =  0.8545454545454545\n",
      "balanced_accuracy  =  0.8205705705705706\n",
      "average_precision_score_macro  =  0.9029463682548229\n",
      "precision_score_macro  =  0.8421474358974359\n",
      "f1_score_micro  =  0.8545454545454545\n",
      "AUC_micro  =  0.9206611570247933\n",
      "AUC_weighted  =  0.9114114114114115\n",
      "recall_score_micro  =  0.8545454545454545\n",
      "recall_score_macro  =  0.8205705705705706\n",
      "recall_score_weighted  =  0.8545454545454545\n",
      "matthews_correlation  =  0.6623666620381758\n",
      "log_loss  =  0.4006248030665477\n",
      "f1_score_macro  =  0.8297213622291022\n",
      "norm_macro_recall  =  0.6411411411411412\n",
      "weighted_accuracy  =  0.8812758417011222\n",
      "f1_score_weighted  =  0.8521812552772305\n",
      "AUC_macro  =  0.9114114114114114\n",
      "accuracy  =  0.8545454545454545\n",
      "precision_score_weighted  =  0.8523892773892774\n",
      "accuracy_table  =  aml://artifactId/ExperimentRun/dcid.AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18/accuracy_table\n",
      "confusion_matrix  =  aml://artifactId/ExperimentRun/dcid.AutoML_68d7346d-5e57-4610-a182-8e4c2aa1429b_18/confusion_matrix\n"
     ]
    }
   ],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print()\n",
    "print(fitted_model)\n",
    "print()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "for metric_name in best_run_metrics:\n",
    "    metric = best_run_metrics[metric_name]\n",
    "    print(metric_name, ' = ', metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='Titanicworkspace', subscription_id='70b8f39e-8863-49f7-b6ba-34a80799550c', resource_group='titanicworkspaceRG'), name=Titanic-model, id=Titanic-model:10, version=10, tags={'Training context': 'Azure Auto ML SDK'}, properties={'AUC': '0.9114114114114115', 'Accuracy': '0.8545454545454545'})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from azureml.core import Model\n",
    "\n",
    "best_run.register_model(model_path='outputs/model.pkl', model_name='Titanic-model',\n",
    "                        tags={'Training context':'Azure Auto ML SDK'},\n",
    "                        properties={'AUC': best_run_metrics['AUC_weighted'], 'Accuracy': best_run_metrics['accuracy']})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Titanic-model version = 10\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9114114114114115\n",
      "\t Accuracy : 0.8545454545454545\n",
      "\n",
      "\n",
      "Titanic-model version = 9\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9054054054054054\n",
      "\t Accuracy : 0.7818181818181819\n",
      "\n",
      "\n",
      "Titanic-model version = 8\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9294294294294293\n",
      "\t Accuracy : 0.7818181818181819\n",
      "\n",
      "\n",
      "Titanic-model version = 7\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9159159159159158\n",
      "\t Accuracy : 0.8\n",
      "\n",
      "\n",
      "Titanic-model version = 6\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9151651651651651\n",
      "\t Accuracy : 0.8181818181818182\n",
      "\n",
      "\n",
      "Titanic-model version = 5\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.8918918918918918\n",
      "\t Accuracy : 0.7636363636363637\n",
      "\n",
      "\n",
      "Titanic-model version = 4\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9166666666666667\n",
      "\t Accuracy : 0.6727272727272727\n",
      "\n",
      "\n",
      "Titanic-model version = 3\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.9129129129129129\n",
      "\t Accuracy : 0.8\n",
      "\n",
      "\n",
      "Titanic-model version = 2\n",
      "\t Training context : Azure Auto ML SDK\n",
      "\t AUC : 0.8948948948948948\n",
      "\t Accuracy : 0.7818181818181819\n",
      "\n",
      "\n",
      "amlstudio-titanic-real-time-in version = 2\n",
      "\t CreatedByAMLStudio : true\n",
      "\n",
      "\n",
      "Titanic-model version = 1\n",
      "\t Training context : Azure Auto ML\n",
      "\t AUC : 0.924174174174174\n",
      "\t Accuracy : 0.8181818181818182\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Liste des modèles référencés\n",
    "for model in Model.list(ws):\n",
    "    print(model.name, 'version =', model.version)\n",
    "    for tag_name in model.tags:\n",
    "        tag = model.tags[tag_name]\n",
    "        print ('\\t',tag_name, ':', tag)\n",
    "    for prop_name in model.properties:\n",
    "        prop = model.properties[prop_name]\n",
    "        print ('\\t',prop_name, ':', prop)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 On applique le modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcul de la décision\n",
    "y_test_pred = fitted_model.predict(X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "Decision=pd.DataFrame(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtention des scores\n",
    "proba=fitted_model.predict_proba(X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Decision\n",
       "0  1       \n",
       "1  1       \n",
       "2  0       \n",
       "3  1       \n",
       "4  1       "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Decision.columns = ['Decision']\n",
    "Decision.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "Proba=pd.DataFrame(proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "Proba.columns = ['Proba_0', 'Proba_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Proba_0</th>\n",
       "      <th>Proba_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Proba_0  Proba_1\n",
       "0 0.05     0.95    \n",
       "1 0.08     0.92    \n",
       "2 0.72     0.28    \n",
       "3 0.10     0.90    \n",
       "4 0.13     0.87    "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Proba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scoring = pd.concat([Decision, Proba], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Résultats du scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Decision</th>\n",
       "      <th>Proba_0</th>\n",
       "      <th>Proba_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Decision  Proba_0  Proba_1\n",
       "0  1        0.05     0.95    \n",
       "1  1        0.08     0.92    \n",
       "2  0        0.72     0.28    \n",
       "3  1        0.10     0.90    \n",
       "4  1        0.13     0.87    "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scoring.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d697cf860>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cjc+fQjkReFFV3UJ/WmRq7vaq+gnwxJz57YuBu5BWiUcJatUJ3RTJWuAA8I/Ax7ptn6I/hXFvkgCzwIXdVxJMATNJfg7cAlwx5z1fCNyU5Hj6R+t/MeBztwCf6P4n8CDg16Vq1XgaoSQ1yikUSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqP+DzdYcaFwa5l6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Scoring.groupby('Decision').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Decision</th>\n",
       "      <th>Proba_0</th>\n",
       "      <th>Proba_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Decision  Proba_0  Proba_1\n",
       "count 55.00     55.00    55.00   \n",
       "mean  0.71      0.33     0.67    \n",
       "std   0.46      0.23     0.23    \n",
       "min   0.00      0.05     0.28    \n",
       "25%   0.00      0.08     0.49    \n",
       "50%   1.00      0.39     0.61    \n",
       "75%   1.00      0.51     0.92    \n",
       "max   1.00      0.72     0.95    "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scoring.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Exportation des scores dans un fichier CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "datedujour = time.strftime(\"%Y-%m-%d-%H%M%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export des scores dans un fichier CSV\n",
    "Scoring.to_csv(r'Exports/Scores_Titanic_'+datedujour+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 -rwxrwxrwx 1 root root 2394 Jun 15 08:06 \u001b[0m\u001b[01;32mExports/Scores_Titanic_2020-06-15-075905.csv\u001b[0m*\r\n",
      "3 -rwxrwxrwx 1 root root 2394 Jun 23 12:11 \u001b[01;32mExports/Scores_Titanic_2020-06-23-121107.csv\u001b[0m*\r\n",
      "3 -rwxrwxrwx 1 root root 2405 Sep 30 15:44 \u001b[01;32mExports/Scores_Titanic_2020-09-30-154445.csv\u001b[0m*\r\n"
     ]
    }
   ],
   "source": [
    "# Visualisation des fichiers CSV exportés\n",
    "%ls Exports/*.csv -ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> End of notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6 - AzureML",
   "language": "python",
   "name": "python3-azureml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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