Session 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette formation a pour but de donner une introduction aux librairies jugées fondamentales pour le data scientist en Python. Elle vient à la suite d'une formation sur les bases du langage même.\n",
    "\n",
    "Les trois packages que l'on va couvrir sont les acteurs historiques majeurs, et sont souvent incontournables :\n",
    "1. **Pandas** est une librairie de manipulation de données (*équivalent le plus proche en R : dplyr*)\n",
    "1. **Matplotlib** permet de réaliser des graphiques (*équivalents en R : ggplot2, lattice, base*)\n",
    "1. **Scikit-learning** regroupe de nombreux algorithmes et modèles pour le machine learning (*équivalents en R : caret, mlr*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lors de la formation sur les bases de Python, on a appris à manipuler les types de bases de Python ; listes, dictionnaires, ...\n",
    "On a vu que Python était un langage généraliste, qui n'est pas spécialisé dans le traitement de données. Les types de base se prêtent donc mal aux calculs mathématiques.\n",
    "\n",
    "Ainsi, la notion d'addition de listes n'est pas l'addition mathématique terme à terme, mais plutôt la concaténation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[1, 2, 3] + [4, 5, 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et l'addition d'un scalaire à une liste n'est pas possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    [1, 2, 3] + 4\n",
    "except:\n",
    "    print(\"Erreur : Python ne sait pas faire ça !\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais ces limitations initiales peuvent être comblées facilement par ce qui fait la force de Python : l'utilisation de packages spécialisés.\n",
    "\n",
    "Par exemple, Numpy apporte le calcul matriciel. Par habitude et flemme, on l'importe toujours sous le nom **np**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.array([1,2,3]) + 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais la puissance de Numpy pour le calcul matriciel et scientifique n'est pas forcément suffisante pour faire du traitement de données. On souhaiterait avoir des objets et des opérations de plus haut niveau. Par exemple les data frames, objets centraux en R, permettent de manipuler plusieurs listes en gardant une cohérence. Et on souhaiterait pouvoir manipuler une ou plusieurs colonnes pour réaliser des opérations complexes (group by, ...).\n",
    "\n",
    "C'est ce pour quoi Pandas a été développé !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L'objet de base de Pandas : Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Création"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas s'importe usuellement sous le nom **pd**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'objet le plus fondamental dans Pandas est la série (**Series**). Il permet de représenter une série de données, c'ets-à-dire une liste unidimensionnelle d'objets de tout type. Chaque donnée dispose d'un label. Ces labels forment ce qu'on appelle l'**index** de la série. On peut construire une série à partir par exemple de listes ou de tableaux NumPy :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series([1, 'toto', None], index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si on ne précise pas d'index, Pandas utilise par défaut les numéros de lignes (en commençant à 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series([1, 'toto', None])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addition d'un scalaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que donne $s+1$ ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "s + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addition de deux séries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series([1, 2, 3], index=['c', 'b', 'd'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que donne $s + s2$ ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On voit déjà une différence notable avec Numpy. L'addition de deux séries alignent les index, pour additionner les lignes correspondantes. L'**index** a un rôle crucial en Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'index permet d'extraire un élément de la série directement par son label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut aussi sélectionner par numéro de ligne, avec la même syntaxe. Pandas se débrouille pour savoir si on parle d'un index ou d'un numéro de ligne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme sur les listes, on peut prendre des tranches (slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut aussi faire de l'indexation booléenne "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[[True, False, True]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cela permet de filtrer une série suivant une condition avec une syntaxe simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[s>1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut modifier ou ajouter des éléments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['d'] = 4\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[3] = 0\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loc et iloc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a vu que s[x] peut signifier soit l'élément ayant le label x, soit le x-ème élément si c'est un nombre. Pandas sait choisir la bonne signification... quand il n'y a pas d'ambiguïté. Mais il est facile d'imaginer des cas problématiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(['a', 'b', 'c'], index=[2, 3, 4])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour prendre l'élement n°2, il faut être plus explicite dans notre façon d'indexer. Pandas met à disposition deux indexeurs :\n",
    "\n",
    "- **.loc** permet d'accéder par label\n",
    "\n",
    "- **.iloc** permet d'accéder par numéro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L'objet le plus utilie : le DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Création"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un DataFrame est une structure bidimensionnel. Il s'agit d'un ensemble de Series. Une Series est une colonne de DataFrame. On peut le construire par exemple à partir d'un dict dont les valeurs sont des Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1, 2, 3], index = ['a', 'b', 'c'])\n",
    "s2 = pd.Series([4, 5, 6], index = ['c', 'b', 'd'])\n",
    "\n",
    "df = pd.DataFrame({'toto': s1, 'titi': s2})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il existe de nombreuses autres façons de construire un DataFrame. Par exemple à partir d'un dict de lists :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'toto': [1, 2, 3, np.nan], 'titi': [np.nan, 5, 4, 6]}\n",
    "\n",
    "pd.DataFrame(d, index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou encore une liste de listes. On peut alors préciser l'index et le nom des colonnes (sinon Pandas utilise des numéros)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame([[1, np.nan], [2, 5], [3, 4], [np.nan, 6]], index=['a', 'b', 'c', 'd'], columns=['toto', 'titi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les crochets permettent d'extraire une colonne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['toto']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il s'agit alors d'une Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df['toto'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut aussi utiliser :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.toto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour extraire une ligne, on peut utiliser **loc** ou **iloc**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df.loc['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[['a', 'c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "type(df.loc[['a', 'c']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour extraire plusieurs lignes, on peut aussi utiliser des crochets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Par contre, pour une seule ligne cela ne marche pas. Il faut utiliser iloc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df[0]\n",
    "except:\n",
    "    print(\"Il n'y a pas de colonne nommée '0'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut aussi faire de l'indexation booléenne, pour sélectionner des lignes suivant une condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['toto'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour extraire un élément, on peut sélectionner la série puis l'élément"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['toto']['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plus directement, on peut passer la ligne et la colonne à loc et iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['b', 'toto']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour toutes ces fonctions, on peut faire du slicing, ou passer des listes, pour extraire plusieurs éléments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manipulations globales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a vu comment extraire des lignes ou des colonnes, mais il existe aussi de nombreuses fonctions s'appliquant à l'ensemble du data frame. Ces fonctions sont disponibles sous la forme *objet.fonction* (en termes de programmation orientée objet, ce sont des méthodes de l'objet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Voir les premières lignes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Trier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suivant l'index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suivant une colonne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('titi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ces fonctions retournent un nouveau DataFrame et ne modifient pas l'objet d'origine. Il faut les assigner à un nouvel objet (avec le même nom ou pas), ou utiliser l'argument *inplace=True*.\n",
    "\n",
    "Cela permet aussi d'enchaîner les fonctions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('titi').head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compter les occurrences des valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['toto'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gestion des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Opérations mathématiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ces fonctions ignorent les valeurs manquantes par défaut (argument *skipna*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour calculer la moyenne des lignes plutôt que des colonnes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_na'] = df['titi'].isnull()\n",
    "\n",
    "df.groupby('is_na').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import et export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il est possible d'importer et d'exporter vers de nombreux formats de fichiers.\n",
    "\n",
    "Pour l'export, on utilise les méthodes du data frame nommées *to_** (to_csv, to_excel, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour l'import, l'objet n'existe pas encore, on utilise donc les fonctions de Pandas nommées *read_** qui retournent un data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv('df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On voit que par défaut l'export écrit l'index comme une colonne normale. Mais l'import ne le charge pas comme index par défaut. On peut préciser l'argument *index_col* pour indiquer le numéro de la colonne qui doit servir d'index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv('df.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphiques"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour réaliser des graphiques, il existe de nombreux packages. Les plus courants sont les suivants :\n",
    "\n",
    "1. **Matplotlib** : le package le plus ancien, que l'on va voir dans la suite\n",
    "\n",
    "1. **Seaborn** : une surcouche à Matplotlib qui permet de tracer des graphiques statistiques poussés et améliore l'esthétique\n",
    "\n",
    "1. **Plotly** : aussi disponible en R, il permet de faire des graphiques interactifs. Il fournit aussi **Plotly express** pour faire rapidement des graphiques avec une approche \"grammaire de graphiques\"\n",
    "\n",
    "1. **Bokeh** : pour faire des graphiques interactifs complexes\n",
    "\n",
    "1. **Altair** : qui combine \"grammaire de graphiques\" et interactivité et reposant sur un langage de spécification de visualisations (Vega)\n",
    "\n",
    "Pour avoir une vision plus détaillée de cet écosystème riche, on peut par exemple voir cette présentation du créateur d'Altair : https://www.youtube.com/watch?v=FytuB8nFHPQ (slides : https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017 )\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib remonte à l'époque où l'outil de référence pour faire de la manipulation et de la visualisation de données est Matlab ! Numpy a apporté le calcul matriciel mais il manque des outils graphiques. Matplotlib cherche donc initialement à copier ce qui se fait en Matlab. L'objectif est de concevoir un équivalent gratuit et open source à Matlab, pour venir le concurrencer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib reprend différents concepts de Matlab :\n",
    "\n",
    "1. Une **Figure** comprend un ou plusieurs graphiques. On peut avoir plusieurs figure.\n",
    "\n",
    "1. Un **Axes** est un ensemble d'axes, c'est-à-dire un graphique sur lequel on peut tracer des données.\n",
    "\n",
    "1. Un **Axis** est un axe du graphique (abscisses, ordonnées, ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il existe deux façons de faire un graphique :\n",
    "\n",
    "1. On peut manipuler ces objets (figure, axes, ...) avec une approche programmation orientée objet\n",
    "1. On peut utiliser Pyplot, un module de Matplotlib, qui se veut plus facile à utiliser en gérant les aspects techniques à notre place et en gérant l'état du graphique (axes courant, ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour les données à tracer, Matplotlib utilise des tableaux Numpy. Néanmoins tout objet qui peut être converti directement peut être utilisé (listes, séries Pandas, ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faire un graphique simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour commencer, on importe Pyplot sous son alias usuel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans un notebook Jupyter, une petite manipulation est nécessaire pour afficher directement les graphiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le graphique le plus simple se fait avec la fonction *plot* de Pyplot, en lui fournissant la liste des abscisses en premier argument et la liste des ordonnées en deuxième argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot([1, 2, 3], [1, 4, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D'autres fonctions sont disponibles pour les autres types de graphiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter([1, 2, 3], [1, 4, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans l'approche orientée objet, on crée d'abord la figure et les axes et on utilise les méthodes de ces objets. Cela peut s'avérer plus simple si on réalise plusieurs graphiques à la fois."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter([1, 2, 3], [1, 4, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Personnalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tous les éléments du graphique sont personnalisables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar([1, 2, 3], [1, 4, 9], color='red')\n",
    "plt.plot([1, 2, 3], [1, 4, 9], linewidth=10)\n",
    "plt.title('Graphique')\n",
    "\n",
    "plt.xlabel('Abscisses')\n",
    "plt.ylabel('Ordonnées')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut s'inspirer de la galerie d'exemples disponible sur le site de Matplotlib : https://matplotlib.org/gallery/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utiliser des data frames Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour illustrer l'utilisation de Matplotlib à partir de data frames Pandas, chargeons le jeu de données classique Iris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris.csv')\n",
    "\n",
    "iris.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut utiliser Matplotlib directement en lui donnant des Series, qui peuvent être converties directement en tableaux Numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(iris['sepal_length'], iris['sepal_width'], c=iris['petal_length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib fournit aussi l'argument *data* qui permet de donner le data frame et de ne faire référence qu'aux noms des colonnes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter('sepal_length', 'sepal_width', c='petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas fournit aussi une interface s'appuyrant sur Matplotlib. Il faut pour cela utiliser la méthode *plot* du data frame (pour des lignes) ou les méthodes plot.scatter (pour des nuages de points), plot.bar (pour des histogrammes) , ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.plot.scatter('sepal_length', 'sepal_width', c='petal_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut donc utiliser toute la puissance de Matplotlib pour customiser le graphique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['species'].value_counts().plot.bar(color='cyan')\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Espèce')\n",
    "plt.ylabel('Nombre')\n",
    "plt.title('Répartition des espèces')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib a une approche bas niveau : on définit ce qu'on trace élément par élément. Cela permet d'avoir quelque chose de très polyvalent mais peut s'avérer laborieux pour certains graphiques.\n",
    "\n",
    "Les packages plus récents ont une approche plutôt \"grammar of graphics\". Dans cette approche, introduite en 1999 par Leland Wilkinson et notamment implémentée par Hadley Wickham dans ggplot2, on décrit les différents composants syntaxiques du graphique (données, conversions esthétiques, géométries, échelles, ...).\n",
    "\n",
    "Pour illustrer cela, essayons de tracer le graphique précédent mais en utilisant l'espèce pour colorer les points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    plt.scatter('sepal_length', 'sepal_width', c='species', data=iris)\n",
    "except:\n",
    "    print(\"Erreur : setosa n'est pas une couleur !\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib demande de spécifier exactement la couleur, et ne sait pas encoder des variables catégorielles. Il faut donc le faire manuellement.\n",
    "\n",
    "On peut par exemple pré-calculer la couleur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = {'setosa': 'red',\n",
    "          'versicolor': 'blue',\n",
    "           'virginica': 'green'}\n",
    "\n",
    "iris['couleur'] = iris['species'].map(palette)\n",
    "\n",
    "plt.scatter('sepal_length', 'sepal_width', c='couleur', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou bien on peut boucler sur les différentes espèces pour superposer trois nuages de points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for espece in iris['species'].unique():\n",
    "    plt.scatter('sepal_length', 'sepal_width', data=iris[iris['species']==espece], label=espece)\n",
    "    \n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "D'autres packages basés sur la grammaire des graphiques peuvent le faire directement (Altair, Plotly Express, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "alt.Chart(iris).mark_point().encode(x='sepal_length', y='sepal_width', color='species').interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.scatter(iris, x='sepal_length', y='sepal_width', color='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scikit Learn** est la librairie de référence pour le machine learning en Python. Elle regroupe de nombreux algorithmes d'apprentissage, mais aussi des fonctions de prétraitement des données, de sélection des modèles, etc. Cela permet de couvrir tout le processus de modélisation avec une librairie bien organisée et cohérente. Elle est donc une raison importante qui a permis à Python de devenir le langage de premier plan pour le machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Même si son nom complet est Scikit Learn, la librairie s'appelle par le diminutif **sklearn**. Il est toutefois peu courant de l'importer directement car les différentes fonctions sont réparties en différents modules, desquels on importe les fonctions souhaitées. Parmi ces modules, on peut citer :\n",
    "- **cluster** pour tout ce qui concerne le clustering\n",
    "- **datasets** pour des exemples de jeux de données\n",
    "- **ensemble** pour les méthodes ensemblistes (combinaisons de modèles, random forest, gradient boosting, ...)\n",
    "- **linear_model** pour les modèles linéaires (régression linéaire (classique ou régularisée), régression logistique, ...)\n",
    "- **metrics** pour le calcul de scores\n",
    "- **model_selection** pour tout ce qui concerne la cross validation, l'optimisation d'hyperparamètres, ...\n",
    "- **neighbors** pour les méthodes basées sur les voisins (KNN)\n",
    "- **pipeline** pour enchaîner les modèles\n",
    "- **preprocessing** pour le prétraitement des données (standardisation, ...)\n",
    "- **svm** pour les Support Vector Machines\n",
    "- **tree** pour les arbres de décision\n",
    "\n",
    "Pour une liste plus complète et plus détaillée, on peut se référer à la documentation officielle : https://scikit-learn.org/stable/modules/classes.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Un premier modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour illustrer les possibilités de Scikit learn, prenons un des jeux de données disponibles au sein de la librairie. Il s'agit d'un jeu de données décrivant le prix de l'immobilier à Boston, en fonction de diférentes variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au sein du module **datasets**, on peut trouver une fonction **load_boston** qui permet de charger les données et retourne un dictionnaire avec différents champs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "boston = load_boston()\n",
    "\n",
    "boston.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **data** contient les observations sous forme matricielle\n",
    "- **target** contient la variable cible (prix) \n",
    "- **feature_names** donne le nom des colonnes de *data*\n",
    "- **DESCR** est une documentation du jeu de données\n",
    "- **filename** est le nom du fichier dans lequel le jeu de données est stocké"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(boston['DESCR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour pouvoir mieux le manipuler, mettons **data** sous la forme d'un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_boston = pd.DataFrame(boston['data'], columns=boston['feature_names'])\n",
    "\n",
    "df_boston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**target** est un array numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = load_boston()['target']\n",
    "\n",
    "target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une des deux premières choses à faire est de séparer les données en jeux d'entraînement et de test, pour éviter le surapprentissage. Le module **model_selection** propore la fonction **train_test_split** pour cela. Elle prend les features, la cible et le taux de répartition. Pour garder une répétabilité, on peut aussi lui spécifier une graîne pour le générateur de nombres aléatoires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_boston, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour un premier essai de modèle, commençons simple : une régression linéaire. Elle est disponible dans le module **linear_model**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appeler la fonction **LinearRegression** crée un objet que l'on va manipuler pour faire l'apprentissage et les prédictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour l'entraînement, on utilise la méthode **fit** du modèle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'entraînement modifie le contenu de l'objet. Il contient maintient tous les paramètres nécessaires, que l'on peut regarder si on est curieux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour la prédiction, on utilise la méthode **predict**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut comparer graphiquement les prédictions aux valeurs réelles, pour vérifier qu'elles sont raisonnables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(y_test, y_pred)\n",
    "plt.plot([0,50], [0,50], color='black', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('Réel')\n",
    "plt.ylabel('Prédiction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour avoir une vision plus précise, on peut utiliser des métriques (disponibles dans le module **metrics**), comme par exemple le MSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changer de modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tout l'intérêt de Scikit learn est que les modèles suivent tous la même interface : une méthode **fit** pour l'apprentissage et une méthode **predict** pour l'inférence. Il est donc très facile de changer de modèle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Par exemple, pour faire une random forest, il suffit d'aller chercher la fonction **RandomForestRegressor** (il existe aussi RandomForestClassifier pour la classification) dans le module **ensemble**. On l'appelle ensuite pour créer notre modèle. On pourrait lui spécifier des arguments (hyperparamètres, ...), mais dans un premier temps gardons les valeurs par défaut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred=rf.predict(X_test)\n",
    "\n",
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On voit tout de suite le gain apporté par ce modèle non-linéaire. Mais on peut facilement contnuer notre exploration de différents modèles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "dict_modeles = {'Régression linéaire': LinearRegression(),\n",
    "               'Random forest': RandomForestRegressor(),\n",
    "               'Gradient boosting': GradientBoostingRegressor()}\n",
    "\n",
    "df_resultats = pd.DataFrame([], columns=['MSE']) # data frame pour stocker les résultats\n",
    "\n",
    "for nom_modele, modele in dict_modeles.items():\n",
    "    modele.fit(X_train, y_train)\n",
    "    y_pred = modele.predict(X_test)\n",
    "    \n",
    "    df_resultats.loc[nom_modele, 'MSE'] = mean_squared_error(y_pred, y_test)\n",
    "\n",
    "df_resultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_resultats.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prétraitement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outre le choix et l'optimisation du modèle, un levier important d'amélioration dans le machine learning est le prétraitement des données. Certains modèles peuvent par exemple nécessiter des variables normalisées. Le module **preprocessing** contient différentes fonctions utiles.\n",
    "\n",
    "Les modèles qu'on a essayés sont linéaires ou basés sur des arbres, donc insensibles à la normalisation. Mais on peut essayer des transformations non-linéaires. Essayons par exemple de voir si la régression linéaire serait plus performante si on applique une loi de puissance sur les features pour les rendre plus gaussiennes. L'utilisation des transformations est très similaire à celle des modèles :\n",
    "- une méthode **fit** permet de déterminer les paramètres de la transformation\n",
    "- une méthode **transform** permet d'appliquer la transformation aux données (si on transforme la variable cible, il existe la méthode **inverse_transform** pour obtenir les prédictions dans l'échelle d'origine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "\n",
    "prepro = PowerTransformer()\n",
    "\n",
    "prepro.fit(X_train)\n",
    "\n",
    "X_train_prepro = prepro.transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut ensuite entraîner un modèle sur ces données transformées :  il faut simplement bien veiller à appliquer la même transformation sur les données de test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train_prepro, y_train)\n",
    "\n",
    "X_test_prepro = prepro.transform(X_test)\n",
    "y_pred = lr.predict(X_test_prepro)\n",
    "\n",
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lorsqu'on enchaîne ainsi différentes étapes, il devient facile de se tromper : oubli d'une transformation sur les données de test, mélange des données de test et d'entraînement, fit sur les données de test, ...\n",
    "\n",
    "Pour cela, Scikit learn propose la notion de **Pipeline**. Il s'agit d'un enchaînement de transformations et de modèles, qui pourra être utilisé en bloc. Ainsi la fonciton **fit** entraînera toutes les étapes du traitement, et la fonction **predict** appliquera toutes les transformations et prédictions nécessaires pour arriver au résultat final.\n",
    "\n",
    "Pour construire un pipeline, on fournit une liste de tuples de la forme $(nom, objet)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe = Pipeline([('scaler', PowerTransformer()), ('modèle', RandomForestRegressor())])\n",
    "\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour finir, regardons rapidement un pan important de la modélisation pour lequel Scikit learn apporte des outils puissants : l'optimisation d'hyperparamètres.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imaginons par exemple que l'on souhaite par exemple trouver les meilleurs hyperparamètres pour un random forest. On peut imaginer le faire manuellement en spécifiant les hyperparamètres lors de la création du modèle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf1 = RandomForestRegressor(n_estimators=50)\n",
    "rf1.fit(X_train, y_train)\n",
    "print(mean_squared_error(rf1.predict(X_test), y_test))\n",
    "\n",
    "rf2 = RandomForestRegressor(n_estimators=200)\n",
    "rf2.fit(X_train, y_train)\n",
    "print(mean_squared_error(rf2.predict(X_test), y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais on peut aussi faire plus précis, plus puissant, plus rapide de manière plus simple !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La fonction **GridSearchCV** (disponible dans le module **model_selection**) par exemple va permettre de :\n",
    "1. générer toutes les combinaisons de valeurs d'hyperparamètres que l'on souhaite (*grid search*)\n",
    "1. pour chaque combinaison, calculer plusieurs fois le modèle sur différents découpages des données (*cross validation*)\n",
    "1. choisir la meilleure combinaison\n",
    "\n",
    "Et avec l'argument *n_jobs*, elle le fait en parallèle (la valeur -1 signifie l'utilisation de tous les coeurs) !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   32.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   40.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [2, 3, 4, 5],\n",
       "                         'n_estimators': [10, 20, 50, 100, 200]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parametres = {'n_estimators': [10, 20, 50, 100, 200], 'max_depth': [2, 3, 4, 5]}\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "grid = GridSearchCV(rf, parametres, n_jobs=-1, verbose=1)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si cela nous intéresse, on peut regarder les valeurs sélectionnées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais cela n'est pas nécessaire : on peut simplement utiliser la méthode **predict** pour faire les prédictions avec le meilleur modèle retenu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid.predict(X_test)\n",
    "\n",
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Même si on a juste donné un aperçu rapide des possibilités de Scikit learn, il faut bien garder en tête ses forces : permettre de manipuler simplement et de manière cohérente des modèles mais aussi fournir des outils puissants pour simplifier chaque étape du processus de  machine learning."
   ]
  }
 ],
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