.. _seance6graphesmlcorrectionrst:

========================================
Graphes en machine learning - correction
========================================


.. only:: html

    **Links:** :download:`notebook <seance6_graphes_ml_correction.ipynb>`, :downloadlink:`html <seance6_graphes_ml_correction2html.html>`, :download:`PDF <seance6_graphes_ml_correction.pdf>`, :download:`python <seance6_graphes_ml_correction.py>`, :downloadlink:`slides <seance6_graphes_ml_correction.slides.html>`, :githublink:`GitHub|_doc/notebooks/sessions/seance6_graphes_ml_correction.ipynb|*`


Correction (en cours de rédaction) des exercices autour des graphes
courants en machine learning.

.. code:: ipython3

    %matplotlib inline
    %load_ext pyensae

.. code:: ipython3

    import matplotlib.pyplot as plt
    plt.style.use('ggplot')

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





Le module utilise des données issue de `Wine Quality Data
Set <https://archive.ics.uci.edu/ml/datasets/Wine+Quality>`__ pour
lequel on essaye de prédire la qualité du vin en fonction de ses
caractéristiques chimiques.

.. code:: ipython3

    from pyensae.datasource import download_data, DownloadDataException
    uci = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/"
    try:
        download_data("winequality-red.csv", url=uci)
        download_data("winequality-white.csv", url=uci)
    except DownloadDataException:
        print("backup")
        download_data("winequality-red.csv", website="xd")
        download_data("winequality-white.csv", website="xd")

.. code:: ipython3

    %head winequality-red.csv






.. raw:: html

    <pre>
    "fixed acidity";"volatile acidity";"citric acid";"residual sugar";"chlorides";"free sulfur dioxide";"total sulfur dioxide";"density";"pH";"sulphates";"alcohol";"quality"
    7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
    7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5
    7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;9.8;5
    11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58;9.8;6
    7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
    7.4;0.66;0;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5
    7.9;0.6;0.06;1.6;0.069;15;59;0.9964;3.3;0.46;9.4;5
    7.3;0.65;0;1.2;0.065;15;21;0.9946;3.39;0.47;10;7
    7.8;0.58;0.02;2;0.073;9;18;0.9968;3.36;0.57;9.5;7

    </pre>



.. code:: ipython3

    import pandas
    red_wine = pandas.read_csv("winequality-red.csv", sep=";")
    red_wine["red"] = 1
    white_wine = pandas.read_csv("winequality-white.csv", sep=";")
    white_wine["red"] = 0
    wines = pandas.concat([red_wine, white_wine])
    wines.head()






.. raw:: html

    <div>
    <style>
        .dataframe thead tr:only-child th {
            text-align: right;
        }

        .dataframe thead th {
            text-align: left;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>fixed acidity</th>
          <th>volatile acidity</th>
          <th>citric acid</th>
          <th>residual sugar</th>
          <th>chlorides</th>
          <th>free sulfur dioxide</th>
          <th>total sulfur dioxide</th>
          <th>density</th>
          <th>pH</th>
          <th>sulphates</th>
          <th>alcohol</th>
          <th>quality</th>
          <th>red</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>7.4</td>
          <td>0.70</td>
          <td>0.00</td>
          <td>1.9</td>
          <td>0.076</td>
          <td>11.0</td>
          <td>34.0</td>
          <td>0.9978</td>
          <td>3.51</td>
          <td>0.56</td>
          <td>9.4</td>
          <td>5</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>7.8</td>
          <td>0.88</td>
          <td>0.00</td>
          <td>2.6</td>
          <td>0.098</td>
          <td>25.0</td>
          <td>67.0</td>
          <td>0.9968</td>
          <td>3.20</td>
          <td>0.68</td>
          <td>9.8</td>
          <td>5</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>7.8</td>
          <td>0.76</td>
          <td>0.04</td>
          <td>2.3</td>
          <td>0.092</td>
          <td>15.0</td>
          <td>54.0</td>
          <td>0.9970</td>
          <td>3.26</td>
          <td>0.65</td>
          <td>9.8</td>
          <td>5</td>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>11.2</td>
          <td>0.28</td>
          <td>0.56</td>
          <td>1.9</td>
          <td>0.075</td>
          <td>17.0</td>
          <td>60.0</td>
          <td>0.9980</td>
          <td>3.16</td>
          <td>0.58</td>
          <td>9.8</td>
          <td>6</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>7.4</td>
          <td>0.70</td>
          <td>0.00</td>
          <td>1.9</td>
          <td>0.076</td>
          <td>11.0</td>
          <td>34.0</td>
          <td>0.9978</td>
          <td>3.51</td>
          <td>0.56</td>
          <td>9.4</td>
          <td>5</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    </div>



On découpe en base d’apprentissage, base de test :

.. code:: ipython3

    from sklearn.model_selection import train_test_split
    X = wines[[c for c in wines.columns if c != "quality"]]
    Y = wines["quality"]
    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
    type(x_train), type(y_train)




.. parsed-literal::
    (pandas.core.frame.DataFrame, pandas.core.series.Series)



.. code:: ipython3

    wines.shape, x_train.shape, y_train.shape




.. parsed-literal::
    ((6497, 13), (4352, 12), (4352,))



Exercice 1 : créer une fonction pour automatiser la création de ce graphe
-------------------------------------------------------------------------


Exercice 2 : simplifier l’apprentissage de chaque modèle
--------------------------------------------------------


Exercice 3 : grid_search
------------------------

Considérer un modèle et estimer au mieux ses paramètres.