1_Tuto_ACP.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"1_Tuto_ACP.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"iVHcuD6FlP03"},"source":["# <font color=green>**REDUCTION DE LA DIMENSION D'UNE ENSEMBLE DE DONNEES PAR L'ANALYSE EN COMPOSANTES PRINCIPALES**\n","\n","*Auteur : Josué Afouda*\n"]},{"cell_type":"markdown","metadata":{"id":"WQ2LMsp4039V"},"source":["# ***Librairies***"]},{"cell_type":"code","metadata":{"id":"ashBgkuV0ofU"},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.decomposition import PCA\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.pipeline import Pipeline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mSnvkIMvculM"},"source":["# ***ACP dans un Pipeline***"]},{"cell_type":"markdown","metadata":{"id":"PrJEX17Yc1_x"},"source":["L'ensemble des données de cette partie est un tableau d'échantillons donnant des mesures (le poids en grammes, la longueur en centimètres, le rapport en pourcentage de la hauteur à la longueur, etc.) de 7 espèces de poissons codées de 1 à 7. Chaque ligne représente un poisson individuel. Ces données proviennent de [Journal of Statistics Education](http://jse.amstat.org/jse_data_archive.htm). Consultez la [description](http://jse.amstat.org/datasets/fishcatch.txt) de ces données que vous pouvez télécharger via cette [page](http://jse.amstat.org/datasets/fishcatch.dat.txt)."]},{"cell_type":"code","metadata":{"id":"iLVbziBIfUi4","executionInfo":{"status":"ok","timestamp":1604053286411,"user_tz":-60,"elapsed":575,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"b94352b8-e1e6-4840-e18c-483fe5016688","colab":{"base_uri":"https://localhost:8080/","height":417}},"source":["# Importation des données\n","\n","columns = ['Species', 'Weight', 'Length1', 'Length2', 'Length3', 'Height%', 'Width%', 'Sex'] \n","\n","fishes = pd.read_csv('http://jse.amstat.org/datasets/fishcatch.dat.txt', delimiter='\\s+', header=None, names=columns)\n","\n","fishes"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Species</th>\n","      <th>Weight</th>\n","      <th>Length1</th>\n","      <th>Length2</th>\n","      <th>Length3</th>\n","      <th>Height%</th>\n","      <th>Width%</th>\n","      <th>Sex</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>242.0</td>\n","      <td>23.2</td>\n","      <td>25.4</td>\n","      <td>30.0</td>\n","      <td>38.4</td>\n","      <td>13.4</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>290.0</td>\n","      <td>24.0</td>\n","      <td>26.3</td>\n","      <td>31.2</td>\n","      <td>40.0</td>\n","      <td>13.8</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>340.0</td>\n","      <td>23.9</td>\n","      <td>26.5</td>\n","      <td>31.1</td>\n","      <td>39.8</td>\n","      <td>15.1</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>363.0</td>\n","      <td>26.3</td>\n","      <td>29.0</td>\n","      <td>33.5</td>\n","      <td>38.0</td>\n","      <td>13.3</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1</td>\n","      <td>430.0</td>\n","      <td>26.5</td>\n","      <td>29.0</td>\n","      <td>34.0</td>\n","      <td>36.6</td>\n","      <td>15.1</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\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>155</th>\n","      <td>7</td>\n","      <td>1100.0</td>\n","      <td>39.0</td>\n","      <td>42.0</td>\n","      <td>44.6</td>\n","      <td>28.7</td>\n","      <td>15.4</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>156</th>\n","      <td>7</td>\n","      <td>1000.0</td>\n","      <td>39.8</td>\n","      <td>43.0</td>\n","      <td>45.2</td>\n","      <td>26.4</td>\n","      <td>16.1</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>157</th>\n","      <td>7</td>\n","      <td>1100.0</td>\n","      <td>40.1</td>\n","      <td>43.0</td>\n","      <td>45.5</td>\n","      <td>27.5</td>\n","      <td>16.3</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>158</th>\n","      <td>7</td>\n","      <td>1000.0</td>\n","      <td>40.2</td>\n","      <td>43.5</td>\n","      <td>46.0</td>\n","      <td>27.4</td>\n","      <td>17.7</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>159</th>\n","      <td>7</td>\n","      <td>1000.0</td>\n","      <td>41.1</td>\n","      <td>44.0</td>\n","      <td>46.6</td>\n","      <td>26.8</td>\n","      <td>16.3</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>159 rows × 8 columns</p>\n","</div>"],"text/plain":["     Species  Weight  Length1  Length2  Length3  Height%  Width%  Sex\n","1          1   242.0     23.2     25.4     30.0     38.4    13.4  NaN\n","2          1   290.0     24.0     26.3     31.2     40.0    13.8  NaN\n","3          1   340.0     23.9     26.5     31.1     39.8    15.1  NaN\n","4          1   363.0     26.3     29.0     33.5     38.0    13.3  NaN\n","5          1   430.0     26.5     29.0     34.0     36.6    15.1  NaN\n","..       ...     ...      ...      ...      ...      ...     ...  ...\n","155        7  1100.0     39.0     42.0     44.6     28.7    15.4  0.0\n","156        7  1000.0     39.8     43.0     45.2     26.4    16.1  0.0\n","157        7  1100.0     40.1     43.0     45.5     27.5    16.3  0.0\n","158        7  1000.0     40.2     43.5     46.0     27.4    17.7  1.0\n","159        7  1000.0     41.1     44.0     46.6     26.8    16.3  0.0\n","\n","[159 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"CKV3uB1hgsi_","executionInfo":{"status":"ok","timestamp":1604053387623,"user_tz":-60,"elapsed":554,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"77a8caee-e526-4d17-ddfe-4e035c77d1b4","colab":{"base_uri":"https://localhost:8080/"}},"source":["# Suppression des colonnes 'Species' et 'Sex'\n","fishes.drop(columns=['Species', 'Sex'], inplace=True)\n","fishes.info()\n","\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 159 entries, 1 to 159\n","Data columns (total 6 columns):\n"," #   Column   Non-Null Count  Dtype  \n","---  ------   --------------  -----  \n"," 0   Weight   158 non-null    float64\n"," 1   Length1  159 non-null    float64\n"," 2   Length2  159 non-null    float64\n"," 3   Length3  159 non-null    float64\n"," 4   Height%  159 non-null    float64\n"," 5   Width%   159 non-null    float64\n","dtypes: float64(6)\n","memory usage: 8.7 KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"552dq8cSCAOk","executionInfo":{"status":"ok","timestamp":1604053478391,"user_tz":-60,"elapsed":561,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"42b70b1e-c35e-4f82-d7f9-6c09ff784f83","colab":{"base_uri":"https://localhost:8080/"}},"source":["# Remplacement de la valeur manquante au niveau de la variable 'Weight'\n","fishes['Weight'] = fishes['Weight'].fillna(fishes['Weight'].mean(axis=0))\n","\n","# Vérifions qu'il n'y a plus de données manquantes\n","fishes.isna().sum()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Weight     0\n","Length1    0\n","Length2    0\n","Length3    0\n","Height%    0\n","Width%     0\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"qvOPCU-rhEIZ"},"source":["L'ensemble des données ci-dessus est de 6 dimensions (6 variables). A l'aide de la technique de l'ACP, Réduisez la dimensionnalité de cet ensemble afin de trouver sa dimension intrinsèque."]},{"cell_type":"code","metadata":{"id":"WZKMz4rwh-tU","executionInfo":{"status":"ok","timestamp":1604054174413,"user_tz":-60,"elapsed":562,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"ab3dddc9-6af3-44ac-9723-c63fc8fd3b86","colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# Création d'un tableau numpy 2D à partir de la dataframe fishes\n","fishes_array = fishes.values\n","\n","# Création d'un objet Pipeline\n","pipe = Pipeline([('scaler', StandardScaler()),\n","                 ('pca', PCA())])\n","\n","# Application de l'objet pipe aux données\n","pipe.fit(fishes_array)\n","\n","# Visualisation du pourcentage de variance expliquée par chaque composante\n","plt.bar(range(pipe[1].n_components_), pipe[1].explained_variance_ratio_)\n","plt.xlabel('Composantes principales')\n","plt.ylabel('Pourcentage de variance')\n","plt.xticks(range(pipe[1].n_components_))\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"8qYQvHBtmNUa","executionInfo":{"status":"ok","timestamp":1604054277811,"user_tz":-60,"elapsed":577,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"f25fcfb4-4d15-4d39-9984-e9e5c1ac5ecd","colab":{"base_uri":"https://localhost:8080/"}},"source":["# Cumul du pourcentage de variance expliquée\n","pipe[1].explained_variance_ratio_.cumsum()\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.64945036, 0.89047222, 0.98237637, 0.99955204, 0.99995958,\n","       1.        ])"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"__RW2Fx9nACh"},"source":["Les deux premières composantes expliquent 89% de la variance totale des données. On peut donc dire que la dimension intrinsèque de cet ensemble de données est raisonnablement égale à 2. Transformons donc la dataframe originelle de 6 variables en une dataframe de 2 variables."]},{"cell_type":"code","metadata":{"id":"ispDz8PooviH","executionInfo":{"status":"ok","timestamp":1604054382221,"user_tz":-60,"elapsed":560,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"cca0b90e-0745-4bde-88db-44b171781d5e","colab":{"base_uri":"https://localhost:8080/"}},"source":["# Création d'un pipeline\n","pipe2 = Pipeline([('scaler', StandardScaler()),\n","                 ('pca', PCA(n_components=2))])\n","\n","# Transformation des données\n","fishes_transformed = pipe2.fit_transform(fishes_array)\n","fishes_transformed.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(159, 2)"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"EVapYfuXF6IG","executionInfo":{"status":"ok","timestamp":1604054400652,"user_tz":-60,"elapsed":486,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"a0bcd41b-7dba-41b2-de68-f2b938a7f19c","colab":{"base_uri":"https://localhost:8080/"}},"source":["fishes_array.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(159, 6)"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"wWPGFAJjF9xr","executionInfo":{"status":"ok","timestamp":1604054414408,"user_tz":-60,"elapsed":530,"user":{"displayName":"J.A DATATECH CONSULTING","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64","userId":"06410945177748020218"}},"outputId":"d3164a9e-003b-4a90-a1ab-39163d3ca2bb","colab":{"base_uri":"https://localhost:8080/"}},"source":["fishes_transformed"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-4.80116754e-01, -6.68315517e-01],\n","       [-2.55219975e-01, -9.13015811e-01],\n","       [-1.58349536e-01, -1.30447046e+00],\n","       [ 1.55073466e-01, -5.45322026e-01],\n","       [ 3.06983365e-01, -9.88607676e-01],\n","       [ 4.16523550e-01, -9.20230768e-01],\n","       [ 5.20354586e-01, -1.42775330e+00],\n","       [ 3.54099424e-01, -4.01105918e-01],\n","       [ 4.85505153e-01, -8.43555574e-01],\n","       [ 6.71206851e-01, -7.49157091e-01],\n","       [ 6.56684573e-01, -8.77178513e-01],\n","       [ 6.89764492e-01, -6.54721470e-01],\n","       [ 6.92811404e-01, -8.33548429e-02],\n","       [ 6.70605048e-01, 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-2.90389007e-01]])"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"g3nB0Qtyps3P"},"source":["Le nouveau jeu de données comporte 159 lignes et 2 colonnes. La réduction de dimensionnalité a été parfaitement opérée :)"]},{"cell_type":"markdown","metadata":{"id":"Yo2PNc0Q_QIm"},"source":["# ***Sélection des composants principaux selon le pourcentage de variance expliquée***\n","\n","\n","\n","\n"]}]}