iris_dataset_with_various_machine_learning_algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rapport TP Data Minig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On importe les différents bibliothéques. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import itertools\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scale_plot_size(factor=1.5):\n",
    "    import matplotlib as mpl\n",
    "    default_dpi = mpl.rcParamsDefault['figure.dpi']\n",
    "    mpl.rcParams['figure.dpi'] = default_dpi*factor\n",
    "scale_plot_size(1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On charge le dataset 'iris'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = iris_data[\"target_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = iris_data[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame(features, columns=iris_data[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = iris_data[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, (150, 4))"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.size, features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Vérifier si une des colonnes contient une valeur nulle **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    False\n",
       "sepal width (cm)     False\n",
       "petal length (cm)    False\n",
       "petal width (cm)     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.isnull().any() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Vérifier si une des lignes contient une valeur nulle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.isnull().any(axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On divise le dataset en 2 parties : Training et Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape, labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 4), 100)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,  y_train.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50, 4), 50)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier_nb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = classifier_nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 2, 2, 1, 1, 2, 0, 2, 0,\n",
       "       2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0, 0, 1,\n",
       "       1, 2, 1, 2])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2, 0,\n",
       "       2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0, 0, 1,\n",
       "       2, 2, 1, 2])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre des cas incorrects est : 2 \n"
     ]
    }
   ],
   "source": [
    "print(\"Le nombre des cas incorrects est : %d \" % (predictions != y_test).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## La matrice de confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 14,  1],\n",
       "       [ 0,  1, 15]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix_classifier_naive_bayes = confusion_matrix(y_test, predictions)\n",
    "confusion_matrix_classifier_naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[19  0  0]\n",
      " [ 0 14  1]\n",
      " [ 0  1 15]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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/N1tv3Ms+zmcQLUmSpIHgwIrXx4cQLgEOjjHOqrONVbL10zX2l7avmrdzlQyi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\nMzMzs5QcRJuZmZmZpfT/0jFVAmd7x7kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe5b34d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix_classifier_naive_bayes, classes=class_names, \n",
    "                      title='matrice de confusion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La précision du modéle Naive Bayes est : 0.96 (96.0%)\n"
     ]
    }
   ],
   "source": [
    "getAccuracy(\"Naive Bayes\", accuracy_score(y_test, predictions))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction qui aide à l'affichage de la matrice de confusion\n",
    "%load /home/neo/Bureau/vI-Workspace/python/plot_confusion_matrix.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbe431c39b0>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Na0v/bo2BvDzIyQltWyQh6c6VBING7kSW8k8t5R+/Kf9IolH+kWAIdf7RmjsC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\n8g9wOoo7AFBAnc7gpcgnsVZaWUlmPAAAAEkj/wCno7gDAAXjef7hgWG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe43157f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale_plot_size(1.6)\n",
    "colors = itertools.cycle('rgb')\n",
    "fig = plt.figure()\n",
    "axes_nb = fig.add_axes([0,0,0.5,0.5])\n",
    "target_ids = range(len(iris_data.target_names))\n",
    "for i, c, label in zip(target_ids, colors, iris_data.target_names):\n",
    "    axes_nb.scatter(features_pca[y_test == i, 0], features_pca[y_test == i, 1],\n",
    "        c=c, label=label)\n",
    "\n",
    "axes_nb.set_title(\"attendu\", color=\"orange\")\n",
    "axes_nb.legend()\n",
    "\n",
    "axes1_nb = fig.add_axes([0.6,0,0.5,0.5])\n",
    "\n",
    "for i, c, label in zip(target_ids, colors, iris_data.target_names):\n",
    "    axes1_nb.scatter(features_test_pca[predictions == i, 0], features_test_pca[predictions == i, 1],\n",
    "        c=c, label=label)\n",
    "\n",
    "axes1_nb.set_title(\"prédit\", color=\"orange\")\n",
    "axes1_nb.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getAccuracy(model, score):\n",
    "    print(\"La précision du modéle {} est : {} ({}%)\".format(model, score, score * 100 ))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load /home/neo/Bureau/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# **Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier_svc = SVC() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2, 0,\n",
       "       2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0, 0, 1,\n",
       "       2, 2, 1, 2])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictsions_svc = classifier_svc.predict(X_test)\n",
    "predictsions_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 15,  0],\n",
       "       [ 0,  0, 16]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_mx_svc = confusion_matrix(y_test, predictsions_svc)\n",
    "confusion_mx_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[19  0  0]\n",
      " [ 0 15  0]\n",
      " [ 0  0 16]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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7LTPRkiRJTShQbJa3AcLo3UIIW5BmSX4c6Igx5qqHJk16B/CvGOPrFfZPzdab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\nMBH9vVkZ5dUPQ08yPgC+a/0mm5WeS9yZ1Z7kIJ6k5YAvUAC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gD0I3Ynsyf2qHa0C3Xbm/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\nPoC8WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe5bb09a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_mx_svc,classes=class_names, title='Matrice de confusion' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La précision du modéle SVM est : 1.0 (100.0%)\n"
     ]
    }
   ],
   "source": [
    "getAccuracy(\"SVM\", accuracy_score(y_test, predictsions_svc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2, whiten=True).fit(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_test = PCA(n_components=2, whiten=True).fit(X_test)\n",
    "features_test_pca = pca_test.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbe43295550>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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APGvt/gi0R0RERCTUlH9ERETEJ1E9Lcta+6C11gDJwBlALnA1UGqMGd3Ka6Q39QC+Dl3r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\nAnfiBJtx1toNEW6SiIiISEg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6OtJNEgm6Nm3a4HK56NKli4KNiEQX5Z+IUP6RRKD8I7FE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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe42cb4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale_plot_size(1.6)\n",
    "colors = itertools.cycle('rgb')\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0,0,0.5,0.5])\n",
    "target_ids = range(len(iris_data.target_names))\n",
    "for i, c, label in zip(target_ids, colors, iris_data.target_names):\n",
    "    axes.scatter(features_pca[y_test == i, 0], features_pca[y_test == i, 1],\n",
    "        c=c, label=label)\n",
    "axes.legend()\n",
    "axes.set_title(\"attendu\", color=\"orange\")\n",
    "\n",
    "axes1 = fig.add_axes([0.6,0,0.5,0.5])\n",
    "\n",
    "for i, c, label in zip(target_ids, colors, iris_data.target_names):\n",
    "    axes1.scatter(features_test_pca[predictsions_svc == i, 0], features_test_pca[predictsions_svc == i, 1],\n",
    "        c=c, label=label)\n",
    "axes1.set_title(\"prédit\", color=\"orange\")\n",
    "axes1.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3.6",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}