{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<span style=\"color:red\">Ajout d'étapes à la fin</span>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "# Recognizing hand-written digits\n",
        "\n",
        "This example shows how scikit-learn can be used to recognize images of\n",
        "hand-written digits, from 0-9.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>\n",
        "# License: BSD 3 clause\n",
        "\n",
        "# Standard scientific Python imports\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import datasets, classifiers and performance metrics\n",
        "from sklearn import datasets, svm, metrics\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Digits dataset\n",
        "\n",
        "The digits dataset consists of 8x8\n",
        "pixel images of digits. The ``images`` attribute of the dataset stores\n",
        "8x8 arrays of grayscale values for each image. We will use these arrays to\n",
        "visualize the first 4 images. The ``target`` attribute of the dataset stores\n",
        "the digit each image represents and this is included in the title of the 4\n",
        "plots below.\n",
        "\n",
        "Note: if we were working from image files (e.g., 'png' files), we would load\n",
        "them using :func:`matplotlib.pyplot.imread`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACXCAYAAAARS4GeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAALBUlEQVR4nO3dX2yd510H8O+vi8ooW2tnE0wU1sSdBAK0mqZTmZBQqjnSuJgcMRJNDDRXmhJxA5G4cG5gjsZQghByxYYWEGoZMFgjIJ2QCmq0uqMXgGLhTipsF2lamNikQp1uHfsjwcvFcUbUpmnzvufkxE8+HymSz+n5vs9j95dzvnlfH7u6rgsAQMtumvYGAAAmTeEBAJqn8AAAzVN4AIDmKTwAQPMUHgCgeU0Xnqp6tKo+NO7HcmMxRwxlhhgHczRMXW8/h6eqXrrk5i1Jvp3kf7ZuH+667s+u/a7Gq6rek+QTSd6e5B+TLHVd99x0d9WW1ueoqm5O8ukk9yS5I8l9XdetTXVTjbkBZuinknw0yZ6MPq+1JL/Sdd1Xprmv1twAc/RjST6V5M6tu9YzmqN/md6uLu+6O8PTdd2bLv5J8m9J3nfJfd8djKraMb1d9ldVb03yV0l+PcnOJGeTfGaqm2pQ63O05ckkv5jkq9PeSItugBmaTfIHSXZlVJq/nuTBaW6oRTfAHP1Hkp/P6PXsrUk+m+QvprqjV3HdFZ5XU1V7q+rLVbVcVV9N8mBVzVbV31TV81W1ufXxD12SWauqD299vFRVT1bV72w99nxV/WzPx+6uqs9X1der6kxVfaKq/vR1fio/l+TprutOdV33rSQrSe6qqh8d/lXitbQyR13XfafrutWu657M//9rkWugoRl6dOt56Gtd1/13ko8n+ekxfZl4DQ3N0YWu657tRpeLKqPno3eM56s0Xtum8Gx5W0Yt8o4khzLa/4Nbt9+e5JsZ/aV9Nfcm+VJGLfS3k/xRVVWPx346yT8leUtGheWXLg1W1Req6hde5bg/nuSpize6rvtGknNb93NttDBHTFeLM/QzSZ5+nY9lPJqZo6q6kORbSX4vyW9d6bHTst1Oof1vko90XfftrdvfTPKXF/9jVX0syeNXyD/Xdd0fbj32j5P8fpIfyOUvCVz2sTX63ol3JXlP13XfSfJkVX320mDXde+8wh7elOT5l933YpI3XyHDeLUwR0xXUzNUVe9M8htJFl/P4xmbZuao67qZqvq+JB9Kcl1+T+p2O8Pz/NZloCRJVd1SVSer6rmq+lqSzyeZqao3vEr+u0OwdQo3GRWQq3nsDyZ54ZL7kuTfr+JzeCnJrS+779aMrp9zbbQwR0xXMzNUVe9I8miSX+267u+vNs8gzczR1nG/keSTST5VVd/f5xiTtN0Kz8vfUvZrSX4kyb1d192a0SnZZHQdcVK+kmRnVd1yyX0/fBX5p5PcdfHGViO+M04lX0stzBHT1cQMVdUdSc4k+WjXdX8yzs3xujQxRy9zU0bvRrt90K4mYLsVnpd7c0anAC9U1c4kH5n0gltvHz+bZKWqbq6qdyd531Uc4q+T/ERVvb+q3pjRaeQvdF33xQlsl9dnO85Rqup7tmYoSW6uqjde4fo9k7XtZqiqbk/yuSQf77rukxPaJldnO87Rvqr6yap6Q1XdmuR3k2wm+dfJ7Li/7V54VpN8b5L/TPIPSf72Gq37wSTvTvJfSX4zo7eVX7wGm6p6uqo+eLlg13XPJ3l/ko9lNBT3JvnApDfMFa1mm83Rli9l9OR4e5K/2/r4jontlitZzfaboQ8nmcvohe6li38mvWGuaDXbb45mkvx5Rt+Lei6jKxbvvfRS3fXiuvvBg9tRVX0myRe7rpt4G6dd5oihzBDj0OocbfczPFNRVe+qqjur6qaqem9G72w4PeVtsc2YI4YyQ4zDjTJH2+1t6deLt2X005LfkuTLSX6567p/nu6W2IbMEUOZIcbhhpgjl7QAgOa5pAUANO+1LmlN5fTPqVOnBuWXl5d7Z/ft29c7e/z48d7Z2dnZ3tkxmPRbmbflacS9e/f2zl64cKF39tixY72zi4tT/UG5k5yjbTlDa2trvbP79+/vnZ2fn++dHbLnMWjyuejEiROD8kePHu2d3b17d+/s+vp67+z1+JrmDA8A0DyFBwBonsIDADRP4QEAmqfwAADNU3gAgOYpPABA8xQeAKB5Cg8A0DyFBwBonsIDADRP4QEAmqfwAADNU3gAgObtmPYGLmd5eXlQ/vz5872zm5ubvbM7d+7snX344Yd7Z5PkwIEDg/K80szMTO/sE0880Tv7+OOP984uLi72zvJKGxsbg/L33Xdf7+xtt93WO/vss8/2znJ5R48e7Z0d+vx+8uTJ3tnDhw/3zq6vr/fOLiws9M5OijM8AEDzFB4AoHkKDwDQPIUHAGiewgMANE/hAQCap/AAAM1TeACA5ik8AEDzFB4AoHkKDwDQPIUHAGiewgMANE/hAQCat2NSBx7ya+XPnz8/aO1z5871zs7NzfXO7tu3r3d2yNcrSQ4cODAo36KNjY1B+bW1tbHs42rNz89PZV1e6fTp04Pyd911V+/s/v37e2ePHTvWO8vlHTp0qHd2eXl50Np79uzpnd29e3fv7MLCQu/s9cgZHgCgeQoPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPMUHgCgeQoPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDm7ZjUgTc3N3tn77777kFrz83NDcr3tWfPnqms27LV1dXe2ZWVlUFrv/jii4Pyfe3du3cq6/JKR44cGZTftWvXVNZeXFzsneXyhryuPPPMM4PWPn/+fO/swsJC7+yQ1/HZ2dne2UlxhgcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPMUHgCgeQoPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPN2TOrAQ36t/L59+8a4k2tnyOc8Ozs7xp2048iRI72zS0tLg9ae1v+TCxcuTGXdVg35eq6urg5a+/Tp04PyfT300ENTWZfLm5ubG5R/4YUXemcXFhamkj1z5kzvbDKZ519neACA5ik8AEDzFB4AoHkKDwDQPIUHAGiewgMANE/hAQCap/AAAM1TeACA5ik8AEDzFB4AoHkKDwDQPIUHAGiewgMANG/HpA485Fe7r6+vj3EnV2dzc7N39uzZs72zBw8e7J2lLRsbG72z8/PzY9tHK1ZWVnpnH3jggfFt5CqdPn26d3ZmZmZs+2D6hryenjlzpnf28OHDvbMnTpzonU2S48ePD8pfjjM8AEDzFB4AoHkKDwDQPIUHAGiewgMANE/hAQCap/AAAM1TeACA5ik8AEDzFB4AoHkKDwDQPIUHAGiewgMANE/hAQCat2NSB56bm+udPXv27KC1T506NZXsEMvLy1NZF1q3tLTUO7u2tjZo7aeeeqp3dv/+/b2zi4uLvbP3339/7+zQtVt19OjRQfmFhYXe2c3Nzd7Zxx57rHf24MGDvbOT4gwPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPMUHgCgeQoPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPN2TOrAc3NzvbMnTpwYtPby8nLv7D333NM7u76+3jvL+M3MzAzKLy4u9s4+8sgjvbNra2u9s0tLS72zrZqfn++d3djYGLT2kPzKykrv7JD527VrV+9sMuzvTatmZ2cH5Q8dOjSmnVydgwcP9s6ePHlyjDsZD2d4AIDmKTwAQPMUHgCgeQoPANA8hQcAaJ7CAwA0T+EBAJqn8AAAzVN4AIDmKTwAQPMUHgCgeQoPANA8hQcAaJ7CAwA0r7qum/YeAAAmyhkeAKB5Cg8A0DyFBwBonsIDADRP4QEAmqfwAADN+z+hHt0iyNm/ygAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 720x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
        "for ax, image, label in zip(axes, digits.images, digits.target):\n",
        "    ax.set_axis_off()\n",
        "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
        "    ax.set_title(\"Training: %i\" % label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Classification\n",
        "\n",
        "To apply a classifier on this data, we need to flatten the images, turning\n",
        "each 2-D array of grayscale values from shape ``(8, 8)`` into shape\n",
        "``(64,)``. Subsequently, the entire dataset will be of shape\n",
        "``(n_samples, n_features)``, where ``n_samples`` is the number of images and\n",
        "``n_features`` is the total number of pixels in each image.\n",
        "\n",
        "We can then split the data into train and test subsets and fit a support\n",
        "vector classifier on the train samples. The fitted classifier can\n",
        "subsequently be used to predict the value of the digit for the samples\n",
        "in the test subset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# flatten the images\n",
        "n_samples = len(digits.images)\n",
        "data = digits.images.reshape((n_samples, -1))\n",
        "\n",
        "# Create a classifier: a support vector classifier\n",
        "clf = svm.SVC(gamma=0.001)\n",
        "\n",
        "# Split data into 50% train and 50% test subsets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    data, digits.target, test_size=0.5, shuffle=False\n",
        ")\n",
        "\n",
        "# Learn the digits on the train subset\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict the value of the digit on the test subset\n",
        "predicted = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Below we visualize the first 4 test samples and show their predicted\n",
        "digit value in the title.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACXCAYAAAARS4GeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAL5UlEQVR4nO3dX4xjZR3G8eeHw4KyOl2MUXZdpojBCAY6iiJR3Ak3JgiZBoKaqNnWhLiJ/8Ybw4W4TVj8d+NIEDBEqdEEkEg6xERjEKYRIYiy7YUxBOJ0ZBd3g0AnrhB18fXiHLSsM7Oz73tKZ37z/SSTzLR9znnb/vb0mdPuroUQBAAA4NlJo14AAADAsFF4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7bguPmTXNbF/+/SVm9njkdm41s+uKXR02CuYIRWCOkIoZSjfSwmNmPTN70cyOmNnh/AndWvR+Qgi/DiG8Yw3rqZnZg8dk94QQri96Tcvs28xsn5kdNLMlM5s3s/OGvV8PmKNX7Js5isQcrbiOX5lZMLOxV3O/GxEz9Ip9n2Jm3zazp83seTO72cxOHvZ+V7MezvBcEULYKundki6U9JVjb7BJ/qBdLenTki6RdLqkhyX9aKQr2liYowxzlIY5GmBmn5A00hepDYgZylyr7P6/S9I5yh6P/3ssXk3rofBIkkIIByX9XNmDo/w3is+a2ROSnsgvu9zMOmbWN7OHzOz8l/NmNmlmj5nZ38zsLkmnDlw3ZWYHBn7eaWb3mNkzZvasmd1kZu+UdKuki/N23s9v+9/TiPnP15jZk2b2nJnda2bbB64LZrbHzJ7I1/hdM7M1PgRnSXowhPCnEMJLkn4s6dwTfBg3PeaIOSoCcySZ2bikvZK+fIIPH8QMSbpC0o0hhOdCCM9IulHZL2Mjs24Kj5ntlHSZpP0DF1clXSTpXDOblPQDSZ+R9EZJ35N0r2WnzbZIain7TfZ0SXdLumqF/bxG0s8kLUoqS9oh6c4Qwh8l7ZH0cAhhawihtEz2Uklfl/RRSWfk27jzmJtdLum9ks7Pb/fhPHtmPjBnrvAQ3CnpbDM7x7LTfrsl/WKF22IFzBFzVATmSJL0NUm3SDq0ym2wAmYo28Ux3781L9KjEUIY2ZeknqQjkvrKHuibJb02vy5IunTgtrdIuv6Y/OOSdkn6kKSnJdnAdQ9J2pd/PyXpQP79xZKekTS2zHpqyn47HrysObCd70v61sB1WyX9S1J5YM0fHLj+J5KuXeNjsUXSd/JtHJW0IOmsUT4/G+WLOWKOmKPC5+hCSR1JY8peRMNya+SLGVrlsdgn6TeS3iTpLZIeybd3xqien/XwPmI1hHDfCtc9NfD9hKTdZvb5gcu2SNqu7EE8GPJHObe4wjZ3SloMIRyNWOt2SY+9/EMI4YiZPausUffyiwd/G3pB2QCtxVeVteid+TY+Kel+MzsvhPBCxFo3G+Yowxyl2fRzZGYnKXuh/mII4egJvAuGzKafodwNkkrKivM/JN0maVLS4Yh1FmLdvKW1gsEn+ylJN4QQSgNfrwsh3CHpL5J2HPPe4kqn2Z6SdKYt/6Gx4/3X8U8rG1JJkpmdpuxU5MHj3ZE1qEi6K4RwIIRwNITQlLRNfP6iCMwRc1SEzTJHb1B2hucuMzsk6dH88gNmdknitje7zTJDCiG8GEL4XAhhRwjhbZKelfT7EMK/U7cda70XnkG3SdpjZhdZ5jQz+4iZvV7Z30Q5KukLZnaymV0p6X0rbOe3yobpG/k2TjWzD+TXHVb2HuOWFbJ3SKqbWcXMTlH2HvcjIYReAffvUUlXm9mbzewkM/uUsr8d8WQB28b/MEcoguc5WlL2m38l/7osv/w9yt6WQDE8z5DMbIeZbc/v2/slXafsQ/Ajs2EKTwjhd5KukXSTpOeVHcBr+XX/lHRl/vNzkj4m6Z4VtvOSsk+Pv13SnyUdyG8vSfdL+oOkQ2b212Wy9yl70n6qbMDOlvTxtaw//4DXkVU+4PVNSV1lp//6kr4k6aoQQn8t28faMEcoguc5CplDL38p+3yIJB3O7xsK4HmGcmcr+9zR3yX9UNlnf365lm0Pi73yLUIAAAB/NswZHgAAgFgUHgAA4B6FBwAAuEfhAQAA7h3vHx4cySea+/1+Ur5Wq0VnO51OdDZl3fPz89FZSapUKinxYf/LYiOZo2azmZRvNBrR2cXFlf6NsONrtVrR2enp6ehsAYY5Rxvyb1ek/LmuVqvR2dnZ2ehsyvGzAC6PRamvaSnHopTj4NTUVHQ2Zc3ScF7TOMMDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3xoa14X6/H51N+S/pJanb7UZnd+3aFZ1tt9vR2VarFZ2VpEqlkpRfr3q9XnS2Xq8Xt5BX0cLCwqiXgILMzMxEZ8vlcnS2Wq1GZ1G81Ocj5fiecgyt1WrR2U6nE52VhvOaxhkeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4NzasDc/OzkZnu91u0r4feOCB6Gyv14vOttvt6Ozk5GR0FssbHx9Pyi8tLY1k39VqNTqLYqUcx6S0Y9nCwkJ0tlQqRWdRvH6/n5Qvl8vR2VarFZ2dm5uLzlYqlejssHCGBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAe2PD2vDk5GR0dnx8PGnfs7Oz0dlerxednZiYiM5OT09HZz0rl8vR2ZQ5kKR6vZ6Uj9VqtaKzMzMzha3Di/n5+ehso9FI2vfevXujsymzPzc3F53lWFS81GNRs9mMzqa8pqW8Fk9NTUVnh4UzPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcM9CCKtdv+qVw5Ly39lLUq1Wi8622+3o7AUXXBCd7XQ60dkC2JC3P5I5KpfLSfmpqamRZOv1enR2//790VlJqlQqKfFhzlH0DFWr1eidpv65TMm3Wq3obMoMpexXkqanp1PiLo9FG1XKcSzldbiA/LJzxBkeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4NzbqBSynXC4n5fv9fiHrOFHdbjc622w2k/Zdq9WS8utVynO5uLiYtO+ZmZnobKVSic7W6/Xo7Pz8fHRWSlv3MKXMwdzcXHR2YmIiOitJ1Wo1Ottut5P2HSv1PnvVaDSis6VSKWnfKceiFJ1OJzq7bdu24hZSEM7wAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAvbFRL2AYut3uqJdwwvr9/qiXsC6VSqXo7O7du5P23Wg0kvKxxsfHo7NTU1PFLWQdGdUc9Hq96Kwklcvl6Gy73Y7OptznSqUSnfVsZmYmOlutVpP23el0orO1Wi06u7S0FJ2dmJiIzg4LZ3gAAIB7FB4AAOAehQcAALhH4QEAAO5ReAAAgHsUHgAA4B6FBwAAuEfhAQAA7lF4AACAexQeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALhnIYTVrl/1yvWqWq1GZ3u9XnS2VCpFZ1utVnQ2dd+SLGnnxzeSOep0Okn5lDlaXFyMzt5+++3R2VqtFp0twDDnaEMei5rNZnS2Xq9HZxcWFqKz5XI5OlsAl8eiVJVKJTrb7Xajs3v37o3ONhqN6GwBlp0jzvAAAAD3KDwAAMA9Cg8AAHCPwgMAANyj8AAAAPcoPAAAwD0KDwAAcI/CAwAA3KPwAAAA9yg8AADAPQoPAABwj8IDAADco/AAAAD3KDwAAMA9CyGMeg0AAABDxRkeAADgHoUHAAC4R+EBAADuUXgAAIB7FB4AAOAehQcAALj3H7xpoGkHOBS+AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 720x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n",
        "for ax, image, prediction in zip(axes, X_test, predicted):\n",
        "    ax.set_axis_off()\n",
        "    image = image.reshape(8, 8)\n",
        "    ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
        "    ax.set_title(f\"Prediction: {prediction}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":func:`~sklearn.metrics.classification_report` builds a text report showing\n",
        "the main classification metrics.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification report for classifier SVC(gamma=0.001):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        88\n",
            "           1       0.99      0.97      0.98        91\n",
            "           2       0.99      0.99      0.99        86\n",
            "           3       0.98      0.87      0.92        91\n",
            "           4       0.99      0.96      0.97        92\n",
            "           5       0.95      0.97      0.96        91\n",
            "           6       0.99      0.99      0.99        91\n",
            "           7       0.96      0.99      0.97        89\n",
            "           8       0.94      1.00      0.97        88\n",
            "           9       0.93      0.98      0.95        92\n",
            "\n",
            "    accuracy                           0.97       899\n",
            "   macro avg       0.97      0.97      0.97       899\n",
            "weighted avg       0.97      0.97      0.97       899\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\n",
        "    f\"Classification report for classifier {clf}:\\n\"\n",
        "    f\"{metrics.classification_report(y_test, predicted)}\\n\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can also plot a `confusion matrix <confusion_matrix>` of the\n",
        "true digit values and the predicted digit values.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            "[[87  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 88  1  0  0  0  0  0  1  1]\n",
            " [ 0  0 85  1  0  0  0  0  0  0]\n",
            " [ 0  0  0 79  0  3  0  4  5  0]\n",
            " [ 0  0  0  0 88  0  0  0  0  4]\n",
            " [ 0  0  0  0  0 88  1  0  0  2]\n",
            " [ 0  1  0  0  0  0 90  0  0  0]\n",
            " [ 0  0  0  0  0  1  0 88  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 88  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 90]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n",
        "disp.figure_.suptitle(\"Confusion Matrix\")\n",
        "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If the results from evaluating a classifier are stored in the form of a\n",
        "`confusion matrix <confusion_matrix>` and not in terms of `y_true` and\n",
        "`y_pred`, one can still build a :func:`~sklearn.metrics.classification_report`\n",
        "as follows:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification report rebuilt from confusion matrix:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        88\n",
            "           1       0.99      0.97      0.98        91\n",
            "           2       0.99      0.99      0.99        86\n",
            "           3       0.98      0.87      0.92        91\n",
            "           4       0.99      0.96      0.97        92\n",
            "           5       0.95      0.97      0.96        91\n",
            "           6       0.99      0.99      0.99        91\n",
            "           7       0.96      0.99      0.97        89\n",
            "           8       0.94      1.00      0.97        88\n",
            "           9       0.93      0.98      0.95        92\n",
            "\n",
            "    accuracy                           0.97       899\n",
            "   macro avg       0.97      0.97      0.97       899\n",
            "weighted avg       0.97      0.97      0.97       899\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# The ground truth and predicted lists\n",
        "y_true = []\n",
        "y_pred = []\n",
        "cm = disp.confusion_matrix\n",
        "\n",
        "# For each cell in the confusion matrix, add the corresponding ground truths\n",
        "# and predictions to the lists\n",
        "for gt in range(len(cm)):\n",
        "    for pred in range(len(cm)):\n",
        "        y_true += [gt] * cm[gt][pred]\n",
        "        y_pred += [pred] * cm[gt][pred]\n",
        "\n",
        "print(\n",
        "    \"Classification report rebuilt from confusion matrix:\\n\"\n",
        "    f\"{metrics.classification_report(y_true, y_pred)}\\n\"\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Ajout d'étapes \n",
        "On souhaite, à partir de l'exemple précédent, ajouter des étapes dans un problème de machine learning. \n",
        "- Choix du bon model (recherche de noyau, et valeur de C)\n",
        "- Interprétation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'C': 4, 'coef0': 0, 'degree': 1, 'kernel': 'rbf'}\n",
            "Confusion matrix:\n",
            "[[87  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 87  1  0  0  0  0  0  2  1]\n",
            " [ 0  0 85  1  0  0  0  0  0  0]\n",
            " [ 0  0  0 82  0  3  0  2  4  0]\n",
            " [ 0  0  0  0 88  0  0  0  0  4]\n",
            " [ 0  0  0  0  0 87  1  0  0  3]\n",
            " [ 0  1  0  0  0  0 90  0  0  0]\n",
            " [ 0  0  0  0  0  1  0 88  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 88  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 90]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "# Flattening des données\n",
        "n_samples = len(digits.images)\n",
        "data = digits.images.reshape((n_samples, -1))\n",
        "\n",
        "# SVM classifier\n",
        "classifier = svm.SVC(gamma=0.001)\n",
        "\n",
        "# Recherche des meilleurs paramètres\n",
        "parameters = {'kernel':('linear','poly', 'rbf', 'sigmoid'), 'C':[0.1, 0.5, 1, 3, 4, 5, 10], 'degree':[1,2,3,5], 'coef0':[0, 0.1, 0.5, 1, 10]}\n",
        "classifier = GridSearchCV(classifier, parameters)\n",
        "\n",
        "# Split data\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    data, digits.target, test_size=0.5, shuffle=False\n",
        ")\n",
        "\n",
        "classifier.fit(X_train, y_train)\n",
        "print(classifier.best_params_)\n",
        "\n",
        "predicted = classifier.predict(X_test)\n",
        "\n",
        "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n",
        "disp.figure_.suptitle(\"Confusion Matrix\")\n",
        "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On peut comprendre pourquoi le kernel n'est pas de type linéaire, polynomial ou sigmoid, il est donc de kernel rbf (gaussien).  Le coefficient C est de 4, permettant de ne pas trop rigide avec la classification (0 -> rigide, +inf -> flexible)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row 32 has been classified as  9 and should be  5\n",
            "Row 53 has been classified as  9 and should be  5\n",
            "Row 120 has been classified as  9 and should be  5\n",
            "Row 197 has been classified as  9 and should be  4\n",
            "Row 215 has been classified as  5 and should be  7\n",
            "Row 220 has been classified as  7 and should be  3\n",
            "Row 280 has been classified as  8 and should be  1\n",
            "Row 344 has been classified as  2 and should be  1\n",
            "Row 366 has been classified as  8 and should be  1\n",
            "Row 390 has been classified as  9 and should be  1\n",
            "Row 463 has been classified as  6 and should be  5\n",
            "Row 466 has been classified as  3 and should be  2\n",
            "Row 653 has been classified as  1 and should be  6\n",
            "Row 675 has been classified as  4 and should be  0\n",
            "Row 704 has been classified as  8 and should be  3\n",
            "Row 707 has been classified as  7 and should be  3\n",
            "Row 713 has been classified as  9 and should be  4\n",
            "Row 730 has been classified as  9 and should be  4\n",
            "Row 760 has been classified as  3 and should be  9\n",
            "Row 762 has been classified as  9 and should be  4\n",
            "Row 764 has been classified as  5 and should be  9\n",
            "Row 792 has been classified as  5 and should be  3\n",
            "Row 828 has been classified as  8 and should be  3\n",
            "Row 829 has been classified as  8 and should be  3\n",
            "Row 831 has been classified as  5 and should be  3\n",
            "Row 832 has been classified as  8 and should be  3\n",
            "Row 867 has been classified as  5 and should be  3\n"
          ]
        },
        {
          "data": {
            "image/png": 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1gENtlphahFrSOZJekLRB0tXdrqcTJB0u6QlJayU9L+nKbtfUSZLGSFot6YFu19JJkiZKukfSeknrJJ3c7Zqa1fVz6nyCgJ+SXS6pH3gWWBARa7taWJskHQYcFhGrJE0AVgLzR/t+DZD0ZWAO8J6IOK/b9XSKpDuAH0bELfkVdA+OiNe6XFZT6tBTzwU2RMTGiNgL3A1c0OWa2hYRWyNiVX5/F7AOmNbdqjpD0nTgY8At3a6lkyQdApwG3AoQEXtHW6ChHqGeBmxueNxPIj/8AyT1ACcCT3e5lE65AbgKeLPLdXTaEcB24Pb81OKW/KKbo0odQp00SeOB7wK9EbGz2/W0S9J5wLaIWNntWkowFjgJuCkiTgR2A6NujKcOod4CHN7weHr+3Kgn6UCyQC+NiFQur3wKcL6kPrJTpTMl3dndkjqmH+iPiIEjqnvIQj6q1CHUzwLHSDoiH5i4ELi/yzW1TZLIzs3WRcT13a6nUyLimoiYHhE9ZN+rxyPi4i6X1RER8TKwWdLM/KmzgFE3sFnout9lioh9kr4IPAyMAW6LiOe7XFYnnAJ8GviJpDX5c9dGxIPdK8kKuAJYmncwG4FLulxP07r+Jy0z66w6HH6bWQc51GaJcajNEuNQmyXGoTZLjENtlhiH2iwx/w9A2318bMOR3gAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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96k4+/TZLjENtlphuCvVXO11AhVLdN+9XF+qaa2ozK0c39dRmVgKH2iwxXRFqSWdKelrSekmXdrqeMkiaLul+SWslPSlpcadrKpOkcZJWS7qr07WUSdJkSbdKekrSOkmndrqmZnX8mjqfIOBHZMMlDQCPAhdExNqOFtYmSccAx0TEKkkTgZXAgrG+X4MkfRLoBd4QEed0up6ySPoG8EBEXJuPoDshIrZ1uKymdENPPQdYHxEbImIPcDPw3g7X1LaI2BwRq/LHO4B1wNTOVlUOSdOAs4FrO11LmSRNAs4ArgOIiD1jLdDQHaGeCmxseD5AIr/8gyT1ALOBRzpcSlmuAi4BXu9wHWU7DtgKfD2/tLg2H3RzTOmGUCdN0hHAbcCSiHi10/W0S9I5wJaIWNnpWipwEHAKcHVEzAZ2AWPuHk83hHoTML3h+bT8tTFP0sFkgb4hIlIZXvk04DxJ/WSXSvMkfbOzJZVmABiIiMEzqlvJQj6mdEOoHwVOkHRcfmPifODODtfUNkkiuzZbFxFf6nQ9ZYmIyyJiWkT0kP1b3RcRH+hwWaWIiBeAjZJm5i/NB8bcjc1C435XKSL2Svo48B1gHHB9RDzZ4bLKcBrwQeBxSWvy1y6PiLs7V5IV8AnghryD2QAs6nA9Tev4R1pmVq5uOP02sxI51GaJcajNEuNQmyXGoTZLjENtlhiH2iwx/w/eQK6EmFWZVgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "count = 0\n",
        "for row_index, (input, prediction, label) in enumerate(zip (X_test, predicted, y_test)):\n",
        "  if prediction != label:\n",
        "    print('Row', row_index, 'has been classified as ', prediction, 'and should be ', label)\n",
        "    count = count +1\n",
        "    if count <= 5:\n",
        "      plt.figure()\n",
        "      input = input.reshape(8, 8)\n",
        "      plt.imshow(input, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
        "      plt.title(f\"Target: {label} - Prediction: {prediction}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On peut observer que le modèle reste affecté par des niveaux de gris reliés entre eux.\n",
        "\n",
        "## Conclusion\n",
        "Grace à ce TP, nous avons pu découvrir les méthodes de classification avec noyaux et la manière d'optimiser les paramètres du modèle avec la fonction `GridSearchCV`"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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",
<<<<<<< HEAD
      "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
=======
      "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
>>>>>>> 0d48cc505a5d44d994fe9539c0d65ab13c368b9e
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    "vscode": {
      "interpreter": {
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  "nbformat": 4,
  "nbformat_minor": 0
}