20 - Sklearn (les Bases).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled16.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPrsB1/Z8APdONadrKuKH9L",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MachineLearnia/Python-Machine-Learning/blob/master/20%20-%20Sklearn%20(les%20Bases).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFK37edpc2z0",
        "colab_type": "text"
      },
      "source": [
        "# 20/30 Apprentissage Supervisé avec Sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCAljzPhcdpO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "5edfade1-9342-4ba5-8975-f4ddc92d75a1"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbURdA5ZdZd5",
        "colab_type": "text"
      },
      "source": [
        "##1. Régression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ShWbEJqdYKU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "13ccaae7-16c7-4bf8-ac4c-def9e08a9bee"
      },
      "source": [
        "np.random.seed(0)\n",
        "m = 100 # creation de 100 échantillons\n",
        "X = np.linspace(0, 10, m).reshape(m,1)\n",
        "y = X + np.random.randn(m, 1)\n",
        "\n",
        "plt.scatter(X, y)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fd2009afdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "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": "WFkeFbj-doir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4yCQzEqdudw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4dddea23-4be0-47cc-d9f2-d630a8d357a7"
      },
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X, y) # entrainement du modele\n",
        "model.score(X, y) # évaluation avec le coefficient de corrélation"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8881140743377214"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8J9kM8vd5X_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "1a1590f3-e520-4f7a-d6e7-c28d9e64b99e"
      },
      "source": [
        "plt.scatter(X, y)\n",
        "plt.plot(X, model.predict(X), c='red')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1f10e9438>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/vhRzo6lk1QzCy9ZUbFnArBZLb/G6biSxVLOIArhIoISmEPb79gt6vz+cExfPgGbN4L77oEcPaNgw5WOpGYT9rhjJl37eyVIZoUiAVJfgtVq9jGunjKDTgsms3WZbnj/mXP46ZkjURlMSTCojFMkB/dpuy/qb76bz3Lf5tX4D/rf9OTzX4U/ceEEHBe88pAAugZK3mztWroQBAzj50UfZhDGmfTmDDvkjDUqac2O+/DuQrSiAS2Dk5eaO1avhn/+EBx6ADRvg0kspuPVWztl9d87J9Ngk4xTAJTDyqlXp2rVw//1wzz2wbh2cfz7cfjvsvXett6XjiSRvn3oCQAFcAiNomzsSCnwVFfDQQzBwIHz/Pfzxj3DHHd5uyjCfn+onkrx86gmQpHZimlmxmb1oZp+Z2QIza+/XwERCxbIFO1tEPWcy1IYN8PDDsNde0KePt4vygw9g7NiwwRvSs91cW9qzW7Iz8CHA6865P5lZA6CRD2MSCStbN3eEm2nHnO7ZtAmGD/fSI19+CX/4A7zwAhx9dNTrxvpEkkwKJGhPPfkm4Rm4me0IHA08AeCc2+CcW+PXwERClbcr4a4ubSkpLkq6l4dfIs20w20DhxqBb/NmePFFb3Z9ySXQpAm89hpMnhxT8IbYnkjifhJI4BqSOcmkUFoBq4CnzGy2mT1uZtuGvsnMupvZTDObuWrVqiQuJ+IF8al9j+OLgaczte9xGc/DRpppF5iFfX/zHRvChAlQWgpnnw316sGYMTBzJpxyCkT4uXBiaQqVbAoklmtI5iQTwOsDhwCPOOfaAT8BfUPf5Jwb5pwrdc6VNmvWLInLiWSfSKmETc5tFfiOXvYp40f1hTPOgB9/hGefhblzoUuXLYE7aqfBGmJ5Ikk2BZKNTz3ym2Ry4EuBpc65GVXfv0iYAC6SyyJ1zQPYpn49GhbWo+XiT7hp2ggOWzwLmjeHoUPh0ku3ajSVSMVHtKZQfnT186uDofgv4Rm4c24l8LWZVT9LHQ/M92VUIgERLsVQ7Xdf/ZfBI27jped6c9iaJXDvvbB4Mfz1r2G7BKai4kMpkNyWbBXK1cCIqgqUz4FuyQ9JJDhC26oCtFy9jGunjOTMBZNZv00jhp3Yje5jhsD222/5uXCVIamo+FBXv9ymboSS8+Ito0u07K7siqe4euoL/Gne22yoX8hTh3bi0SPOYl3D7WodGhzuQOCiwgIaFtbjh58rt/rc0MN+Jf+oG6HkpXjzygntPFy5Ev7xD94dNhSH49lDzuDh9mfz3baNga0PDY6UKtmmfj2KCgu2Oul92ZoKygZO1MxZtqIALjkt3v4psb5/3OxlDH3pQzq/OYJLZr3CNpsrWd7pHC7d7RQ+b7TTlveFyzdHSomsqaikuKhwy0y85ik32sIu4SiAS06LN68cy+uvTFnIklsGMHraGLbbUMH4/Y9maMeL6PHnU7iG6PnmuipX1lRUUlRYQONGhVulU3K2cZckTAFcclq8ZXR1vr+iAh5+mLLb7uSMn37kjdZHcu9RF7KwWUvAC9yxbC6KdiBwReWmiP9MW9ilJgVwyWnx9k8J9/4d6m3mobUzYO+LYPly5rVsx+CzLmLurvvU+tm6gmvowuhZh5bw7merIs7EI9EWdqlJAVxyWrxldDXfv3L1erp9MZVrp73AtsuWQFkZjBzJjdM2xTWrD7cwOuajZdzVpW2t8sOaiosK+XXj5qxr3CXZRQFccl68OwnLD9qV8v+bBv36wYIFcMgh8NjQLb1K+uwQvgwwUnCta2E00hPC7Z3abPlZ1W9LJArgItWc8zoC3nILzJ4Nv/+91zGwRq8SiH9WX9fCaLTPiidg6+Sc/KMALgIwaRLcfDNMnQp77uk1mjr/fCgIv00+nll9tIVUP3qN6OSc/JTUiTwisYqny15affABnHgidOwIX3wBjzwCn30GF10UMXjHKx39SHRyTn7SDFxSpvqRftmaiuzblDJ3Ltx6K4wfD02beocHX345FPlf5ZGOfiQ6OSc/KYBLSoQ+0od23MnYppT//hduuw1GjfKaS91xB/TqVavRVCqkuiWrH21jJXiUQpGUCPdIHyqts8OvvoLLLoP99/dm3TfcwISXp1JWWEarAZMjpnWyNvUTQm1j85Nm4Dki2yoQYgnOaZkdVjWa4tFHve+vvBJuvJFxKzZFXfQL0sKg2sbmJwXwHJCNgaaufh+Qhtnh6tUwaBA88AD8+it06+blvFu0AGDQ0xOjNq2KtxFWKsVyg9bJOflHKZQckI0VCOEe6asrqVN6ruK6dV5eu1UruPtuKC/3NuM89tiW4A2xLfply8JgsifLS+7SDDwHZEugqSntj/RVjaYYOBC++84L3HfcAW3bhn17LIt+2bIwmE1PApJdFMBzQLKBJlX587Q80m/YAE88Af37w/LlcNJJ3teHHVbnj8XS5CreRlipko03aMkOCuA5IJFAk9U12rHYtAmGD4e//93bgFPVaIpjjonpx2N5QsiWhcFseRKQ7KMzMXNEPLPocGcyhsracxg3b4axY2s3murff0ujqVwU6QzNlK0jSNbRmZg5Lp50RdbVaMcixkZTuShbngQk+yiA56GsqdGO1XvveYF76lSvuiRKo6lExPIEk8lae5UISjgK4Hko4zXasfrgA69D4NtvQ/PmXqOpSy+FBg3Cvj3RABtLHX021tqLqA48D2WsRjtWc+dC585wxBEwZ47XaGrxYujRo87gnWitdCx19NlYay+S9AzczAqAmcAy59wZyQ9JUi1rc6qLFnmNpl54AXbYAe68E3r2jKnRVDK10kHa1CNSkx8plJ7AAmAHHz5L0iSrcqpLlnibbp5+GrbZBvr2heuugyZN6vyxmimTSLVUseb7g7KpR6SmpFIoZrYbcDrwuD/DkbyyciVccw20bg3PPec1mvr8c6/5VAzBu2bKJJJYAmwsnfzU7U+yUbIz8PuB64GIz7hm1h3oDtCiRi8KyWNRGk3FIpZSyFgDbJA29YjUlPBGHjM7AzjNOXeFmXUErouWA9dGnjy3bh0MGeIF73XroGtXbydl69Zxf1SrvhMizrwNFGAlp6RiI08Z0MnMTgMaAjuY2XDn3IVJfKYEXNhSvv2aeCWAd90VU6OpWETKSWftDlKRFEg4B+6cu9E5t5tzriXQFZio4J3fQvPS336/ljk3D6Rij1bQuze0awczZsBLL20J3omeeKOctIg28oiPqvPS9TZvonz+e/SaMpIWP37D3D3acOB7o7ZqNJXM5hjlpEV8CuDOufeA9/z4LMke8e5sXPHDT5y68D/8bcoIWn//NZ/ssheXnNiDSXuW8kWYLoHJ9rnOqlJIkQzQDFzCimt27By8/jqvDu/NfssXsWin3bm8c19e37cDzupRElLKV7OVbTjaHCMSGwVwCSvm2fGkSV6/kqlT2b2kBX079Wb0vkezuZ6Xnw7NS8fSytaPzTHZdsizSCqoF4qEFXXr+IcfeqffdOzoHajwyCNs+/kijrz9WnZtsh1G+L4q0eq3/ViI1BmSki80A5ewIpXp/eGXFV4Z4Msvw047weDBcMUVUOTNmqPlpetKj5T4NFOO1nhKM3PJFQrgUkuko9b2+GE5100dyRnzJ3nNpe64A3r1iqnRVE3pqN+OdJOonomrJazkCgXwNAhKPjY0P+2A5mtXcdV/XuCceW9Bg22wG26APn2i9iqJJB0HBUe6SRSY6XR3ySkK4Cnm90EAqbwZ1Ew9NP3pB66cNprz57yGYdS/8kq46Sb43e+SGlM66rcj3SQi5d5V9SJBpQCeYsnWOteU6lNhlq+pYIdf1vPXGWPo9tF4Gmys5MW2J/BgWVemPtDNtzFFypP7dXOKdJOIVLqolrASVArgKebnQQB+3gy2sm4dN84aQ9fJo9nh158Y//ujue8PF/BFk5Kt6rhTMSa/b06RbhKpTt+IpJMCeIr5eRBASk6FqajY0miq+3ff8c4+RzKo7AI+27kVED3A+TWmlN6cqmj7veQaBfAU83PRztdTYSor4cknvWPLli2DE06A/v1Z12A31r2xEIsxwPk1pnQdWabt95JLFMBTzI9ZX6TSPkjgZrBpE4wcCbff7p1+06EDDB/ubcgByokvZeHXDUpHlonEL+EDHRKhAx3iF27reXUQj2vji3Mwdiz06wfz53utXfv3h1NPBbPoPx9ljMmmJXz7PUVyUCoOdJA0CJcbrg5qsWx8GTdrKe8/+ByXvPYEbb/5P9a13JvtR4+Gs86Cev50UvAjLVHzSSX0SUMbbkTCUy+UGCV68ECykskNv//4i+ze+WTuefomin9ZT+/TrqX9+fczbu8OvgVvP5W3K2Fq3+MoKS7a6ri0mlvhRcSTff8vTkCqg2smmyNFygHXmRuuajR11F/OpmTNSm456QqO+8tQxrQ9nvWboNeoOWm9CcWrrq3w2TxukXQLfABPR3CN1hwpleI6OmzePK/R1OGHw6xZDDj2Uo7p/hjD251GZUFhrbdmc4e+um5O2TxukXQLfABPR3BNV4lbOOXtSrirS1tKiositmhl8WK44AI46CB4912v0dQXX/DqSRfwa+E2ET87W9MS4W5aNWXruEXSLfCLmOkIrpkucYu4SLhkiVfH/dRT0KABXH+996eq0VS4Er9Q8fx7SldTrtAFzXDUv0QkB2bgkYKoA9/ypVl3Avo330DPntC6NTzzDFx+uVfTPXBgrS6BNWfvkcR6E0r3OkDNBc1wVB8ukgMBvK7Hbb+CTExpjHT44QevI+Cee8JDD8FFF8GiRfDgg2G7BFaPfWrf47j/3IOTugllah0g626eIlkk8CmUaI/bfvXTSNUW7JjSEuvWwZAh3uk3P/4IXbvC3/8O++wT83WS3RGaqXUA9S8RiSzwARx+C66t+k7Yqn4YsjdfGrUDX41GU3z3HXTq5OW8DzwwoeslcxPK5DqA+peIhJdwCsXMdjezd81svpl9amY9/RxYIhKqmc6gSGmJ+yZ8AkOHejnu3r3h4INh+nTvHMoEg3eylMoQyT7JzMA3Ar2dc7PMbHvgIzN7yzk336exxS0dx3WBf9UYoU8G9TZvovP8SVw7ZQT8+A20bw/PPQfHHuvX0BOmVIZI9kk4gDvnVgArqr5eZ2YLgBIgYwE8HUHGz4MHtqQlnOOU//6Hv70/gn2+X8LC5nvDyCd9aTTlJ6UyRLKLLzlwM2sJtANm+PF5yUh1kIlWjVF989ixqBAzWPNzZcQbSZ+T9mHC4Ke5+t1nOHDlYhY32Y1eXW6i40092PfQ3VP2O4hIbki6nayZbQdMAgY458aG+efdge4ALVq0OPSrr76K6/Oz7UT3SAulUPfBuUWFBbVLDydPhptvhilTWN74d9zT/jw+KDuV3qfur1muiNQSqZ1sUgHczAqBV4A3nHP3Rnt/vP3Aw/WI3ioQplnZwIlhqzEKzNgU5d9lSXERU4/fHm65Bd58E5o3h1tvhUsv9XZSioiEESmAJ1OFYsATwIJYgnciMtlEKpJI1RjRgvc+q76k35M3e42mPvrIq+levBh69FDwFpGEJJMDLwMuAuaZ2Zyq125yzr2a/LA8mWwiFUmkhdJIG4n2+GE5vaaMpPP8Sfy8TSOv0VSvXrD99kmPJdvSSyKSXslUoUzBO/UqZfzePOJXwIu0UFoz3bPr2lVc/Z8XOGfuW1QWFPJEhz/RfEA/Tu94QEJjD+VnNYyIBFNW78T0s6471QGv+jMeGzOds15/lgvnvAbA8ENOZ8zJ/8Nlf2rP6e1KfLuJ1JVeUgAXyQ9ZHcD9rOtOVcCrDsjrV66i98cv8/L0cdSv3AAXXwz9+nHJHntwSY33+nUTycb0koikV1YHcPCvrjsVAW/c7GXc+fwMzpv+Et0/eIkdfv2JCfsfQ4MBd3Ji+VFbvd/Pm0ime5Qr/y6SeVkfwP0Sa8CLOTD98gtLbh3AG++OpOnPP/LW3kdwz1EX8tnOrSj5rJITw4zBz5tIutoGhKP8u0h2CHw/8FjF0owppkMLKivh0Udh7725ZsIjLGjWivKL7uEvZ93KZzu3AiIHZD+bbWWyR3k2lneK5KPAzsDjfYSPJZ8eKTD1GjWHe16bz32bF1D61BDv9Jv27bn61Gv59077bRows28AAAfJSURBVHWtSAHZ71lzpnqTKP8ukh0CGcATfYSPFvDCBqCQRlNr9m1D8SuvwGmncfyc5bwdR0DOlY5+mc6/i4gnkAE8VRUltQKTcxzzxSx6v//clkZTV3Tuy9zDj2fK6ScAiQXkum4ifi0MpnqBMZP5dxH5TSADeKoe4asDU9vPP+a6yc9y+NL5fL3jLlx3Wi9eanMsm+oVYGt/rfUzfqUx/FoYTMcCY648SYgEXSADeKoe4cs3raD92wPZZfokvtmuCbeceDmjDjqJyoJC364RiV9PFena4KPe4CKZF8gqFN+P9/r0U+jSBQ47jF0WfQKDBjHjzRmMOaJTreCdyjSBX08VWmAUyR+BnIH79gi/eDHcfjuMHAnbbed9fe21sMMOdAI2NyxKW5rAr6cKLTCK5I+kD3SIR7z9wFPm66+9092ffNJr5Xr11XD99bDTThkbkl+9z7Oxh7qIJCdSP/BAzsAT9u23cNdd8MgjsHkzXH453HQT41ZuZtBjH2d0Qc6vpwotMIrkj/yYgf/wg3eAwpAhUFEBl1wC/frBHntoxioiWc/3E3kCYf16GDAA9twT/vEPOPNMWLAAnniCcavrUzZwIr1GzdG2cBEJpEClUOJpNMXQoV7QXrXKC9x33gkHHbTlc0Jn3aFUtSEi2S4wM/CYG00NGwZ77+1Vkxx4IEybBuPHbwneEL5WOpSqNkQk2wUmgNfZAW/TJhg+HH7/e/jrX6FFC3jnHXj7bTjyyK0+K9rsWtvCRSQIApNCidRoqu2Md+DAq2H+fDj4YKhqNIVFPq4zUq00eG1ZVbUhIkEQmABeV6Mp9tsPRo+Gs86CetEfKiI1Y1LliYgESWBSKNXb5w/7+hNGjezLM/+6jSYV65h1+70wbx6cfXZMwRsyexiCiIhfAjMDL9+0gvbvDGSXaZP4dtvGDO50Da1v7kXnw1sl9nlqxiQiAReMAN6nDwwezC5NmsA//8nOV17JdY0aZXpUIiIZlVQAN7NTgCFAAfC4c26gL6MKddRRXrOpqkZTIiKSRAA3swLgIeBEYCnwoZmNd87N92twW3Tq5P0REZEtklnEPBxY7Jz73Dm3AXgB6OzPsEREJJpkAngJ8HWN75dWvVaLmXU3s5lmNnPVqlVJXE5ERGpKeRmhc26Yc67UOVfarFmzVF9ORCRvJBPAlwG71/h+t6rXREQkDZIJ4B8Crc2slZk1ALoC4/0ZloiIRJNwFYpzbqOZXQW8gVdG+KRz7lPfRiYiInVKqg7cOfcq8KpPY0mJmHuIi4gETDB2YiYo9OCG6h7iQNYHcd14RCSawDSzSkSdPcSzWEyHV4hI3svpAB7p4IZsPy4tqDceEUmvnA7gkY5Fy/bj0oJ64xGR9MrpAF7dQ7ymIByXFtQbj4ikV04H8KAe3BDUG4+IpFdOV6FAMA9uqB6vqlBEpC45H8CDKog3HhFJr5xOoYiI5DIFcBGRgFIAFxEJKAVwEZGAUgAXEQkoc86l72Jmq4CvEvzxpsB3Pg4nCPQ75wf9zvkhmd95D+fcVkeapTWAJ8PMZjrnSjM9jnTS75wf9Dvnh1T8zkqhiIgElAK4iEhABSmAD8v0ADJAv3N+0O+cH3z/nQOTAxcRkdqCNAMXEZEaFMBFRAIqEAHczE4xs4VmttjM+mZ6PKlmZrub2btmNt/MPjWznpkeUzqYWYGZzTazVzI9lnQws2Ize9HMPjOzBWbWPtNjSjUzu7bqv+lPzOx5M2uY6TH5zcyeNLNvzeyTGq81MbO3zGxR1d+N/bhW1gdwMysAHgJOBfYHzjOz/TM7qpTbCPR2zu0PHAlcmQe/M0BPYEGmB5FGQ4DXnXP7AQeR47+7mZUA1wClzrkDgAKga2ZHlRJPA6eEvNYXeMc51xp4p+r7pGV9AAcOBxY75z53zm0AXgA6Z3hMKeWcW+Gcm1X19Tq8/2PndHNwM9sNOB14PNNjSQcz2xE4GngCwDm3wTm3JrOjSov6QJGZ1QcaAcszPB7fOecmA6tDXu4MPFP19TNAuR/XCkIALwG+rvH9UnI8mNVkZi2BdsCMzI4k5e4Hrgc2Z3ogadIKWAU8VZU2etzMts30oFLJObcMGAwsAVYAPzrn3szsqNJmF+fciqqvVwK7+PGhQQjgecvMtgPGAL2cc2szPZ5UMbMzgG+dcx9leixpVB84BHjEOdcO+AmfHquzVVXetzPezas5sK2ZXZjZUaWf82q3fanfDkIAXwbsXuP73apey2lmVogXvEc458ZmejwpVgZ0MrMv8VJkx5nZ8MwOKeWWAkudc9VPVi/iBfRcdgLwhXNulXOuEhgLdMjwmNLlGzPbFaDq72/9+NAgBPAPgdZm1srMGuAteozP8JhSyswMLze6wDl3b6bHk2rOuRudc7s551ri/e870TmX0zMz59xK4Gsz27fqpeOB+RkcUjosAY40s0ZV/40fT44v3NYwHri46uuLgZf9+NCsP9TYObfRzK4C3sBbtX7SOfdphoeVamXARcA8M5tT9dpNzrlXMzgm8d/VwIiqicnnQLcMjyelnHMzzOxFYBZepdVscnBLvZk9D3QEmprZUuA2YCAw2swuw2upfY4v19JWehGRYApCCkVERMJQABcRCSgFcBGRgFIAFxEJKAVwEZGAUgAXEQkoBXARkYD6f5fkd6mQNoyHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiLDacOweJGj",
        "colab_type": "text"
      },
      "source": [
        "##2. Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO3lTzLNeFjx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d544b300-0bdb-49a8-e9d4-149bc2d31cbf"
      },
      "source": [
        "titanic = sns.load_dataset('titanic')\n",
        "titanic = titanic[['survived', 'pclass', 'sex', 'age']]\n",
        "titanic.dropna(axis=0, inplace=True)\n",
        "titanic['sex'].replace(['male', 'female'], [0, 1], inplace=True)\n",
        "titanic.head()"
      ],
      "execution_count": 8,
      "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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  sex   age\n",
              "0         0       3    0  22.0\n",
              "1         1       1    1  38.0\n",
              "2         1       3    1  26.0\n",
              "3         1       1    1  35.0\n",
              "4         0       3    0  35.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqHeWY4KeA2W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWsleD6DegS7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = KNeighborsClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52gq2y2AekXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = titanic['survived']\n",
        "X = titanic.drop('survived', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-pyGxmles41",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5818300f-596a-410a-cb8f-eada9abec898"
      },
      "source": [
        "model.fit(X, y) # entrainement du modele\n",
        "model.score(X, y) # évaluation"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8319327731092437"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLeWNuYMfdFG",
        "colab_type": "text"
      },
      "source": [
        "##3. Prediction de survie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PenJkj9Metw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def survie(model, pclass=3, sex=0, age=26):\n",
        "  x = np.array([pclass, sex, age]).reshape(1, 3)\n",
        "  print(model.predict(x))\n",
        "  print(model.predict_proba(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqLWXmljfY7N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3a2ad14b-5418-4a4b-f2dd-8d2169cb3f71"
      },
      "source": [
        "survie(model)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n",
            "[[0.6 0.4]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Bv5FnyTfmZi",
        "colab_type": "text"
      },
      "source": [
        "## 4. Exercice et Solution\n",
        "Écrire un code qui permet de trouver la meilleure valeur de voisin n_neighbors pour le modele de KNeighborsClassifier.\n",
        "\n",
        "Dans sklearn, il est possible de faire cela avec la classe GridSearchCV. Mais il peut également etre utile de savoir écrire soi-meme ce genre de code de recherche."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJ0iAJt9f8f2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "d1ff75d1-4176-4723-d14c-8ff887efe610"
      },
      "source": [
        "# SOLUTION\n",
        "score = []\n",
        "best_k = 1\n",
        "best_score = 0\n",
        "\n",
        "for k in range(best_k, 30):\n",
        "    model = KNeighborsClassifier(n_neighbors=k)\n",
        "    model.fit(X, y)\n",
        "    score.append(model.score(X, y))\n",
        "    \n",
        "    if best_score < model.score(X, y):\n",
        "        best_k = k\n",
        "        best_score = model.score(X, y)\n",
        "\n",
        "print(best_k)\n",
        "plt.plot(score)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1eef752b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "mrbIE8rLgWOC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}