svm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "svm.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOaVEEPoFYJXXb4cKAUGF6h"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvGw8LZYM_C-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Libs\n",
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "import scipy.optimize as opt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "%matplotlib inline \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV-r_H6FNGF_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4704716d-b118-456f-cb06-a9dd4cd9476c"
      },
      "source": [
        "#Dataset\n",
        "!wget -O cell_samples.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-18 19:53:01--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20675 (20K) [text/csv]\n",
            "Saving to: ‘cell_samples.csv’\n",
            "\n",
            "\rcell_samples.csv      0%[                    ]       0  --.-KB/s               \rcell_samples.csv    100%[===================>]  20.19K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-03-18 19:53:02 (1.58 MB/s) - ‘cell_samples.csv’ saved [20675/20675]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEq1LebVNOVC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e430e1bf-8f0b-4661-eb2b-783e3564139d"
      },
      "source": [
        "#Load data\n",
        "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
        "cell_df.head()"
      ],
      "execution_count": 3,
      "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>ID</th>\n",
              "      <th>Clump</th>\n",
              "      <th>UnifSize</th>\n",
              "      <th>UnifShape</th>\n",
              "      <th>MargAdh</th>\n",
              "      <th>SingEpiSize</th>\n",
              "      <th>BareNuc</th>\n",
              "      <th>BlandChrom</th>\n",
              "      <th>NormNucl</th>\n",
              "      <th>Mit</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000025</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002945</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1015425</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1016277</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1017023</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        ID  Clump  UnifSize  UnifShape  ...  BlandChrom  NormNucl Mit  Class\n",
              "0  1000025      5         1          1  ...           3         1   1      2\n",
              "1  1002945      5         4          4  ...           3         2   1      2\n",
              "2  1015425      3         1          1  ...           3         1   1      2\n",
              "3  1016277      6         8          8  ...           3         7   1      2\n",
              "4  1017023      4         1          1  ...           3         1   1      2\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hm1uQuEyNX8o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "172c4bf4-d8f7-4805-dff8-4795434934e9"
      },
      "source": [
        "#One-dimension\n",
        "ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
        "cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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4RURiRsEvIhIzkwp+M9PfUouIzHATCn4zu8DMOoB/yt8/z8z+oqSViYhISUx0xP9loAno\nAnD3nwG/W6qiRESkdCY81ePur4xqOl7kWkREJAATDf5XzOwCwM2s3MxuBlJTPaiZnWFmO83sn8ws\nZWbvnuq+ZOIymSx79nSSyWTDLiV00emLDLAnv5WoiML5kUp1sX37AVKprqLve6LB/9+AG4EzgX8B\n6vP3p+orwKPufi5wHtP4JSIT09qaoqZmK+vWfYeamq20tsa3y6PTF61ADbAuv20NqQ4ZKQrnx5Yt\nu6mr28b11z9KXd02tmzZXdT9T2gFLjM7a/RUj5m93d1/OekDmlUB+4Hf9gku/6UVuKYnk8lSU7OV\nnp7+obZEooxDhzbHbtGL6PRFhlzY94xoSwCHgOoA65CRonB+pFJd1NVtO6G9o2PjpBdmGWsFromO\n+F82s1YzS4xo++GkKhi2lNxZv83M9pnZN8zstNEPMrPNZtZuZu2ZjF4GT0c63U1FReFTXV4+h3S6\nO6SKwhOdvkgDFaPayvPtEpYonB9tbZ2Tap+KiQb/c8CTwNNm9o58m03xmGXAu4Cvu/sq4HXg1tEP\ncvet7t7g7g3V1RoBTUcyWUVv70BBW1/fAMlkVUgVhSc6fZEEeke19eXbJSxROD8aGxdPqn0qJhr8\n7u5/AWwBfmBm7wOmukr7EeCIu/8kf38nuV8EUiLV1ZW0tDSRSJSxYEEFiUQZLS1NsZvmgSj1RTXQ\nQm56Z0F+24KmecIVhfOjtnYhzc31BW3NzfVFXX93onP8+/Kjc8xsMfBtYLW7T6k3zOxJ4MPu/ryZ\n3Q6c5u6fHOvxmuMvjkwmSzrdTTJZFcvQHyk6fZEhN72TRKEfHVE4P1KpLtraOmlsXDzl0B9rjn+i\nwb/Y3TtH3C8DLnD3f5hiMfXAN8hNcv4zsNHdXxvr8Qp+EZHJGyv4y8b5puvc/ZvABrOTTulPKfjd\nfT9wQjEiIlJ6pwx+YPDTNvNLXYiIiATjlMHv7v8nv70jmHJERKTUTvmpHjO7wczOzt82M7vXzLrN\n7FkzWxVMiSIiUkzjfZzzYwz/RckGcpdX+G3gJuDPS1eWiIiUynjB3+/uffnb64G/cvcud9/N8Py/\niIjMIOMF/4CZLTazecClwMgrBSXG+B4REYmw8T7V8xmgHZgLPOzuBwHM7CJyn78XEZEZZrzgryR3\nCcF3uvtPR7S3A/+5ZFWJiEjJjDfV82l37yf3V7ZD3P11d/916coSEZFSGW/E32Vmu4ClZvbw6H90\n9ytLU5aIiJTKeMF/BbkrZ/418L9KX46IiJTaeH+52wv82MwucHethiIiMguMd5G2u93948C9ZnbC\nZTw11SMiMvOMN9Xz1/ntF0tdyGwWhWt7R6mOKFBfFFJ/FCrGtfCjXMN4Uz1789v/W9Sjxkhra4pN\nmx6jomIOvb0DtLQ0sWFDbWzriAL1RSH1R6EtW3Zzzz37h+43N9fz1a9eNqtqmOhCLGuB28l9pr+M\n3Hq77u6/XbRKTmGmLsSSyWSpqdlKT0//UFsiUcahQ5sDHVVFpY4oUF8UUn8USqW6qKvbdkJ7R8fG\nwEb+xaxhrIVYJrrmbgvwJeBC4Hxyi6icP6kKYiid7qaiorCLy8vnkE53x7KOKFBfFFJ/FGpr65xU\n+0ytYbw5/kHd7v63RTtqTCSTVfT2DhS09fUNkExWxbKOKFBfFFJ/FGpsXDyp9plaw0RH/E+Y2RfM\n7N1m9q7Br6JVMUtVV1fS0tJEIlHGggUVJBJltLQ0Bf4SOip1RIH6opD6o1Bt7UKam+sL2pqb6wN9\ngzeIGiY6x/9E/ubggwfn+N9TtEpOYabO8Q+KyicmolJHFKgvCqk/Cs2WT/WMNcd/yuA3s5sGb+a3\nDmSAp9z95SlVMgUzPfhFRMIw1Td35+e/Ts9/zSf3xu7fmtkHi16liIiU3Hif4z/pIutm9hZyi7Lc\nX4qiRESkdCb65m4Bd3+V4ekfERGZQaYU/GZ2CfBakWsREZEAjHeRtucY/iTPoLcAvwD+a6mKEhGR\n0hnvD7jWj7rvQJe7v16iekREpMTGe3P3UFCFiIhIMKY0xy8iIjOXgl9EJGYU/CIiMaPgFxGJGQW/\niEjMKPhFRGJGwS8iEjOhBb+ZzTWzfWb2SFg1SHwdO3aIjo6/5dgx/akK5K7Hv2dPJ5lMVnVEpI5U\nqovt2w+QSnUVfd9hjvg/BqRCPL7E1NNPf5HKyrM588yrqaw8m6ef/mLYJYWqtTVFTc1W1q37DjU1\nW2ltDefHUnUM27JlN3V127j++kepq9vGli27i7r/Ca3AVWxmtgTYDvx34CZ3H31piAJaiEWK5dix\nQ1RWnk1lZd9QWzZbTjb7IosW1YRYWTgymSw1NVvp6ekfakskyjh0aHOgK3GpjmGpVBd1ddtOaO/o\n2DjplbimuhBLqdwNfAoYGOsBZrbZzNrNrD2TyQRXmcxqR4920Nc3t6Ctr28uR492hFRRuNLpbioq\nCmOgvHwO6XS36gipjra2zkm1T0XgwW9m64Gj7r73VI9z963u3uDuDdXV1QFVJ7PdW99aR3n58YK2\n8vLjvPWtdSFVFK5ksore3sLxV1/fAMlkleoIqY7GxsWTap+KMEb8a4ErzSxNbgWv95jZN0OoQ2Jo\n0aIa9u37PNlsOd3d88hmy9m37/OxnOYBqK6upKWliUSijAULKkgkymhpaQp8wXXVMay2diHNzfUF\nbc3N9UVd9D2UOf6hg5tdDNysOX4J2rFjhzh6tIO3vrUutqE/UiaTJZ3uJpmsCjxsVcfJpVJdtLV1\n0ti4eMqhP9Yc/3jX4xeZlRYtqlHgj1BdXRlq0KqOE9XWLizqKH+kUIPf3X8E/CjMGkRE4kZ/uSsi\nEjMKfhGRmFHwi4jEjIJfRCRmFPwiIjGj4BcRiRkFv4hIzCj4RURiRsEvIhIzCn4RkZhR8IuIxIyC\nX0QkZhT8IiIxM8uDPwPsyW9DrCKTZc+eTjKZrOqIiF272vizP/tzdu1qC7WOqDwnqVQX27cfIJXq\nCrWOqPRHFJT0OXH3yH+tXr3aJ2+HuyfcvSq/3TGFfUzfjh0dnkh82auqvuKJxJd9x46OWNcRBZ/8\n5GZ//fVyf+21ef766+X+yU9uDqWOqDwnzc2PO3xh6Ku5+fFQ6ohKf0RBsZ4ToN1PkqmhrsA1UZNf\ngSsD1AA9I9oSwCEguPV7M5ksNTVb6enpH64iUcahQ5sDXeQhKnVEwa5dbVx44YVUVvYNtWWz5Tz1\n1FNcfnljYHVE5TlJpbqoq9t2QntHx8aSLQJyMlHpjygo5nMy1gpcs3SqJw1UjGorz7cHWEW6m4qK\nwi4uL59DOt0dyzqioK3tx/T2zi1o6+ubQ1vbjwOtIyrPSVtb56TaSyUq/REFQTwnszT4k0DvqLa+\nfHuAVSSr6O0dKKyib4BksiqWdURBY+MaKiqOF7SVlw/Q2Lgm0Dqi8pw0Ni6eVHupRKU/oiCI52SW\nBn810EJuemdBfttCkNM8kFu3s6WliUSijAULKkgkymhpaQr8pWtU6oiCyy9v5PbbN5LNltPd/Say\n2XJuv31joNM8EJ3npLZ2Ic3N9QVtzc31gU7zQHT6IwqCeE5m6Rz/oAy56Z0kQYd+QRWZLOl0N8lk\nVagnclTqiIJdu9poa/sxjY1rAg/9kaLynKRSXbS1ddLYuDjw0B8pKv0RBcV4Tsaa45/lwS8iEl8x\ne3NXRETGouAXEYkZBb+ISMwo+EVEYkbBLyISMwp+EZGYUfCLiMSMgl9EJGYU/CIiMaPgFxGJGQW/\niEjMKPhFRGJGwS8iEjMKfhGRmAk8+M3sLDN7wsw6zOygmX0s6BqClwH25LdxryMKNcALLzzPI4/c\nzwsvPB9qHVHpj6hIpbrYvv0AqVRX2KXMamGM+PuBT7h7HbAGuNHM6kKoIyCt5BZ+X5fftsa4jijU\nANu2/TFLlqzkwgs3smTJSrZt++NQ6ohKf0TFli27qavbxvXXP0pd3Ta2bNkddkmzVugLsZjZ94F7\n3P3xsR4zcxdiyZD7ge4Z0ZYADhHsimBRqCMKNeRG+kuWrKSysm+oLZst58iR51i27JzA6ohKf0RF\nKtVFXd22E9o7OjaGuiLYTBfJhVjMLAmsAn5ykn/bbGbtZtaeyczUl8FpoGJUW3m+PW51RKEGeOGF\nffT2zi1o6+ubwwsv7Au0jqj0R1S0tXVOql2mJ7TgN7PTge8CH3f3fxv97+6+1d0b3L2hunqmjoCS\nQO+otr58e9zqiEINsGzZKioqjhe0lZcPsGzZqkDriEp/REVj4+JJtcv0hBL8ZlZOLvS/5e7fC6OG\nYFQDLeRewi/Ib1sI/qV8FOqIQg2wbNk5PPDAzWSz5XR3v4lstpwHHrg54GkeiEp/REVt7UKam+sL\n2pqb6zXNUyKBz/GbmQHbgVfd/eMT+Z6ZO8c/KEPuJXyScH+wo1BHFGrIzfW/8MI+li1bFULojxSN\n/oiKVKqLtrZOGhsXK/SLYKw5/jCC/0LgSeA5YCDf/Mfu/sOxvmfmB7+ISPDGCv6yoAtx96cAC/q4\nIiKSo7/cFRGJGQW/iEjMKPhFRGJGwS8iEjMKfhGRmFHwi4jEjIJfRCRmFPwiIjGj4BcRiRkFv4hI\nzCj4RURiRsEvIhIzCn4RkZiZ5cH/fuD0/DZMnyC3vuonQq7jLnIrXd4V8xoAngZuy2/DlAH25Lci\nwQh9sfWJmNr1+E925ecw/q9zGV52YPB+fwh1nAZkR93/dQxrALgceHzU/cdCqKMV2ERu7d1ecitw\nbQihDpmtIrnYeumMNcIPeuT/CQpDH+A4wY/876IwcAFeJ9hRdxRqgNwI//FRbbsIfuSfIRf6PUB3\nfrsJjfwlCLM0+HdPsr1Udk6yvVRaJ9k+W2uAXMhPpr1U0uRG+iOV59tFSmuWBv9lk2wvlWsm2V4q\nY00fBDmtEIUaIDetM5n2UkmSm94ZqS/fLlJamuMvuTJy0zuDwprjP53c1MqgMObXo1ADQBOFI/yw\n5/jLyYW+5viluGI2xw+5kL+KXLhcRTihD7mQvwn4d/ltGKEPuYD9n0B9fhtG4EahBsiF/FPAZ/Pb\nMEIfciF/iNwU5CEU+hKUWTziFxGJtxiO+EVE5GQU/CIiMaPgFxGJGQW/iEjMKPhFRGJmRnyqx8wy\n5D7vNpMtAo6FXUREqC8KqT8KqT+GTbcvaty9enTjjAj+2cDM2k/2sao4Ul8UUn8UUn8MK1VfaKpH\nRCRmFPwiIjGj4A/O1rALiBD1RSH1RyH1x7CS9IXm+EVEYkYjfhGRmFHwi4jEjIK/hMzsLDN7wsw6\nzOygmX0s7JqiwMzmmtk+M3sk7FrCZmZnmNlOM/snM0uZ2bvDriksZvZH+Z+TA2bWambzwq4pSGZ2\nr5kdNbMDI9reYmaPm9mL+e2bi3EsBX9p9QOfcPc6YA1wo5nVhVxTFHwMSIVdRER8BXjU3c8FziOm\n/WJmZwIfBRrcfQW5FYs+GG5VgbsPeO+otluBv3P3s4G/y9+fNgV/Cbl7p7v/NH/7V+R+qM8Mt6pw\nmdkS4ArgG2HXEjYzqwJ+l9zSW7h7r7v/v3CrClUZkDCzMqAS+EXI9QTK3f8BeHVU81XA9vzt7cD7\ni3EsBX9AzCwJrAJ+Em4lobsb+BQwEHYhEbAUyADb8lNf3zCz08IuKgzu/i/AF4HDQCfQ7e67Tv1d\nsfA2d+/M3/4l8LZi7FTBHwAzOx34LvBxd/+3sOsJi5mtB466+96wa4mIMuBdwNfdfRW5BYmL8lJ+\npsnPXV9F7pfhbwGnmdl14VYVLZ777H1RPn+v4C8xMysnF/rfcvfvhV1PyNYCV5pZGrgfeI+ZfTPc\nkkJ1BDji7oOvAneS+0UQR5cBL7t7xt37gO8BF4RcUxT8q5ktBshvjxZjpwr+EjIzIzd/m3L3L4Vd\nT9jc/dPuvsTdk+TeuPt7d4/tqM7dfwm8Ymbn5JsuBTpCLClMh4E1ZlaZ/7m5lJi+0T3Kw8CH8rc/\nBHy/GDtV8JfWWuC/kBvZ7s9//Yewi5JI2QJ8y8yeBeqBz4dcTyjyr3p2Aj8FniOXTbG6dIOZtQLP\nAOeY2REz2wTcCawzsxfJvSq6syjH0iUbRETiRSN+EZGYUfCLiMSMgl9EJGYU/CIiMaPgFxGJGQW/\nCGBmbzez+83sJTPba2Y/NLNlI6+UKDJblIVdgEjY8n8w9CCw3d0/mG87jyJdF0UkajTiF4FLgD53\n/9+DDe7+M+CVwftmdr2Z3SirXMcAAAFBSURBVDPi/iNmdnH+9q/N7Av5a8nvNrNGM/uRmf2zmV05\n4vu/n29/0cxuC+x/JzKKgl8EVgDTuXDcaeQuP7Ec+BXwZ8A64PeAz414XCPwH4F3Av/JzBqmcUyR\nKdNUj8j09QKP5m8/B/zG3fvM7DkgOeJxj7t7F4CZfQ+4EGgPslAR0IhfBOAgsHqcx/RT+PMyclnA\nPh++9skA8BsAdx+gcHA1+vooul6KhELBLwJ/D7zJzDYPNpjZO4GzRjwmDdSb2RwzO4vctM1krcuv\noZogt5LS09OoWWTKFPwSe/nR+u8Bl+U/znkQ+B/kVjwa9DTwMrnLJv85uatITlYbubUZngW+6+6a\n5pFQ6OqcIgEws+vJLSTeHHYtIhrxi4jEjEb8IiIxoxG/iEjMKPhFRGJGwS8iEjMKfhGRmFHwi4jE\nzP8HFGUq3Sq16FQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JPNxydSNfoq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "6bc3f452-af34-4487-8e00-ad5d2f3c008b"
      },
      "source": [
        "#Data pre-processing and selection\n",
        "cell_df.dtypes"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID              int64\n",
              "Clump           int64\n",
              "UnifSize        int64\n",
              "UnifShape       int64\n",
              "MargAdh         int64\n",
              "SingEpiSize     int64\n",
              "BareNuc        object\n",
              "BlandChrom      int64\n",
              "NormNucl        int64\n",
              "Mit             int64\n",
              "Class           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wq_lfRA2Nibe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "68ee29fb-4361-4697-c3d2-07241ab91550"
      },
      "source": [
        "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
        "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
        "cell_df.dtypes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID             int64\n",
              "Clump          int64\n",
              "UnifSize       int64\n",
              "UnifShape      int64\n",
              "MargAdh        int64\n",
              "SingEpiSize    int64\n",
              "BareNuc        int64\n",
              "BlandChrom     int64\n",
              "NormNucl       int64\n",
              "Mit            int64\n",
              "Class          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_-4zApUNnvy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "81bc9e22-1aea-439e-e5cb-41c6077e1a92"
      },
      "source": [
        "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
        "X = np.asarray(feature_df)\n",
        "X[0:5]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  1,  1,  1,  2,  1,  3,  1,  1],\n",
              "       [ 5,  4,  4,  5,  7, 10,  3,  2,  1],\n",
              "       [ 3,  1,  1,  1,  2,  2,  3,  1,  1],\n",
              "       [ 6,  8,  8,  1,  3,  4,  3,  7,  1],\n",
              "       [ 4,  1,  1,  3,  2,  1,  3,  1,  1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9aCQ8uwNr1P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42b1d5a6-bc0d-4db0-8615-4299599e0c7c"
      },
      "source": [
        "cell_df['Class'] = cell_df['Class'].astype('int')\n",
        "y = np.asarray(cell_df['Class'])\n",
        "y [0:5]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYZVK4AJNvUU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "650e5256-648b-45de-d308-d39e9b7a0a59"
      },
      "source": [
        "#Train/Test\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Test set:', X_test.shape,  y_test.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train set: (546, 9) (546,)\n",
            "Test set: (137, 9) (137,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kApdfgOkNyJf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c34ca637-8fca-4f8f-9938-766a5ce32479"
      },
      "source": [
        "from sklearn import svm\n",
        "clf = svm.SVC(kernel='rbf')\n",
        "clf.fit(X_train, y_train) "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2jxAc2RN1Jq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3000cd5b-dbe7-4a9a-d5b6-2ff4ae7d8962"
      },
      "source": [
        "yhat = clf.predict(X_test)\n",
        "yhat [0:5]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 2, 4, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFrCLPfJN3G9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Evaluation\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RacmYflZN6aq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxA3o_dqN9Fi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "outputId": "232599b3-e489-4ac1-de4b-ccb077a029d3"
      },
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "print (classification_report(y_test, yhat))\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       1.00      0.94      0.97        90\n",
            "           4       0.90      1.00      0.95        47\n",
            "\n",
            "    accuracy                           0.96       137\n",
            "   macro avg       0.95      0.97      0.96       137\n",
            "weighted avg       0.97      0.96      0.96       137\n",
            "\n",
            "Confusion matrix, without normalization\n",
            "[[85  5]\n",
            " [ 0 47]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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MeUlOpmbWkgpDo6qV5/RY2KUAVyc1s56hqx8ZLcXJ1MxaVhPlUidTM2tNkp/NNzOrga6f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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jekJLd-sOBaq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2afb5d05-9ff9-4ccc-a7c4-7cbac1468490"
      },
      "source": [
        "#F1\n",
        "from sklearn.metrics import f1_score\n",
        "f1_score(y_test, yhat, average='weighted') "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9639038982104676"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9r9ppvdODQT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "e8648243-e540-4dce-90e4-bc8e2136dbe7"
      },
      "source": [
        "#Jaccard\n",
        "from sklearn.metrics import jaccard_similarity_score\n",
        "jaccard_similarity_score(y_test, yhat)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9635036496350365"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    }
  ]
}