SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5c5c2b4-ce22-4dc1-8ce3-55172a4372b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as py\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0706510-cf4f-40bf-98d4-5d27bc4b99e9",
   "metadata": {},
   "source": [
    "Load Data From CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "229c05c2-6765-4e54-9633-3c974b4ae52f",
   "metadata": {},
   "outputs": [
    {
     "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  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df=pd.read_csv(\"cell_samples.csv\")\n",
    "cell_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e8e6989-bae4-49b0-96eb-09f0cd2805af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db5b0ef-9f16-4bd6-88a5-6061c5975f1b",
   "metadata": {},
   "source": [
    "Data Pre-processing and selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f70d084-54d9-4037-b14a-5028e3a45e70",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a60388a4-3789-4e21-990a-49be2ef9ca80",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3cb1315-3525-4967-9039-47f89ef04eef",
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asarray(feat_df)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ab3ddbd-97e1-4ae7-ae97-90ab7ddbb659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df['Class'] = cell_df['Class'].astype('int')\n",
    "y = np.asarray(cell_df['Class'])\n",
    "y [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245eda5f-a59b-41a0-81c1-d062c7a7cfe7",
   "metadata": {},
   "source": [
    "Train/Test dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f97ab010-a58c-48d4-a08e-d7a48919b1b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (546, 9) (546,)\n",
      "Test set: (137, 9) (137,)\n"
     ]
    }
   ],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4602d6-ed49-486e-b5fe-3053ac56951e",
   "metadata": {},
   "source": [
    "Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bb17c1d-e14a-44e4-9068-c780fe0fc618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf=svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31f4f96d-735c-46fc-96c7-0d197256fdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat=clf.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73428da7-f895-4ce2-b113-59a6640482b0",
   "metadata": {},
   "source": [
    "Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e32d361b-47b6-4214-ae44-6d106957c0d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fbf49c4-4ebb-4806-ae16-90eaaee671f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Predicted label')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm,classes,normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm=cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]\n",
    "        print(\"Normalized confusiion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "    print(cm)\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\",color=\"white\" if cm[i,j]>thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a925854f-75c7-4af7-aa5c-61715f9f5058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix=confusion_matrix(y_test,yhat,labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "print(classification_report(y_test,yhat))\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix,classes=['Benign(2)','Malignant(4)'],normalize=False,title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "14016a19-ef60-4a3a-8123-da8e8c8a3a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9639038982104676"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test,yhat,average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6544512-7307-4661-988c-07b28416d9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "jaccard_score(y_test,yhat,pos_label=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdf314af-4282-4b85-852c-8d054f455afa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}