svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "markdown",
   "metadata": {},
   "source": [
    "sample class are benign (value = 2) or malignant (value = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cell_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 1, 1, 1, 2, 3, 1, 1],\n",
       "       [5, 4, 4, 5, 7, 3, 2, 1],\n",
       "       [3, 1, 1, 1, 2, 3, 1, 1],\n",
       "       [6, 8, 8, 1, 3, 3, 7, 1],\n",
       "       [4, 1, 1, 3, 2, 3, 1, 1]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asarray(feature_df)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 4, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df['Class'] = cell_df['Class'].astype('int')\n",
    "y = np.asarray(cell_df['Class'])\n",
    "y [0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (559, 8) (559,)\n",
      "Test set: (140, 8) (140,)\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": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/envs/py36/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf') #radial basis function kernel\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 4, 4, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(X_test)\n",
    "yhat [0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[83  5]\n",
      " [ 2 50]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        Positive     TP | FN\n",
    "        Negative     FP | TN\n",
    "                Positive  Negative\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}