final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando as Bibliotecas e a Base de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tratando o Dataset para ser usado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 902,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = load_breast_cancer()\n",
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 903,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 903,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 904,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dataset['data'] # daset das features\n",
    "#dataset['target'] # diagnósticos\n",
    "#dataset['target_names'] # nomes dos diagnósticos\n",
    "#dataset['DESCR'] # descrição do dataset \n",
    "#dataset['feature_names'] # nome das features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfData = pd.DataFrame(dataset['data'], columns = dataset['feature_names'])\n",
    "dfTarget = pd.DataFrame(dataset['target'], columns = ['diagnosis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 1022,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "   diagnosis\n",
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0"
      ]
     },
     "execution_count": 1023,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTarget.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 908,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(dfData, np.ravel(dfTarget), test_size=0.30, random_state = 1)\n",
    "\n",
    "# colocando o random state para sempre que reexecutar gerar a mesma combinação aleatória de treino e teste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 910,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 911,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "svm = LinearSVC()\n",
    "#svm.kernel = 'linear'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 960,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 960,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.52      1.00      0.68        63\n",
      "          1       1.00      0.46      0.63       108\n",
      "\n",
      "avg / total       0.82      0.66      0.65       171\n",
      "\n",
      "[[63  0]\n",
      " [58 50]]\n"
     ]
    }
   ],
   "source": [
    "pred  = svm.predict(X_test)\n",
    "print(classification_report(y_test, pred))\n",
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 965,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "paramGrid = {'C': [0.01, 0.1,1, 10, 100, 1000, 10000, 100000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid = GridSearchCV(svm, paramGrid, verbose = 3, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ................. C=0.01, score=0.8195488721804511, total=   0.0s\n",
      "[CV] ................. C=0.01, score=0.9323308270676691, total=   0.0s\n",
      "[CV] .................. C=0.1, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ................. C=0.01, score=0.8787878787878788, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] C=1 .............................................................\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................... C=0.1, score=0.924812030075188, total=   0.0s\n",
      "[CV] .................... C=1, score=0.8872180451127819, total=   0.0s\n",
      "[CV] C=10000 .........................................................\n",
      "[CV] C=1 .............................................................\n",
      "[CV] .................... C=1, score=0.8872180451127819, total=   0.0s\n",
      "[CV] .................. C=0.1, score=0.8787878787878788, total=   0.0s\n",
      "[CV] ................ C=10000, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=10000 .........................................................\n",
      "[CV] .................... C=1, score=0.9545454545454546, total=   0.0s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] ................ C=10000, score=0.8796992481203008, total=   0.0s\n",
      "[CV] C=10000 .........................................................\n",
      "[CV] ................... C=10, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] .................... C=10, score=0.924812030075188, total=   0.0s\n",
      "[CV] .............................. C=10000, score=0.75, total=   0.0s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] C=100000 ........................................................\n",
      "[CV] ............... C=100000, score=0.8646616541353384, total=   0.0s\n",
      "[CV] C=100000 ........................................................\n",
      "[CV] ................... C=10, score=0.9393939393939394, total=   0.0s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] .................. C=100, score=0.7744360902255639, total=   0.0s\n",
      "[CV] ............... C=100000, score=0.9323308270676691, total=   0.0s\n",
      "[CV] C=100000 ........................................................\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] .................. C=100, score=0.9097744360902256, total=   0.0s\n",
      "[CV] ............... C=100000, score=0.8560606060606061, total=   0.0s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] .................. C=100, score=0.9545454545454546, total=   0.0s\n",
      "[CV] C=1000 ..........................................................\n",
      "[CV] ................. C=1000, score=0.9022556390977443, total=   0.0s\n",
      "[CV] C=1000 ..........................................................\n",
      "[CV] ................. C=1000, score=0.9398496240601504, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   8 out of  24 | elapsed:    0.1s remaining:    0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=1000 ..........................................................\n",
      "[CV] ................. C=1000, score=0.9545454545454546, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': [0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 970,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:  {'C': 1000}\n",
      "\n",
      "Best estimator:  LinearSVC(C=1000, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n",
      "\n",
      "Best score:  0.9321608040201005\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params: \", grid.best_params_, end = \"\\n\\n\")\n",
    "print(\"Best estimator: \", grid.best_estimator_, end = \"\\n\\n\")\n",
    "print(\"Best score: \", grid.best_score_, end = \"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 972,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.92      0.90        63\n",
      "          1       0.95      0.93      0.94       108\n",
      "\n",
      "avg / total       0.93      0.92      0.92       171\n",
      "\n",
      "[[ 58   5]\n",
      " [  8 100]]\n"
     ]
    }
   ],
   "source": [
    "gridPred = grid.predict(X_test)\n",
    "print(classification_report(y_test, gridPred))\n",
    "print(confusion_matrix(y_test, gridPred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 973,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 974,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "listaScore = []\n",
    "listaC = []\n",
    "for score, C in zip(grid.cv_results_['mean_test_score'], grid.cv_results_['param_C']):\n",
    "    listaScore.append(score)\n",
    "    listaC.append(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 975,
   "metadata": {
    "collapsed": false
   },
   "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": [
    "plt.subplot()\n",
    "plt.loglog(listaC, listaScore, basex=2)\n",
    "plt.grid(True)\n",
    "plt.yscale(\"linear\")\n",
    "plt.xlabel(\"C\")\n",
    "plt.ylabel(\"Test Score\")\n",
    "plt.title(\"Test Score x C\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {
    "collapsed": false
   },
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension    ...      worst texture  worst perimeter  \\\n",
       "0                 0.07871    ...              17.33           184.60   \n",
       "1                 0.05667    ...              23.41           158.80   \n",
       "2                 0.05999    ...              25.53           152.50   \n",
       "3                 0.09744    ...              26.50            98.87   \n",
       "4                 0.05883    ...              16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  diagnosis  \n",
       "0                0.2654          0.4601                  0.11890          0  \n",
       "1                0.1860          0.2750                  0.08902          0  \n",
       "2                0.2430          0.3613                  0.08758          0  \n",
       "3                0.2575          0.6638                  0.17300          0  \n",
       "4                0.1625          0.2364                  0.07678          0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 1024,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "result = pd.concat([dfData, dfTarget], axis=1, join_axes=[dfData.index])\n",
    "\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#p = sns.PairGrid(result, hue = \"diagnosis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 978,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#p.map(plt.hist);\n",
    "#plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 979,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "svmG = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 980,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 980,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svmG.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        63\n",
      "          1       0.63      1.00      0.77       108\n",
      "\n",
      "avg / total       0.40      0.63      0.49       171\n",
      "\n",
      "[[  0  63]\n",
      " [  0 108]]\n"
     ]
    }
   ],
   "source": [
    "predGaussian  = svmG.predict(X_test)\n",
    "print(classification_report(y_test, predGaussian))\n",
    "print(confusion_matrix(y_test, predGaussian))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# sigma = 1/gamma\n",
    "\n",
    "paramGridGaussian = {'C': [0.01, 0.1,1, 10, 100, 1000, 10000, 100000],\n",
    "                    'gamma': [1000, 100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gridGaussian = GridSearchCV(svmG, paramGridGaussian, verbose = 3, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 64 candidates, totalling 192 fits\n",
      "[CV] C=0.01, gamma=1000 ..............................................\n",
      "[CV] C=0.01, gamma=1000 ..............................................\n",
      "[CV] C=0.01, gamma=1000 ..............................................\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] ..... C=0.01, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=0.01, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=0.01, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=0.01, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ...... C=0.01, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....... C=0.01, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....... C=0.01, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=0.01, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] C=0.1, gamma=100 ................................................\n",
      "[CV] ....... C=0.01, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] .... C=0.01, gamma=0.001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] C=0.1, gamma=100 ................................................\n",
      "[CV] ....... C=0.1, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ........ C=0.01, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ....... C=0.1, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ....... C=0.1, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] .... C=0.01, gamma=0.001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] ........ C=0.01, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ........ C=0.1, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ...... C=0.1, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] .... C=0.01, gamma=0.001, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ........ C=0.01, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ........ C=0.1, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] ...... C=0.1, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ... C=0.01, gamma=0.0001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ...... C=0.01, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ........ C=0.1, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ...... C=0.1, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ... C=0.01, gamma=0.0001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.0001 ............................................\n",
      "[CV] ...... C=0.01, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ......... C=0.1, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ... C=0.01, gamma=0.0001, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=1000 ...............................................\n",
      "[CV] ...... C=0.01, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ......... C=0.1, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ...... C=0.1, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=1000 ...............................................\n",
      "[CV] ......... C=0.1, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ..... C=0.1, gamma=0.001, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ..... C=0.01, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] ...... C=0.1, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=1000 ...............................................\n",
      "[CV] ....... C=0.1, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] ..... C=0.01, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ...... C=0.1, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=0.1, gamma=100 ................................................\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.9022556390977443, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] ....... C=0.1, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=0.01, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1, gamma=1000 .................................................\n",
      "[CV] ....... C=0.1, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=0.1, gamma=0.0001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ........... C=1, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ........ C=1, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] C=1, gamma=1000 .................................................\n",
      "[CV] ....... C=1, gamma=0.001, score=0.9318181818181818, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ......... C=10, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ........... C=1, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ...... C=1, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ........ C=1, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=1000 .................................................\n",
      "[CV] ......... C=10, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=1, gamma=0.0001, score=0.9323308270676691, total=   0.0s\n",
      "[CV] ......... C=1, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ........ C=1, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] ......... C=10, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ...... C=1, gamma=0.0001, score=0.9318181818181818, total=   0.0s\n",
      "[CV] C=10, gamma=1000 ................................................\n",
      "[CV] ......... C=1, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......... C=1, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] .......... C=10, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ....... C=10, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] ......... C=1, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] C=10, gamma=1000 ................................................\n",
      "[CV] ......... C=1, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] .......... C=10, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ........ C=1, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] ....... C=10, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=1000 ................................................\n",
      "[CV] ......... C=1, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] .......... C=10, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ........ C=1, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ....... C=10, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] .......... C=1, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] ........ C=10, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ........ C=1, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ........ C=10, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........ C=10, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .......... C=1, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ....... C=1, gamma=0.001, score=0.8646616541353384, total=   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ........ C=10, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........ C=10, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .......... C=1, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ....... C=1, gamma=0.001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ........ C=10, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ....... C=10, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ........... C=1, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=1000 ...............................................\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .... C=100, gamma=0.0001, score=0.9172932330827067, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] ....... C=10, gamma=0.01, score=0.6165413533834586, total=   0.0s\n",
      "[CV] ...... C=100, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....... C=100, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] .... C=100, gamma=0.0001, score=0.9318181818181818, total=   0.0s\n",
      "[CV] C=1000, gamma=1000 ..............................................\n",
      "[CV] ....... C=100, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....... C=10, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ....... C=100, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=1000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] C=1000, gamma=1000 ..............................................\n",
      "[CV] ....... C=100, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=10, gamma=0.001, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....... C=100, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ..... C=1000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=1000 ..............................................\n",
      "[CV] ....... C=100, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ...... C=10, gamma=0.001, score=0.8872180451127819, total=   0.0s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...... C=100, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ..... C=1000, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1000, gamma=100 ...............................................\n",
      "[CV] ........ C=100, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=10, gamma=0.001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ...... C=100, gamma=0.01, score=0.6165413533834586, total=   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] C=1000, gamma=100 ...............................................\n",
      "[CV] ...... C=1000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=10, gamma=0.0001, score=0.8796992481203008, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ........ C=100, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ...... C=100, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ...... C=1000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..... C=10, gamma=0.0001, score=0.9323308270676691, total=   0.0s\n",
      "[CV] C=1000, gamma=100 ...............................................\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ........ C=100, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ..... C=100, gamma=0.001, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..... C=10, gamma=0.0001, score=0.9393939393939394, total=   0.0s\n",
      "[CV] C=100, gamma=1000 ...............................................\n",
      "[CV] ...... C=1000, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ......... C=100, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] C=1000, gamma=10 ................................................\n",
      "[CV] ..... C=100, gamma=0.001, score=0.8872180451127819, total=   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ...... C=100, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=1000 ...............................................\n",
      "[CV] ......... C=100, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ....... C=1000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] C=1000, gamma=10 ................................................\n",
      "[CV] ..... C=100, gamma=0.001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] ...... C=100, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ....... C=1000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ......... C=100, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] .... C=100, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=1000, gamma=10 ................................................\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] C=10000, gamma=0.1 ..............................................\n",
      "[CV] C=10000, gamma=100 ..............................................\n",
      "[CV] ....... C=1000, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] .... C=1000, gamma=0.001, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ..... C=10000, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ..... C=10000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10000, gamma=0.01 .............................................\n",
      "[CV] C=10000, gamma=100 ..............................................\n",
      "[CV] .... C=1000, gamma=0.001, score=0.8872180451127819, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] ........ C=1000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=10000, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] C=10000, gamma=0.01 .............................................\n",
      "[CV] ..... C=10000, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10000, gamma=10 ...............................................\n",
      "[CV] .... C=1000, gamma=0.001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ........ C=1000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] .... C=10000, gamma=0.01, score=0.6165413533834586, total=   0.0s\n",
      "[CV] ... C=1000, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ...... C=10000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10000, gamma=0.01 .............................................\n",
      "[CV] C=10000, gamma=10 ...............................................\n",
      "[CV] ........ C=1000, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ... C=1000, gamma=0.0001, score=0.9323308270676691, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] .... C=10000, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10000, gamma=0.001 ............................................\n",
      "[CV] ...... C=1000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] .... C=1000, gamma=0.0001, score=0.946969696969697, total=   0.0s\n",
      "[CV] ...... C=10000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10000, gamma=1000 .............................................\n",
      "[CV] ... C=10000, gamma=0.001, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=10000, gamma=10 ...............................................\n",
      "[CV] C=10000, gamma=0.001 ............................................\n",
      "[CV] ...... C=1000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=10000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...... C=10000, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10000, gamma=1000 .............................................\n",
      "[CV] C=10000, gamma=1 ................................................\n",
      "[CV] ... C=10000, gamma=0.001, score=0.8872180451127819, total=   0.0s\n",
      "[CV] C=10000, gamma=0.001 ............................................\n",
      "[CV] ...... C=1000, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ....... C=10000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=10000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=10000, gamma=1000 .............................................\n",
      "[CV] ... C=10000, gamma=0.001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=10000, gamma=1 ................................................\n",
      "[CV] C=10000, gamma=0.0001 ...........................................\n",
      "[CV] ..... C=1000, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .... C=10000, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10000, gamma=100 ..............................................\n",
      "[CV] ....... C=10000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .. C=10000, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=10000, gamma=1 ................................................\n",
      "[CV] C=10000, gamma=0.0001 ...........................................\n",
      "[CV] ..... C=1000, gamma=0.01, score=0.6165413533834586, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] .. C=10000, gamma=0.0001, score=0.9323308270676691, total=   0.0s\n",
      "[CV] ..... C=10000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=1000 ............................................\n",
      "[CV] C=10000, gamma=0.0001 ...........................................\n",
      "[CV] ....... C=10000, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=10000, gamma=0.1 ..............................................\n",
      "[CV] ..... C=1000, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=1 ...............................................\n",
      "[CV] ... C=100000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ..... C=10000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=1000 ............................................\n",
      "[CV] ... C=10000, gamma=0.0001, score=0.946969696969697, total=   0.0s\n",
      "[CV] C=100000, gamma=0.001 ...........................................\n",
      "[CV] C=10000, gamma=0.1 ..............................................\n",
      "[CV] ...... C=100000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=1 ...............................................\n",
      "[CV] .. C=100000, gamma=0.001, score=0.9242424242424242, total=   0.0s\n",
      "[CV] C=100000, gamma=0.0001 ..........................................\n",
      "[CV] ... C=100000, gamma=1000, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=1000 ............................................\n",
      "[CV] ..... C=10000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] ...... C=100000, gamma=1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=0.1 .............................................\n",
      "[CV] . C=100000, gamma=0.0001, score=0.8947368421052632, total=   0.0s\n",
      "[CV] C=100000, gamma=0.0001 ..........................................\n",
      "[CV] ... C=100000, gamma=1000, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=100 .............................................\n",
      "[CV] .... C=100000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=0.1 .............................................\n",
      "[CV] . C=100000, gamma=0.0001, score=0.9323308270676691, total=   0.0s\n",
      "[CV] C=100000, gamma=0.0001 ..........................................\n",
      "[CV] .... C=100000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .... C=100000, gamma=0.1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=0.1 .............................................\n",
      "[CV] C=100000, gamma=100 .............................................\n",
      "[CV] .. C=100000, gamma=0.0001, score=0.946969696969697, total=   0.0s\n",
      "[CV] .... C=100000, gamma=0.1, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=0.01 ............................................\n",
      "[CV] .... C=100000, gamma=100, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=100 .............................................\n",
      "[CV] .... C=100000, gamma=100, score=0.6287878787878788, total=   0.0s\n",
      "[CV] ... C=100000, gamma=0.01, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=10 ..............................................\n",
      "[CV] C=100000, gamma=0.01 ............................................\n",
      "[CV] ..... C=100000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=10 ..............................................\n",
      "[CV] ... C=100000, gamma=0.01, score=0.6165413533834586, total=   0.0s\n",
      "[CV] C=100000, gamma=0.01 ............................................\n",
      "[CV] ..... C=100000, gamma=10, score=0.6240601503759399, total=   0.0s\n",
      "[CV] C=100000, gamma=10 ..............................................\n",
      "[CV] ... C=100000, gamma=0.01, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=0.001 ...........................................\n",
      "[CV] ..... C=100000, gamma=10, score=0.6287878787878788, total=   0.0s\n",
      "[CV] C=100000, gamma=1 ...............................................\n",
      "[CV] .. C=100000, gamma=0.001, score=0.8721804511278195, total=   0.0s\n",
      "[CV] C=100000, gamma=0.001 ...........................................\n",
      "[CV] ...... C=100000, gamma=1, score=0.6240601503759399, total=   0.0s\n",
      "[CV] .. C=100000, gamma=0.001, score=0.8872180451127819, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 192 out of 192 | elapsed:    1.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': [0.01, 0.1, 1, 10, 100, 1000, 10000, 100000], 'gamma': [1000, 100, 10, 1, 0.1, 0.01, 0.001, 0.0001]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 984,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridGaussian.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params for gaussian kernel:  {'C': 1000, 'gamma': 0.0001}\n",
      "\n",
      "Best estimator for gaussian kernel:  SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "\n",
      "Best score for gaussian kernel:  0.9246231155778895\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params for gaussian kernel: \", gridGaussian.best_params_, end = \"\\n\\n\")\n",
    "print(\"Best estimator for gaussian kernel: \", gridGaussian.best_estimator_, end = \"\\n\\n\")\n",
    "print(\"Best score for gaussian kernel: \", gridGaussian.best_score_, end = \"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.90      0.90        63\n",
      "          1       0.94      0.94      0.94       108\n",
      "\n",
      "avg / total       0.93      0.93      0.93       171\n",
      "\n",
      "[[ 57   6]\n",
      " [  6 102]]\n"
     ]
    }
   ],
   "source": [
    "gridPredGaussian = gridGaussian.predict(X_test)\n",
    "print(classification_report(y_test, gridPredGaussian))\n",
    "print(confusion_matrix(y_test, gridPredGaussian))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "listaScoreG = []\n",
    "listaCG = []\n",
    "listaGammaG = []\n",
    "dicio = {}\n",
    "for score, C, gamma in zip(gridGaussian.cv_results_['mean_test_score'], gridGaussian.cv_results_['param_C'], gridGaussian.cv_results_['param_gamma']):\n",
    "    if (score, gamma) not in dicio:\n",
    "        listaScoreG.append(score)\n",
    "        listaCG.append(C)\n",
    "        listaGammaG.append(gamma)\n",
    "        dicio[score, gamma] = 1\n",
    "        #print(score, C, gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#gridGaussian.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "metadata": {
    "collapsed": false
   },
   "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": [
    "plt.subplot()\n",
    "plt.loglog(listaCG, listaScoreG, basex=2)\n",
    "plt.grid(True)\n",
    "plt.yscale(\"linear\")\n",
    "plt.xlabel(\"C\")\n",
    "plt.ylabel(\"Test Score\")\n",
    "plt.title(\"Test Score x C\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RBF Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svmRBF = SVC(kernel = 'rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 1208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svmRBF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.75      0.85        63\n",
      "          1       0.87      1.00      0.93       108\n",
      "\n",
      "avg / total       0.92      0.91      0.90       171\n",
      "\n",
      "[[ 47  16]\n",
      " [  0 108]]\n"
     ]
    }
   ],
   "source": [
    "predRBF  = svmRBF.predict(X_test)\n",
    "print(classification_report(y_test, predRBF))\n",
    "print(confusion_matrix(y_test, predRBF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1210,
   "metadata": {
    "collapsed": false
   },
   "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>diagnosis</th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.1189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  mean radius  mean texture  mean perimeter  mean area  \\\n",
       "0          0        17.99         10.38           122.8     1001.0   \n",
       "\n",
       "   mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "0           0.1184            0.2776          0.3001               0.1471   \n",
       "\n",
       "   mean symmetry           ...             worst radius  worst texture  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "\n",
       "   worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0            184.6      2019.0            0.1622             0.6656   \n",
       "\n",
       "   worst concavity  worst concave points  worst symmetry  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                   0.1189  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 1210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "result = pd.concat([dfTarget,dfData], axis=1, join_axes=[dfData.index])\n",
    "\n",
    "result.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f28d43ad160>"
      ]
     },
     "execution_count": 1241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = result.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}