SVC (Using Grid Search) on breat_cancer dataset from sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "%matplotlib inline\n",
    "from sklearn.datasets import load_breast_cancer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'],\n",
       "      dtype='|S9')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "\n",
       "[4 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      "mean radius                569 non-null float64\n",
      "mean texture               569 non-null float64\n",
      "mean perimeter             569 non-null float64\n",
      "mean area                  569 non-null float64\n",
      "mean smoothness            569 non-null float64\n",
      "mean compactness           569 non-null float64\n",
      "mean concavity             569 non-null float64\n",
      "mean concave points        569 non-null float64\n",
      "mean symmetry              569 non-null float64\n",
      "mean fractal dimension     569 non-null float64\n",
      "radius error               569 non-null float64\n",
      "texture error              569 non-null float64\n",
      "perimeter error            569 non-null float64\n",
      "area error                 569 non-null float64\n",
      "smoothness error           569 non-null float64\n",
      "compactness error          569 non-null float64\n",
      "concavity error            569 non-null float64\n",
      "concave points error       569 non-null float64\n",
      "symmetry error             569 non-null float64\n",
      "fractal dimension error    569 non-null float64\n",
      "worst radius               569 non-null float64\n",
      "worst texture              569 non-null float64\n",
      "worst perimeter            569 non-null float64\n",
      "worst area                 569 non-null float64\n",
      "worst smoothness           569 non-null float64\n",
      "worst compactness          569 non-null float64\n",
      "worst concavity            569 non-null float64\n",
      "worst concave points       569 non-null float64\n",
      "worst symmetry             569 non-null float64\n",
      "worst fractal dimension    569 non-null float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X= df\n",
    "y = cancer['target']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, 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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  67]\n",
      " [  0 121]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        67\n",
      "          1       0.64      1.00      0.78       121\n",
      "\n",
      "avg / total       0.41      0.64      0.50       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_grid = {'C':[0.1,1,10,100,1000], 'gamma':[1,0.1,0.01,0.001,0.0001,0.00001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(), param_grid, verbose = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................... C=0.1, gamma=1, score=0.617188 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................... C=0.1, gamma=1, score=0.622047 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......................... C=0.1, gamma=1, score=0.619048 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.617188 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.622047 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.619048 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.617188 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.622047 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.619048 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.617188 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.622047 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.619048 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.882812 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.897638 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.888889 -   0.0s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=1e-05, score=0.898438 -   0.0s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=1e-05, score=0.929134 -   0.0s\n",
      "[CV] C=0.1, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=1e-05, score=0.865079 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.617188 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.622047 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.619048 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................... C=1, gamma=0.1, score=0.617188 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................... C=1, gamma=0.1, score=0.622047 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................... C=1, gamma=0.1, score=0.619048 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.617188 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.622047 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.619048 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.929688 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.905512 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.896825 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.921875 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.929134 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.904762 -   0.0s\n",
      "[CV] C=1, gamma=1e-05 ................................................\n",
      "[CV] ....................... C=1, gamma=1e-05, score=0.882812 -   0.0s\n",
      "[CV] C=1, gamma=1e-05 ................................................\n",
      "[CV] ....................... C=1, gamma=1e-05, score=0.937008 -   0.0s\n",
      "[CV] C=1, gamma=1e-05 ................................................\n",
      "[CV] ....................... C=1, gamma=1e-05, score=0.873016 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.617188 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.622047 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.619048 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.617188 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.622047 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.619048 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.617188 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.629921 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.626984 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.921875 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.905512 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.873016 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.953125 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.929134 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.920635 -   0.0s\n",
      "[CV] C=10, gamma=1e-05 ...............................................\n",
      "[CV] ...................... C=10, gamma=1e-05, score=0.898438 -   0.0s\n",
      "[CV] C=10, gamma=1e-05 ...............................................\n",
      "[CV] ...................... C=10, gamma=1e-05, score=0.921260 -   0.0s\n",
      "[CV] C=10, gamma=1e-05 ...............................................\n",
      "[CV] ...................... C=10, gamma=1e-05, score=0.904762 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.617188 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.622047 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.619048 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.617188 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.622047 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.619048 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.617188 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.629921 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.626984 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... C=100, gamma=0.001, score=0.921875 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=100, gamma=0.001, score=0.905512 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=100, gamma=0.001, score=0.873016 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.953125 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.921260 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.920635 -   0.0s\n",
      "[CV] C=100, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=100, gamma=1e-05, score=0.937500 -   0.0s\n",
      "[CV] C=100, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=100, gamma=1e-05, score=0.937008 -   0.0s\n",
      "[CV] C=100, gamma=1e-05 ..............................................\n",
      "[CV] ..................... C=100, gamma=1e-05, score=0.920635 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.617188 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.622047 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.619048 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.617188 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.622047 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.619048 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.617188 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.629921 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.626984 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.921875 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.905512 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.873016 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.945312 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.889764 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.912698 -   0.0s\n",
      "[CV] C=1000, gamma=1e-05 .............................................\n",
      "[CV] .................... C=1000, gamma=1e-05, score=0.953125 -   0.0s\n",
      "[CV] C=1000, gamma=1e-05 .............................................\n",
      "[CV] .................... C=1000, gamma=1e-05, score=0.937008 -   0.0s\n",
      "[CV] C=1000, gamma=1e-05 .............................................\n",
      "[CV] .................... C=1000, gamma=1e-05, score=0.912698 -   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  90 out of  90 | elapsed:    4.1s 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=None, 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={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.1, 1, 10, 100, 1000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001, 1e-05]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=3)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.0001}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.94      0.95        67\n",
      "          1       0.97      0.98      0.97       121\n",
      "\n",
      "avg / total       0.96      0.96      0.96       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 63   4]\n",
      " [  3 118]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model2 = SVC(C=100, gamma=0.000001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=1e-06, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.98        67\n",
      "          1       0.98      1.00      0.99       121\n",
      "\n",
      "avg / total       0.99      0.99      0.99       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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