11_svm/11_01_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unfamiliar SVMs with a Familiar Dataset\n",
    "\n",
    "To motivate our basic understandings of SVMs, lets explore SVMs through our favorite iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = iris.target\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty standard procedure so far. We've created a design matrix X, and a vector of responses y.\n",
    "\n",
    "We'll also fit, predict, score, etc. in the same way as other sklearn models. Let's take a look at the SVC arguments..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's look at some of the parameters\n",
    "model = svm.SVC() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98666666666666669"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pretty good, though we kind of expected this\n",
    "model.score(X,y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7, 19, 19], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many support vectors exist for each class\n",
    "model.n_support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13,  15,  18,  23,  24,  41,  44,  50,  52,  54,  56,  57,  60,\n",
       "        63,  66,  68,  70,  72,  76,  77,  78,  83,  84,  85,  86,  98,\n",
       "       100, 106, 110, 118, 119, 121, 123, 126, 127, 129, 131, 133, 134,\n",
       "       138, 141, 142, 146, 147, 149], dtype=int32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indices of the support vectors\n",
    "model.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locations of the support vectors\n",
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Decision Boundaries\n",
    "\n",
    "We are going to explore what happens to the **decision boundary** and **support vectors** as we change C, kernel, and kernel parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data[:,[0,2]]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Support Vectors Per Class: [ 5 18 19]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a grid of points to predict over\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Support Vectors Per Class: [ 4  7 12]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10dcca350>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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huH8MDAxw7drVkK+9v9/NxYsjx5r7/X7Ky8tDxx6Ph0uXysbVXlnZBdrb29A0LTR5W15+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\n/wEZXjv/507X9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d73ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = svm.SVC(C=100000000)\n",
    "model.fit(X,y)\n",
    "print \"# of Support Vectors Per Class:\", model.n_support_\n",
    "\n",
    "Z = model.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "Z = Z.reshape(xx.shape) \n",
    "\n",
    "\n",
    "plt.contourf(xx,yy,Z,alpha=0.4)\n",
    "plt.scatter(X[:,0],X[:,1],c=y,alpha=0.8)\n",
    "plt.xlabel(iris.feature_names[0]), plt.ylabel(iris.feature_names[2])\n",
    "plt.title(\"Iris Decision Boundaries for SVM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "1. Use the \"rbf\" (radial basis function) kernel in creating svm.SVC classifier. This is the gaussian drop off we learned.\n",
    "  1. Modulate the gamma parameter from .001 to 100. Observe what happens to the number of support vectors as well as the shape of the decision boundaries as you increase gamma?\n",
    "  2. How does this relate to our conversation of underfitting/overfitting?\n",
    "2. Using the same \"rbf\" kernel, do not supply an argument for gamma. This automatically sets gamma = 1 / n_features.\n",
    "  1. Now, modulate \"C\" from .1 to 1 to 1000. What happens to the decision boundaries? C is the penalty parameter of the errors. High C tells the SVM to work harder to find a less imperfect boundary.\n",
    "3. If you finish early, explore using the \"poly\" (polynomial degree--requires the \"degree\" parameter), as well as the \"linear\" kernel.\n",
    "\n",
    "There are also other SVM classifiers within sklearn including LinearSVC. LinearSVC is slightly different from SVM with a linear kernel.\n",
    "\n",
    "More information here: http://scikit-learn.org/stable/modules/svm.html#svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter search\n",
    "\n",
    "Ah, so we've seen that in the case of \"rbf\" or radial basis kernels, we have to modulate the C parameter of the SVM, which modulates how much errors should be avoided. We also have to modulate gamma, which dictates the width of the gaussian kernels.\n",
    "\n",
    "We're going to use GridSearchCV to search for both parameters. In addition, we're going to finalize our example. Remember that SVM uses distance features but we have not normalized any of our features!! We'll start doing that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5],\n",
       "       [ 4.9,  3. ],\n",
       "       [ 4.7,  3.2],\n",
       "       [ 4.6,  3.1],\n",
       "       [ 5. ,  3.6]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data\n",
    "y = iris.target\n",
    "X[0:5,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.90068117,  1.03205722],\n",
       "       [-1.14301691, -0.1249576 ],\n",
       "       [-1.38535265,  0.33784833],\n",
       "       [-1.50652052,  0.10644536],\n",
       "       [-1.02184904,  1.26346019]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler() \n",
    "X = scaler.fit_transform(X) \n",
    "X[0:5,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.69031455e-15  -1.63702385e-15  -1.48251781e-15  -1.62314606e-15]\n",
      "[ 1.  1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "# Let's confirm...\n",
    "print np.mean(X,axis=0)\n",
    "print np.var(X,axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': array([  1.00000000e-03,   1.21152766e-02,   1.46779927e-01,\n",
       "          1.77827941e+00,   2.15443469e+01,   2.61015722e+02,\n",
       "          3.16227766e+03,   3.83118685e+04,   4.64158883e+05,\n",
       "          5.62341325e+06,   6.81292069e+07,   8.25404185e+08,\n",
       "          1.00000000e+10]),\n",
       " 'gamma': array([  1.00000000e-13,   2.15443469e-12,   4.64158883e-11,\n",
       "          1.00000000e-09,   2.15443469e-08,   4.64158883e-07,\n",
       "          1.00000000e-05,   2.15443469e-04,   4.64158883e-03,\n",
       "          1.00000000e-01,   2.15443469e+00,   4.64158883e+01,\n",
       "          1.00000000e+03]),\n",
       " 'kernel': ['linear', 'rbf', 'poly']}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We're going to create a space of C and gamma values to grid search over\n",
    "# And then map it to a dictionary \n",
    "\n",
    "C_range = np.logspace(-3, 10, 13) \n",
    "gamma_range = np.logspace(-13, 3, 13) \n",
    "kernel_range=[\"linear\",\"rbf\",\"poly\"]\n",
    "param_grid = dict(gamma=gamma_range, C=C_range,kernel=kernel_range)\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-8adb8eb487f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStratifiedShuffleSplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m         \"\"\"\n\u001b[0;32m--> 732\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, parameter_iterable)\u001b[0m\n\u001b[1;32m    503\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m                                     error_score=self.error_score)\n\u001b[0;32m--> 505\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m                 for train, test in cv)\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    657\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpre_dispatch\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36mdispatch\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m         \"\"\"\n\u001b[1;32m    405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_verbosity_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, func, args, kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)\u001b[0m\n\u001b[1;32m   1457\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1458\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1459\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36m_dense_fit\u001b[0;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[1;32m    234\u001b[0m                 \u001b[0mcache_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m                 \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m                 max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cv = StratifiedShuffleSplit(y, n_iter=5, test_size=0.2)\n",
    "grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)\n",
    "grid.fit(X, y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Params: {'C': 38311.868495572853, 'gamma': 1.0000000000000001e-05}\n",
      "Best Score: 0.986666666667\n"
     ]
    }
   ],
   "source": [
    "print \"Best Params:\", grid.best_params_\n",
    "print \"Best Score:\", grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's see how the score changes over range of C and gamma\n",
    "scores = [x[1] for x in grid.grid_scores_]\n",
    "scores = np.array(scores).reshape(len(C_range), len(gamma_range))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dc9acd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EIwuCIAjqmlgjC4pm7uLFHTfqBL3mF2oOgAVLlxZq78OHnynUHsDafYoXFP39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\nIc1fjmjcnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfe64d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap( scores,xticklabels=C_range,yticklabels=gamma_range,cmap=plt.cm.Reds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}