10_svm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue' align=\"center\">Support Vector Machine Tutorial Using Python Sklearn</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img height=300 width=300 src=\"iris_petal_sepal.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>0.2</td>\n",
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      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>6.4</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
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       "      <td>6.9</td>\n",
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       "      <td>1</td>\n",
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       "      <th>54</th>\n",
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      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "\n",
       "    target  \n",
       "50       1  \n",
       "51       1  \n",
       "52       1  \n",
       "53       1  \n",
       "54       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df[df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "100                6.3               3.3                6.0               2.5   \n",
       "101                5.8               2.7                5.1               1.9   \n",
       "102                7.1               3.0                5.9               2.1   \n",
       "103                6.3               2.9                5.6               1.8   \n",
       "104                6.5               3.0                5.8               2.2   \n",
       "\n",
       "     target  \n",
       "100       2  \n",
       "101       2  \n",
       "102       2  \n",
       "103       2  \n",
       "104       2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <th>flower_name</th>\n",
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       "      <td>0.2</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>0</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
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       "      <td>setosa</td>\n",
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       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "45                4.8               3.0                1.4               0.3   \n",
       "46                5.1               3.8                1.6               0.2   \n",
       "47                4.6               3.2                1.4               0.2   \n",
       "48                5.3               3.7                1.5               0.2   \n",
       "49                5.0               3.3                1.4               0.2   \n",
       "50                7.0               3.2                4.7               1.4   \n",
       "51                6.4               3.2                4.5               1.5   \n",
       "52                6.9               3.1                4.9               1.5   \n",
       "53                5.5               2.3                4.0               1.3   \n",
       "54                6.5               2.8                4.6               1.5   \n",
       "\n",
       "    target flower_name  \n",
       "45       0      setosa  \n",
       "46       0      setosa  \n",
       "47       0      setosa  \n",
       "48       0      setosa  \n",
       "49       0      setosa  \n",
       "50       1  versicolor  \n",
       "51       1  versicolor  \n",
       "52       1  versicolor  \n",
       "53       1  versicolor  \n",
       "54       1  versicolor  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[45:55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df0 = df[:50]\n",
    "df1 = df[50:100]\n",
    "df2 = df[100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sepal length vs Sepal Width (Setosa vs Versicolor)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f1b16976a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1XVL4Tn4zOyR9EpiJiA/m7zdGxOE9jLdrHqdgZlZeL8YpLJLUOL10InBb02fdXKA2M7Oa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1b1990f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Sepal Width')\n",
    "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color=\"green\",marker='+')\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color=\"blue\",marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Petal length vs Pepal Width (Setosa vs Versicolor)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f1b2018390>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EH1RJGF8AVgGHSPoz4LvAZyrsdxjwZMv8lnLZa0jaH1gKrGxZbOBOSeslLatwvoiIqFGV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\nSRJGRERU8v8BEmPulfCH7tYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1b1f82588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Petal Length')\n",
    "plt.ylabel('Petal Width')\n",
    "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color=\"green\",marker='+')\n",
    "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"blue\",marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Using Support Vector Machine (SVM)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.drop(['target','flower_name'], axis='columns')\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93333333333333335"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[4.8,3.0,1.5,0.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tune parameters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Regularization (C)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93333333333333335"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C = SVC(C=1)\n",
    "model_C.fit(X_train, y_train)\n",
    "model_C.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96666666666666667"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C = SVC(C=10)\n",
    "model_C.fit(X_train, y_train)\n",
    "model_C.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Gamma**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90000000000000002"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_g = SVC(gamma=10)\n",
    "model_g.fit(X_train, y_train)\n",
    "model_g.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Kernel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_linear_kernal = SVC(kernel='linear')\n",
    "model_linear_kernal.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96666666666666667"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_linear_kernal.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train SVM classifier using sklearn digits dataset (i.e. from sklearn.datasets import load_digits) and then,\n",
    "\n",
    "1. Measure accuracy of your model using different kernels such as rbf and linear.\n",
    "2. Tune your model further using regularization and gamma parameters and try to come up with highest accurancy score\n",
    "3. Use 80% of samples as training data size\n"
   ]
  }
 ],
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