letter_recognition_MultiLable_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multilable classification demo\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn import svm ,naive_bayes\n",
    "from sklearn.model_selection import train_test_split\n",
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "# from sklearn.neighbors import KNeighborsClassifier as KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "indexes = ['letter','x-box','y-box','width','height','onpix','x-bar','y-bar','x2bar','y2bar','xybar','x2ybr','xy2br','x-ege','xegvy','y-ege','yegvx']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/letter-recognition/letter-recognition.data',names=indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df = dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dff = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>x-box</th>\n",
       "      <th>y-box</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>onpix</th>\n",
       "      <th>x-bar</th>\n",
       "      <th>y-bar</th>\n",
       "      <th>x2bar</th>\n",
       "      <th>y2bar</th>\n",
       "      <th>xybar</th>\n",
       "      <th>x2ybr</th>\n",
       "      <th>xy2br</th>\n",
       "      <th>x-ege</th>\n",
       "      <th>xegvy</th>\n",
       "      <th>y-ege</th>\n",
       "      <th>yegvx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  letter  x-box  y-box  width  height  onpix  x-bar  y-bar  x2bar  y2bar  \\\n",
       "0      T      2      8      3       5      1      8     13      0      6   \n",
       "1      I      5     12      3       7      2     10      5      5      4   \n",
       "2      D      4     11      6       8      6     10      6      2      6   \n",
       "3      N      7     11      6       6      3      5      9      4      6   \n",
       "4      G      2      1      3       1      1      8      6      6      6   \n",
       "\n",
       "   xybar  x2ybr  xy2br  x-ege  xegvy  y-ege  yegvx  \n",
       "0      6     10      8      0      8      0      8  \n",
       "1     13      3      9      2      8      4     10  \n",
       "2     10      3      7      3      7      3      9  \n",
       "3      4      4     10      6     10      2      8  \n",
       "4      6      5      9      1      7      5     10  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    T\n",
       "1    I\n",
       "2    D\n",
       "3    N\n",
       "4    G\n",
       "Name: letter, dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['letter']\n",
    "x = df\n",
    "x = x.drop('letter',axis=1)\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = pd.get_dummies(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    T\n",
       "1    I\n",
       "2    D\n",
       "3    N\n",
       "4    G\n",
       "Name: letter, dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2835     Z\n",
       "17868    F\n",
       "10780    M\n",
       "13502    L\n",
       "11657    O\n",
       "Name: letter, dtype: object"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          n_jobs=1)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model = OneVsRestClassifier(KNN()) #0.9436\n",
    "model = OneVsRestClassifier(svm.SVC()) # 0.9666\n",
    "# model = OneVsRestClassifier(DecisionTreeClassifier()) # 0.8308\n",
    "# model = OneVsRestClassifier(naive_bayes.MultinomialNB()) # 0.6364 GNB, 0.5382 MNB, 0.1078 BN\n",
    "model.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_x,test_y)"
   ]
  }
 ],
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