nine_ClassifyNames.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gender_features(word):\n",
    "    return{'last_letter': word[-1]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'last_letter': 'k'}\n"
     ]
    }
   ],
   "source": [
    "print(gender_features('Shrek'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aamir',\n",
       " 'Aaron',\n",
       " 'Abbey',\n",
       " 'Abbie',\n",
       " 'Abbot',\n",
       " 'Abbott',\n",
       " 'Abby',\n",
       " 'Abdel',\n",
       " 'Abdul',\n",
       " 'Abdulkarim',\n",
       " 'Abdullah',\n",
       " 'Abe',\n",
       " 'Abel',\n",
       " 'Abelard',\n",
       " 'Abner',\n",
       " 'Abraham',\n",
       " 'Abram',\n",
       " 'Ace',\n",
       " 'Adair',\n",
       " 'Adam']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.words('male.txt')[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abagael',\n",
       " 'Abagail',\n",
       " 'Abbe',\n",
       " 'Abbey',\n",
       " 'Abbi',\n",
       " 'Abbie',\n",
       " 'Abby',\n",
       " 'Abigael',\n",
       " 'Abigail',\n",
       " 'Abigale',\n",
       " 'Abra',\n",
       " 'Acacia',\n",
       " 'Ada',\n",
       " 'Adah',\n",
       " 'Adaline',\n",
       " 'Adara',\n",
       " 'Addie',\n",
       " 'Addis',\n",
       " 'Adel',\n",
       " 'Adela']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.words('female.txt')[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "namesgender = ([(name, 'male') for name in names.words('male.txt')] +\n",
    "          [(name, 'female') for name in names.words('female.txt')])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7944"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(namesgender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Aamir', 'male'),\n",
       " ('Aaron', 'male'),\n",
       " ('Abbey', 'male'),\n",
       " ('Abbie', 'male'),\n",
       " ('Abbot', 'male'),\n",
       " ('Abbott', 'male'),\n",
       " ('Abby', 'male'),\n",
       " ('Abdel', 'male'),\n",
       " ('Abdul', 'male'),\n",
       " ('Abdulkarim', 'male'),\n",
       " ('Abdullah', 'male'),\n",
       " ('Abe', 'male'),\n",
       " ('Abel', 'male'),\n",
       " ('Abelard', 'male'),\n",
       " ('Abner', 'male'),\n",
       " ('Abraham', 'male'),\n",
       " ('Abram', 'male'),\n",
       " ('Ace', 'male'),\n",
       " ('Adair', 'male'),\n",
       " ('Adam', 'male')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "namesgender[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Zena', 'female'),\n",
       " ('Zenia', 'female'),\n",
       " ('Zia', 'female'),\n",
       " ('Zilvia', 'female'),\n",
       " ('Zita', 'female'),\n",
       " ('Zitella', 'female'),\n",
       " ('Zoe', 'female'),\n",
       " ('Zola', 'female'),\n",
       " ('Zonda', 'female'),\n",
       " ('Zondra', 'female'),\n",
       " ('Zonnya', 'female'),\n",
       " ('Zora', 'female'),\n",
       " ('Zorah', 'female'),\n",
       " ('Zorana', 'female'),\n",
       " ('Zorina', 'female'),\n",
       " ('Zorine', 'female'),\n",
       " ('Zsa Zsa', 'female'),\n",
       " ('Zsazsa', 'female'),\n",
       " ('Zulema', 'female'),\n",
       " ('Zuzana', 'female')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "namesgender[7924:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(namesgender)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[({'last_letter': 'n'}, 'male'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 's'}, 'female'),\n",
       " ({'last_letter': 'y'}, 'male'),\n",
       " ({'last_letter': 't'}, 'male'),\n",
       " ({'last_letter': 'e'}, 'female'),\n",
       " ({'last_letter': 'n'}, 'male'),\n",
       " ({'last_letter': 'h'}, 'male'),\n",
       " ({'last_letter': 'e'}, 'female'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 'd'}, 'male'),\n",
       " ({'last_letter': 'e'}, 'female'),\n",
       " ({'last_letter': 'l'}, 'female'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 'i'}, 'female'),\n",
       " ({'last_letter': 'y'}, 'female'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 'a'}, 'female'),\n",
       " ({'last_letter': 'e'}, 'female')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featuresets = [(gender_features(n), g) for (n, g) in namesgender]\n",
    "\n",
    "featuresets[:20]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = featuresets[500:], featuresets[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.796\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'female'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(gender_features('Neo'))\n",
    "classifier.classify(gender_features('Trinity'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "             last_letter = 'k'              male : female =     73.6 : 1.0\n",
      "             last_letter = 'a'            female : male   =     34.1 : 1.0\n",
      "             last_letter = 'f'              male : female =     15.8 : 1.0\n",
      "             last_letter = 'p'              male : female =     11.8 : 1.0\n",
      "             last_letter = 'v'              male : female =      9.8 : 1.0\n",
      "             last_letter = 'd'              male : female =      9.5 : 1.0\n",
      "             last_letter = 'm'              male : female =      8.4 : 1.0\n",
      "             last_letter = 'w'              male : female =      8.4 : 1.0\n",
      "             last_letter = 'o'              male : female =      8.3 : 1.0\n",
      "             last_letter = 'r'              male : female =      7.0 : 1.0\n",
      "             last_letter = 'g'              male : female =      5.1 : 1.0\n",
      "             last_letter = 's'              male : female =      4.4 : 1.0\n",
      "             last_letter = 'z'              male : female =      4.3 : 1.0\n",
      "             last_letter = 't'              male : female =      4.3 : 1.0\n",
      "             last_letter = 'b'              male : female =      3.8 : 1.0\n",
      "             last_letter = 'i'            female : male   =      3.6 : 1.0\n",
      "             last_letter = 'u'              male : female =      3.0 : 1.0\n",
      "             last_letter = 'j'              male : female =      2.8 : 1.0\n",
      "             last_letter = 'n'              male : female =      2.0 : 1.0\n",
      "             last_letter = 'e'            female : male   =      1.8 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count(a)': 0,\n",
       " 'count(b)': 0,\n",
       " 'count(c)': 0,\n",
       " 'count(d)': 0,\n",
       " 'count(e)': 1,\n",
       " 'count(f)': 0,\n",
       " 'count(g)': 0,\n",
       " 'count(h)': 1,\n",
       " 'count(i)': 0,\n",
       " 'count(j)': 0,\n",
       " 'count(k)': 1,\n",
       " 'count(l)': 0,\n",
       " 'count(m)': 0,\n",
       " 'count(n)': 0,\n",
       " 'count(o)': 0,\n",
       " 'count(p)': 0,\n",
       " 'count(q)': 0,\n",
       " 'count(r)': 1,\n",
       " 'count(s)': 1,\n",
       " 'count(t)': 0,\n",
       " 'count(u)': 0,\n",
       " 'count(v)': 0,\n",
       " 'count(w)': 0,\n",
       " 'count(x)': 0,\n",
       " 'count(y)': 0,\n",
       " 'count(z)': 0,\n",
       " 'firstletter': 's',\n",
       " 'has(a)': False,\n",
       " 'has(b)': False,\n",
       " 'has(c)': False,\n",
       " 'has(d)': False,\n",
       " 'has(e)': True,\n",
       " 'has(f)': False,\n",
       " 'has(g)': False,\n",
       " 'has(h)': True,\n",
       " 'has(i)': False,\n",
       " 'has(j)': False,\n",
       " 'has(k)': True,\n",
       " 'has(l)': False,\n",
       " 'has(m)': False,\n",
       " 'has(n)': False,\n",
       " 'has(o)': False,\n",
       " 'has(p)': False,\n",
       " 'has(q)': False,\n",
       " 'has(r)': True,\n",
       " 'has(s)': True,\n",
       " 'has(t)': False,\n",
       " 'has(u)': False,\n",
       " 'has(v)': False,\n",
       " 'has(w)': False,\n",
       " 'has(x)': False,\n",
       " 'has(y)': False,\n",
       " 'has(z)': False,\n",
       " 'lastletter': 'k'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gender_features2(name):\n",
    "    features = {}\n",
    "    features[\"firstletter\"] = name[0].lower()\n",
    "    features[\"lastletter\"] = name[-1].lower()\n",
    "    for letter in 'abcdefghijklmnopqrstuvwxyz':\n",
    "        features[\"count({})\".format(letter)] = name.lower().count(letter)\n",
    "        features[\"has({})\".format(letter)] = (letter in name.lower())\n",
    "    return features\n",
    "\n",
    "features = gender_features2('Shrek')\n",
    "len(features)\n",
    "features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "featuresets2 = [(gender_features2(n), g) for (n, g) in namesgender]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Don {'firstletter': 'd', 'lastletter': 'n', 'count(a)': 0, 'has(a)': False, 'count(b)': 0, 'has(b)': False, 'count(c)': 0, 'has(c)': False, 'count(d)': 1, 'has(d)': True, 'count(e)': 0, 'has(e)': False, 'count(f)': 0, 'has(f)': False, 'count(g)': 0, 'has(g)': False, 'count(h)': 0, 'has(h)': False, 'count(i)': 0, 'has(i)': False, 'count(j)': 0, 'has(j)': False, 'count(k)': 0, 'has(k)': False, 'count(l)': 0, 'has(l)': False, 'count(m)': 0, 'has(m)': False, 'count(n)': 1, 'has(n)': True, 'count(o)': 1, 'has(o)': True, 'count(p)': 0, 'has(p)': False, 'count(q)': 0, 'has(q)': False, 'count(r)': 0, 'has(r)': False, 'count(s)': 0, 'has(s)': False, 'count(t)': 0, 'has(t)': False, 'count(u)': 0, 'has(u)': False, 'count(v)': 0, 'has(v)': False, 'count(w)': 0, 'has(w)': False, 'count(x)': 0, 'has(x)': False, 'count(y)': 0, 'has(y)': False, 'count(z)': 0, 'has(z)': False} \n",
      "\n",
      "Olva {'firstletter': 'o', 'lastletter': 'a', 'count(a)': 1, 'has(a)': True, 'count(b)': 0, 'has(b)': False, 'count(c)': 0, 'has(c)': False, 'count(d)': 0, 'has(d)': False, 'count(e)': 0, 'has(e)': False, 'count(f)': 0, 'has(f)': False, 'count(g)': 0, 'has(g)': False, 'count(h)': 0, 'has(h)': False, 'count(i)': 0, 'has(i)': False, 'count(j)': 0, 'has(j)': False, 'count(k)': 0, 'has(k)': False, 'count(l)': 1, 'has(l)': True, 'count(m)': 0, 'has(m)': False, 'count(n)': 0, 'has(n)': False, 'count(o)': 1, 'has(o)': True, 'count(p)': 0, 'has(p)': False, 'count(q)': 0, 'has(q)': False, 'count(r)': 0, 'has(r)': False, 'count(s)': 0, 'has(s)': False, 'count(t)': 0, 'has(t)': False, 'count(u)': 0, 'has(u)': False, 'count(v)': 1, 'has(v)': True, 'count(w)': 0, 'has(w)': False, 'count(x)': 0, 'has(x)': False, 'count(y)': 0, 'has(y)': False, 'count(z)': 0, 'has(z)': False} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for (n, g) in namesgender[:2]:\n",
    "    print(n, gender_features2(n), '\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.764\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_set, test_set = featuresets2[500:], featuresets2[:500]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(nltk.classify.accuracy(classifier, test_set))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_names = namesgender[500:]\n",
    "test_names = namesgender[:500]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set = [(gender_features(n), g) for (n, g) in train_names]\n",
    "test_set = [(gender_features(n), g) for (n, g) in test_names]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.796\n"
     ]
    }
   ],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(nltk.classify.accuracy(classifier, test_set))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def geterrors(test):\n",
    "    errors = []\n",
    "    for (name, tag) in test:\n",
    "        guess = classifier.classify(gender_features(name))\n",
    "        if guess != tag:\n",
    "            errors.append( (tag, guess, name) )\n",
    "    return errors\n",
    "\n",
    "errors = geterrors(test_names)\n",
    "len(errors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct=female   guess=male     name=Aimil                         \n",
      "correct=female   guess=male     name=Alys                          \n",
      "correct=female   guess=male     name=Caryl                         \n",
      "correct=female   guess=male     name=Charis                        \n",
      "correct=female   guess=male     name=Cloris                        \n",
      "correct=female   guess=male     name=Dew                           \n",
      "correct=female   guess=male     name=Easter                        \n",
      "correct=female   guess=male     name=Estell                        \n",
      "correct=female   guess=male     name=Ethelin                       \n",
      "correct=female   guess=male     name=Faun                          \n",
      "correct=female   guess=male     name=Frank                         \n",
      "correct=female   guess=male     name=Ginger                        \n",
      "correct=female   guess=male     name=Gwendolen                     \n",
      "correct=female   guess=male     name=Harriet                       \n",
      "correct=female   guess=male     name=Jo Ann                        \n",
      "correct=female   guess=male     name=Jordain                       \n",
      "correct=female   guess=male     name=Madalyn                       \n",
      "correct=female   guess=male     name=Pet                           \n",
      "correct=female   guess=male     name=Phil                          \n",
      "correct=female   guess=male     name=Philis                        \n",
      "correct=female   guess=male     name=Ros                           \n",
      "correct=female   guess=male     name=Sibeal                        \n",
      "correct=male     guess=female   name=Aditya                        \n",
      "correct=male     guess=female   name=Alfonse                       \n",
      "correct=male     guess=female   name=Ali                           \n",
      "correct=male     guess=female   name=Anatoly                       \n",
      "correct=male     guess=female   name=Andy                          \n",
      "correct=male     guess=female   name=Baily                         \n",
      "correct=male     guess=female   name=Bartholemy                    \n",
      "correct=male     guess=female   name=Bartie                        \n",
      "correct=male     guess=female   name=Chaunce                       \n",
      "correct=male     guess=female   name=Esme                          \n",
      "correct=male     guess=female   name=Hilary                        \n",
      "correct=male     guess=female   name=Keith                         \n",
      "correct=male     guess=female   name=Lawrence                      \n",
      "correct=male     guess=female   name=Osborne                       \n",
      "correct=male     guess=female   name=Sammie                        \n",
      "correct=male     guess=female   name=Sinclare                      \n",
      "correct=male     guess=female   name=Warde                         \n",
      "correct=male     guess=female   name=Wolfy                         \n"
     ]
    }
   ],
   "source": [
    "def printerrors(errors):\n",
    "    for (tag, guess, name) in sorted(errors):\n",
    "        print('correct={:<8s} guess={:<8s} name={:<30s}'.format(tag, guess, name))\n",
    "\n",
    "printerrors(errors[:40])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.metrics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reflist = []\n",
    "testlist = []\n",
    "for (features, label) in test_set:\n",
    "    reflist.append(label)\n",
    "    testlist.append(classifier.classify(features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['male',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'male',\n",
       " 'male',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'female',\n",
       " 'male',\n",
       " 'female']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reflist[:30]\n",
    "testlist[:30]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       |   f     |\n",
      "       |   e     |\n",
      "       |   m   m |\n",
      "       |   a   a |\n",
      "       |   l   l |\n",
      "       |   e   e |\n",
      "-------+---------+\n",
      "female |<268> 57 |\n",
      "  male |  45<130>|\n",
      "-------+---------+\n",
      "(row = reference; col = test)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = ConfusionMatrix(reflist, testlist)\n",
    "print(cm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reffemale = set()\n",
    "refmale = set()\n",
    "testfemale = set()\n",
    "testmale = set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0,\n",
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       " 487,\n",
       " 497,\n",
       " 499}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, label in enumerate(reflist):\n",
    "    if label == 'female': reffemale.add(i)\n",
    "    if label == 'male': refmale.add(i)\n",
    "\n",
    "for i, label in enumerate(testlist):\n",
    "    if label == 'female': testfemale.add(i)\n",
    "    if label == 'male': testmale.add(i)\n",
    "\n",
    "reffemale\n",
    "testfemale\n",
    "refmale\n",
    "testmale\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def printmeasures(label, refset, testset):\n",
    "    print(label, 'precision:', precision(refset, testset))\n",
    "    print(label, 'recall:', recall(refset, testset)) \n",
    "    print(label, 'F-measure:', f_measure(refset, testset))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "female precision: 0.8562300319488818\n",
      "female recall: 0.8246153846153846\n",
      "female F-measure: 0.84012539184953\n",
      "male precision: 0.6951871657754011\n",
      "male recall: 0.7428571428571429\n",
      "male F-measure: 0.7182320441988951\n"
     ]
    }
   ],
   "source": [
    "printmeasures('female', reffemale, testfemale)\n",
    "printmeasures('male', refmale, testmale)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'suffix1': 'k', 'suffix2': 'e'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gender_features3(word):\n",
    "    return {'suffix1': word[-1],'suffix2': word[-2]}\n",
    "\n",
    "gender_features3('Shrek')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'v'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 't', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'h', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 's'}, 'female'),\n",
       " ({'suffix1': 'd', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'h'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'k'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'w', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'h'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'f'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 't', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'h', 'suffix2': 'c'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'c'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'm'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 's'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 's'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'f', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'k', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'd'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'r', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'v', 'suffix2': 'E'}, 'male'),\n",
       " ({'suffix1': 'k', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'u'}, 'male'),\n",
       " ({'suffix1': 'o', 'suffix2': 'h'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'r', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'k', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'c'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'o'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'k', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'h', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'd'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'b'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'z'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 's'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'male'),\n",
       " ({'suffix1': 'o', 'suffix2': 'f'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'u', 'suffix2': 'h'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'v'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'o', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'f'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'k', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'o', 'suffix2': 'J'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'c'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'j'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'n'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'r', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'f'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'u'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'y'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'j'}, 'female'),\n",
       " ({'suffix1': 'f', 'suffix2': 'f'}, 'male'),\n",
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       " ({'suffix1': 'i', 'suffix2': 'b'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'p'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 's'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'h', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'd', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'h'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'd', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'u'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'h', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'n'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 't'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'b'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'p', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'u'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 'o'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'd', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 't', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'd', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'h', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'y'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'n'}, 'male'),\n",
       " ({'suffix1': 'm', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'c'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'b'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'd', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'p'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'l'}, 'male'),\n",
       " ({'suffix1': 'h', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 's'}, 'female'),\n",
       " ({'suffix1': 's', 'suffix2': 'u'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'h', 'suffix2': 'p'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'm', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'd'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'a'}, 'female'),\n",
       " ({'suffix1': 'l', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'n', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'y', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'n'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'h'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'v'}, 'male'),\n",
       " ({'suffix1': 'm', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'e', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'c'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'd'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'r'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'h'}, 'female'),\n",
       " ({'suffix1': 't', 'suffix2': 't'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'i'}, 'female'),\n",
       " ({'suffix1': 'e', 'suffix2': 'r'}, 'male'),\n",
       " ({'suffix1': 'n', 'suffix2': 'o'}, 'male'),\n",
       " ({'suffix1': 'a', 'suffix2': 'e'}, 'female'),\n",
       " ({'suffix1': 'r', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 's', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'y', 'suffix2': 'f'}, 'female'),\n",
       " ({'suffix1': 'o', 'suffix2': 'e'}, 'male'),\n",
       " ({'suffix1': 'd', 'suffix2': 'n'}, 'male'),\n",
       " ({'suffix1': 'l', 'suffix2': 'i'}, 'male'),\n",
       " ({'suffix1': 'd', 'suffix2': 'a'}, 'male'),\n",
       " ({'suffix1': 'i', 'suffix2': 'd'}, 'female'),\n",
       " ({'suffix1': 'i', 'suffix2': 'l'}, 'female'),\n",
       " ({'suffix1': 'a', 'suffix2': 'c'}, 'female'),\n",
       " ...]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gender_features3(word):   \n",
    "    return {'suffix1': word[-1],'suffix2': word[-2]}\n",
    "\n",
    "featuresets3 = [(gender_features3(n), g) for (n, g) in namesgender]\n",
    "featuresets3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Don {'suffix1': 'n', 'suffix2': 'o'} \n",
      "\n",
      "Olva {'suffix1': 'a', 'suffix2': 'v'} \n",
      "\n",
      "Charis {'suffix1': 's', 'suffix2': 'i'} \n",
      "\n",
      "Hilary {'suffix1': 'y', 'suffix2': 'r'} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for (n, g) in namesgender[:4]:\n",
    "    print(n, gender_features3(n), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.776\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = featuresets3[500:], featuresets3[:500]\n",
    "\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "\n",
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_names = namesgender[500:]\n",
    "\n",
    "test_names = namesgender[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def geterrors(test):\n",
    "    errors = []\n",
    "    for (name, tag) in test:\n",
    "        guess = classifier.classify(gender_features(name))\n",
    "        if guess != tag:\n",
    "            errors.append( (tag, guess, name) )\n",
    "    return errors\n",
    "\n",
    "errors = geterrors(test_names)\n",
    "len(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct=male     guess=female   name=Adolpho                       \n",
      "correct=male     guess=female   name=Adrick                        \n",
      "correct=male     guess=female   name=Anatoly                       \n",
      "correct=male     guess=female   name=Andy                          \n",
      "correct=male     guess=female   name=Bartie                        \n",
      "correct=male     guess=female   name=Bronson                       \n",
      "correct=male     guess=female   name=Cyrillus                      \n",
      "correct=male     guess=female   name=Don                           \n",
      "correct=male     guess=female   name=Donal                         \n",
      "correct=male     guess=female   name=Dorian                        \n",
      "correct=male     guess=female   name=Elden                         \n",
      "correct=male     guess=female   name=Emilio                        \n",
      "correct=male     guess=female   name=Emmanuel                      \n",
      "correct=male     guess=female   name=Esme                          \n",
      "correct=male     guess=female   name=Ev                            \n",
      "correct=male     guess=female   name=Hewett                        \n",
      "correct=male     guess=female   name=Hilary                        \n",
      "correct=male     guess=female   name=Husain                        \n",
      "correct=male     guess=female   name=Ishmael                       \n",
      "correct=male     guess=female   name=Johan                         \n",
      "correct=male     guess=female   name=Jules                         \n",
      "correct=male     guess=female   name=Kaspar                        \n",
      "correct=male     guess=female   name=Keith                         \n",
      "correct=male     guess=female   name=Lester                        \n",
      "correct=male     guess=female   name=Mack                          \n",
      "correct=male     guess=female   name=Miguel                        \n",
      "correct=male     guess=female   name=Morgan                        \n",
      "correct=male     guess=female   name=Morris                        \n",
      "correct=male     guess=female   name=Nealon                        \n",
      "correct=male     guess=female   name=Paton                         \n",
      "correct=male     guess=female   name=Piotr                         \n",
      "correct=male     guess=female   name=Rickard                       \n",
      "correct=male     guess=female   name=Rodrick                       \n",
      "correct=male     guess=female   name=Sammie                        \n",
      "correct=male     guess=female   name=Sheldon                       \n",
      "correct=male     guess=female   name=Sinclare                      \n",
      "correct=male     guess=female   name=Warde                         \n",
      "correct=male     guess=female   name=Wes                           \n",
      "correct=male     guess=female   name=Wolf                          \n",
      "correct=male     guess=female   name=Wolfy                         \n"
     ]
    }
   ],
   "source": [
    "def printerrors(errors):\n",
    "    for (tag, guess, name) in sorted(errors):\n",
    "        print('correct={:<8s} guess={:<8s} name={:<30s}'.format(tag, guess, name))\n",
    "\n",
    "printerrors(errors[:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def printmeasures(label, refset, testset):\n",
    "    print(label, 'precision:', precision(refset, testset))\n",
    "    print(label, 'recall:', recall(refset, testset)) \n",
    "    print(label, 'F-measure:', f_measure(refset, testset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "female precision: 0.8562300319488818\n",
      "female recall: 0.8246153846153846\n",
      "female F-measure: 0.84012539184953\n",
      "male precision: 0.6951871657754011\n",
      "male recall: 0.7428571428571429\n",
      "male F-measure: 0.7182320441988951\n"
     ]
    }
   ],
   "source": [
    "printmeasures('female', reffemale, testfemale)\n",
    "printmeasures('male', refmale, testmale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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