0 - Initial notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First kaggle submission\n",
    "\n",
    "This notebook is needed to test that everything works for you.\n",
    "\n",
    "Also it introduces first challenge.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello, world\n"
     ]
    }
   ],
   "source": [
    "print 'hello, world'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading spam collection\n",
    "Each line is label + TAB + message. Label is either 'spam' or 'ham'. \n",
    "\n",
    "Let's look at this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ham\tDid he say how fantastic I am by any chance, or anything need a bigger life lift as losing the will 2 live, do you think I would be the first person 2 die from N V Q? \r\n",
      "ham\tBlack shirt n blue jeans... I thk i c ü...\r\n",
      "ham\tIf e timing can, then i go w u lor...\r\n",
      "ham\tThey r giving a second chance to rahul dengra.\r\n",
      "ham\tI cant pick the phone right now. Pls send a message\r\n",
      "ham\tHaha good to hear, I'm officially paid and on the market for an 8th\r\n",
      "ham\tFfffffffff. Alright no way I can meet up with you sooner?\r\n",
      "ham\tBut i'm really really broke oh. No amount is too small even  &lt;#&gt; \r\n",
      "ham\tOnly 2% students solved this CAT question in 'xam... 5+3+2= &lt;#&gt;  9+2+4= &lt;#&gt;  8+6+3= &lt;#&gt;  then 7+2+5=????? Tell me the answer if u r brilliant...1thing.i got d answr.\r\n",
      "spam\t<Forwarded from 21870000>Hi - this is your Mailbox Messaging SMS alert. You have 4 messages. You have 21 matches. Please call back on 09056242159 to retrieve your messages and matches\r\n",
      "ham\tNo da:)he is stupid da..always sending like this:)don believe any of those message.pandy is a mental:)\r\n",
      "spam\tYou won't believe it but it's true. It's Incredible Txts! Reply G now to learn truly amazing things that will blow your mind. From O2FWD only 18p/txt\r\n",
      "ham\tIt's fine, imma get a drink or somethin. Want me to come find you?\r\n",
      "ham\tI can probably come by, everybody's done around  &lt;#&gt;  right?\r\n",
      "spam\tsexy sexy cum and text me im wet and warm and ready for some porn! u up for some fun? THIS MSG IS FREE RECD MSGS 150P INC VAT 2 CANCEL TEXT STOP\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 15 ./data/1-sms-spam-train.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import codecs\n",
    "with codecs.open('./data/1-sms-spam-train.txt', encoding='utf-8') as f:\n",
    "    train_labels_textual, train_messages = zip(*[line.split('\\t') for line in f.readlines()])\n",
    "    \n",
    "with codecs.open('./data/1-sms-spam-test.txt', encoding='utf-8') as f:\n",
    "    test_messages = f.readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting labels to 0 and 1\n",
    "\n",
    "Just for convenience. 1 is spam, 0 is ham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'ham' u'ham' u'ham' ..., u'ham' u'spam' u'ham']\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "print numpy.array(train_labels_textual)\n",
    "train_labels = (numpy.array(train_labels_textual) == 'spam') * 1\n",
    "print train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing minimal stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spam messages 389\n",
      "ham messages  2611\n",
      "spam part     0.129666666667\n"
     ]
    }
   ],
   "source": [
    "print 'spam messages', train_labels.sum()\n",
    "print 'ham messages ', (1 - train_labels).sum()\n",
    "print 'spam part    ', train_labels.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining simple evaluation procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate(message):\n",
    "    message = message.lower()\n",
    "    positive_patterns = ['me ', 'he ', 'i ']\n",
    "    negative_patterns = ['call', 'free', 'won', 'sms',]\n",
    "    score = 0\n",
    "    for pattern in positive_patterns:\n",
    "        score -= (pattern in message)\n",
    "    for pattern in negative_patterns:\n",
    "        score += (pattern in message)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_scores = map(evaluate, train_messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the quality on available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.803854859655462"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(train_labels, train_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the basic solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = map(evaluate, test_messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "result = pandas.DataFrame({'Id': numpy.arange(len(test_messages)), 'Label': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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    "result[:10]"
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  {
   "cell_type": "code",
   "execution_count": 13,
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   "outputs": [],
   "source": [
    "result.to_csv('data/1-sms-spam-predictions.csv', index=False)"
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      "1949,2\r\n",
      "1950,-1\r\n",
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      "1972,-1\r\n",
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      "1978,0\r\n",
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      "1981,1\r\n",
      "1982,0\r\n",
      "1983,-2\r\n",
      "1984,0\r\n",
      "1985,0\r\n",
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      "1987,-3\r\n",
      "1988,0\r\n",
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      "1990,-1\r\n",
      "1991,0\r\n",
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      "1993,-1\r\n",
      "1994,0\r\n",
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      "1998,0\r\n",
      "1999,-1\r\n",
      "2000,-3\r\n",
      "2001,-1\r\n",
      "2002,-1\r\n",
      "2003,0\r\n",
      "2004,-1\r\n",
      "2005,-1\r\n",
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      "2007,-2\r\n",
      "2008,-2\r\n",
      "2009,-1\r\n",
      "2010,-3\r\n",
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      "2013,3\r\n",
      "2014,-2\r\n",
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      "2572,0\r\n",
      "2573,3\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/1-sms-spam-predictions.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Click link below to download solution\n",
    "\n",
    "The solution is to be submitted to kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import FileLink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='data/1-sms-spam-predictions.csv' target='_blank'>data/1-sms-spam-predictions.csv</a><br>"
      ],
      "text/plain": [
       "/Users/axelr/ipython/MLatImperial2016/data/1-sms-spam-predictions.csv"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileLink('data/1-sms-spam-predictions.csv')"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
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}