nateve/WarrantyAnalysis-NLTK.ipynb

## WarrantyAnalysis-NLTK.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import random\n",
    "import re\n",
    "from string import lower\n",
    "from nltk import word_tokenize\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.parse import stanford\n",
    "from nltk.tree import ParentedTree, Tree\n",
    "from matplotlib import pyplot\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32957\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index([u'ID', u'EventNo', u'Prefix', u'Model', u'MfgFac', u'ProdFam',\n",
       "       u'ProdGrp', u'EngModel', u'EngFac', u'DT', u'F', u'PD', u'MajorSystem',\n",
       "       u'System', u'SubSystem', u'Component', u'WorkCd', u'PartNum',\n",
       "       u'PartName', u'PartNumName', u'GroupNum', u'GroupName', u'GroupNumName',\n",
       "       u'Warranty', u'WarrantyRange', u'Comment', u'Hours', u'HrRng',\n",
       "       u'FlrInd', u'BldDt', u'BldMth', u'RprDt', u'RprMth', u'RprDlr',\n",
       "       u'MktSub', u'Story', u'Claim', u'PaidDt', u'SN', u'OwningIssueTag',\n",
       "       u'OwningIssue', u'IssueStatus', u'IssueType', u'CreateDt', u'MIS',\n",
       "       u'RPT_LVL'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/QR_JMC_MWL_Aurora.csv')\n",
    "print len(data)\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#drop unwanted columns\n",
    "data = data.loc[:,('ID', 'Model', 'EngModel', 'MajorSystem', 'Component', 'WarrantyRange', 'Story')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>EngModel</th>\n",
       "      <th>MajorSystem</th>\n",
       "      <th>Component</th>\n",
       "      <th>WarrantyRange</th>\n",
       "      <th>Story</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53141536</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>MACHINE WONT REGEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52657918</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>%%EXTENDED COVERAGE ENROLLMENT CONTRACT #3177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52612200</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>%%EXTENDED COVERAGE ENROLLMENT CONTRACT #3176241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53055761</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52638503</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>COMPLETED SL PS44446 DTD 19 AUG 2014 THIS IS A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52687891</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>COMPLETED PS44446. BUT HAS THE FOLLOWING ISSUE...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Model EngModel   MajorSystem       Component WarrantyRange  \\\n",
       "0  53141536  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "1  52657918  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "2  52612200  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "3  53055761  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "4  52638503  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "5  52687891  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "\n",
       "                                               Story  \n",
       "0                                 MACHINE WONT REGEN  \n",
       "1   %%EXTENDED COVERAGE ENROLLMENT CONTRACT #3177441  \n",
       "2   %%EXTENDED COVERAGE ENROLLMENT CONTRACT #3176241  \n",
       "3                                                NaN  \n",
       "4  COMPLETED SL PS44446 DTD 19 AUG 2014 THIS IS A...  \n",
       "5  COMPLETED PS44446. BUT HAS THE FOLLOWING ISSUE...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>EngModel</th>\n",
       "      <th>MajorSystem</th>\n",
       "      <th>Component</th>\n",
       "      <th>WarrantyRange</th>\n",
       "      <th>Story</th>\n",
       "      <th>CleanText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53141536</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>MACHINE WONT REGEN</td>\n",
       "      <td>machine wont regen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52657918</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>%%EXTENDED COVERAGE ENROLLMENT CONTRACT #3177441</td>\n",
       "      <td>extended coverage enrollment contract 3177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52612200</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>%%EXTENDED COVERAGE ENROLLMENT CONTRACT #3176241</td>\n",
       "      <td>extended coverage enrollment contract 3176241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53055761</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52638503</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>COMPLETED SL PS44446 DTD 19 AUG 2014 THIS IS A...</td>\n",
       "      <td>completed sl ps44446 dtd 19 aug 2014 this is a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Model EngModel   MajorSystem       Component WarrantyRange  \\\n",
       "0  53141536  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "1  52657918  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "2  52612200  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "3  53055761  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "4  52638503  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "\n",
       "                                               Story  \\\n",
       "0                                 MACHINE WONT REGEN   \n",
       "1   %%EXTENDED COVERAGE ENROLLMENT CONTRACT #3177441   \n",
       "2   %%EXTENDED COVERAGE ENROLLMENT CONTRACT #3176241   \n",
       "3                                                NaN   \n",
       "4  COMPLETED SL PS44446 DTD 19 AUG 2014 THIS IS A...   \n",
       "\n",
       "                                           CleanText  \n",
       "0                                 machine wont regen  \n",
       "1      extended coverage enrollment contract 3177441  \n",
       "2      extended coverage enrollment contract 3176241  \n",
       "3                                                NaN  \n",
       "4  completed sl ps44446 dtd 19 aug 2014 this is a...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add cleaned column\n",
    "data['CleanText'] = data.Story.str.replace(r'[^a-zA-Z0-9,:.\\s]+', '')\n",
    "data.CleanText = data.CleanText.str.lower()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with nulls 32957\n",
      "without nulls 22000\n"
     ]
    }
   ],
   "source": [
    "#get rid of nulls\n",
    "print 'with nulls', len(data.Story)\n",
    "df_subset = data[pd.isnull(data.loc[:,('CleanText')])==False]\n",
    "#drop Story column\n",
    "df_subset = df_subset.loc[:,('ID', 'Model', 'EngModel', 'MajorSystem', 'Component', 'WarrantyRange', 'CleanText')]\n",
    "print 'without nulls', len(df_subset.CleanText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>EngModel</th>\n",
       "      <th>MajorSystem</th>\n",
       "      <th>Component</th>\n",
       "      <th>WarrantyRange</th>\n",
       "      <th>CleanText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53141536</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>machine wont regen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52657918</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>extended coverage enrollment contract 3177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52612200</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Non Failure</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>Negative</td>\n",
       "      <td>extended coverage enrollment contract 3176241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52638503</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>completed sl ps44446 dtd 19 aug 2014 this is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52687891</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>completed ps44446. but has the following issue...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Model EngModel   MajorSystem       Component WarrantyRange  \\\n",
       "0  53141536  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "1  52657918  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "2  52612200  950K     C7.1   Non Failure   PRIME PRODUCT      Negative   \n",
       "4  52638503  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "5  52687891  950K     C7.1  Base Machine  MACHINE ENGINE      0 to 500   \n",
       "\n",
       "                                           CleanText  \n",
       "0                                 machine wont regen  \n",
       "1      extended coverage enrollment contract 3177441  \n",
       "2      extended coverage enrollment contract 3176241  \n",
       "4  completed sl ps44446 dtd 19 aug 2014 this is a...  \n",
       "5  completed ps44446. but has the following issue...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# write out to file\n",
    "# df_subset.to_csv('./output/AuroraSubset.csv', na_rep='NA', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenize and Classify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('complaint:', 4396),\n",
       " ('customer complaint:', 2028),\n",
       " ('submitted by:', 754),\n",
       " ('al claim identified as follows:', 303),\n",
       " ('customer concern:', 173),\n",
       " ('concern:', 129),\n",
       " ('cat complaint:', 122),\n",
       " ('fmp:', 116),\n",
       " ('c:', 112),\n",
       " ('client complaint:', 105),\n",
       " ('note:', 101),\n",
       " ('i note:', 82),\n",
       " ('ca i note:', 82),\n",
       " ('customer:', 75),\n",
       " ('misc:', 67),\n",
       " ('claim:', 56),\n",
       " ('repair process comments:', 48),\n",
       " ('tted by dolores nunez coverage:', 35),\n",
       " ('cat support:', 34),\n",
       " ('cause of failure:', 34),\n",
       " ('complications in repair:', 32),\n",
       " ('entered by:', 30),\n",
       " ('emissions warranty  complaint:', 26),\n",
       " ('rtrain nongovernmental premium:', 23),\n",
       " ('emission warranty  complaint:', 22),\n",
       " ('equipment location:', 21),\n",
       " ('with lakeisha from cat i note:', 21),\n",
       " ('im  policy code xnd  complaint:', 20),\n",
       " ('sumbittedd by:', 18),\n",
       " ('pip claimpi32366 sl date:', 18),\n",
       " ('ery repair  customer complaint:', 17),\n",
       " ('requested by:', 17),\n",
       " ('slmsg5:', 16),\n",
       " ('pip claimpi32313 sl date:', 15),\n",
       " ('aulics nongovernmental premium:', 15),\n",
       " ('age policy code xnd  complaint:', 13),\n",
       " ('cause:', 12),\n",
       " ('correction:', 12),\n",
       " ('12 month premier  complaint:', 12),\n",
       " (':', 12)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# search for common tags\n",
    "tags = []\n",
    "for i in df_subset[df_subset.CleanText.str.contains(\":\")].CleanText:\n",
    "    tag = re.search(\"([\\w\\s,]{,30})(:)\", i)\n",
    "    if tag:\n",
    "        tags.append(tag.group(1).strip() + tag.group(2))\n",
    "#         print tag.group(1).strip() + tag.group(2)\n",
    "\n",
    "tags = nltk.Text(tags)\n",
    "tags_dist = nltk.FreqDist(tags)\n",
    "\n",
    "tags_dist.most_common(40)\n",
    "\n",
    "# tags_dict = dict(tags_dist)\n",
    "\n",
    "# for k in tags_dict:\n",
    "#     if 'cause' in k:\n",
    "#         print k, tags_dict[k]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Model</th>\n",
       "      <th>EngModel</th>\n",
       "      <th>MajorSystem</th>\n",
       "      <th>Component</th>\n",
       "      <th>WarrantyRange</th>\n",
       "      <th>CleanText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52687891</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>completed ps44446. but has the following issue...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52942304</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Implement Controls &amp; Hydraulic</td>\n",
       "      <td>PRIME PRODUCT</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>.complaint: there was a hyd leak on the left c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>53009984</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>CAB</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>.complaint: windshield wiper not working right...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>52616388</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Base Machine</td>\n",
       "      <td>MACHINE ENGINE</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>.note: this machine is not in service with 5 h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>52783863</td>\n",
       "      <td>950K</td>\n",
       "      <td>C7.1</td>\n",
       "      <td>Drive Train</td>\n",
       "      <td>PARTS</td>\n",
       "      <td>0 to 500</td>\n",
       "      <td>.complaint: . repair hyd leak. .cause: .      ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Model EngModel                     MajorSystem       Component  \\\n",
       "5   52687891  950K     C7.1                    Base Machine  MACHINE ENGINE   \n",
       "8   52942304  950K     C7.1  Implement Controls & Hydraulic   PRIME PRODUCT   \n",
       "14  53009984  950K     C7.1                    Base Machine             CAB   \n",
       "21  52616388  950K     C7.1                    Base Machine  MACHINE ENGINE   \n",
       "34  52783863  950K     C7.1                     Drive Train           PARTS   \n",
       "\n",
       "   WarrantyRange                                          CleanText  \n",
       "5       0 to 500  completed ps44446. but has the following issue...  \n",
       "8       0 to 500  .complaint: there was a hyd leak on the left c...  \n",
       "14      0 to 500  .complaint: windshield wiper not working right...  \n",
       "21      0 to 500  .note: this machine is not in service with 5 h...  \n",
       "34      0 to 500  .complaint: . repair hyd leak. .cause: .      ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_subset.CleanText = df_subset.CleanText.str.replace('customer complaint:', 'complaint:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('customer concern:', 'complaint:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('concern:', 'complaint:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('cause of failure:', 'cause:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('notes:', 'note:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('repair comment:', 'repair:')\n",
    "df_subset.CleanText = df_subset.CleanText.str.replace('repair process comments:', 'repair:')\n",
    "\n",
    "\n",
    "\n",
    "match_words = ['cause:', 'complaint:', 'note:', 'correction:', 'repair:']\n",
    "for word in match_words:\n",
    "    df_subset.loc[:,('CleanText')] = df_subset.CleanText.str.replace(word, '.%s' % word)\n",
    "\n",
    "display(df_subset[df_subset.CleanText.str.contains(':')].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7391\n",
      "4666\n",
      "8078\n",
      "915\n",
      "2619\n"
     ]
    }
   ],
   "source": [
    "print len(df_subset[df_subset.CleanText.str.contains('cause:')])\n",
    "print len(df_subset[df_subset.CleanText.str.contains('correction:')])\n",
    "print len(df_subset[df_subset.CleanText.str.contains('complaint:')])\n",
    "print len(df_subset[df_subset.CleanText.str.contains('note:')])\n",
    "print len(df_subset[df_subset.CleanText.str.contains('repair:')])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113869\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimID</th>\n",
       "      <th>Classification</th>\n",
       "      <th>SentenceNo</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53141536</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>machine wont regen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52657918</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>extended coverage enrollment contract 3177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52612200</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>extended coverage enrollment contract 3176241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52638503</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completed sl ps44446 dtd 19 aug 2014 this is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completed ps44446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>but has the following issues: code 56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>dsn  cat991603026y erform update ps44446 went...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52757454</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completd ps44446 as needed,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52942304</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>complaint: there was a hyd leak on the left cy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52942304</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>cause: 2168994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ClaimID Classification  SentenceNo  \\\n",
       "0  53141536           None           1   \n",
       "1  52657918           None           1   \n",
       "2  52612200           None           1   \n",
       "3  52638503           None           1   \n",
       "4  52687891           None           1   \n",
       "5  52687891           None           2   \n",
       "6  52687891           None           3   \n",
       "7  52757454           None           1   \n",
       "8  52942304           None           1   \n",
       "9  52942304           None           2   \n",
       "\n",
       "                                              Tokens  \n",
       "0                                 machine wont regen  \n",
       "1      extended coverage enrollment contract 3177441  \n",
       "2      extended coverage enrollment contract 3176241  \n",
       "3  completed sl ps44446 dtd 19 aug 2014 this is a...  \n",
       "4                                  completed ps44446  \n",
       "5              but has the following issues: code 56  \n",
       "6   dsn  cat991603026y erform update ps44446 went...  \n",
       "7                        completd ps44446 as needed,  \n",
       "8  complaint: there was a hyd leak on the left cy...  \n",
       "9                                    cause: 2168994   "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(r\"[^.]*\")\n",
    "# comma_tokenizer = RegexpTokenizer(r\"[^,]*\")\n",
    "\n",
    "tokens = []\n",
    "claims = []\n",
    "counts = []\n",
    "\n",
    "for index,row in df_subset.iterrows():\n",
    "    current_text = row['CleanText']\n",
    "    current_claim = row['ID']\n",
    "    sentence_tokens = tokenizer.tokenize(current_text)\n",
    "    counter = 1\n",
    "    for token in sentence_tokens:\n",
    "        if len(token.strip()) > 0:\n",
    "            tokens.append(token)\n",
    "            claims.append(current_claim)\n",
    "            counts.append(counter)\n",
    "            counter = counter + 1\n",
    "            \n",
    "sentence_grain = pd.DataFrame({\n",
    "                'Tokens': tokens, \n",
    "                'ClaimID': claims, \n",
    "                'SentenceNo': counts, \n",
    "                'Classification': None})\n",
    "print len(sentence_grain)\n",
    "sentence_grain.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nataliedeclerck/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "match_words = ['cause', 'complaint', 'note', 'correction', 'repair']\n",
    "# add classification words\n",
    "for word in match_words:\n",
    "    sentence_grain.loc[:,('Classification')][ sentence_grain.loc[:,('Tokens')].str.contains(word) ] = word\n",
    "\n",
    "\n",
    "# remove classifier words from sentence\n",
    "for word in match_words:\n",
    "    sentence_grain.loc[:,('Tokens')] = sentence_grain.loc[:,('Tokens')].str.replace(word,'')\\\n",
    "    .str.replace(':', ' ').str.replace('  ', ' ')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimID</th>\n",
       "      <th>Classification</th>\n",
       "      <th>SentenceNo</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53141536</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>machine wont regen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52657918</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>extended coverage enrollment contract 3177441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52612200</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>extended coverage enrollment contract 3176241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52638503</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completed sl ps44446 dtd 19 aug 2014 this is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completed ps44446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>but has the following issues code 56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52687891</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>dsn cat991603026y erform update ps44446 went ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52757454</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>completd ps44446 as needed,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52942304</td>\n",
       "      <td>complaint</td>\n",
       "      <td>1</td>\n",
       "      <td>there was a hyd leak on the left cylinder of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52942304</td>\n",
       "      <td>cause</td>\n",
       "      <td>2</td>\n",
       "      <td>2168994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ClaimID Classification  SentenceNo  \\\n",
       "0  53141536           None           1   \n",
       "1  52657918           None           1   \n",
       "2  52612200           None           1   \n",
       "3  52638503           None           1   \n",
       "4  52687891           None           1   \n",
       "5  52687891           None           2   \n",
       "6  52687891           None           3   \n",
       "7  52757454           None           1   \n",
       "8  52942304      complaint           1   \n",
       "9  52942304          cause           2   \n",
       "\n",
       "                                              Tokens  \n",
       "0                                 machine wont regen  \n",
       "1      extended coverage enrollment contract 3177441  \n",
       "2      extended coverage enrollment contract 3176241  \n",
       "3  completed sl ps44446 dtd 19 aug 2014 this is a...  \n",
       "4                                  completed ps44446  \n",
       "5               but has the following issues code 56  \n",
       "6   dsn cat991603026y erform update ps44446 went ...  \n",
       "7                        completd ps44446 as needed,  \n",
       "8   there was a hyd leak on the left cylinder of ...  \n",
       "9                                           2168994   "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_grain['Tokens'].replace(' ', np.nan, inplace=True)\n",
    "sentence_grain.dropna(subset=['Tokens'], inplace=True)\n",
    "sentence_grain.reset_index(drop=True, inplace=True)\n",
    "\n",
    "sentence_grain.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sentence_grain.to_csv('./output/AuroraClassify.csv', na_rep='NA', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113263\n",
      "[['machine', 'wont', 'regen']\n",
      " ['extended', 'coverage', 'enrollment', 'contract', '3177441']\n",
      " ['extended', 'coverage', 'enrollment', 'contract', '3176241']\n",
      " ['completed', 'sl', 'ps44446', 'dtd', '19', 'aug', '2014', 'this', 'is', 'a', 'before', 'failure']\n",
      " ['completed', 'ps44446']]\n"
     ]
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(r\"\\w+'\\w+|\\w+\")\n",
    "stories_tokens = np.array([tokenizer.tokenize(story) for story in sentence_grain.Tokens.str.lower()])\n",
    "print(len(stories_tokens))\n",
    "print stories_tokens[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['machine', 'wont', 'regen', 'extended', 'coverage', 'enrollment', 'contract', '3177441', 'extended', 'coverage', 'enrollment', 'contract', '3176241', 'completed', 'sl', 'ps44446', 'dtd', '19', 'aug', '2014', 'this', 'is', 'a', 'before', 'failure']\n"
     ]
    }
   ],
   "source": [
    "#flatten words into one list\n",
    "token_list = []\n",
    "for row in stories_tokens:\n",
    "    for word in row:\n",
    "        token_list.append(word)\n",
    "print(token_list[0:25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 25 of 3829 matches:\n",
      "td ps44446 as needed there was a hyd leak on the left cylinder of the quick co\n",
      "e o ring i checked operation the oil leak had been ed completed sl ps44446 dtd\n",
      "rage enrollment contract 3126505 hyd leak damaged seal for hydraulic filter by\n",
      "achine ran machine machine to verify leak has been ed perform ps44446 update e\n",
      "ans oils ok customer concern coolant leak of failure hose clamps not properly \n",
      "aner and tested machine to make sure leak had stopped extended coverage enroll\n",
      "o fill windshield wiper tank and had leak ordered new one and replaced it per \n",
      "rage enrollment contract 3386191 oil leak steering pressure relief valve is le\n",
      "relief valve is leaking verified oil leak on steering valve found leak coming \n",
      "ied oil leak on steering valve found leak coming from the 1325258 pressure red\n",
      "258 pressure reducing valve tried to leak by tightening the lock nut more but \n",
      " by tightening the lock nut more but leak still on going ordered and installed\n",
      "alled new 1325258 valve retested for leak all o k coolant leak thermostat is l\n",
      "ve retested for leak all o k coolant leak thermostat is leaking coolant not se\n",
      " not seating properly verified found leak at the 4201395 thermostat inspected \n",
      " smcs code 7594 engine has a coolant leak remove and replace the engine as per\n",
      " rolled and deformed allowing oil to leak removed right side tilt cylinder and\n",
      "ts pat flanagan 8454374127 hydraulic leak hose connector came off during the p\n",
      "et i noticed a significant hydraulic leak at the rear of the machine upon furt\n",
      "rollment contract 3334692 of coolant leak smell in cab went to field jobsite t\n",
      "olts per pip letter of hydraulic oil leak had to run machine to locate leak so\n",
      "il leak had to run machine to locate leak source found oil leak at accumulator\n",
      "hine to locate leak source found oil leak at accumulator manifold inspect mani\n",
      "ial leaking paint on the yoke ts oil leak remove belly pan and drive shaft rem\n",
      "rage enrollment contract 3279750 oil leak rear axle pinion seal yoke ts remove\n"
     ]
    }
   ],
   "source": [
    "text = nltk.Text(token_list)\n",
    "text.concordance('leak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P9ZtxP5FS2q0XpNMhndhsnwzpy5COgnQ7SM+GdDWkOzTHb92kHQHptpCeDul3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\nUucYAEmSpM75/yAB89fJxMbsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c75dc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text.dispersion_plot([\"burnt\", \"broken\", \"leaking\", \"cracked\", \"lost\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of_and hydraulic_around of_installed hydraulic_test hydraulic_removed\n",
      "transmission_removed hyd_that hydraulic_failed the_and hyd_removed\n",
      "hydraulic_when hydraulic_on hydraulic_and hydraulic_to hydraulic_from\n",
      "hydraulic_ts hydraulic_leaking found_on hydraulic_leak\n",
      "hydraulic_cleaned\n"
     ]
    }
   ],
   "source": [
    "text.common_contexts([\"leak\",\"fluid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extended coverage; enrollment contract; coverage enrollment; resultant\n",
      "damage; service letter; nacd 2012; 2012 subsidy; cat support; ride\n",
      "control; installed new; suren reddy; cat dealer; 6042473763\n",
      "sreddyfinning; reddy 6042473763; ard head; oil leak; control valve;\n",
      "insurance yes; non governmental; ran machine; parking brake; dealer\n",
      "codeclaim; follows dealer; see additional; support 000; claim\n",
      "identified; also see; regular operations; additional claim; hydraulic\n",
      "oil; customer name; gain access; year labor; fuel pressure; manual\n",
      "regen; 12mosunlimited efm; expected cat; first year; product link; wty\n",
      "12mosunlimited; fuel rail; efm labour; status report; engine oil; 1st\n",
      "year; labor registration; pressure sensor; active code; park brake;\n",
      "right side\n"
     ]
    }
   ],
   "source": [
    "# common bi-grams\n",
    "text.collocations(num=50, window_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0307926569598717"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lexical diversity\n",
    "# small domain, not lexically diverse\n",
    "len(set(text)) / len(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('the', 42596),\n",
       " ('and', 39310),\n",
       " ('to', 24823),\n",
       " ('machine', 14260),\n",
       " ('was', 11275),\n",
       " ('for', 10675),\n",
       " ('found', 9836),\n",
       " ('of', 9628),\n",
       " ('on', 9186),\n",
       " ('in', 8163)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FreqDist similar to Counter object\n",
    "fdist = nltk.FreqDist(text)\n",
    "print len(fdist)\n",
    "fdist.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CGuNo3rx52jhSZI6juLh42zjWZlTApcZRnPHtnjFjRo1xVFRU5Pw8wuMoLi7O+XmkxhHW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\nEx5HeHy5ritVzfl5hPVyfR7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g7qisxM0qehToW2Bf43HTwL+T43iuouCzQ+9rKwruHiQd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NoQkUMonqoi1XSgiEwBRqjq7UVoBmNKzB6YwvcvTIFs1hwuReQpmqbD7TtHVS8ScyLfxg1GDscy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\nzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b5fc9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compare to curve in tokens_dist.png\n",
    "fdist.plot(50, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['6042473763',\n",
       " 'accumulator',\n",
       " 'accumulators',\n",
       " 'additional',\n",
       " 'calibration',\n",
       " 'complication',\n",
       " 'compressor',\n",
       " 'connection',\n",
       " 'connections',\n",
       " 'differential',\n",
       " 'disassembled',\n",
       " 'disconnected',\n",
       " 'enrollment',\n",
       " 'governmental',\n",
       " 'guidelines',\n",
       " 'identified',\n",
       " 'operations',\n",
       " 'powertrain',\n",
       " 'reassembled',\n",
       " 'regeneration',\n",
       " 'reinstalled',\n",
       " 'sreddyfinning',\n",
       " 'temperature',\n",
       " 'transmission',\n",
       " 'troubleshoot',\n",
       " 'troubleshooting',\n",
       " 'troubleshot',\n",
       " 'windshield']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorted unique words longer than 9 characters that occur more than 300 times\n",
    "sorted(w for w in set(text) if len(w) > 9 and fdist[w] > 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All rows= 113263\n",
      "Rows with labels= 30502\n"
     ]
    }
   ],
   "source": [
    "#Remove nulls\n",
    "print 'All rows=', len(sentence_grain)\n",
    "sentence_grain_labeled = sentence_grain.dropna(how='any')\n",
    "print 'Rows with labels=', len(sentence_grain_labeled)\n",
    "sentence_grain_labeled.reset_index(drop = True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimID</th>\n",
       "      <th>Classification</th>\n",
       "      <th>SentenceNo</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52942304</td>\n",
       "      <td>complaint</td>\n",
       "      <td>1</td>\n",
       "      <td>there was a hyd leak on the left cylinder of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52942304</td>\n",
       "      <td>cause</td>\n",
       "      <td>2</td>\n",
       "      <td>2168994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52942304</td>\n",
       "      <td>correction</td>\n",
       "      <td>3</td>\n",
       "      <td>i used the shut valves to shut the flow of oi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52942304</td>\n",
       "      <td>repair</td>\n",
       "      <td>5</td>\n",
       "      <td>i checked operation the oil leak had been ed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53009984</td>\n",
       "      <td>complaint</td>\n",
       "      <td>1</td>\n",
       "      <td>windshield wiper not working right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>53009984</td>\n",
       "      <td>cause</td>\n",
       "      <td>2</td>\n",
       "      <td>mounting bolts loose wiper was stuck to one s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52616388</td>\n",
       "      <td>note</td>\n",
       "      <td>1</td>\n",
       "      <td>this machine is not in service with 5 hours o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52616388</td>\n",
       "      <td>complaint</td>\n",
       "      <td>2</td>\n",
       "      <td>perform service letter ps44446 dated 11aug2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52616388</td>\n",
       "      <td>cause</td>\n",
       "      <td>3</td>\n",
       "      <td>the existing software may lead to the operato...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52616388</td>\n",
       "      <td>cause</td>\n",
       "      <td>5</td>\n",
       "      <td>if the issue is d by the software debounce th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ClaimID Classification  SentenceNo  \\\n",
       "0  52942304      complaint           1   \n",
       "1  52942304          cause           2   \n",
       "2  52942304     correction           3   \n",
       "3  52942304         repair           5   \n",
       "4  53009984      complaint           1   \n",
       "5  53009984          cause           2   \n",
       "6  52616388           note           1   \n",
       "7  52616388      complaint           2   \n",
       "8  52616388          cause           3   \n",
       "9  52616388          cause           5   \n",
       "\n",
       "                                              Tokens  \n",
       "0   there was a hyd leak on the left cylinder of ...  \n",
       "1                                           2168994   \n",
       "2   i used the shut valves to shut the flow of oi...  \n",
       "3       i checked operation the oil leak had been ed  \n",
       "4                 windshield wiper not working right  \n",
       "5   mounting bolts loose wiper was stuck to one s...  \n",
       "6   this machine is not in service with 5 hours o...  \n",
       "7     perform service letter ps44446 dated 11aug2014  \n",
       "8   the existing software may lead to the operato...  \n",
       "9   if the issue is d by the software debounce th...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ClaimID': 52942304,\n",
       " 'Classification': 'complaint',\n",
       " 'SentenceNo': 1,\n",
       " 'Tokens': ' there was a hyd leak on the left cylinder of the quick coupler'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(sentence_grain_labeled.head(10))\n",
    "#Create dictionaries from each row\n",
    "rows_dict = sentence_grain_labeled.to_dict(orient='records')\n",
    "rows_dict[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Frequency Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Words categorized by classification keyword\n",
    "cfd = nltk.ConditionalFreqDist(\n",
    "    (genre, words)\n",
    "    for genre in set(d['Classification'] for d in rows_dict)\n",
    "    for d in rows_dict\n",
    "    if d['Classification'] == genre\n",
    "    for words in word_tokenize(d['Tokens']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "767"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of complaints with the word \"engine\"\n",
    "cfd['complaint']['engine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            leaking    light      low   failed   damage pressure  cracked    loose   faulty  warning  removed \n",
      "      note        3        2        0        8        9        7        3        3        1        4        9 \n",
      "    repair      209       72      139       80      110      246       55       83       20       49      689 \n",
      " complaint      715      388      411       44       21      430      136      120        4      247       71 \n",
      "     cause      603       54      216     1004      844      484      216      583      223       22       63 \n",
      "correction      238       87      149      106       31      313       56      126       21       39      758 \n"
     ]
    }
   ],
   "source": [
    "keywords = set(d['Classification'] for d in rows_dict)\n",
    "problem_words = ['leaking', 'light', 'low', 'failed', 'damage', 'pressure', 'cracked', 'loose', 'faulty', 'warning', 'removed']\n",
    "cfd.tabulate(conditions=keywords, samples=problem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p0qUMGzbMbA1DlPcLoQJW9f7vxYtkOSbb9YmIwFbO2G+g3J25VG7+8Ud25eW5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\n6NbRRLeOrpHz4Q+5RUWcu2IF3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wBEZCumcNZV9lWTHZTv0AwVAioUaQtnvRCRKRhZcFf2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\nuBLrpFgsFovFYnEl1kmxWCwWi8XiSv4fBJNUsEkAayoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c3a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfd.plot(conditions=keywords, samples=problem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              valve  coolant      oil   engine    brake     seal     hose     code    regen   sensor software \n",
      "      note        8        6       39       10        4       10        4       87        7        8       15 \n",
      "    repair      309      148      586      296      174      114      190      278       94      225       66 \n",
      " complaint      222      285     1338      767      476       93      122      729      311      190       50 \n",
      "     cause      853      139      600      214      293      543      464      160       90      663      266 \n",
      "correction      386      138      582      299      198      168      239      349      119      311      137 \n"
     ]
    }
   ],
   "source": [
    "keywords = set(d['Classification'] for d in rows_dict)\n",
    "problem_words = ['valve', 'coolant', 'oil', 'engine', 'brake', 'seal', 'hose', 'code', 'regen', 'sensor', 'software']\n",
    "cfd.tabulate(conditions=keywords, samples=problem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GVaSU+lXEC2jbsGFDmZSUJFXk3Llz5gr07CmlkZkiZXJyiT8WEO9rrsl32b3b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\nnY1eSHy8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c64a690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfd.plot(conditions=keywords, samples=problem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('and', 3745),\n",
       " ('the', 3476),\n",
       " ('to', 2452),\n",
       " (',', 1785),\n",
       " ('machine', 1723),\n",
       " ('for', 1549),\n",
       " ('was', 1014),\n",
       " ('found', 848),\n",
       " ('on', 789),\n",
       " ('of', 789),\n",
       " ('after', 719),\n",
       " ('check', 702),\n",
       " ('removed', 689),\n",
       " ('in', 660),\n",
       " ('this', 597)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view most common by keyword category\n",
    "cfd['repair'].most_common(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw bar charts from Conditional Frequency Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "colors = 'rgbcmyk' # red, green, blue, cyan, magenta, yellow, black\n",
    "\n",
    "# plot a bar chart showing counts for each word by category\n",
    "def bar_chart(categories, words, counts, out_file):\n",
    "    fig = pyplot.figure(figsize=(25, 10))\n",
    "    ind = np.arange(len(words))\n",
    "    width = 1 / (len(categories) + 1)\n",
    "    bar_groups = []\n",
    "    for c in range(len(categories)):\n",
    "        bars = pyplot.bar(ind+c*width, counts[categories[c]], width, color=colors[c % len(colors)])\n",
    "        bar_groups.append(bars)\n",
    "    pyplot.xticks(ind+width, words, fontsize=20)\n",
    "    pyplot.yticks(fontsize=20)\n",
    "    pyplot.legend([b[0] for b in bar_groups], categories, loc='upper right', fontsize=20)\n",
    "    pyplot.ylabel('Frequency', fontsize=25)\n",
    "    pyplot.title('Frequency of Words by Category', fontsize=30)\n",
    "    #pyplot.savefig(out_file)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zJKM623Yk9k6XvGiCztvS0pwqPJjYqcJPBT5KOddBGbnc7DCsRzJPdGdiPb1/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\nZFdF0hitssoqXH311RMSbGCggSRJkiR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Z7Zyg3oj3MCixAPHjrNU6XNXRVY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\nO7aq4jrs/ZwELAPshb2j99FN7qNU9TEROQ57hl/C5MHpmDx+F5tYWTPk4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c24fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parts that cause complaint/failure\n",
    "keywords = ['note', 'repair', 'complaint', 'cause', 'correction']\n",
    "problem_words = ['valve', 'coolant', 'hydraulic', 'oil', 'engine', 'brake', 'seal', 'hose', 'steering', 'regen', 'sensor', 'software']\n",
    "\n",
    "counts = {}\n",
    "for keyword in keywords:\n",
    "    counts[keyword] = [cfd[keyword][word] for word in problem_words]\n",
    "bar_chart(keywords, problem_words, counts, './output/cfd_plot_parts.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UZ9uOxN7pkpdM0HFbUZpThQcTO1X4qcBHKcc6KCOXmx2G9Ujmie5MrKf3r8+X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\nW1FmgrmAZa+VT2XmW8ajDkm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8LqyS7YAv8ROeHXw8rvSaxc78TK4ZMpfyfT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d6c5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# problem words associated with failure\n",
    "keywords = ['note', 'repair', 'complaint', 'cause', 'correction']\n",
    "problem_words = ['leaking', 'light', 'low', 'failed', 'damage', 'pressure', 'cracked', 'loose', 'faulty', 'code', 'removed', 'warning', 'broken']\n",
    "\n",
    "counts = {}\n",
    "for keyword in keywords:\n",
    "    counts[keyword] = [cfd[keyword][word] for word in problem_words]\n",
    "bar_chart(keywords, problem_words, counts, './output/cfd_plot_problems.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 6\n",
    "### Supervised Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coolant': 1,\n",
       " 'from': 1,\n",
       " 'leaking': 1,\n",
       " 'on': 1,\n",
       " 'pump': 1,\n",
       " 'seal': 1,\n",
       " 'shaft': 1,\n",
       " 'water': 1}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def string2word_vector(text):\n",
    "    fdist = nltk.FreqDist(word_tokenize(text))\n",
    "    return dict(fdist)\n",
    "\n",
    "string2word_vector('coolant leaking from shaft seal on water pump')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(' traveled to customer site new glasgow', 'cause')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = ([ (i['Tokens'], 'cause' ) for i in rows_dict if i['Classification'] == 'cause' ] +\n",
    "          [ (i['Tokens'], 'complaint' ) for i in rows_dict if i['Classification'] == 'complaint' ] +\n",
    "          [ (i['Tokens'], 'correction' ) for i in rows_dict if i['Classification'] == 'correction' ] +\n",
    "          [ (i['Tokens'], 'repair' ) for i in rows_dict if i['Classification'] == 'repair' ] +\n",
    "          [ (i['Tokens'], 'note' ) for i in rows_dict if i['Classification'] == 'note' ])\n",
    "\n",
    "random.shuffle(classes)\n",
    "classes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'customer': 1, 'to': 1, 'glasgow': 1, 'new': 1, 'site': 1, 'traveled': 1}, 'cause')\n",
      "({'rocker': 1, 'reassembled': 1, '10r2134': 1, 'installed': 1, 'same': 1, 'rocked': 1, 'rough': 1, 'adjustment': 1, 'unit': 1, 'orig': 2, 'cut': 1, 'checked': 1, 'service': 1, 'system': 2, 'avail': 2, 'to': 1, '1506': 1, '4': 1, 'fuel': 1, '3855474': 1, 'engine': 1, 'that': 1, 'failure': 1, 'suspected': 1, '550413svj2': 1, 'cyl': 1, 'did': 1, 'air': 1, 'dpf': 2, 'of': 1, 'found': 3, 'and': 4, 'cylinder': 1, 'cover': 1, 'is': 1, 'as': 1, 'at': 3, 'arm': 2, 'out': 1, 'screw': 1, 'p': 1, 'valve': 1, 'per': 1, 'dsn': 1, 'new': 1, 'ref': 1, 'be': 1, 'we': 1, 'assembly': 1, '207': 1, '208': 1, '20r1133': 1, 'pressure': 1, 'running': 1, 'injector': 1, 'reman': 4, 'a': 1, 'ok': 4, 'tested': 2, 'fault': 1, 'removed': 2, 'sepd': 1, 'filter': 1, 'magazine': 1, 'time': 1, '3225763': 1, 'the': 2, 'broken': 1}, 'cause')\n",
      "({'open': 1, 'solenoid': 1}, 'cause')\n",
      "({'1': 1, 'injector': 1, 'line': 1, 'leaking': 1}, 'cause')\n",
      "({'broken': 1, 'fiber': 2, 'broke': 1, 'glass': 2, 'mounts': 1, 'where': 1, 'precleaner': 1}, 'cause')\n"
     ]
    }
   ],
   "source": [
    "# need features to look like this:\n",
    "for t,c in classes[:5]:\n",
    "    print (string2word_vector(t), c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create list of featuresets\n",
    "featuresets = [(string2word_vector(t), c) for (t,c) in classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24402\n",
      "6100\n"
     ]
    }
   ],
   "source": [
    "# 80% training set; 20% test set\n",
    "size = int(len(featuresets) * 0.2)\n",
    "train_set, test_set = featuresets[size:], featuresets[:size]\n",
    "\n",
    "print len(train_set)\n",
    "print len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complaint\n",
      "complaint\n",
      "correction\n",
      "repair\n",
      "cause\n",
      "complaint\n"
     ]
    }
   ],
   "source": [
    "# show classifier predictions for sample sentences\n",
    "print classifier.classify(string2word_vector('machine wont regen'))\n",
    "print classifier.classify(string2word_vector('windshield wiper not working right'))\n",
    "print classifier.classify(string2word_vector('oring was replaced'))\n",
    "print classifier.classify(string2word_vector('i used the shut valves to shut the flow of oil of to the quick coupler cylinders'))\n",
    "print classifier.classify(string2word_vector('mounting bolts loose wiper was stuck to one side of windshield'))\n",
    "print classifier.classify(string2word_vector('there was a hyd leak on the left cylinder of the quick coupler'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ClaimID': 52942304,\n",
       " 'Classification': 'complaint',\n",
       " 'PredictedClassification': 'complaint',\n",
       " 'SentenceNo': 1,\n",
       " 'Tokens': ' there was a hyd leak on the left cylinder of the quick coupler'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(rows_dict)):\n",
    "    token = rows_dict[i]['Tokens']\n",
    "    rows_dict[i]['PredictedClassification'] = classifier.classify(string2word_vector(token))\n",
    "\n",
    "rows_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimID</th>\n",
       "      <th>SentenceNo</th>\n",
       "      <th>Classification</th>\n",
       "      <th>PredictedClassification</th>\n",
       "      <th>Tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52942304</td>\n",
       "      <td>1</td>\n",
       "      <td>complaint</td>\n",
       "      <td>complaint</td>\n",
       "      <td>there was a hyd leak on the left cylinder of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52942304</td>\n",
       "      <td>2</td>\n",
       "      <td>cause</td>\n",
       "      <td>complaint</td>\n",
       "      <td>2168994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52942304</td>\n",
       "      <td>3</td>\n",
       "      <td>correction</td>\n",
       "      <td>repair</td>\n",
       "      <td>i used the shut valves to shut the flow of oi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52942304</td>\n",
       "      <td>5</td>\n",
       "      <td>repair</td>\n",
       "      <td>repair</td>\n",
       "      <td>i checked operation the oil leak had been ed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53009984</td>\n",
       "      <td>1</td>\n",
       "      <td>complaint</td>\n",
       "      <td>complaint</td>\n",
       "      <td>windshield wiper not working right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ClaimID  SentenceNo Classification PredictedClassification  \\\n",
       "0  52942304           1      complaint               complaint   \n",
       "1  52942304           2          cause               complaint   \n",
       "2  52942304           3     correction                  repair   \n",
       "3  52942304           5         repair                  repair   \n",
       "4  53009984           1      complaint               complaint   \n",
       "\n",
       "                                              Tokens  \n",
       "0   there was a hyd leak on the left cylinder of ...  \n",
       "1                                           2168994   \n",
       "2   i used the shut valves to shut the flow of oi...  \n",
       "3       i checked operation the oil leak had been ed  \n",
       "4                 windshield wiper not working right  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new df with predicted classification column, re-order columns\n",
    "naivebayes = pd.DataFrame(rows_dict, columns=['ClaimID', 'SentenceNo', 'Classification', 'PredictedClassification', 'Tokens'])\n",
    "naivebayes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# output to csv\n",
    "# naivebayes.to_csv('./output/AuroraNaiveBayes.csv', na_rep='NA', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Accuracy: 0.696393442623\n"
     ]
    }
   ],
   "source": [
    "print 'Naive Bayes Accuracy:',nltk.classify.accuracy(classifier, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "                      pd = 1                note : compla =    862.3 : 1.0\n",
      "                      15 = 1                note : compla =    529.0 : 1.0\n",
      "                  limits = 1                note : cause  =    521.0 : 1.0\n",
      "               indicates = 1                note : compla =    504.8 : 1.0\n",
      "                   claim = 1                note : cause  =    465.0 : 1.0\n",
      "               confirmed = 1              correc : cause  =    420.0 : 1.0\n",
      "                    late = 1                note : cause  =    331.3 : 1.0\n",
      "                    ship = 1                note : compla =    292.1 : 1.0\n",
      "               exception = 1                note : repair =    252.9 : 1.0\n",
      "            supplemental = 1                note : correc =    202.9 : 1.0\n",
      "                   short = 1                note : compla =    198.5 : 1.0\n",
      "                warranty = 1                note : cause  =    184.0 : 1.0\n",
      "                      ed = 1              repair : compla =    174.6 : 1.0\n",
      "                approved = 1                note : repair =    170.3 : 1.0\n",
      "                      17 = 1                note : cause  =    149.9 : 1.0\n",
      "                  please = 1                note : cause  =    149.8 : 1.0\n",
      "                verified = 1              correc : cause  =    142.0 : 1.0\n",
      "                       d = 1                note : compla =    131.4 : 1.0\n",
      "                comments = 1              repair : cause  =    100.9 : 1.0\n",
      "                 charges = 1                note : cause  =     97.5 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 8\n",
    "### Analyzing sentence structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parser = stanford.StanfordParser(model_path=\"edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' there was a hyd leak on the left cylinder of the quick coupler'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_grain_labeled.Tokens[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(test\n",
      "  (ROOT\n",
      "    (S\n",
      "      (NP (EX there))\n",
      "      (VP\n",
      "        (VBD was)\n",
      "        (NP\n",
      "          (NP (DT a) (NN hyd) (NN leak))\n",
      "          (PP\n",
      "            (IN on)\n",
      "            (NP\n",
      "              (NP (DT the) (JJ left) (NN cylinder))\n",
      "              (PP (IN of) (NP (DT the) (JJ quick) (NN coupler))))))))))\n"
     ]
    }
   ],
   "source": [
    "parse = parser.raw_parse(sentence_grain_labeled.Tokens[0])\n",
    "parse_list = list(parse)\n",
    "#print parse_list\n",
    "parse_tree = Tree('test',parse_list)\n",
    "print parse_tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              test                                                     \n",
      "               |                                                        \n",
      "              ROOT                                                     \n",
      "               |                                                        \n",
      "               S                                                       \n",
      "   ____________|_____________                                           \n",
      "  |                          VP                                        \n",
      "  |     _____________________|___                                       \n",
      "  |    |                         NP                                    \n",
      "  |    |        _________________|______                                \n",
      "  |    |       |                        PP                             \n",
      "  |    |       |          ______________|______                         \n",
      "  |    |       |         |                     NP                      \n",
      "  |    |       |         |        _____________|_______                 \n",
      "  |    |       |         |       |                     PP              \n",
      "  |    |       |         |       |              _______|____            \n",
      "  NP   |       NP        |       NP            |            NP         \n",
      "  |    |    ___|____     |    ___|______       |    ________|______     \n",
      "  EX  VBD  DT  NN   NN   IN  DT  JJ     NN     IN  DT       JJ     NN  \n",
      "  |    |   |   |    |    |   |   |      |      |   |        |      |    \n",
      "there was  a  hyd  leak  on the left cylinder  of the     quick coupler\n",
      "\n"
     ]
    }
   ],
   "source": [
    "parse_tree.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#function for getting label path\n",
    "def getPath(tree, leaf):\n",
    "    if leaf in tree.leaves():\n",
    "        leaf_values = tree.leaves()\n",
    "        leaf_index = leaf_values.index(leaf)\n",
    "        #print leaf_index\n",
    "        tree_location = tree.leaf_treeposition(leaf_index)\n",
    "        print 'leaf=', tree[tree_location]\n",
    "        print 'root to leaf position path=', tree_location\n",
    "        labels = []\n",
    "        counter = -1\n",
    "        for i in tree_location:\n",
    "            parent = tree[tree_location[:counter]]\n",
    "            labels.append(parent.label())\n",
    "            counter = counter - 1\n",
    "        return 'leaf to root label path=', labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "leaf= cylinder\n",
      "root to leaf position path= (0, 0, 1, 1, 1, 1, 0, 2, 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('leaf to root label path=',\n",
       " [u'NN', u'NP', u'NP', u'PP', u'NP', u'VP', u'S', u'ROOT', 'test'])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPath(parse_tree, 'cylinder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "leaf= leak\n",
      "root to leaf position path= (0, 0, 1, 1, 0, 2, 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('leaf to root label path=',\n",
       " [u'NN', u'NP', u'NP', u'VP', u'S', u'ROOT', 'test'])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPath(parse_tree, 'leak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "leaf= hyd\n",
      "root to leaf position path= (0, 0, 1, 1, 0, 1, 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('leaf to root label path=',\n",
       " [u'NN', u'NP', u'NP', u'VP', u'S', u'ROOT', 'test'])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPath(parse_tree, 'hyd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "leaf= coupler\n",
      "root to leaf position path= (0, 0, 1, 1, 1, 1, 1, 1, 2, 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('leaf to root label path=',\n",
       " [u'NN', u'NP', u'PP', u'NP', u'PP', u'NP', u'VP', u'S', u'ROOT', 'test'])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPath(parse_tree, 'coupler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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