fuzzy_search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "30 min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><h1>PyCon 2017</h1></center>\n",
    "<center><h2>Portland, Oregon</h2></center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "\n",
    "- we are going to talk about fuzzy search.\n",
    "- It's Approximate string matching\n",
    "- Why? Sometime we don't really know what we are looking for"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "** Fuzzy Search **    \n",
    "Approximate string matching\n",
    "\n",
    "- Why\n",
    "    - Abbreviation: what does \"LGTM\" mean?\n",
    "    - Misspellings: \"I sware\" or \"I swear\"?\n",
    "    - Lack of whitespaces: BEDBATHANDBEYOND\n",
    "    - Cut-off words\n",
    "    - etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "\n",
    "- Both US and USA point to the same entity: United States\n",
    "- Fuzzy search usually based on some sort of string distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "- Applications\n",
    "    - Entity resolution\n",
    "        - \"US\" and \"USA\"\n",
    "    - Search Engines\n",
    "    - Auto correct\n",
    "- Search based on String distance\n",
    "    - Quantify qualitative data for analytical purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Soundex**\n",
    "\n",
    "- phonetic algorithm\n",
    "- index by sound as pronounced in English\n",
    "- assigns a soundex coding \n",
    "- ideal for spelling inconsistencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**American Soundex Coding**\n",
    "\n",
    "[http://www.archives.gov/research/census/soundex.html](http://www.archives.gov/research/census/soundex.html)\n",
    "\n",
    "every soundex code is a letter and three numbers\n",
    "\n",
    "| Number | Letter |\n",
    "|---------|----------|\n",
    "|1|B,F,P,V|\n",
    "|2|C,G,J,K,Q,S,X,Z|\n",
    "|3|D,T|\n",
    "|4|L|\n",
    "|5|M,N|\n",
    "|6|R|\n",
    "\n",
    "Ignore A,E,I,O,U,H,W,Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P634\n",
      "P634\n"
     ]
    }
   ],
   "source": [
    "import jellyfish as j\n",
    "\n",
    "a=j.soundex('PORTLAND')\n",
    "print(a)\n",
    "b=j.soundex('PRTLAND')\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W630\n",
      "W630\n"
     ]
    }
   ],
   "source": [
    "# Numbers and Letters\n",
    "a=j.soundex('Word #123')\n",
    "print(a)\n",
    "b=j.soundex('Word')\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A213\n",
      "E213\n"
     ]
    }
   ],
   "source": [
    "a=j.soundex('accept')\n",
    "print(a)\n",
    "a2=j.soundex('except')\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A300\n",
      "A300\n"
     ]
    }
   ],
   "source": [
    "#homophones\n",
    "a=j.soundex('ado')\n",
    "print(a)\n",
    "a2=j.soundex('adieu')\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F630\n",
      "F630\n"
     ]
    }
   ],
   "source": [
    "a=j.soundex('forth')\n",
    "print(a)\n",
    "a2=j.soundex('fourth')\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Soundex with PostgreSQL\n",
    "\n",
    "https://www.postgresql.org/docs/9.1/static/fuzzystrmatch.html\n",
    "\n",
    "`CREATE EXTENSION fuzzystrmatch;`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine('postgresql://jiaqi@localhost/pycon')\n",
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('A500', 'A500', 4)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sqlalchemy.sql import text\n",
    "\n",
    "query = \"select soundex('Anne'), soundex('Ann'), difference('Anne','Ann')\"\n",
    "res = engine.execute(text(query))\n",
    "res.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Create Table & Load Data\n",
    "\n",
    "`Create table pypi \n",
    "(packages varchar, description varchar);`\n",
    "\n",
    "`COPY pypi from 'pypi.csv' delimiter ',' csv;`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('facehugger\\xa00.1.6',),\n",
       " ('face_scrambler\\xa00.0.2',),\n",
       " ('fakechroot\\xa00.2.1',),\n",
       " ('fake-useragent\\xa00.1.7',),\n",
       " ('fakeUserData\\xa01',),\n",
       " ('Focusr\\xa00.3',),\n",
       " ('fuzzysearch\\xa00.3.0',),\n",
       " ('FuzzySortedDict\\xa01.0.1',),\n",
       " ('fzysearch\\xa00.3',)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT    packages \n",
    "        FROM      pypi \n",
    "        WHERE     soundex(packages) = soundex('fuzzysearch')\"\"\"\n",
    "res = engine.execute(text(query))\n",
    "res.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Fake User-Agents of many real devices',),\n",
       " ('Fuchsia reduces differential equations for Feynman master integrals to canonical form',),\n",
       " ('fuzzysearch is useful for finding approximate subsequence matches',),\n",
       " ('fuzzy searches a string input to a list and returns results from the list in order of relevance',),\n",
       " ('FPGA core/image generator and FPGA communication',),\n",
       " ('Fuzzy search with the PostgreSQL trigram extension',),\n",
       " ('Fuzzy search with the PostgreSQL trigram extension',),\n",
       " ('Fuzzy search with the PostgreSQL trigram extension',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',),\n",
       " ('Fake servers for testing',)]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT    description\n",
    "        FROM      pypi \n",
    "        WHERE     soundex(description) = soundex('fuzzysearch')\"\"\"\n",
    "res = engine.execute(text(query))\n",
    "res.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('199fix exception logger for Django',),\n",
       " ('quick sort',),\n",
       " ('Fisica para Jogoso FGA',),\n",
       " ('Asychronous downloads made easy',),\n",
       " ('HashiCorp Vault API client',),\n",
       " ('Fix skew in images',),\n",
       " ('Caches your Django ORM queries and automatically invalidates them.',),\n",
       " ('Flash remoting framework. Includes support for NetConnection, RemoteObject, IExternizeable, Flex Producer/Consumer messaging, custom type serialization, and a C-based AMF encoder/decoder.',),\n",
       " ('This is wrapper for Apple Push Notification Service.',),\n",
       " ('Check script scheduler',)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT    description\n",
    "        FROM      pypi \n",
    "        WHERE     difference(description, 'fuzzysearch') > 2 limit 10\"\"\"\n",
    "res = engine.execute(text(query))\n",
    "res.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "**Soundex**\n",
    "- Soundex is pretty easy to implement\n",
    "- Computationally fast\n",
    "- only works on ASCII characters (no foreign languages)\n",
    "- How do you calculate distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "**Levenshtein distance**\n",
    "- also call edit distance\n",
    "- accounts for how many characters you have to change to have the same string\n",
    "- computationally fast (can handle real time processing)\n",
    "- pairwise comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Levenshtein as l\n",
    "\n",
    "l.distance('SMYTHE', 'SMITH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.distance('pypi', 'pypy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pitfall: Comparing Addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str99 = '99 Broadway'\n",
    "str100 = '100 Broadway'\n",
    "str999 = '999 Broadway'\n",
    "\n",
    "l.distance(str99, str100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.distance(str99, str999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.distance(str999, str100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Longer Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str1='Mike\\'s New York Deli and co'\n",
    "str2='Sam\\'s New York Deli and co'\n",
    "l.distance(str1,str2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Levenshtein as l\n",
    "str1='Mike\\'s Deli'\n",
    "str2='Sam\\'s Deli'\n",
    "l.distance(str1,str2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Levenshtein**\n",
    "- counting raw edits penalizes long strings: use a ratio of edits to length\n",
    "- weighing numbers differently from letters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "N-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def ngram(tokens, n):\n",
    "    grams =[tokens[i:i+n] for i in range(len(tokens)-(n-1))]\n",
    "    return grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The', 'quick', 'brown']\n",
      "['quick', 'brown', 'fox']\n",
      "['brown', 'fox', 'jumped']\n",
      "['fox', 'jumped', 'over']\n",
      "['jumped', 'over', 'a']\n",
      "['over', 'a', 'lazy']\n",
      "['a', 'lazy', 'dog']\n"
     ]
    }
   ],
   "source": [
    "sentence_gram = \"The quick brown fox jumped over a lazy dog\".split()\n",
    "grams = ngram(sentence_gram, 3)\n",
    "\n",
    "for gram in grams:\n",
    "    print(gram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyc\n",
      "yco\n",
      "con\n",
      "on2\n",
      "n20\n",
      "201\n",
      "017\n"
     ]
    }
   ],
   "source": [
    "word_gram = \"pycon2017\"\n",
    "grams = ngram(word_gram, 3)\n",
    "\n",
    "for gram in grams:\n",
    "    print(gram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Scoring Similarity: Jaccard similarity**\n",
    "\n",
    "intersection over the union "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def get_sim(a_tri,b_tri):\n",
    "    intersect = len(set(a_tri) & set(b_tri))\n",
    "    union = len(set(a_tri) | set(b_tri))\n",
    "    return float(intersect)/(union)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pyc', 'yco', 'con', 'on2', 'n20', '201', '017']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(grams)\n",
    "get_sim(grams, grams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_gram = ngram('aabcccdeeeffgghhh', 3)\n",
    "b_gram = ngram('abcccdeeffgghhh', 3)\n",
    "get_sim(a_gram, b_gram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Trigam Search with Postgres\n",
    "\n",
    "https://www.postgresql.org/docs/9.1/static/pgtrgm.html\n",
    "\n",
    "`create extension pg_trgm;`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine('postgresql://jiaqi@localhost/pycon')\n",
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "query_des=\"\"\"\n",
    "    SELECT \n",
    "                a.description, \n",
    "                similarity(lower(a.description), :descript) as similarity \n",
    "    FROM        pypi as a \n",
    "    WHERE       lower(a.description) % :descript\n",
    "    ORDER BY    similarity DESC\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sqlalchemy.sql import text\n",
    "\n",
    "description = 'fuzzy search'\n",
    "res = engine.execute(text(query_des), descript=description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('All Search', 0.411765),\n",
       " ('All Search', 0.411765),\n",
       " ('Fuzzy path searching for shells', 0.387097),\n",
       " ('Fuzzy path searching for shells', 0.387097),\n",
       " ('Fuzzy path searching for shells', 0.387097),\n",
       " ('Fuzzy path searching for shells', 0.387097),\n",
       " ('Media search', 0.368421),\n",
       " ('Kagiso Search', 0.35),\n",
       " ('fuzzy.ai API', 0.315789),\n",
       " ('Search forms for django', 0.307692),\n",
       " ('Search in google', 0.304348),\n",
       " ('Stabilizer Search', 0.304348),\n",
       " ('fuzzy.io API', 0.3)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions = res.fetchall()\n",
    "descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "query_package=\"\"\"\n",
    "    SELECT \n",
    "                a.packages, \n",
    "                similarity(lower(a.packages), :p) as similarity \n",
    "    FROM        pypi as a \n",
    "    WHERE       lower(a.packages) % :p\n",
    "    ORDER BY    similarity DESC\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fuzzysearch\\xa00.3.0', 0.75),\n",
       " ('simple-fuzzysearch\\xa00.0.5', 0.521739),\n",
       " ('fzysearch\\xa00.3', 0.444444),\n",
       " ('c2.search.fuzzy\\xa01.0a5', 0.416667),\n",
       " ('fuzzyset\\xa00.0.9', 0.388889),\n",
       " ('mysearch\\xa01.1', 0.352941),\n",
       " ('Fuzzy\\xa01.1', 0.333333),\n",
       " ('mysearch\\xa01.5', 0.315789),\n",
       " ('pYsearch\\xa03.1', 0.315789)]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk = \"fuzzysearch\"\n",
    "res = engine.execute(text(query_package), p=pk)\n",
    "packages = res.fetchall()\n",
    "packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for elem in packages:\n",
    "    if elem >="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "heuristics = for both fields over \n",
    "heuristics - well different situations have differnet codes - statistical model leveraging the trigram score as a feature\n",
    "(back to slides here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Other Similarity Metrics**\n",
    "- NLTK: wordnet\n",
    "- Word2Vec: uses cosine distance\n",
    "    - cosine distance between two vectors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "word1 = wordnet.synsets(\"blue\")\n",
    "word2 = wordnet.synsets(\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word1[0].wup_similarity(word2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Starting training using file text/text8\\n'b'Words processed: 100K     Vocab size: 72K  \\rWords processed: 200K     Vocab size: 130K  \\rWords processed: 300K     Vocab size: 181K  \\rWords processed: 400K     Vocab size: 231K  \\rWords processed: 500K     Vocab size: 278K  \\rWords processed: 600K     Vocab size: 323K  \\rWords processed: 700K     Vocab size: 366K  \\rWords processed: 800K     Vocab size: 407K  \\rWords processed: 900K     Vocab size: 450K  \\rWords processed: 1000K     Vocab size: 489K  \\rWords processed: 1100K     Vocab size: 525K  \\rWords processed: 1200K     Vocab size: 562K  \\rWords processed: 1300K     Vocab size: 599K  \\rWords processed: 1400K     Vocab size: 634K  \\rWords processed: 1500K     Vocab size: 669K  \\rWords processed: 1600K     Vocab size: 706K  \\rWords processed: 1700K     Vocab size: 741K  \\rWords processed: 1800K     Vocab size: 778K  \\rWords processed: 1900K     Vocab size: 812K  \\rWords processed: 2000K     Vocab size: 848K  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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Vocab size (unigrams + bigrams): 2419827\\n'b'Words in train file: 17005206\\n'b'Words written: 100K\\rWords written: 200K\\rWords written: 300K\\rWords written: 400K\\rWords written: 500K\\rWords written: 600K\\rWords written: 700K\\rWords written: 800K\\rWords written: 900K\\rWords written: 1000K\\rWords written: 1100K\\rWords written: 1200K\\rWords written: 1300K\\rWords written: 1400K\\rWords written: 1500K\\rWords written: 1600K\\rWords written: 1700K\\rWords written: 1800K\\rWords written: 1900K\\rWords written: 2000K\\rWords written: 2100K\\rWords written: 2200K\\rWords written: 2300K\\rWords written: 2400K\\rWords written: 2500K\\rWords written: 2600K\\rWords written: 2700K\\rWords written: 2800K\\rWords written: 2900K\\rWords written: 3000K\\rWords written: 3100K\\rWords written: 3200K\\rWords written: 3300K\\rWords written: 3400K\\rWords written: 3500K\\rWords written: 3600K\\rWords written: 3700K\\rWords written: 3800K\\rWords written: 3900K\\rWords written: 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written: 16700K\\rWords written: 16800K\\rWords written: 16900K\\rWords written: 17000K\\r'b'Starting training using file text/text8-phrases\\n'b'100K\\r200K\\r300K\\r400K\\r500K\\r600K\\r700K\\r800K\\r900K\\r1000K\\r1100K\\r1200K\\r1300K\\r1400K\\r1500K\\r1600K\\r1700K\\r1800K\\r1900K\\r2000K\\r2100K\\r2200K\\r2300K\\r2400K\\r2500K\\r2600K\\r2700K\\r2800K\\r2900K\\r3000K\\r3100K\\r3200K\\r3300K\\r3400K\\r3500K\\r3600K\\r3700K\\r3800K\\r3900K\\r4000K\\r4100K\\r4200K\\r4300K\\r4400K\\r4500K\\r4600K\\r4700K\\r4800K\\r4900K\\r5000K\\r5100K\\r5200K\\r5300K\\r5400K\\r5500K\\r5600K\\r5700K\\r5800K\\r5900K\\r6000K\\r6100K\\r6200K\\r6300K\\r6400K\\r6500K\\r6600K\\r6700K\\r6800K\\r6900K\\r7000K\\r7100K\\r7200K\\r7300K\\r7400K\\r7500K\\r7600K\\r7700K\\r7800K\\r7900K\\r8000K\\r8100K\\r8200K\\r8300K\\r8400K\\r8500K\\r8600K\\r8700K\\r8800K\\r8900K\\r9000K\\r9100K\\r9200K\\r9300K\\r9400K\\r9500K\\r9600K\\r9700K\\r9800K\\r9900K\\r10000K\\r10100K\\r10200K\\r10300K\\r10400K\\r10500K\\r10600K\\r10700K\\r10800K\\r10900K\\r11000K\\r11100K\\r11200K\\r11300K\\r11400K\\r11500K\\r11600K\\r11700K\\r11800K\\r11900K\\r12000K\\r12100K\\r12200K\\r12300K\\r12400K\\r12500K\\r12600K\\r12700K\\r12800K\\r12900K\\r13000K\\r13100K\\r13200K\\r13300K\\r13400K\\r13500K\\r13600K\\r13700K\\r13800K\\r13900K\\r14000K\\r14100K\\r14200K\\r14300K\\r14400K\\r14500K\\r14600K\\r14700K\\r14800K\\r14900K\\r15000K\\r15100K\\r15200K\\r15300K\\r15400K\\r15500K\\r15600K\\r15700K\\r15800K\\r15900K\\r16000K\\r16100K\\rVocab 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     ]
    }
   ],
   "source": [
    "#sample data set from: http://mattmahoney.net/dc/text8.zip\n",
    "import word2vec\n",
    "word2vec.word2phrase('text/text8', 'text/text8-phrases', verbose=True)\n",
    "word2vec.word2vec('text/text8-phrases', 'text/text8.bin', size=100, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05433614,  0.12524973,  0.07404984, -0.08754835,  0.14115146,\n",
       "        0.02670596, -0.00371378, -0.0822072 ,  0.13078709,  0.17228104,\n",
       "        0.04973774,  0.0019975 ,  0.1577177 , -0.05208512, -0.14908025,\n",
       "       -0.01790344,  0.12454432,  0.02813962,  0.07449716,  0.10513708,\n",
       "       -0.21924408,  0.01712829, -0.06005951, -0.03893688,  0.17716517,\n",
       "       -0.0377395 , -0.14821038, -0.01041527,  0.07213477,  0.05798329,\n",
       "        0.06121416,  0.07777782, -0.05813833, -0.09764528,  0.19129921,\n",
       "        0.09042751,  0.0471188 ,  0.01570847, -0.05679421,  0.12045967,\n",
       "       -0.04221033,  0.15452832, -0.13602494, -0.08144774, -0.00420029,\n",
       "        0.15539469,  0.02450533, -0.05251949,  0.01617594, -0.10412274,\n",
       "        0.01644405, -0.01289717,  0.15754297, -0.06625356,  0.04333038,\n",
       "       -0.14395064, -0.03137844,  0.01774955, -0.0368893 , -0.01210278,\n",
       "       -0.14220341, -0.13698573, -0.03015008,  0.0602311 ,  0.00937867,\n",
       "        0.02154245, -0.09614298, -0.24017119, -0.07919069,  0.01427343,\n",
       "        0.14461903, -0.01650186, -0.06899033, -0.10703208,  0.00148559,\n",
       "        0.03204586, -0.2283708 , -0.02625657,  0.03003199, -0.06079603,\n",
       "        0.07720963,  0.04048733,  0.1023441 , -0.13308406,  0.03832683,\n",
       "        0.15545969,  0.04695185,  0.06050506, -0.03850128,  0.15793142,\n",
       "        0.04807498, -0.0725477 ,  0.06874809,  0.15540673,  0.09727377,\n",
       "        0.10471302, -0.02086591,  0.19859764, -0.18967381, -0.14002228])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import word2vec\n",
    "model = word2vec.load('text/text8.bin')\n",
    "model['coffee']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def get_similar_words(word):\n",
    "    indexes, metrics = model.cosine(word)\n",
    "    return model.generate_response(indexes, metrics).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wheat', 0.8717711986650305),\n",
       " ('cotton', 0.8716775132711713),\n",
       " ('beef', 0.869811888579094),\n",
       " ('tea', 0.8669866678285572),\n",
       " ('bananas', 0.8623041870046427),\n",
       " ('sugar', 0.856468674871585),\n",
       " ('cocoa', 0.8556310923408067),\n",
       " ('rice', 0.8497382416920862),\n",
       " ('poultry', 0.8476775486170876),\n",
       " ('potatoes', 0.8473502626191239)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_similar_words('coffee')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('tea', 0.3349493584833404),\n",
       " ('chicken', 0.3331083672705435),\n",
       " ('cigarettes', 0.3314079955476932),\n",
       " ('rubber', 0.32838851367457095),\n",
       " ('soda', 0.32784664748106473),\n",
       " ('tin', 0.3275660350090016),\n",
       " ('beans', 0.32454398748329094),\n",
       " ('cream', 0.3238316700128127),\n",
       " ('cotton', 0.32298672573669773),\n",
       " ('rice', 0.3226631851510718)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexes, metrics = model.analogy(pos=[\"coffee\", \"night\"], neg=[\"day\"], n=10)\n",
    "model.generate_response(indexes, metrics).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#analogy\n",
    "#sun + cold - warm\n",
    "indexes, metrics = model.analogy(pos=[\"sun\", \"cold\"], neg=[\"warm\"], n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('moon', 0.28099868884410184),\n",
       " ('earth', 0.2570855551214004),\n",
       " ('heavens', 0.25232329108750745),\n",
       " ('neptune', 0.2485093399462911),\n",
       " ('jupiter', 0.24012433433820843),\n",
       " ('orbit_around', 0.2395745361408647),\n",
       " ('sky', 0.23716435455645374),\n",
       " ('saturn', 0.23677076081542567),\n",
       " ('solar_system', 0.23490988018948508),\n",
       " ('venus', 0.23345560237762197)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.generate_response(indexes, metrics).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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