{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#(Easy) High performance text processing in Machine Learning*\n", "\n", "#ML meetup\n", "##*Joint work with Ian Langmore" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#[Rosetta](https://github.com/columbia-applied-data-science/rosetta) \n", "\n", "##Tools for data science with a focus on text processing.\n", "\n", "* Focuses on \"medium data\", i.e. data too big to fit into memory but too small to necessitate the use of a cluster.\n", "* Integrates with existing scientific Python stack as well as select outside tools.\n", "\n", "## Tools and utilities \n", "\n", "### `cmd` \n", "* Unix-like command line utilities. Filters (read from stdin/write to stdout) for files\n", "\n", "### `parallel` \n", "* Wrappers for Python multiprocessing that add ease of use\n", "* Memory-friendly multiprocessing\n", "\n", "### `text`\n", "* Stream text from disk to formats used in common ML processes\n", "* Write processed text to sparse formats\n", "* Helpers for ML tools (e.g. Vowpal Wabbit, Gensim, etc...)\n", "* Other general utilities\n", "\n", "### `workflow`\n", "* High-level wrappers that have helped with our workflow and provide additional examples of code use\n", "\n", "### `modeling`\n", "* General ML modeling utilities\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Lets begin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###for the purpose of this tutorial we will be working with a collection of about 1000 declassified government embassy cables\n", "###" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Text Processors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Streamers\n", "* Data in many text processing problems comes in the form of \n", " * flat files\n", " * repeated calls to an DB or API\n", " * some 'online' stream\n", "* A lot of these can be handled streaming the data either from disk, DB, API minimizing CPU use\n", "* In addition, a lot of streaming is embarassingly parallel so can be easily scaled\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#all you realy need to know is that CABLES is the directory where the data (or cables)\n", "#are stored on your machine\n", "DATA = os.environ['DATA']\n", "CABLES = os.path.join(DATA, 'declass', 'cables_short')\n", "RAW = os.path.join(CABLES, 'raw')\n", "PROCESSED = os.path.join(CABLES, 'processed')\n", "SPARSE = os.path.join(CABLES, 'sparse')\n", "\n", "sfile_path = os.path.join(SPARSE, 'cables-short.vw')\n", "filtered_sfile_path = os.path.join(PROCESSED, 'cables-short-filtered.vw')\n", "sff_path = os.path.join(PROCESSED, 'sff.pkl')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Streaming: given a collection of objects streaming is the paradigm of processing these objects one at a time in memory, extracting relevant information, writing the information, and discarding the original object \n", "\n", "###Note: after a streaming process is complete, the original collection should no longer be needed for the analysis at hand" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Lets write a simple file streamer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#filefilter is a module which helps with basic file/dir functions, such as\n", "#retrieving all paths from a given directory and it's subdir's\n", "from rosetta.text import filefilter" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "def simple_file_streamer(base_path):\n", " paths = filefilter.get_paths(base_path, get_iter=True)\n", " for p in paths:\n", " with open(p) as f:\n", " text = f.read()\n", " yield(text)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###In case you haven't worked much with iterators explicitely, here is a small refresher... \n", "* For those familiar with generator functions, or iterators, you'll notice that this is exactly what we mean by \"streamer,\" i.e. anything that retrieves files or extracts information from therein and has a .next() method\n", "* python docs have a short intro about generators (http://docs.python.org/2/tutorial/classes.html#generators)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_iter(N):\n", " i=0\n", " while True:\n", " if i == N:\n", " raise StopIteration\n", " else:\n", " yield i\n", " i += 1\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "mi = my_iter(5)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": true, "input": [ "mi.next()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "0" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "#note the raised StopIteration; lets see how a for look handles this\n", "\n", "for i in my_iter(5):\n", " print i\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###so even if you have not thought about iterators, you have been using them throughout \n", "###now back to our streamer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "simple_stream = simple_file_streamer(RAW)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "#lets look at what this object is\n", "type(simple_stream)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "generator" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Note: this is the first time anything is read into memory" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#lets see what the .next() yields (and splitlines to make it more readable)\n", "simple_stream.next().splitlines()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "StopIteration", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#lets see what the .next() yields (and splitlines to make it more readable)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msimple_stream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mStopIteration\u001b[0m: " ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "* Now since our end goal is to build a topic model we probably want to have a more fexible streamer, i.e. one that can return file ids, text or tokens (based on some predefined tokenizer)\n", " * luckily we have one such streamer written" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from rosetta import TextFileStreamer, TokenizerBasic\n", "text_streamer = TextFileStreamer(text_base_path=RAW, file_type='*', \n", " tokenizer=TokenizerBasic())\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from rosetta.text import streamers" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "stream = text_streamer.info_stream()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": true, "input": [ "stream.next()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "{'atime': 1393622776.0,\n", " 'cached_path': '/Users/danielkrasner/DATA_master/prod/declass/cables_short/raw/1976ZANZIB00097',\n", " 'doc_id': '1976ZANZIB00097',\n", " 'mtime': 1383854248.0,\n", " 'size': 118,\n", " 'text': 'MRN: 1976ZANZIB000097 SEGMENT NUMBER: 000001 EXPAND ERROR ENCOUNTERED;\\nTELEGRAM TEXT FOR THIS SEGMENT IS UNAVAILABLE',\n", " 'tokens': ['mrn',\n", " 'segment',\n", " 'number',\n", " 'expand',\n", " 'error',\n", " 'encountered',\n", " 'telegram',\n", " 'text',\n", " 'segment',\n", " 'unavailable']}" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": true, "input": [ "#lets take a quick look at TextFileStreamer\n", "TextFileStreamer?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Note: you can pass a tokenizer function to TextFileStreamer(), i.e. any function that takes a string of text and returns a list of strings (the \"tokens\")\n", " * We have written a basic tokenizer function and class to add functionality and because the nltk.word_tokenize() was slow\n", "* It also has a few other nice options such as shuffle, file_type, etc and a bunch of methods" ] }, { "cell_type": "code", "collapsed": false, "input": [ "text = stream.next()['text']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": true, "input": [ "print text" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "LIMITED OFFICIAL USE\n", "\n", "PAGE 01 YAOUND 00757 010739Z\n", "\n", "17\n", "ACTION OES-05\n", "\n", "INFO OCT-01 AF-06 ISO-00 EB-07 AID-05 /024 W\n", " --------------------- 068311\n", "R 010645Z MAR 76\n", "FM AMEMBASSY YAOUNDE\n", "TO SECSTATE WASHDC 7910\n", "\n", "\n", "LIMITED OFFICIAL USE YAOUNDE 0757\n", "\n", "E.O.: 11652: N/A\n", "TAGS: SPOP\n", "SUBJ: IMPLICATION OF WORLDWIDE POPULATION GROWTH FOR UNITED\n", " STATES SECURITY AND OVERSEAS INTERESTS\n", "\n", "REFS: (A) STATE 301427 (B) STATE 297241\n", "\n", "COMMENTS BELOW ARE KEYED TO LETTERED SECTIONS PARA 4 REF (A):\n", "\n", "A. CAMEROON'S BASIC POPULATION POLICY: CAMEROON HAS NO CLEARLY\n", "STATED POPULATION POLICY. CAMEROON'S PRESENT PRACTICE REFLECTS\n", "THE INFLUENCE OF OLD FRENCH LAW, TRADITIONAL DESIRE FOR LARGE\n", "FAMILIES, AND INCREASING AWARENESS BY YOUNGER CAMEROONIANS OF\n", "NEED FOR FAMILY PLANNING AND TO A CERTAIN EXTENT, BIRTH CONTROL.\n", "THERE IS NO CLEAR LEGAL PROHIBITION OF CONTRACEPTIVE PRACTICES,\n", "YET REFERENCES TO EARLIER FRENCH LAWS LEAVE LOCAL LAWYERS IN\n", "AGREEMENT THAT CAMEROON HAS NEITHER A PRO-FAMILY PLANNING POLICY\n", "NOR A LEGISLATIVE ENVIRONMENT CONDUCIVE TO THE ESTABLISHMENT OF\n", "FAMILY PLANNING SERVICES. NEVERTHELESS, CAMEROON IS NOT PRO-\n", "NATALIST, AND LEADING GOVERNMENT OFFICIALS ARE INVOLVED IN\n", "FAMILY PLANNING TRAINING AND THE DEVELOPMENT OF PILOT FAMILY\n", "PLANNING SERVICE PROGRAMS. THE GOVERNMENT OF CAMEROON (GURC) IS\n", "NOW IN THE FINAL STATES OF DRAFTING THE FOURTH FIVE-YEAR PLAN.\n", "AT EARLIER STAGES IN THE DRAFTING, IT WAS DECIDED TO CREATE AN\n", "INTERMINISTERIAL COMMISSION TO DEAL WITH POPULATION ISSUES. THE\n", "FOURTH FIVE-YEAR PLAN, WHEN IT EMERGES IN FINAL, WILL REVEAL THE\n", "DEGREE TO WHICH CAMEROON HAS BEEN ABLE TO DEFINE A POPULATION\n", "POLICY.\n", "\n", "LIMITED OFFICIAL USE\n", "\n", "LIMITED OFFICIAL USE\n", "\n", "PAGE 02 YAOUND 00757 010739Z\n", "\n", "B. CAMEROON'S POPULATION PROGRAM: DEFINED AS SUCH, CAMEROON DOES\n", "NOT HAVE A POPULATION PROGRAM. HOWEVER, THERE ARE A NUMBER OF\n", "CURRENT PROJECTS WHICH REFLECT THE PERMISSIVE STANCE OF THE GOV-\n", "ERNMENT TOWARD FAMILY PLANNING ACTIVITIES. AID IS SUPPORTING A\n", "NUMBER OF THESE ACTIVITIES WHICH RELATE TO FERTILITY DECLINE:\n", "THESE INCLUDE THE TRAINING OF MIDWIVES IN FAMILY PLANNING SER-\n", "VICE DEVELOPMENT AND ADMINISTRATION; ASSISTING WITH THE FIRST\n", "NATIONAL CENSUS; SUPPORTING FAMILY PLANNING CLINICS AT URBAN\n", "HEALTH CENTERS AND THE NATIONAL HOSPITAL (CUSS); AND THE DEVELOP-\n", "MENT OF EDUCATIONAL MATERIALS AIMED AT INTEGRATING FAMILY PLAN-\n", "NING INTO FAMILY HEALTH TRAINING PROGRAMS. THESE ACTIVITIES ARE\n", "NOT COSTLY AND CONTRIBUTE SIGNIFICANTLY TO THE PROCESS WHICH\n", "WILL EVENTUALLY REQUIRE GURC TO DEVELOP A POPULATION POLICY\n", "FAVORING THE AVAILABILITY OF FAMILY PLANNING SERVICES. CONTINUED\n", "SUPPORT IS JUSTIFIED IN ORDER TO SUSTAIN THESE FORCES UNTIL A\n", "FAVORABLE GOVERNMENT POLICY OPENS THE DOOR FOR MUCH STRONGER\n", "SUPPORT.\n", "\n", "C. POPULATION GROWTH IN CAMEROON IS AT AN ESTIMATED RATE OF 2.2\n", "PERCENT WHICH IN URBAN AREAS IS AS HIGH AS 8 PERCENT BECAUSE OF\n", "MIGRATION. THE INFLUENCE OF THIS GROWTH UPON NATIONAL DEVELOP-\n", "MENT HAS TO DATE BEEN ONLY MARGINAL AT THE NATIONAL LEVEL. EX-\n", "PENDITURES FOR SOCIAL SERVICES ARE STEADILY INCREASING, BUT THUS\n", "FAR THERE HAVE BEEN NO SIGNIFICANT EFFORTS ON FOOD IMPORTS,\n", "DOMESTIC SAVINGS AND THE BALANCE OF PAYMENTS.\n", "\n", "D. SOCIO-ECONOMIC DEVELOPMENT, ON THE OTHER HAND, HAS FELT THE\n", "PRESSURE OF POPULATION GROWTH, PARTICULARLY IN THE RAPIDLY EX-\n", "PANDING CITIES. UNEMPLOYMENT LEVELS ARE HIGH, THE PRICE OF FOOD\n", "IN THE CITIES IS SOARING, SCHOOLS ARE UNABLE TO ABSORB THE YOUNG,\n", "AND MINOR THEFT IS COMMONPLACE. TRADITIONAL FAMILY TIES ARE NOT\n", "MAINTAINED BECAUSE OF THE INABILITY TO AFFORD THE EXCHANGE OF\n", "GIFTS.\n", "\n", "E. THE URBAN ENVIRONMENT ALSO REFLECTS THE OVERCROWDED CONDI-\n", "TIONS AS WASTE REMOVAL CANNOT KEEP PACE WITH THE NEED. IN NORTH\n", "CAMEROON, WHICH HAS SOME OF THE MOST DENSELY POPULATED AREAS OF\n", "THE COUNTRY, THE COMBINATION OF OVERGRAZING AND THE DROUGHT HAS\n", "LED TO A DEGENERATION OF LAND AND WATER RESOURCES TO A POINT\n", "WHERE IT CAN BE SEEN THAT, IF THIS PROCESS IS NOT REVERSED, NORTH\n", "CAMEROON WILL NOT BE ABLE TO SUPPORT ITS PEOPLE AFTER TEN TO\n", "LIMITED OFFICIAL USE\n", "\n", "LIMITED OFFICIAL USE\n", "\n", "PAGE 03 YAOUND 00757 010739Z\n", "\n", "FIFTEEN YEARS.\n", "\n", "F. THE CURRENT POLITICAL CLIMATE IN CAMEROON IS STABLE AND NOT\n", "LIKELY TO BE THREATENED BY POPULATION PRESSURE IN THE FORESEEABLE\n", "FUTURE. UNEMPLOYMENT AND INFLATION HAVE RESULTED IN INCREASED\n", "WORKER DEMANDS AND PETTY CRIME BUT THIS IS NOT LIKELY TO INVOLVE\n", "OTHER CENTRAL AFRICAN COUNTRIES. PRESENT GURC THINKING ON POPU-\n", "LATION PROBLEMS EMPHASIZES RESETTLEMENT IN NEW DEVELOPMENT AREAS\n", "RATHER THAN LIMITATION OF GROWTH TO RELIEVE PRESSURE.\n", "\n", "G. THE UNITED STATES CAN BEST CONTRIBUTE TO THE EVOLUATION OF A\n", "FAVORABLE POPULATION POLICY IN CAMEROON BY CONTINUING TO OFFER\n", "A VARIETY OF TYPES OF ASSISTANCE THROUGH A COMBINATION OF GOVERN-\n", "MENTAL AND PRIVATE ORGANIZATIONS. THIS APPROACH PERMITS THE APPLI-\n", "CATION OF RESOURCES TO SMALL EFFORTS WHERE THE GREATEST POLITI-\n", "CAL IMPACT CAN BE EXPECTED, WHILE RESERVING LARGE-SCALE SUPPORT\n", "UNTIL SUCH TIME AS THERE IS AN OFFICIAL EFFORT TO DEVELOP NAT-\n", "IONAL FAMILY PLANNING SERVICES.\n", "SPIRO\n", "\n", "\n", "LIMITED OFFICIAL USE\n", "\n", "\n", "\n", "\n", "NNN\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "text_streamer.tokenizer.text_to_token_list(text)\n", "#text_streamer.tokenizer.text_to_counter(text)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#lets look at a few methods\n", "token_stream = text_streamer.token_stream() # returns a generator function which yields a stream of tokens" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "token_stream.next() # this is what our basic tokenizer returns (we are skipping stop words and numerics by default)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "['unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'action',\n", " 'eur',\n", " 'info',\n", " 'oct',\n", " 'iso',\n", " 'eure',\n", " 'eb',\n", " 'faa',\n", " 'dote',\n", " 'pm',\n", " 'nsc',\n", " 'sp',\n", " 'ss',\n", " 'ciae',\n", " 'dode',\n", " 'inr',\n", " 'nsae',\n", " 'pa',\n", " 'usia',\n", " 'prs',\n", " 'mct',\n", " 'sep',\n", " 'fm',\n", " 'amconsul',\n", " 'zagreb',\n", " 'secstate',\n", " 'washdc',\n", " 'priority',\n", " 'info',\n", " 'amembassy',\n", " 'belgrade',\n", " 'unclas',\n", " 'section',\n", " 'zagreb',\n", " 'eo',\n", " 'na',\n", " 'tags',\n", " 'pfor',\n", " 'eair',\n", " 'yo',\n", " 'subj',\n", " 'twa',\n", " 'hijacking',\n", " 'zagreb',\n", " 'press',\n", " 'reaction',\n", " 'ref',\n", " 'belgrade',\n", " 'both',\n", " 'zagreb',\n", " 'sunday',\n", " 'papers',\n", " 'vjesnik',\n", " 'borba',\n", " 'featured',\n", " 'series',\n", " 'articles',\n", " 'twa',\n", " 'hijacking',\n", " 'borba',\n", " 'carried',\n", " 'tanjujg',\n", " 'commentary',\n", " 'same',\n", " 'editorial',\n", " 'appeared',\n", " 'belgrade',\n", " 'edition',\n", " 'reftel',\n", " 'vjesnik',\n", " 'carried',\n", " 'articles',\n", " 'paris',\n", " 'new',\n", " 'york',\n", " 'correspondents',\n", " 'article',\n", " 'datelined',\n", " 'zagreb',\n", " 'summarizes',\n", " 'tanjug',\n", " 'report',\n", " 'vjesnik',\n", " 'summary',\n", " 'tanjug',\n", " 'report',\n", " 'strongly',\n", " 'criticizes',\n", " 'tolerating',\n", " 'activities',\n", " 'anti',\n", " 'yugoslav',\n", " 'extremist',\n", " 'groups',\n", " 'article',\n", " 'new',\n", " 'york',\n", " 'correspondent',\n", " 'drazen',\n", " 'vukov',\n", " 'colic',\n", " 'contains',\n", " 'different',\n", " 'slants',\n", " 'hijacking',\n", " 'example',\n", " 'article',\n", " 'interprets',\n", " 'hijacking',\n", " 'act',\n", " 'desperation',\n", " 'croatian',\n", " 'emigre',\n", " 'groups',\n", " 'finding',\n", " 'less',\n", " 'less',\n", " 'support',\n", " 'anti',\n", " 'yugoslav',\n", " 'activities',\n", " 'united',\n", " 'states',\n", " 'coming',\n", " 'under',\n", " 'quote',\n", " 'more',\n", " 'determined',\n", " 'unquote',\n", " 'investigation',\n", " 'police',\n", " 'complete',\n", " 'text',\n", " 'latter',\n", " 'article',\n", " 'follows',\n", " 'quote',\n", " 'unclassified',\n", " 'unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'those',\n", " 'insane',\n", " 'people',\n", " 'mayor',\n", " 'new',\n", " 'york',\n", " 'city',\n", " 'abraham',\n", " 'beame',\n", " 'those',\n", " 'lunatics',\n", " 'threaten',\n", " 'many',\n", " 'lives',\n", " 'action',\n", " 'know',\n", " 'achieve',\n", " 'anything',\n", " 'hope',\n", " 'murderers',\n", " 'brought',\n", " 'justice',\n", " 'one',\n", " 'first',\n", " 'reactions',\n", " 'news',\n", " 'self',\n", " 'appointed',\n", " 'fighters',\n", " 'freedom',\n", " 'croatia',\n", " 'hijacked',\n", " 'airplane',\n", " 'american',\n", " 'company',\n", " 'twa',\n", " 'friday',\n", " 'evening',\n", " 'central',\n", " 'european',\n", " 'time',\n", " 'saturday',\n", " 'morning',\n", " 'local',\n", " 'time',\n", " 'new',\n", " 'york',\n", " 'airport',\n", " 'laguardia',\n", " 'starting',\n", " 'drop',\n", " 'leaflets',\n", " 'european',\n", " 'capitals',\n", " 'during',\n", " 'world',\n", " 'war',\n", " 'ii',\n", " 'explains',\n", " 'new',\n", " 'york',\n", " 'post',\n", " 'readers',\n", " 'ustashi',\n", " 'fight',\n", " \"tito's\",\n", " 'partisans',\n", " 'communists',\n", " 'killed',\n", " 'eight',\n", " 'hundred',\n", " 'thousand',\n", " 'jews',\n", " 'serbs',\n", " 'gypsies',\n", " 'together',\n", " 'croats',\n", " 'opponents',\n", " 'regime',\n", " 'newspaper',\n", " 'puppet',\n", " 'italian',\n", " 'german',\n", " 'hands.',\n", " 'thus',\n", " 'address',\n", " 'terrorists',\n", " 'put',\n", " 'end',\n", " 'long',\n", " 'proclamation',\n", " 'number',\n", " 'post',\n", " 'office',\n", " 'box',\n", " 'sharp',\n", " 'condemnations',\n", " 'descriptions',\n", " 'political',\n", " 'background',\n", " 'hijackers',\n", " 'immediately',\n", " 'started',\n", " 'arrive',\n", " 'hoping',\n", " 'praise',\n", " 'really',\n", " 'difficult',\n", " 'night',\n", " 'terror',\n", " 'new',\n", " 'york',\n", " 'post',\n", " 'saturday',\n", " 'morning',\n", " 'american',\n", " 'newspapers',\n", " 'radio',\n", " 'television',\n", " 'stations',\n", " 'agreed',\n", " 'question',\n", " 'propaganda',\n", " 'action',\n", " 'extremists',\n", " 'one',\n", " 'group',\n", " 'passengers',\n", " 'released',\n", " 'hijacked',\n", " 'plane',\n", " 'canada',\n", " 'spoke',\n", " 'propaganda',\n", " 'subjected',\n", " 'time.',\n", " 'bloody',\n", " 'introduction',\n", " 'leaflets',\n", " 'very',\n", " 'seriously',\n", " 'pompously',\n", " 'speak',\n", " 'unbearable',\n", " 'cultural',\n", " 'political',\n", " 'economic',\n", " 'exploitation',\n", " 'croatia',\n", " 'bloody',\n", " 'introduction',\n", " 'prior',\n", " 'hijacking',\n", " 'bomb',\n", " 'placed',\n", " 'one',\n", " 'central',\n", " 'stations',\n", " 'underground',\n", " 'railway',\n", " 'new',\n", " 'york',\n", " 'city',\n", " 'members',\n", " 'new',\n", " 'york',\n", " 'city',\n", " 'police',\n", " 'bomb',\n", " 'disposal',\n", " 'squad',\n", " 'unfortunately',\n", " 'succeed',\n", " 'mastering',\n", " 'monstrous',\n", " 'device',\n", " 'friday',\n", " 'evening',\n", " 'exploded',\n", " 'hands',\n", " 'four',\n", " 'policemen',\n", " 'brian',\n", " \"o'murray\",\n", " 'killed',\n", " 'spot',\n", " 'sergeant',\n", " 'terrence',\n", " 'mctigue',\n", " 'fighting',\n", " 'life',\n", " 'henry',\n", " 'dworkin',\n", " 'fritz',\n", " \"o'behr\",\n", " 'hospital',\n", " 'seriously',\n", " 'wounded',\n", " 'mayor',\n", " 'beame',\n", " 'duty',\n", " 'unclassified',\n", " 'unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'whole',\n", " 'night',\n", " 'hospital',\n", " 'during',\n", " 'operation',\n", " 'wounded',\n", " 'policemen',\n", " 'chicago',\n", " 'airport',\n", " 'families',\n", " 'crying',\n", " 'american',\n", " 'citizens',\n", " 'cross',\n", " 'ocean',\n", " 'airplane',\n", " 'technically',\n", " 'equipped',\n", " 'trip',\n", " 'name',\n", " 'appeal',\n", " 'american',\n", " 'people',\n", " 'fighters',\n", " 'free',\n", " 'croatia.',\n", " 'unclassified',\n", " 'nnn',\n", " 'unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'action',\n", " 'eur',\n", " 'info',\n", " 'oct',\n", " 'iso',\n", " 'eure',\n", " 'eb',\n", " 'faa',\n", " 'dote',\n", " 'pm',\n", " 'nsc',\n", " 'sp',\n", " 'ss',\n", " 'ciae',\n", " 'dode',\n", " 'inr',\n", " 'nsae',\n", " 'pa',\n", " 'usia',\n", " 'prs',\n", " 'mct',\n", " 'sep',\n", " 'fm',\n", " 'amconsul',\n", " 'zagreb',\n", " 'secstate',\n", " 'washdc',\n", " 'priority',\n", " 'info',\n", " 'amembassy',\n", " 'belgrade',\n", " 'unclas',\n", " 'section',\n", " 'zagreb',\n", " 'authors',\n", " 'proclamation',\n", " 'struggling',\n", " 'against',\n", " 'shadows',\n", " 'hitler',\n", " 'mussolini',\n", " 'renunciation',\n", " 'past',\n", " 'admit',\n", " 'even',\n", " 'justified',\n", " 'use',\n", " 'force',\n", " 'cause',\n", " 'fear',\n", " 'discontent',\n", " 'one',\n", " 'part',\n", " 'population',\n", " 'promise',\n", " 'use',\n", " 'force',\n", " 'little',\n", " 'possible',\n", " 'nevertheless',\n", " 'hijacked',\n", " 'airplane',\n", " 'planted',\n", " 'bomb',\n", " 'killed',\n", " 'one',\n", " 'person',\n", " 'announced',\n", " 'second',\n", " 'bomb',\n", " 'explode',\n", " 'time',\n", " 'indignation',\n", " 'such',\n", " 'liberation',\n", " 'policy',\n", " 'support',\n", " 'american',\n", " 'citizens',\n", " 'american',\n", " 'government',\n", " 'death',\n", " 'injuries',\n", " 'members',\n", " 'bomb',\n", " 'disposal',\n", " 'squad',\n", " 'aroused',\n", " 'anger',\n", " 'citizens',\n", " 'new',\n", " 'york',\n", " 'still',\n", " 'forgotten',\n", " 'unexplained',\n", " 'explosion',\n", " 'laguardia',\n", " 'airport',\n", " 'now',\n", " 'again',\n", " 'listen',\n", " 'very',\n", " 'same',\n", " 'airport',\n", " 'another',\n", " 'hijacking',\n", " 'passenger',\n", " 'plane',\n", " 'happened',\n", " 'full',\n", " 'months',\n", " 'peace',\n", " 'entire',\n", " 'usa',\n", " 'usa',\n", " 'even',\n", " 'worst',\n", " 'gangsters',\n", " 'know',\n", " 'murder',\n", " 'policeman',\n", " 'unpardonable',\n", " 'crime',\n", " 'hijacking',\n", " 'airplane',\n", " 'act',\n", " 'detested',\n", " 'united',\n", " 'states',\n", " 'terrorists',\n", " 'decide',\n", " 'commit',\n", " 'two',\n", " 'unpopular',\n", " 'types',\n", " 'crime',\n", " 'reply',\n", " 'given',\n", " 'blackmailing',\n", " 'memorandum',\n", " 'simply',\n", " 'based',\n", " 'fact',\n", " 'unclassified',\n", " 'unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'american',\n", " 'public',\n", " 'less',\n", " 'less',\n", " 'support',\n", " 'anti',\n", " 'yugoslav',\n", " 'disruptive',\n", " 'ideas',\n", " 'awaken',\n", " 'conscience',\n", " 'recently',\n", " 'state',\n", " 'department',\n", " 'declared',\n", " 'future',\n", " 'continue',\n", " 'support',\n", " 'respect',\n", " 'integrity',\n", " 'unity',\n", " 'yugoslavia',\n", " 'fighters',\n", " 'freedom',\n", " 'croatia',\n", " 'denouncing',\n", " 'besides',\n", " 'fighting',\n", " 'against',\n", " 'economic',\n", " 'coopera',\n", " 'tion',\n", " 'between',\n", " 'yugoslavia',\n", " 'usa',\n", " 'disruptive',\n", " 'efforts',\n", " 'less',\n", " 'favorably',\n", " 'judged',\n", " 'american',\n", " 'police',\n", " 'more',\n", " 'determined',\n", " 'investigating',\n", " 'real',\n", " 'content',\n", " 'work',\n", " 'emigre',\n", " 'associations',\n", " 'congressmen',\n", " 'opposing',\n", " 'future',\n", " 'protection',\n", " 'former',\n", " 'war',\n", " 'criminals',\n", " 'now',\n", " 'live',\n", " 'undisturbed',\n", " 'usa',\n", " 'therefore',\n", " 'panic',\n", " 'began',\n", " 'reign',\n", " 'emigre',\n", " 'ranks',\n", " 'therefore',\n", " 'one',\n", " 'invent',\n", " 'something',\n", " 'really',\n", " 'spectacular',\n", " 'hijacking',\n", " 'leaflets',\n", " 'bombs',\n", " 'barter',\n", " 'large',\n", " 'american',\n", " 'newspapers',\n", " 'published',\n", " 'extensive',\n", " 'statement',\n", " 'terrorists',\n", " 'basic',\n", " 'condition',\n", " 'disarming',\n", " 'second',\n", " 'bomb',\n", " 'releasing',\n", " 'kidnapped',\n", " 'passengers',\n", " 'although',\n", " 'majority',\n", " 'newspapers',\n", " 'agree',\n", " 'completely',\n", " 'hijackers',\n", " 'dictate',\n", " 'important',\n", " 'pages',\n", " 'slightest',\n", " 'detail',\n", " 'nevertheless',\n", " 'more',\n", " 'certain',\n", " 'press',\n", " 'fulfilled',\n", " 'part',\n", " 'agreement',\n", " 'new',\n", " 'american',\n", " 'tactic',\n", " 'aims',\n", " 'negotiations',\n", " 'long',\n", " 'possible',\n", " 'sorts',\n", " 'terrorists',\n", " 'order',\n", " 'rescue',\n", " 'many',\n", " 'human',\n", " 'lives',\n", " 'possible',\n", " 'newspapers',\n", " 'consented',\n", " 'barter',\n", " 'terrorists',\n", " 'fulfill',\n", " 'promises',\n", " 'now',\n", " 'clear',\n", " 'one',\n", " 'believed',\n", " 'such',\n", " 'people',\n", " 'sake',\n", " 'truth',\n", " 'one',\n", " 'admit',\n", " 'section',\n", " 'american',\n", " 'newspapers',\n", " 'radio',\n", " 'television',\n", " 'approached',\n", " 'whole',\n", " 'story',\n", " 'purely',\n", " 'reasons',\n", " 'circulation',\n", " 'more',\n", " 'less',\n", " 'seriousness',\n", " 'trying',\n", " 'find',\n", " 'everything',\n", " 'even',\n", " 'deeper',\n", " 'political',\n", " 'reasons.',\n", " 'minority',\n", " 'whether',\n", " 'out',\n", " 'negligence',\n", " 'ignorance',\n", " 'commercialism',\n", " 'politics',\n", " 'last',\n", " 'resort',\n", " 'hijackers',\n", " 'those',\n", " 'sympathizers',\n", " 'croats',\n", " 'many',\n", " 'sometimes',\n", " 'very',\n", " 'loud',\n", " 'especially',\n", " 'stormy',\n", " 'time',\n", " 'america',\n", " 'various',\n", " 'unclassified',\n", " 'unclassified',\n", " 'page',\n", " 'zagreb',\n", " 'groups',\n", " 'try',\n", " 'grab',\n", " 'large',\n", " 'possible',\n", " 'piece',\n", " 'power',\n", " 'election',\n", " 'decision',\n", " 'making',\n", " 'such',\n", " 'battles',\n", " 'maneuvers',\n", " 'sometimes',\n", " 'use',\n", " 'anti',\n", " 'yugoslav',\n", " 'purposes',\n", " 'religion',\n", " 'nation',\n", " 'such',\n", " 'stories',\n", " 'yugoslav',\n", " 'persecution',\n", " 'jews',\n", " 'sometimes',\n", " 'broader',\n", " 'internal',\n", " 'squaring',\n", " 'accounts',\n", " 'effort',\n", " 'show',\n", " 'washington',\n", " 'amoral',\n", " 'cooperates',\n", " 'dictatorial',\n", " 'regimes',\n", " 'people',\n", " 'try',\n", " 'include',\n", " 'yugoslavia',\n", " 'sometimes',\n", " 'general',\n", " 'world',\n", " 'trends',\n", " 'usa',\n", " 'feels',\n", " 'itself',\n", " 'threatened',\n", " 'strengthening',\n", " 'non',\n", " 'aligned',\n", " 'policy',\n", " 'emigre',\n", " 'groups',\n", " 'try',\n", " 'use',\n", " 'ends',\n", " 'such',\n", " 'internal',\n", " 'political',\n", " 'international',\n", " 'factors',\n", " 'temporarily',\n", " 'slow',\n", " 'down',\n", " 'development',\n", " 'american',\n", " 'yugoslav',\n", " 'relations',\n", " 'thus',\n", " 'over',\n", " 'capitals',\n", " 'europe',\n", " 'quite',\n", " 'unusual',\n", " 'leaflets',\n", " 'fluttered',\n", " 'already',\n", " 'many',\n", " 'things',\n", " 'irretrievably',\n", " 'failed',\n", " 'unquote',\n", " 'kaiser',\n", " 'unclassified',\n", " 'nnn']" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "text_streamer.doc_id # returns a list of retrieved doc ids etc \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#if you want to use another tokenizer it's easy\n", "import nltk\n", "nltk.word_tokenize(text)\n", "text_streamer_nltk = TextFileStreamer(text_base_path=RAW, file_type='*', \n", " tokenizer_func=nltk.word_tokenize)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "stream_nltk = text_streamer_nltk.token_stream()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": true, "input": [ "stream_nltk.next()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "['LIMITED',\n", " 'OFFICIAL',\n", " 'USE',\n", " 'PAGE',\n", " '01',\n", " 'ZURICH',\n", " '00534',\n", " '051851Z',\n", " '53',\n", " 'ACTION',\n", " 'SY-05',\n", " 'INFO',\n", " 'OCT-01',\n", " 'EUR-12',\n", " 'ISO-00',\n", " 'SS-15',\n", " '/033',\n", " 'W',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '--',\n", " '-',\n", " '127954',\n", " 'O',\n", " 'R',\n", " '051810Z',\n", " 'SEP',\n", " '76',\n", " 'ZFF4',\n", " 'FM',\n", " 'AMCONSUL',\n", " 'ZURICH',\n", " 'TO',\n", " 'USMISSION',\n", " 'GENEVA',\n", " 'NIACT',\n", " 'IMMEDIATE',\n", " 'INFO',\n", " 'AMCONSUL',\n", " 'JOHANNESBURG',\n", " 'SECSTATE',\n", " 'WASHDC',\n", " '2360',\n", " 'LIMITED',\n", " 'OFFICIAL',\n", " 'USE',\n", " 'ZURICH',\n", " '534',\n", " 'DEPT',\n", " 'FOR',\n", " 'SY/CIC',\n", " 'E.',\n", " 'O.',\n", " '11652',\n", " ':',\n", " 'N/A',\n", " 'TAGS',\n", " ':',\n", " 'ASEC',\n", " 'OVIP',\n", " '(',\n", " 'KISSINGER',\n", " ',',\n", " 'MRS',\n", " 'HENRY',\n", " 'A',\n", " ')',\n", " 'SUBJ',\n", " ':',\n", " 'TRAVEL',\n", " 'OF',\n", " 'SY',\n", " 'ADVANCE',\n", " 'TEAM',\n", " '1.',\n", " 'SPECIAL',\n", " 'AGENTS',\n", " 'WOODS',\n", " 'AND',\n", " 'BALL',\n", " 'TO',\n", " 'DEPART',\n", " 'ZURICH',\n", " '9/6/76',\n", " 'ON',\n", " 'SWISSAIR',\n", " '#',\n", " '942',\n", " 'AT',\n", " '1935.',\n", " 'DUE',\n", " 'TO',\n", " 'CHANGE',\n", " 'IN',\n", " 'ITINERARY',\n", " ',',\n", " 'AGENTS',\n", " 'WILL',\n", " 'NOT',\n", " 'REPEAT',\n", " 'WILL',\n", " 'NOT',\n", " 'CONTINUE',\n", " 'ON',\n", " 'TO',\n", " 'JOHANNESBURG',\n", " 'ON',\n", " 'BRITISH',\n", " 'AIRWAYS',\n", " '#',\n", " '031.',\n", " '2.',\n", " 'AGENTS',\n", " 'WILL',\n", " 'REMAIN',\n", " 'IN',\n", " 'GENEVA',\n", " 'UNTIL',\n", " 'FURTHER',\n", " 'INSTRUCTIONS',\n", " 'FROM',\n", " 'HEADQUARTERS.',\n", " '3.',\n", " 'REQUEST',\n", " 'HOTEL',\n", " 'ACCOMMODATIONS',\n", " '(',\n", " 'LONGCHAMP',\n", " 'OR',\n", " 'EPSOM',\n", " ')',\n", " 'BE',\n", " 'MADE',\n", " 'AND',\n", " 'ASSISTANCE',\n", " 'UPON',\n", " 'ARRIVAL.',\n", " 'NELSON',\n", " 'LIMITED',\n", " 'OFFICIAL',\n", " 'USE',\n", " 'NNN']" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Vowpal Wabbit for LDA Topic Modeling\n", "* LDA = Latent Dirichlet Allocation \n", " * treats each document as a bag of words \n", " * a topic is chosen from a topic distribution $p(k)$ where $k=1, \\dots , K$\n", " * a word is chosen from the k'th topic distribution $p(w|k)$ and thrown into the bag\n", " * distrubutions $p(k)$ depends on $\\theta$ ~ $Dir(\\alpha)$ and $p(w|k)$ depends on $\\beta$ a $k\\times V$ matrix of word probabilties\n", " * these 'latent' variables are chosen to maximize the probability of producing the observed documents, and in turn depend on user chosen parameters $\\alpha$ and $\\eta$ \n", " * the model produces two important probability distributions:\n", " * $p(w|k)$, the probability of $w$ bring generated by topic $k$ and\n", " * $p(k|d)$, the probabilty of topic $k$ being used to generate a randomly chosen word from document $d$ \n", " * these topic and word weights can be used to understand the semantic structure of the documents as well as generate document feature\n", " * for more details about LDA topic modeling see the wonderful Blei, Ng, and Jordan [paper](http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf)\n", "* Vowpal Wabbit\n", " * optimized (very fast) C++ library http://hunch.net/~vw/\n", " * can find tutorials at https://github.com/JohnLangford/vowpal_wabbit/wiki/Tutorial and \n", " https://github.com/columbia-applied-data-science/rosetta/blob/master/examples/vw_helpers.md\n", " \n", "\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from rosetta.text import text_processors, filefilter, streamers, vw_helpers" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "#create the VW format file \n", "my_tokenizer = text_processors.TokenizerBasic()\n", "stream = streamers.TextFileStreamer(text_base_path=RAW, tokenizer=my_tokenizer)\n", "stream.to_vw(sfile_path, n_jobs=-1, raise_on_bad_id=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "### somewhere here run (stick with 5 passes or so...)\n", "# rm -f *cache\n", "#vw --lda 20 --cache_file doc_tokens.cache --passes 5 -p prediction.dat --readable_model topics.dat --bit_precision 16 --lda_D 975 --lda_rho 0.1 --lda_alpha 1 ../sparse/cables-short.vw\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#load the sparse file \n", "formatter = text_processors.VWFormatter()\n", "sff = text_processors.SFileFilter(formatter)\n", "sff.load_sfile(sfile_path)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "#remove \"gaps\" in the sequence of numbers (ids)\n", "sff.compactify()\n", "sff.save(PROCESSED + '/sff_basic.pkl')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Compactification done. self.bit_precision_required = 14\n", "collisions = 0, vocab_size = 14476\n", "All collisions resolved\n" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "sff.to_frame().sort_index(by='doc_fraction', ascending=False).head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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doc_freqtoken_scoredoc_fraction
token
fm 829 845 0.849385
info 829 1432 0.849385
page 829 1325 0.849385
oct 829 973 0.849385
action 829 907 0.849385
iso 828 852 0.848361
secstate 827 854 0.847336
washdc 824 907 0.844262
nnn 819 830 0.839139
tags 782 785 0.801230
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10 rows \u00d7 3 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ " doc_freq token_score doc_fraction\n", "token \n", "fm 829 845 0.849385\n", "info 829 1432 0.849385\n", "page 829 1325 0.849385\n", "oct 829 973 0.849385\n", "action 829 907 0.849385\n", "iso 828 852 0.848361\n", "secstate 827 854 0.847336\n", "washdc 824 907 0.844262\n", "nnn 819 830 0.839139\n", "tags 782 785 0.801230\n", "\n", "[10 rows x 3 columns]" ] } ], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Look at LDA results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#use the LDAResults class from rosetta to convert back to readable, python friendly formats\n", "lda = vw_helpers.LDAResults(PROCESSED + '/topics.dat', \n", " PROCESSED + '/prediction.dat', PROCESSED + '/sff_basic.pkl')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "#look at some of the words\n", "topic_words = lda.pr_token_g_topic.loc[:,'topic_12'].order(ascending=False).index[:10]\n", "lda.sfile_frame.loc[topic_words]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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doc_freqtoken_scoredoc_fraction
token
museums 1 2 0.001025
anniversary 7 10 0.007172
inherent 1 1 0.001025
joaquin 1 1 0.001025
textile 1 7 0.001025
marcel 2 3 0.002049
involved. 1 1 0.001025
disclosure 2 3 0.002049
deportation 2 3 0.002049
conakry 3 14 0.003074
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10 rows \u00d7 3 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ " doc_freq token_score doc_fraction\n", "token \n", "museums 1 2 0.001025\n", "anniversary 7 10 0.007172\n", "inherent 1 1 0.001025\n", "joaquin 1 1 0.001025\n", "textile 1 7 0.001025\n", "marcel 2 3 0.002049\n", "involved. 1 1 0.001025\n", "disclosure 2 3 0.002049\n", "deportation 2 3 0.002049\n", "conakry 3 14 0.003074\n", "\n", "[10 rows x 3 columns]" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "#look at the topics themselves\n", "lda.print_topics(10)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "========== Printing top 10 tokens in every topic==========\n", "------------------------------\n", "Topic name: topic_00. P[topic_00] = 0.0271\n", " topic_00 doc_freq\n", "token \n", "affords 0.020413 1\n", "mattered 0.019496 1\n", "principal 0.001991 20\n", "manager 0.001038 16\n", "shunting 0.000777 2\n", "mac 0.000718 1\n", "cost 0.000718 32\n", "devaluations 0.000718 1\n", "rooms 0.000718 1\n", "interfiew 0.000718 2\n", "\n", "------------------------------\n", "Topic name: topic_01. P[topic_01] = 0.0278\n", " topic_01 doc_freq\n", "token \n", "thirdly 0.063519 1\n", "rigs 0.001077 3\n", "city 0.000728 16\n", "phoned 0.000707 2\n", "feeling 0.000462 8\n", "dissuaded 0.000457 1\n", "minorities 0.000386 4\n", "nyc 0.000373 1\n", "newsroundup 0.000373 1\n", "resrep 0.000373 1\n", "\n", "------------------------------\n", "Topic name: topic_02. P[topic_02] = 0.0392\n", " topic_02 doc_freq\n", "token \n", "companying 0.074200 1\n", "respected 0.032733 3\n", "obtains 0.027652 1\n", "less 0.023041 21\n", "ailment 0.017348 1\n", "palmer 0.016520 2\n", "detecting 0.015943 2\n", "nb 0.015255 1\n", "croatian 0.013441 48\n", "transmitting 0.013423 2\n", "\n", "------------------------------\n", "Topic name: topic_03. P[topic_03] = 0.0261\n", " topic_03 doc_freq\n", "token \n", "epstein 0.018949 1\n", "rigs 0.000455 3\n", "manager 0.000421 16\n", "points 0.000277 23\n", "slavia 0.000261 2\n", "retired 0.000210 2\n", "minorities 0.000200 4\n", "views 0.000196 24\n", "life. 0.000191 1\n", "rene 0.000185 4\n", "\n", "------------------------------\n", "Topic name: topic_04. P[topic_04] = 0.0278\n", " topic_04 doc_freq\n", "token \n", "rhodes 0.023252 1\n", "delivery 0.018596 10\n", "embarrassed 0.010083 3\n", "lazic 0.010066 2\n", "procesd 0.006491 1\n", "rigs 0.000395 3\n", "heinous 0.000355 1\n", "questions 0.000355 33\n", "amounts 0.000355 4\n", "fingerhut 0.000355 2\n", "\n", "------------------------------\n", "Topic name: topic_05. P[topic_05] = 0.0264\n", " topic_05 doc_freq\n", "token \n", "gratuitous 0.028985 1\n", "rigs 0.000447 3\n", "manager 0.000414 16\n", "ound 0.000414 1\n", "points 0.000272 23\n", "yourself 0.000271 2\n", "slavia 0.000261 2\n", "unproductive 0.000206 2\n", "sides 0.000206 7\n", "unfamiliar 0.000204 1\n", "\n", "------------------------------\n", "Topic name: topic_06. P[topic_06] = 0.0255\n", " topic_06 doc_freq\n", "token \n", "rigs 0.000470 3\n", "manager 0.000441 16\n", "points 0.000287 23\n", "slavia 0.000271 2\n", "minorities 0.000206 4\n", "life. 0.000197 1\n", "rene 0.000190 4\n", "disapprovingly 0.000190 1\n", "associati 0.000190 1\n", "claiming 0.000180 2\n", "\n", "------------------------------\n", "Topic name: topic_07. P[topic_07] = 0.1819\n", " topic_07 doc_freq\n", "token \n", "unclas 0.080968 480\n", "exercise 0.061456 8\n", "sites 0.055679 5\n", "urban 0.037215 3\n", "tbio 0.025041 1\n", "leaned 0.020269 1\n", "lady 0.017772 2\n", "custom 0.016974 2\n", "condemnations 0.014577 1\n", "professed 0.014133 2\n", "\n", "------------------------------\n", "Topic name: topic_08. P[topic_08] = 0.0393\n", " topic_08 doc_freq\n", "token \n", "ps 0.207765 2\n", "specifies 0.028863 1\n", "museums 0.025596 1\n", "thereby 0.016587 2\n", "hurt 0.013174 2\n", "signs 0.010361 5\n", "sherman 0.005601 2\n", "businessmen 0.003005 5\n", "morning.nelson 0.002849 1\n", "owner 0.002829 3\n", "\n", "------------------------------\n", "Topic name: topic_09. P[topic_09] = 0.0256\n", " topic_09 doc_freq\n", "token \n", "slavia 0.000695 2\n", "rigs 0.000466 3\n", "manager 0.000433 16\n", "points 0.000286 23\n", "cellucam 0.000239 1\n", "nchama 0.000230 4\n", "texts 0.000230 3\n", "budimir 0.000206 1\n", "fascist 0.000206 2\n", "minorities 0.000204 4\n", "\n", "------------------------------\n", "Topic name: topic_10. P[topic_10] = 0.0425\n", " topic_10 doc_freq\n", "token \n", "stalins 0.021645 1\n", "dsp 0.021625 1\n", "pell 0.017589 1\n", "ndweta 0.016886 1\n", "attempts 0.016374 11\n", "gon 0.015946 2\n", "bern 0.015598 33\n", "moltke 0.011906 2\n", "lisinski 0.011713 1\n", "humanity 0.010704 1\n", "\n", "------------------------------\n", "Topic name: topic_11. P[topic_11] = 0.0301\n", " topic_11 doc_freq\n", "token \n", "repression 0.059625 2\n", "usun 0.023723 42\n", "wolfango 0.022077 1\n", "recipient 0.011955 1\n", "english 0.008423 36\n", "heritage 0.006332 2\n", "ound 0.003699 1\n", "refers 0.002346 3\n", "rigs 0.000363 3\n", "minorities 0.000356 4\n", "\n", "------------------------------\n", "Topic name: topic_12. P[topic_12] = 0.1436\n", " topic_12 doc_freq\n", "token \n", "museums 0.028422 1\n", "anniversary 0.026150 7\n", "inherent 0.025394 1\n", "joaquin 0.016931 1\n", "textile 0.015816 1\n", "marcel 0.015317 2\n", "involved. 0.014861 1\n", "disclosure 0.014578 2\n", "deportation 0.013256 2\n", "conakry 0.013154 3\n", "\n", "------------------------------\n", "Topic name: topic_13. P[topic_13] = 0.0260\n", " topic_13 doc_freq\n", "token \n", "croatia. 0.010828 1\n", "cancel 0.004762 4\n", "rigs 0.000463 3\n", "manager 0.000435 16\n", "disapprovingly 0.000405 1\n", "associati 0.000405 1\n", "points 0.000284 23\n", "slavia 0.000267 2\n", "minorities 0.000203 4\n", "life. 0.000195 1\n", "\n", "------------------------------\n", "Topic name: topic_14. P[topic_14] = 0.0261\n", " topic_14 doc_freq\n", "token \n", "racks 0.018467 1\n", "rigs 0.000451 3\n", "manager 0.000420 16\n", "bradking 0.000408 1\n", "solved 0.000306 1\n", "academics 0.000299 2\n", "bosio 0.000299 1\n", "specifically 0.000299 15\n", "points 0.000273 23\n", "yourself 0.000272 2\n", "\n", "------------------------------\n", "Topic name: topic_15. P[topic_15] = 0.0452\n", " topic_15 doc_freq\n", "token \n", "museums 0.078717 1\n", "kidded 0.032361 1\n", "legality 0.019756 1\n", "viking 0.014038 3\n", "vest 0.012276 2\n", "ruling 0.011657 3\n", "vukoviceva 0.011027 2\n", "esperer 0.008133 1\n", "independistas 0.007940 1\n", "traces 0.007673 1\n", "\n", "------------------------------\n", "Topic name: topic_16. P[topic_16] = 0.0264\n", " topic_16 doc_freq\n", "token \n", "plained 0.022303 1\n", "rigs 0.000427 3\n", "manager 0.000384 16\n", "priority 0.000344 154\n", "shouting 0.000344 3\n", "city 0.000288 16\n", "o'behr 0.000277 1\n", "points 0.000260 23\n", "ound 0.000253 1\n", "slavia 0.000246 2\n", "\n", "------------------------------\n", "Topic name: topic_17. P[topic_17] = 0.0330\n", " topic_17 doc_freq\n", "token \n", "interest 0.050184 74\n", "troops' 0.043989 1\n", "oil 0.036823 17\n", "tye 0.026336 1\n", "sentiments 0.009119 1\n", "originating 0.006924 1\n", "parson' 0.005642 1\n", "desertification 0.004743 1\n", "furnace 0.001297 2\n", "manager 0.000936 16\n", "\n", "------------------------------\n", "Topic name: topic_18. P[topic_18] = 0.0372\n", " topic_18 doc_freq\n", "token \n", "rick 0.132870 1\n", "attendance 0.092382 16\n", "outs 0.087129 1\n", "rigs 0.000250 3\n", "manager 0.000195 16\n", "views 0.000166 24\n", "overtaxed 0.000155 1\n", "fidelity 0.000155 2\n", "ound 0.000154 1\n", "points 0.000152 23\n", "\n", "------------------------------\n", "Topic name: topic_19. P[topic_19] = 0.1430\n", " topic_19 doc_freq\n", "token \n", "negotia 0.031884 1\n", "evenings 0.027105 1\n", "staying 0.025209 7\n", "proceedings 0.019902 7\n", "cudna 0.016990 1\n", "championship 0.016631 1\n", "town 0.016536 17\n", "leaned 0.014484 1\n", "opportunities 0.012323 10\n", "isreal 0.011789 1\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "##\n", "lda.pr_topic_g_doc.T.loc[[0]].plot(kind='bar', figsize=(20,10),\n", " title = 'First Document Topic Weights')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "" ] }, { "metadata": { "png": { "height": 703, "width": 1148 } }, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "#or at the average topic probabilties \n", "import random\n", "r = lambda: random.randint(0,255)\n", "my_colors = ['#%02X%02X%02X' % (r(),r(),r()) for i in range(20)]\n", "#my_colors = 'rgbkymc'\n", "lda.pr_topic_g_doc.mean(axis=1).plot(kind='bar', figsize=(15,10), color=my_colors,\n", " title='Average Topic Probabilities')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "" ] }, { "metadata": { "png": { "height": 632, "width": 876 } }, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Have fun!\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }