Thursday, January 28, 2016

Fall Student D3.js Projects

Here's the followup I promised on my post about teaching D3.js to journalism students: A selection from their projects! Their project goal was to produce a data story using UNICEF data (and possibly related data) about child mortality. The grading criteria were pretty rigorously spelled out as follows in Week 14 of the repo:

  • 20% for using 4 chart types we covered in class (can include small multiples as one)
  • 20% for good interactivity: Transitions, highlights, tooltips, filter/sort, animation...
  • 15% on text: Connective text holding the story together, intro and conclusion, annotations on graphs, good explanations, good writing (good English style)
  • 10% on storytelling: You create a useful, interesting data story flow using a mix of text, steppers/buttons, highlights, scrollytelling. (You don't have to use all of them.)
  • 10% on graph/chart elements: Good labeling of values/axes, tooltips, readability of chart contents and labels
  • 10% on visual style overall: Color scheme, attractiveness, clarity in graphs, use of UNICEF style somewhere in page
  • 10% for good data analysis: Interesting findings/results, nice use of top 10s or top N, relating data sets to each other intelligently
  • 5% for page layout/design: Good visual and functional CSS, useful external links, resume/CV link, header/footer with info about the project and data as needed.

I realized later I should have had a separate line item or aspect of "Good UX," which is embarrassing to me since that was my job for 18 years. Anyway, live and learn. Extra credit was given for using special layouts or interaction methods we didn't cover in class, as well as going above-and-beyond on any single aspect (such as using new external data).

Grading was NOT based on good code. It was primarily based on user-facing results. Expect the code to be not the best, as these were not computer science students and this wasn't a software engineering class! However, everyone is still learning and is interested in doing better, given opportunity to practice.

Also note: Several students were not native English speakers. Regardless of the injunction to check the English, there may be remaining writing issues. It's apparently hard to fit copy-editing into the project delivery cycle at the end of the semester :)



US Child Mortality


One of my favorites, this project by MFA student Louise Whitaker explores child mortality in the US as compared to the rest of the world. She starts with a "scrollytelling" line chart and moves into bar charts and small multiple bar charts with linked mouseovers and linked scatterplots:



There is a lovely tooltip on the map with dual dot plots in it:



And we end with more small multiple linked bar charts showing the relative status of different US states on health issues:


Louise will be looking for work in UX and/or data vis design after this semester. Amazing hire, I'd say.

Fertility and Mortality


Halina Mader's excellent project features a study of fertility and mortality rates for children under five. She uses a "stepper" structure with "next" and "previous" buttons.

Her first view is a world map colored by 2015 infant mortality rates. The tooltips are a lovely detail: a bullet-style bar graph showing the rate of the country vs. the world avg and the worst.


The next state is a little subtle if you aren't watching closely: the map animates shading over time with the decline in death rates. The line chart is synced with the map on rollover:


She shows useful trendlines and correlations on small multiple scatter plots which have linked mouseovers by country:


Earlier, in only Week 6 of my class, Halina also produced this wonderful line chart block that was widely fav'd on Twitter:


You should hire Halina, she's available now and she's outstanding.


Malawi and Under Five Mortality


Graduating senior Barbara Poon produced a lovely project with helpful graphics and a nice analytic edge. Her scrollytelling trends story is particularly good:


She also uses dotplots, one of my favorite plot types:



Barbara is looking for analytics and data visualization work and would be another excellent hire!

The Effect of War


Grad student Shiyan ("Yan") Jiang's project focused on the effect of war on child mortality. She opens with a choropleth map with line chart tooltips (ok, if you see a trend, I maybe have told them all they'd get instant A's for tooltips with charts in them):


She uses a scrollytelling style to walk through her data story. At one point she highlights key sections of trend lines to show long-term impacts of wars:


Yan is a graduate student who is available for summer work and contract work.

Disasters and Mortality


Jiaxin Liu's project uses a unique button legend method for controlling the views. This line chart's focus on worldwide disasters and their impact on child deathrates was especially good:


She also features some synchronized interaction between plots -- highlighting world regions on the line chart also highlights the same countries in the scatterplot on the right:


Jiaxin became such a big fan of D3 during the class that she used it for another web class project as well. Jiaxin will be looking for data journalism jobs after this semester!


Female Education


Zhizhou ("Jo") Wang produced a very graphic, dramatic visual project related to female education and childhood mortality. Her magnum opus interactive piece is the linked map, line charts, and bar charts. Clicking on the map updates all of the data on the right:


She also features a nice "scrollytelling" scatterplot section:


Jo will be pursuing graduate journalism programs after this semester.


A Sad Story: Sub-Saharan African Infant Mortality


Luis Melgar's project focused on the sad story of sub-Saharan Africa. He uses a choropleth map linked to a line chart, animated bar charts, small linked multiples inspired by Jim Vallandingham's Flowing Data tutorial materials taught in my class (Week 10), and an epic scatter plot animation with 11 "stepper" buttons that looks specifically at diarrhea and pneumonia.




Luis Melgar is a journalist at Univision and a grad student at University of Miami. He says he is also a cheese addict, but aren't we all.

Thanks are Due


Thanks to the University of Miami's School of Communication and my visiting Knight Chair position in the Center for Communication, Culture, and Change for giving me some dedicated, hard-working students for the first run of my D3 vis class. The repo materials are here and being tweaked for the second run of the class, with twice as many students!

Also thanks to Guy Taylor of UNICEF in NYC for supporting my students and help with data questions.

Monday, January 11, 2016

Teaching a Semester of D3.js

I spent last semester frantically putting together a course on D3.js for journalism students at the University of Miami at the same time as teaching it and grading it. Wow, teaching a semester course is hard. Teaching coding, especially to non-CS students, is a special challenge. I was lucky to have a small class of very patient and motivated guinea pigs students for the first semester.

The class was meant to be a portfolio-builder, focused on journalistic interactive visualization. We used data from UNICEF in the first semester, visible in the examples and projects. This coming semester has fewer journalism students, which means changing the content a little, a process I'm still going through in the repo. This post is a recap of what we did and what was hard about it. Next post (in a week) will show some of my students' work.

Interactivity and "Journalistic" Vis

Why teach D3? At least one friend teaching journalism students said he'd never do that again. I heard this right before I started on the adventure. But this course was meant to be on interactive data visualization, which means a chart does more than behave like a static bar chart and readers do more than look at the bars. I have talk slides here about designing for interactivity in vis, and primarily the examples I show are built in D3. This is the current lay of the land!

There is still no better library than D3 for building custom data-driven designs, with custom interactions, and integrating them with the web page DOM. I did show Highcharts, and one of the first homeworks was to use it for a few charts. But the animated transitions in D3 (and open palette of design options) are what sell it, and all my students wanted to do fancy artistic animations in their final projects: animated maps, animated lines, animated lines on maps, synchronized lines and maps that animate over time, you name it if it involved lines or maps apparently. :) (It pushed me hard too, to help them figure all that out.)

When I was trying to learn D3, I wanted to know how to hook up a chart to UI elements and make things move, but the books out there didn't get into anything that fancy, sticking mostly to how to create static charts in isolation. Static charts are usually much easier to create with other tools than D3 (unless it's an "unusual" chart type). So for my class I focused a lot on the UI interaction aspects of D3 coding. D3 can do a lot of fancy things, like networks, parallel coordinates, sankey diagrams... But I stuck to the "basics" for journalistic vis in this class:

  • Tables and heatmaps
  • Bars, vertical and horizontal
  • Lines, including handling lots and lots of lines
  • Stream/Area charts
  • Stacked and grouped bars
  • Scatterplots
  • Small multiples
  • Maps

We also covered a lot of key interaction features like animated transitions, swapping out a dataset and animating in a new one, how to hook up various UI elements like select menus, buttons, sliders; making complex tooltips, linking two charts together with a toggle switch or a click/mouseover, annotating particular data points, adding legends. In Javascript, important data concepts included sorting, getting top 10's (or N's), creating calculated variables.

Setting up the Tools: Github and Servers, Oh My

Getting folks set up on day one with a server and Github was a challenge, but luckily most of them had encountered a little bit of git before. However, most students did not know how to use the command line, and two of them had Windows machines, so this was "challenging" for all including me. (I totally forgot that all people don't automatically know Unix and Windows command line. Really threw me for a loop.) I probably oversold how useful "git stash" is when they had conflicts, but I feel no regret. Before too long they were git pulling every week and had learned how to make gists.

Gists are the building blocks of a portfolio of bl.ocks, a key component of the D3 community eco-system. Also, they were required for easier grading and debugging on my part — especially now that Ian Johnson (@enjalot) has released blockbuilder.org, which made debugging a lot simpler.

For some reason, using a server really stumps new web programmers. (After watching people struggle, I've put a bunch of documentation on setting them up in the nascent drafty d3-faq.) Folks who have done only static web design have usually not got a good understanding of why you need to use a server to view and render code. Unlearning that they can just double click on their file to view it takes a lot of time. No, the URL really has to say "localhost://" not "file://". The source of many bugs for the first few weeks was folks not having loaded their page using the server, even after they had set one up. (And note: That's an example of an issue that's harder to debug by email remotely than it is when you're looking over their shoulder. There were a lot like this. My office hours were sometimes busy.)

Javascript with D3

My class came in with required background in HTML and CSS, but little to no Javascript. Heck, this is how a lot of people learn D3, so why not? Well, anyone (like me) who has gone this route knows that the Javascript part is the thing that trips you up the most, even after you start to "get" the D3 paradigm. Just understanding the D3 examples out there (especially Mike Bostock's) requires a fairly advanced understanding of Javascript.

For all data visualization, data "munging" is hard and sometimes very data-set specific. You can either "munge" in a tool outside Javascript — I recommended and showed Excel — but at a certain point, you need to get a grip on the munging that's close to the vis code itself. Structuring your data to make it easy to get at certain values during interaction in the UI is pretty important. Getting data sets merged, looping through them to do calculations, or to create subsets of data, learning and using a functional coding style with forEach and maps — these things were hard for everyone, even the students with some programming background. I gave a few pure Javascript homeworks, on topics like debugging and data manipulations, but honestly, I should have given more of them. (OTOH, this is harder to grade, because it usually requires eyes-on-careful-review of each one. Meh.)

I also should have buckled down on teaching data manipulation tools earlier. In an attempt to be "easier" on them, I didn't teach d3.nest() right away, and helped one poor student (hi Luis!) write a laborious loop in JS to nest his data... After that hour, I realized, "Teach all the tools. Teach the nest()." Students need to know about the helper functions, which will save them time down the road. A homework on nesting data followed. I'll introduce lodash.js this spring semester, too.

A Lack of "Complex" Examples To Teach From

Many of the D3 examples, books, and tutorials are basic or even "toy" (abstract from realistic frames, not using real data, etc). There's a role for the basic — the best intro book is Scott Murray's very simple, unscary starter book, Interactive Data Vis for the Web. We started there, of course, but as we got into complex animations and transitions, there were fewer and fewer good working examples and tutorials out there to inspire class materials.

The big exceptions are the tutorials of Jim Vallandingham and Nathan Yau on Flowing Data; both do "journalistic" vis how-to's on their sites. I borrowed and adapted several of theirs, for small multiples and maps in particular. Jim's code tends towards more "advanced" and I simplified some of it — which I have mixed feelings about and may undo; Nathan's code I sometimes updated when it was using older D3 style or could be made more functional. Scott Murray's intro examples I also updated to use more D3-common conventions (e.g., adding the margin object convention, removing for-loops).

Even after seeing how to use functions for update patterns in D3, when project time came, everyone struggled to organize their code. When I asked people to just make a page combining 3 charts on it, all hell broke loose in the global scope conflict space. While I was quite clear that projects were judged on end-user experience, not code quality, code structure issues made it much harder for the students to modify and debug their own code. I'll be focusing more on code structure this semester.

Unfortunately, there are more examples online of how to use Angular or React to structure big projects, rather than pure Javascript. Obviously those frameworks solve a lot of organizational and architectural issues, but this is a challenge for everyone teaching D3, I feel. I don't want to inflict a framework on students who are just learning Javascript and D3.

Finding a Data Story Is Hard

Almost all of the class had had a static infographics class (from Alberto Cairo), but the practice of finding a story in data is hard, and I considered it outside the scope of the course. I recommended and demoed Excel and Tableau to a few students who were struggling, and luckily several had already had experience using Tableau. (I tried PowerBI briefly and was also very impressed by it!) Nevertheless, data "stories" for their projects were in flux until the very end. It's notoriously difficult to "design" for data vis without using the real data (sketching by hand only gets you so far), and a lack of proficiency with exploratory tools probably impaired some of them.

With a class next semester that's less journalistic, I'll expand the project grading to allow for less data-driven stories and allow a broader range of data visualization. I'll also be exploring a design process that starts with data exploration, then moves to UI sketches, then moves to phased development and feedback cycles.

Debugging is Also Hard

I knew I should teach debugging, and I did, but I think you can only teach it to a point. It's boring to watch someone else doing it, but it's also necessary. Getting students to learn how to use breakpoints in the Chrome console is a necessary evil, as is walking back through the stack trace.

One of the harder aspects of debugging is that you have to have a lot of experience with what can go wrong to be able to guess what it might be this time. It's about hours spent doing it. This is hard to teach; it just requires practice time.

Students Will Find and Replicate All Your Bugs

Because the general practice of learning D3 in the wild is to take examples and modify them to fit your own data, I wanted to support that in my class. I made examples and then had the class plug in their own data (hopefully on the topic of their final project!). This means that code sloppiness, errors, and bad habits in my code ended up replicated and magnified over and over. Including bad UI design — one example with unfortunate bar coloring showed up in a couple of projects.

My homework is to fix all that in the repo and try not to introduce too many new ones.

Thanks for Content I Borrowed, Linked To, or Adapted

People whose work contributed a lot to this repo include Mike Bostock, Scott Murray, Jim Vallandingham, Nathan Yau, Mike Freeman, Ian Johnson.

Course Materials

The repo (that will keep evolving this semester) is here. I expect to be adding more examples — such as for canvas, crossfilter/dc.js, and perhaps other layouts. There might even be data "art." I will post links and examples from student projects for the fall in another week or so!

Sunday, September 13, 2015

Knight Projects for the Year


I am installed in Miami for the academic year as a Visiting Knight Chair in the Journalism department; I've been busy (frantically, insanely busy) trying to put together class materials for the semester, grade stuff, produce talks and workshops, and keep up with Twitter.

As a nice benefit — or responsibility — I have project money to spend on activities or products that will improve the lives of the journalists of the future. Or of the now, if I do it right. Apart from some conference organization with Alberto Cairo, I'm thinking hard about how I'd like to spend that money. Here are a few things I tweeted about a week ago that I think would be of great benefit to data journalists, which don't yet exist fully:

"A few of my Wish List items for improving work, probably out of my project $ and scope:"

  1. "A data-wrangler tool like Trifacta, easy to get/use."
  2. "A customizable, comprehensive interactive vis lib with easy basics - like Vega 2 but maybe more baked? Vega in a year?"
  3. "A non-programming tool for visualization creation that outputs code you can tweak. Lyra, basically, baked."
  4. "A Shiny Server and similar paradigm for Python."
  5. "HTMLwidgets for Python -- we need one ring to bind them, or something. Soooo many attempts to make notebook vis graphics."
  6. "One more - tools/methods for making training and sharing entity recognizers easier. HUGE problem in text analysis."
A few of these tools are under active development in the University of Washington's Interactive Data Lab, particularly Vega and Lyra. (I recommend this video of Arvind Satyanaryan demoing Lyra at OpenVis Conf.) One, Trifacta, is a spin-off company and product from Jeff Heer (Director of the IDL) and student Sean Kandel, who created Data Wrangler. If you want to see some of the excellent tool future in the works at UW's IDL, Jeff Heer's keynote at OpenVis this year was outstanding.

And apparently there's more goodness in the works addressing my needs for IPython notebook interactive widgets in a sub-vega project on Github, pointed out by Rob Story), called ipython-vega right now. Also on the Python front, Rob Story suggests we might want to look at Pyxley from Stitchfix, but to me that still currently looks like a lot of programming and manual setup for a non-programmery analyst. Shiny apps are dead-simple for data analysts with a little gumption to throw up and share with folks right from their R Studio environment.

The future looks great about 5+ years out when all the grad students have finished and productized (or gotten significant coding support). But right now there is still a lot of pain, especially when you're trying to teach folks and recommend tools that are stable, documented, and tested (by people, not unit tests, although those too). Trifacta, of course, is not open-source. A competitor product, Alteryx, looks nice and has an academic license scheme but the non-academic version is $4K! Both for students and data journalists, enterprise level pricing for data wrangling tools is looking scary.

Aside on Entity Recognizers

Oh, a little note on the #6 item, entity recognition tools... Anyone who is trying to do named entity recognition (NER) in text files has a horrible slog getting good results. NER means things like looking up all the people, places, products, or companies in a text. It's hard because different strings are used to refer to the same things. To get results that are any good, especially on dynamic recent data (like news!), you need to train a recognizer with labeled text. (This is because the "out of the box" models and tools like Stanford NER etc. are almost always inadequate for what you really want.) The tools to do the labeling, and the labeling itself, pretty much suck. (Although I admit I haven't looked at the most recent one recommended to me by the Caerus folks.) I know a lot of grad students are suffering with this, when doing research on text in highly specific domains.

I'd love to see a marketplace for trained models customized for different domains, and easy-peasy tools for updating them and sharing improvements. I wish someone's NLP student would tackle this as a startup. Or, I suppose, I could do it with my project money and some help.

Instead, Text Analysis and Vis How-To's?

In the realm of things I can deliver that don't require a corporate team of developers, I'm thinking about doing an online repo ("book") of text analysis and visualization methods. This will be a combination of NLP and corpus analysis methods (in R and Python, I hope) as well as a handbook of visualization methods for text (with sample D3 code). The audience would be journalists with text to analyze, digital humanists with corpora, linguists wanting to get more visual with their work. Because my time is shockingly limited, I'll probably recruit an external helper with my project money to create code samples. If you've seen my epic collection of text vis on Pinterest and want to know "how do I make those?" I hope I'll be able to help you all.


How does this sound? Useful?

Any other ideas from folks out there? I'm chatting with my pals at Bocoup (Irene, Jim, Yannick) about other options for collaborations between us.


Local Workshops on Data Journalism Topics

One of my contributions to the local community at U of Miami is a series of workshops on topics hopefully of interest to data journalists (that I am qualified to teach). The first was a well-attended one on Excel Data Analysis (files here), and upcoming topics include:
  • Excel Charts and Graphs
  • Just What is Big Data (and Data Science) Anyway?
  • Intro to Web Analytics: A/B Testing and Tracking
  • Intro to Tableau
  • Python and R: What Are They Good For?
  • Text Mining with Very Little Programming
  • Visualizing Network Data

I'd like to do one on command line data analysis, and some more on Python and R tools, but am not sure yet where the group wants to go. Stay tuned for more links!

Sunday, March 08, 2015

Teaching News

Overdue for a blog post, and I guess my news needs an official announcement!

I'm happy to announce that I have accepted a visiting post at the University of Miami for 9 months, beginning August 2015 and running through the academic year. This post is financially possible thanks to the generous Knight Foundation, which supports various faculty positions in journalism throughout the country. I’ll be helping Alberto Cairo get his new Data Visualization and Journalism track in the Interactive Media MFA off to a running start; I’ll be teaching data visualization and data analysis, including D3.js. I'll probably keep some side contract work going at the same time. Here's my favorite version of the news on Twitter:




I’ve always been wary of trying to teach D3 in any short workshop format — I’ve been asked and said “no” many times. However, the first class I’ll teach is a semester long, so it seems more feasible. To help prepare for this, I’ll also be a TA in this spring’s online Data Visualization and Infographics with D3 course co-taught by Alberto and Scott Murray (@alignedleft, screen-capped above), who is the author of a very nice introductory D3 book, Interactive Data Visualization for the Web. (If you’re reading about it now for the first time, the class filled up quickly to the cap set at 500 people. Maybe they can do it again if it’s successful.)

In other more minor teaching news, I did a guest lecture at CMU in Golan Levin’s STUDIO for Creative Inquiry on NLP (natural language processing) in Python; the files are all here. The most “interesting” part from Twitter’s perspective is the Bayesian detection of sex scenes in 50 Shades of Gray (because spam is boring). I first did this cocktail-party stunt at OpenVis Conf in 2013, and now I’ve finally released the data and code for it. These introductory lectures cover concepts that would be useful in any more advanced text visualization context; I hope to get a chance to expand on that subject while in Miami, too.

I’m also putting together a lynda.com class, Introduction to Data Analysis with Pandas, although I’ve been doing it veeerrrryyyy slowwwwlllyyyy.

Finally, related to teaching, I’m co-chair of OpenVis Conf this year. We are not quite sold out yet (as of this post), and I think you should come. This is a conference about how the visualization sausage is made — lots of educational talks!

I had planned to write 3 more sections on learning, teaching, and making, but there were some minefields in there about gender and sexism in tech. Not ready for prime time. No navel-gazing for now!

Tuesday, December 30, 2014

A Silly Text Visualization Toy



This little text-to-image replacement toy made me laugh, so I decided to put it up in case it makes you laugh too. In my last project, I did part-of-speech tagging in Python and used that to replace nouns with other nouns (see post and demo); in this one, I did the part-of-speech tagging all in Javascript using the terrific RiTa.js library!

With RiTa, you get the same slightly noisy results I got in the pattern.py tagging I did before: not all the nouns are good "nouns." The API for tagging is super easy:

>RiTa.getPosTagsInline("Silent night, holy night")
>"Silent/jj night/nn , holy/rb night/nn"

After generating the parts of speech, I filtered for just the nouns ("/nn" and "/nns"). I replaced those with words in "span" tags, and then used an ajax call to search for each spanned text in Google's image search API. The whole operation is outlined here, with the logic for getting the local text selected first:

$.when(
      $.get("texts/" + file_name, function (text) {
        lines = text.split('\n');
      })
    )
    .then(function () { 
      return processLines(lines);
    })
    .then(function (text) {
      $(".content").html(text);
    })
    .done(function () {
      $("span.replace").each(function (i, val) {
        getImage(val);
      });
    });


It turns out (of course) that there's a lot of repetition in certain words, especially for holiday songs and poems; so I introduced some random picking of the image thumbnails for variety.

Here's more from "Night Before Christmas" (which is really called "A Visit from St. Nick") -- yes, that's Microsoft Word:


This is the first sentence of Pride & Prejudice; it ends with the single man getting the Good Wife:


And the Road Not Taken:



I think the Night Before Christmas is the best one, but they all have their moments. Try it. Suggestions for other well-known (short) texts to try?

Saturday, November 22, 2014

Visualizing Word Embeddings in Pride and Prejudice

It is a truth universally acknowledged that a weekend web hack can be a lot of work, actually. After my last blog post, I thought I'd do a fast word2vec text experiment for #NaNoGenMo. It turned into a visualization hack, not too surprisingly. The results were mixed, though they might be instructive to someone out there.

Overall, the project as launched consists of the text of Pride and Prejudice, with the nouns replaced by the most similar word in a model trained on all of Jane Austen's books' text. The resulting text is pretty nonsensical. The blue words are the replaced words, shaded by how close a "match" they are to the original word; if you mouse over them, you see a little tooltip telling you the original word and the score.


Meanwhile, the graph shows the 2D reduction of the words, original and replacement, with a line connecting them:

The graph builds up a trace of the words you moused over, a kind of self-created word cloud report.


The final project lives here. The github repo is here, mostly Python processing in an IPython (Jupyter) notebook and then a javascript front-end. This is a blog post about how it started and how it ended.

Data Maneuvers

In a (less meandering than how it really happened) summary, the actual steps to process the data were these:

  1. I downloaded the texts for all Jane Austen novels from Project Gutenberg and reduced the files to just the main book text (no table of contents, etc.).
  2. I then pre-processed them to convert to just nouns (not proper nouns!) using pattern.py's tagger. Those nouns were used to train a word2vec model using gensim. I also later trained on all words, and that turned out to be a better model for the vis.
  3. Then I replaced all nouns inside Pride and Prejudice with their closest match according to the model's similarity function. This means closest based on use of words in the whole Austen oeuvre!
  4. I used a python t-SNE library to reduce the 200 feature dimensions for each word to 2 dimensions and plotted them in matplotlib. I saved out the x/y coordinates for each word in the book, so that I can show those words on the graph as you mouse over the replaced (blue) words.
  5. The interaction uses a "fill in the word cloud" mechanism that leaves a trace of where you've been so that eventually you see theme locations on the graph. (Maybe.) Showing all the words to start is too much, and even after a while of playing with it, I wanted them to either fade or go away--so I added a "clear" button above the graph till I can treat this better.

The UI uses the novel text preprocessed in Python (where I wrote the 'span' tag around each noun with attributes of the score, former word, and current word), a csv file for the word locations on the graph, and a PNG with dots for all word locations on a transparent background. The D3 SVG works on top of that (this is the coolest hack in the project, IMO--see below for a few more details).

Word Similarity Results

The basic goal initially was to take inspiration from the observation that "distances" in word2vec are nicely regular; the distance between "man" and "woman" is analogous to the distance between "king" and "queen." I thought I might get interesting word-swap phenomena using this property, like gender swaps, etc. When I included pronouns and proper nouns in my experiment, I got even limper word salad, so I finally stuck with just the 'NN' noun tag in the pattern.py ptag parser output. (You will notice some errors in the text output; I didn't try to fix the tagging issues.)

I was actually about to launch a different version--a model trained on just the nouns in Austen, but the results left me vaguely dissatisfied. The 2D graph looked like this, including the very crowded lower left tip that's the most popular replacement zone (which in a non-weekend-hacky project this would need some better treatment in the vis, maybe a fisheye or rescaling...):

Because the closest word to most words are the most "central" words for the model--e.g., "brother" and "family", the results are pretty dull: lots of sentences with the same words over-used, like "It is a sister universally acknowledged, that a single brother in retirement of a good man, must be in time of a man."

Right before I put up all the files, I tried training the model on all words in Austen, but still replacing only the nouns in the text. The results are much more interesting in the text as well as the 2D plot; while there is no obvious clustering effect visually, you can start seeing related words together, like the bottom:

There are also some interesting similarity results for gendered words in this model:

model.most_similar(['wife'])
[(u'son', 0.7893723249435425),
 (u'reviving', 0.7113327980041504),
 (u'daughter', 0.7054953575134277),
 (u'admittance', 0.6823280453681946),
 (u'attentions', 0.658092737197876),
 (u'warmed', 0.6542254090309143),
 (u'niece', 0.6514275074005127),
 (u'addresses', 0.6490938663482666),
 (u'proposals', 0.647223174571991),
 (u'behaviour', 0.6413060426712036)]

model.most_similar(['husband'])
[(u'nerves', 0.8918779492378235),
 (u'lifting', 0.7963227033615112),
 (u'wishes', 0.7679949998855591),
 (u'nephew', 0.7674976587295532),
 (u'senses', 0.7639766931533813),
 (u'daughter', 0.7601332664489746),
 (u'ladyship', 0.7527087330818176),
 (u'daughters', 0.7525165677070618),
 (u'thoughts', 0.7426179647445679),
 (u'mother', 0.7310776710510254)]

However, the closest matches for "man" is "woman" and vice versa. I should note that in Radim's gensim demo for the Google News text, "man: woman :: woman: girl," and "husband: wife :: wife : fiancée."

And while most of the text is garbage, with some fun gender riffs here and there, in one version I got this super sentence: "I have been used to consider furniture the estate of man." (Originally: "poetry the food of love.") Unfortunately, in this version of the model and replacements, we get "I have been used to consider sands as the activity of wise."

I saved out the json of the word replacements and scores for future different projects. I should also note that recently gensim added doc2vec (document to vector), promising even more relationship fun.

A Note on Using the Python Graph as SVG Background

To make a dot image background for the graph, I just plotted the t-SNE graph in matplotlib, like this (see the do_tsne_files function) with the axis off:

plt.figure(figsize=(15, 15))
plt.axis('off')
plt.scatter(Y[:,0], Y[:,1], s=10, color='gray', alpha=0.2)

After doing this, I right-clicked the inline image to "save image" from my IPython notebook, and that became the background for drawing the dots, lines, and words for the mouseovers. Using the axis('off') makes it entirely transparent except for the marks on top, it turns out. So the background color works fine, too:

#graph {
  position: fixed;
  top: 150px;
  right: 20px;
  overflow: visible;
  background: url('../data/pride_NN_tsne.png');
  background-color: #FAF8F5;
  background-size: 600px 600px;
  border: 1px #E1D8CF solid;
}

There was a little jiggering by hand of the edge limits in the CSS to make sure the scaling worked right in the D3, but in the end it looks approximately right. My word positioning suffers from a simplification--the dots appear at the point of the word coordinates, but the words are offset from the dots, and I don't re-correct them after the line moves. This means that you can sometimes see a purple and blue word that are the same word, in different spots on the graph. Exercise for the future!

I also borrowed some R code and adapted it for my files, to check the t-SNE output there. One of the functions will execute a graphic callback every N iterations, so you can see a plot of the status of the algorithm. To run this (code in my repo), you'll need to make sure you paste (in the unix sense) the words and coordinates files together and then load them into R. The source for that code is this nice post.

The Original Plan and Its Several Revisions

If I were really cool, I would just say this is what I intended to build all along.

My stages of revision were not pretty, but maybe educational:

  • "Let's just replace the words with closest matches in the word2vec model and see what we get! Oh, it's a bit weird. Also, the text is harder to parse and string replace than I expected, so, crud."
  • ...Lots of experimenting with what words to train the model with, one book or all of them, better results with more data but maybe just nouns...
  • "Maybe I can make a web page view with the replacements highlighted. And maybe add the previous word and score." (You know, since the actual text is itself sucky.)
  • ...A long bad rabbit hole with javascript regular expressions and replacements that were time-consuming for me and the web page to load...
  • "What if I try to visualize the distances between words in the model, since I have this similarity score. t-SNE is what the clever kids are using, let's try that."
  • "Cool, I can output a python plot and draw on top of it in javascript! I'll draw a crosshair on the coordinates for the current word in the graph."
  • "Eh, actually, the original word and the replacement might be interesting in the graph too: Let's regenerate the data files with both words, and show both on the plot."
  • "Oh. The 'close' words in the model aren't close on the 2D plot from the nouns model. I guess that figures. Bummer. This was kind of a dead-end."
  • Post-hoc rationalization via eye-candy: "Still, better to have a graph than just text. Add some D3 dots, a line between them, animate them so it looks cooler." (Plus tweaks like opacity of the line based on closeness score, if I do enough of these no one will notice the crappy text?)
  • Recap: "Maybe this is a project showing results of a bad text replacement, and the un-intuitive graph that goes along with it?"
  • "Well, it's some kind of visualization of some pretty abstract concepts, might be useful to someone. Plus, code."
  • ...Start writing up the steps I took and realize I was doing some of them twice (in Python and JS) and refactor...
  • "Now I still have to solve all the annoying 'final' details like CSS, ajax loading of text parts on scroll, fixing some text replacement stuff for non-words and spaces, making a github with commented code and notebook, add a button to clear the graph since it gets crowded, etc."
  • Then, just as I was about to launch today: "Oh, why don't I just show what the graph looks like based on a model of all the words in Austen, not just nouns. Hey, wait, this is actually more interesting and the close matches are usually actually close on the graph too!"

There were equal amounts of Python hacking and Javascript hacking in this little toy. Building a data interactive requires figuring out the data structures that are best for UI development, which often means going back to the data processing side and doing things differently there. Bugs in the vis itself turned up data issues, too. For a long time I didn't realize I had a newline in a word string that broke importing of the coordinates file after that point; this meant the word "truth" wasn't getting a highlight. That's one of the first words in the text, of course!

And obviously I replaced my word2vec model right at the last second, too. Keep the pipeline for experiments as simple as possible, and it'll all be okay.

Sunday, October 26, 2014

A Roundup of Recent Text Analytics and Vis Work

Some really exciting things in text analysis and visualization have crossed my Twitter feed recently; I thought I'd pull together some pointers in case you missed any of my tweetspam about one of my favorite subjects. Maybe posts like this will become a regular thing!

Shiffman's P5.js and Javascript Text Tutorials


Dan Shiffman, famous for his excellent books and lessons on Processing, is doing a course for ITP that includes a lot of text analytics work done in javascript and p5.js (the new javascript Processing lib). The git repo for his course content (code and tutorials) is here. He includes accessible content on TF-IDF, Markov chains, Naive Bayes, parsing, and text layout for the web.

Topic Modeling News


David Mimno updated Mallet, the Java reference package for LDA, with labeled LDA (topics within labeled documents) and stop word regular expressions. Blog post with some explanation here.

Alan Riddell released a Python implementation of LDA with an interface inspired by scikit-learn. He points to an interesting semi-supervised topic modeling package also in Python, zLabel-LDA.

I liked this paper by Maiya and Rolfe with ideas for improving labeling of topics as compared to using raw LDA results. (Every time I teach topic modeling I confront the "but what do these mean" question, and the notion of post-processing the results for more meaningful representation gets a pretty short answer, because we've usually run out of time.)

Here's a nice recent project release from Peter Organisciak for making timeseries charts of topics across digital books in the Hathitrust Digital archive. Full instructions for the python and R package. Here's a section of his example of some topic distributions across The Scarlet Letter:


Words in Space (Multidimensional Scaling)


I was rather excited when Mario Klingemann posted his evolving project on visualizing the topics of the images in the Internet Archive's Book Collection -- a giant zoomable map of related subjects crunched with t-SNE. The links open the related images collections on flickr (e.g., here's "playing cards"). If you like old book images, especially woodcuts, this is a trap you may never escape from! I got lost in occult symbols and finally had to shut the tab.


Related to topic modeling, Lee and Mimno posted a paper on drawing convex 2d (or 3d) hulls around "anchor words" to outline topics in their co-occurrence spaces, such as from t-SNE (t-Distributed Stochastic Neighbor Embedding) or PCA. From their paper:


Meanwhile, David McClure has an interesting post about creating something like these algorithms "by hand" and generating network diagrams from the results. (Thanks to Ted Underwood for passing this on.) Here's his hand-labeled map of War and Peace:


Other words-in-space multidimensional scaling projects of recent note include word2vec, which has a nice Python gensim implementation (see great blog post and demo by Radim Řehůřek);
and GloVe, which claims to improve on word2vec but looks similar to me from the usage perspective (here's a "maybe buggy" Python implementation). t-SNE also has implementations in lots of languages including Python and R, all listed on their page. Also see a nice overview explanation of word embeddings with t-SNE visual examples by Chris Olah here and his demo of dimensionality reduction and t-SNE here.


Narrative Vis


In a fascinating project on Visualizing.org, Georgia Panagiotidou and Anne Pasanen visualize the oscillation of characters between good and evil in the Finnish Kalevala epic. Really lovely and worth a browse in full screen.


Nick Beauchamp's Plot Mapper: Paste in a text and a complex PCA visualization reduces it to something amazingly simple. He says,

The text is chopped into N chunks, and each "chapter" is plotted in a 2-dimensional space (connected by lines) along with the top X words in the text. You can see how the trajectory of the text moves through the space of words, emphasizing different themes at different stages of the work.

Here's a surprisingly sweet Peter Pan:


Note: Keep options for words to generate low, or you may get an error. Thanks to David Mimno (@dmimno) for passing that one one!

Text Generator Art


Darius Kazemi (@tinysubversions) is doing NaNoGenMo (National Novel Generation Month) again this year - repo and rules here. Let's all work on text generation in November! Instead of, you know, actually writing that novel by hand, like an animal.

It was a few weeks ago, but it still makes me giggle - the Vogon Poetry Generator that uses Google Search to build something based on the title (which you can edit in-page).

Wrapping Up


I really love text visualization projects that combine great analytics with great applications. Keep sending me pointers (@arnicas on Twitter) and maybe I'll do more of these roundups when the awesome gets to me enough. For more inspirational links, try my Pinterest board of text vis, my twitter list of text vis, art, nlp folks (who talk about a lot of other things so YMMV), and this hopefully growing index of academic work from the ISOVIS folks.