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Infrastructure

Nov 13 2010

It’s time for another bookkeeping post. Read below if you want to know about changes I’m making and contemplating to software and data structures, which I ought to put in public somewhere. Henry posted questions in the comments earlier about whether we use Princeton’s supercomputer time, and why I didn’t just create a text scatter chart for evolution like the one I made for scientific method. This answers those questions. It also explains why I continue to drag my feet on letting us segment counts by some sort of genre, which would be very useful.

Currently, I’m using perl for data preprocessing and wordcounts, and R for exploratory analysis. I’m adding MySQL soon (see below). Mostly, that’s because those are the only two scripting languages I know, but they’re fairly well suited for this sort of work. And they’re free. Perl is great at quickly and efficiently reading through large batches of text files, so I use it to first clean up the punctuation on those files and (attempt to) divide it into sentences, and then to create flat text files with various sorts of wordcounts. It’s also not bad for other sorts of preprocessing–I use it, eg, to identify usable volumes in the search results I get from the internet archive (they let you download thousands of entries at a time), and then to batch download the text files from their site.

R is a program I actually like using, that implements all sorts of statistical functions very transparently, and has lots of nice options for graphical output. It takes a lot more time to get a chart looking right than it does in Excel, but once you do, it makes it easy to create a script that will produce the same chart, with user-defined variations, for anything else. There’s no other way these wordcount charts would be worthwhile if I couldn’t just feed a list of words into R and have it spit out a nicely formatted comparison of all of them.

The problem that R and perl share is that they both like to store all the data they’re working on in RAM. This is a problem, because I’m working with quite large stores of data. I have a file that lists just each of my 200,000 words, and then how many times they each appear in a year–that’s 250 MB now. If I wanted to segment it any more (by genre, say, or originating library) the file would be too large to load into R. (Not, perhaps, on the supercomputer–but there’s a way that I can keep this on my laptop).

Anyone who knows about computers is probably wondering why I haven’t just started using a database already. Partly, it’s because I’m worried it’s going to be slower for the exploration than R is. Mostly, it’s because I’m scared of storing data any file format that doesn’t have text files I can check in on. But it’s clear this has to happen before I can make any progress on dealing with genre issues, which I’m worried about. It will have a lot of ancillary benefits too. The current way that I calculate books that appear in a word is hopelessly baroque, involving reading through a number of files twice, and requiring some really ugly code. It also doesn’t leave any easy way on disk to find out coincidences of two words in a book, which is something that would, to put it mildly, be nice to have–that’s the thing Henry asked for above for evolution, and it’s bad that currently I have to run a perl script that takes an hour to run to get an answer. The current system also doesn’t let me do things like see if books that use the phrase “natural selection” use the word “species”, say, more often than books that don’t—it only lets me sees if they use the word species once, and then stops counting. There are a lot of other, little reasons like this that using a database will make more interesting analysis possible.

The reason is that having more memory to work with means, hopefully, that I can switch from storing wordcounts by year (16 million entries) to storing them by book (probably a couple hundred million entries). Each book entry can have lots of data tied to it, and I can extract that in different ways. The next step, which would be great for syntactic analysis in particular, would be having a database entry for every line in a book (yet more entries), and maybe to store the books themselves by sentence in there. That would allow neat things such as actually displaying the sentences that, say, use Lamarck and Darwin together in the course of exploring data.But that would take a lot of space, and I’m already using up about 35 GB of hard drive space for this. I want to make sure it works first.

So that’s been my Saturday project. Hopefully I’ll get it done by the end of the day–I’ve got the perl script putting data into a table running in MySQL, but I still have to get the catalog in so I can make selections by year, genre, etc.