It recently hit me that I see unit tests as a form of penance (in addition to being a great tool for specification and test driven development). If you fix a bug and don’t add a unit test I suspect you are not actually sorry. Read more…
I strongly advise using version control, and usually recommend using git as your version control system. Usually I feel a bit guilty about this advice as git is so general that it is more of a toolkit for a version control system than a complete proscriptive version control system (the missing pieces being the selection and documentation of a workflow and conventions).
But I still feel git is the one to use. My requirements involve not writing dot files in every single directory (breaks some OSX tools, and both CVS and Subversion do this), being able to work disconnected (eliminates Perforce), being cross-platform, being actively maintained, and being able to easily change decision such as where the gold standard repository lives (or even changing your mind on collaborating or not). This makes me lean towards BZR, git and Mecurial. Git is the most popular one of the bunch and has the most popular repository aggregator: GitHub.
For beginners I teach treating git like old-school RCS or SCCS: just use git to maintain versions of your local files. Don’t worry about using it to share or distribute files (but do make sure to back-up you directory in some way). To use git in this way you only need to run three commands regularly: “git status,” “git add,” and “git commit” (see Minimal Version Control Lesson: Use It). Roughly status shows you what is going on and add/commit pairs checkpoint your work. To work in this way you don’t need to know anything about branching (version control nerds’ favorite confusing topic), merging and so on. The idea is that as long as you are running add/commit pairs often enough any other problem you run into can be solved (though it make take an hour of searching books and Stack Overflow to find the answer). Git’s user interface is horrible (in part) because “everything is possible,” but that also means you can (with difficulty) solve just about any problem you run into with git (except, it seems, nested or dependent repositories).
However eventually you want to work with a collaborator or distribute your results to a client. To do that effectively with git you need to start using additional commands such as “git pull,” “git rebase,” and “git push.” Things seem more confusing at this point (though you still do not yet need to worry about branching in its full generality), but are in fact far less confusing and far less error prone than ad-hoc solutions such as emailing zip files. I almost always advise sharing work in “star workflow” where each worker has their own repository and a single common “naked” repository (that is a repository with only git data structures and no ready to use files) is used to coordinate (thought of as a server or gold standard, often named “origin”). This is treating git as if it were just a better CVS or SVN (the difference being if you want to perform a truly distributed step like pushing code to a collaborator without using the main server, you can and git will actually help with the record keeping). The central repository can be GitHub, GitLab or even a directory on a machine with ssh access. A lot of ink is spilled on how such a workflow doesn’t feel like a “distributed workflow,” but it is (you can work when disconnected from the central repository, and if the central repository is lost any up to date worker can provision a new central repository).
To get familiar with git I recommend a good book such as Jon Loeliger and Matthew McCullough’s “Version Control with Git” 2nd Edition, O’Reilly 2012. Or, better yet, work with people who know git. In all cases you need to keep notes, git issues are often solved by sequences of of three to five esoteric commands. Even after working with git for some time I still run into major “hair pullers.” One of these major “hair pullers” I run into is what I call “pseudo conflicts” and is what I am going to describe in this article. Read more…
I was flipping through my copy of William Cleveland’s The Elements of Graphing Data the other day; it’s a book worth revisiting. I’ve always liked Cleveland’s approach to visualization as statistical analysis. His quest to ground visualization principles in the context of human visual cognition (he called it “graphical perception”) generated useful advice for designing effective graphics [1].
I confess I don’t always follow his advice. Sometimes it’s because I don’t agree with him, but also it’s because I use ggplot for visualization, and I’m lazy. I like ggplot because it excels at layering multiple graphics into a single plot and because it looks good; but deviating from the default presentation is often a bit of work. How much am I losing out on by this? I decided to do the work and find out.
Details of specific plots aside, the key points of Cleveland’s philosophy are:
Of course, when you are your own viewer, part of the cognitive strain in visualization comes from difficulty generating the desired graphic. So we’ll start by making the easiest possible ggplot graph, and working our way from there — Cleveland style.
I know “officially” data scientists all always work in “big data” environments with data in a remote database, streaming store or key-value system. But in day to day work Excel files and Excel export files get used a lot and cause a disproportionate amount of pain.
I would like to make a plea to my fellow data scientists to stop using Excel-like formats for informal data exchange and become much stricter in producing and insisting on truly open machine readable files. Open files are those in an open format (not proprietary like Microsoft Excel) and machine readable in this case means readable by a very simple program (preferring simple escaping strategies to complicated quoting strategies). A lot of commonly preferred formats surprisingly do not meet these conditions: for example Microsoft Excel, XML and quoted CSV all fail the test. A few formats that do meet these conditions: SQL dumps, JSON and what I call “strong TSV.” I will illustrate some of the difficulty in using ad-hoc formats in R and suggest work-arounds. Read more…
We have added a worked example to the README of our experimental logistic regression code.
The Logistic codebase is designed to support experimentation on variations of logistic regression including:
What we mean by this code being “experimental” is that it has capabilities that many standard implementations do not. In fact most of the items in the above list are not usually made available to the logistic regression user. But our project is also stand-alone and not as well integrated into existing workflows as standard production systems. Before trying our code you may want to try R or Mahout. Read more…
It’s often the case that I want to write an R script that loops over multiple datasets, or different subsets of a large dataset, running the same procedure over them: generating plots, or fitting a model, perhaps. I set the script running and turn to another task, only to come back later and find the loop has crashed partway through, on an unanticipated error. Here’s a toy example:
> inputs = list(1, 2, 4, -5, 'oops', 0, 10)
> for(input in inputs) {
+ print(paste("log of", input, "=", log(input)))
+ }
[1] "log of 1 = 0"
[1] "log of 2 = 0.693147180559945"
[1] "log of 4 = 1.38629436111989"
[1] "log of -5 = NaN"
Error in log(input) : Non-numeric argument to mathematical function
In addition: Warning message:
In log(input) : NaNs produced
The loop handled the negative arguments more or less gracefully (depending on how you feel about NaN), but crashed on the non-numeric argument, and didn’t finish the list of inputs.
How are we going to handle this?
I am going to come-out and say it: I am emotionally done with 32 bit machines and operating systems. My sympathy for them is at an end.
I know that ARM is still 32 bit, but in that case you get something big back in exchange: the ability to deploy on smartphones and tablets. For PCs and servers 32 bit addressing’s time is long past, yet we still have to code for and regularly run into these machines and operating systems. The time/space savings of 32 bit representations is nothing compared to the loss of capability in sticking with that architecture and the wasted effort in coding around it. My work is largely data analysis in a server environment, and it is just getting ridiculous to not be able to always assume at least a 64 bit machine. Read more…
I would like to remind readers we are sharing more of our project code at https://github.com/WinVector. Read more…
A constant problem for computer science (since its inception) is how to manipulate data that is larger than machine memory. We present here some general strategies for working “out of core” or what you should do when you run out of memory.
Early computers were most limited by their paltry memory sizes. von Neumann himself commented that even a room full of genius mathematicians would not be capable of much if all they could communicate, think upon or remember were the characters on a single type written page (much more memory than the few hundred words available to the Eniac). The most visible portions of early computers are their external memories or secondary stores: card readers, paper tape readers and tape drives.
SDC 920 computer, Computer History Museum, Mountain View CA
Historically computer scientists have concentrated on streaming or online algorithms (that is algorithms that work with the data in the order it is available and use limited memory). For many problems we have found this an insufficient model and it is much better to assume you can re-order and replicate data (such as scattering data to many processors and re-collecting it to sort). The scatter/gather paradigm is ubiquitous and is the underpinning of large scale sorting, databases and Map Reduce. So in one sense databases and Map Reduce different APIs on top of very related technologies (journaling, splitting and merging). Replicating data (or even delaying duplicate elimination) that is already “too large to handle” may seem counterintuitive; but it is exploiting the primary property of secondary storage: that secondary storage tends to be much larger than primary storage (typically by 2 orders of magnitude, compare a 2 terabyte drive to an 8 gigabyte memory stick). Read more…
We extend the ideas of from Automatic Differentiation with Scala to include the reverse accumulation. Reverse accumulation is a non-obvious improvement to automatic differentiation that can in many cases vastly speed up calculations of gradients. Read more…
