Posted on Categories Computer Science, Exciting Techniques, Opinion, Programming, RantsTags , 2 Comments on Thumbs up for Anaconda

Thumbs up for Anaconda

One of the things I like about R is: because it is not used for systems programming you can expect to install your own current version of R without interference from some system version of R that is deliberately being held back at some older version (for reasons of script compatibility). R is conveniently distributed as a single package (with automated install of additional libraries).

Want to do some data analysis? Install R, load your data, and go. You don’t expect to spend hours on system administration just to get back to your task.

Python, being a popular general purpose language does not have this advantage, but thanks to Anaconda from Continuum Analytics you can skip (or at least delegate) a lot of the system environment imposed pain. With Anaconda trying out Python packages (Jupyter, scikit-learn, pandas, numpy, sympy, cvxopt, bokeh, and more) becomes safe and pleasant. Continue reading Thumbs up for Anaconda

Posted on Categories Computer Science, Opinion, RantsTags , , , , , 2 Comments on Text encoding is a convoluted mess

Text encoding is a convoluted mess

Modern text encoding is a convoluted mess where costs can easily exceed benefits. I admit we are in a world that has moved beyond ASCII (which at best served only English, and even then without full punctuation). But modern text encoding standards (utf-x, Unicode) have metastasized to the point you spend more time working around them than benefiting from them.


NewImage
ASCII Code Chart-Quick ref card” by Namazu-tron – See above description. Licensed under Public Domain via Wikimedia Commons
Continue reading Text encoding is a convoluted mess

Posted on Categories Computer Science, MathematicsTags , , 2 Comments on Erdős writing like a programmer

Erdős writing like a programmer

Here is a neat example of famous mathematician Pál Erdős (often rendered in English as Paul Erdős) writing like a programmer in 1961. He goes to some trouble to introduce notation that allows him to index everything from zero. Continue reading Erdős writing like a programmer

Posted on Categories Computer Science, Programming, Public Service Article, TutorialsTags , , , , , 13 Comments on Using closures as objects in R

Using closures as objects in R

For more and more clients we have been using a nice coding pattern taught to us by Garrett Grolemund in his book Hands-On Programming with R: make a function that returns a list of functions. This turns out to be a classic functional programming techique: use closures to implement objects (terminology we will explain).

It is a pattern we strongly recommend, but with one caveat: it can leak references similar to the manner described in here. Once you work out how to stomp out the reference leaks the “function that returns a list of functions” pattern is really strong.

We will discuss this programming pattern and how to use it effectively. Continue reading Using closures as objects in R

Posted on Categories Coding, Computer Science, data science, Expository Writing, math programming, Pragmatic Machine Learning, StatisticsTags , , , , , , , , , 4 Comments on The Geometry of Classifiers

The Geometry of Classifiers

As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data sets, mostly from the UCI Machine Learning Repository. For fun, we decided to do a follow-up study, using their data and several classifier implementations from scikit-learn, the Python machine learning library. We were interested not just in classifier accuracy, but also in seeing if there is a “geometry” of classifiers: which classifiers produce predictions patterns that look similar to each other, and which classifiers produce predictions that are quite different? To examine these questions, we put together a Shiny app to interactively explore how the relative behavior of classifiers changes for different types of data sets.

Continue reading The Geometry of Classifiers

Posted on Categories Computer Science, Opinion, Practical Data Science, Pragmatic Data Science, Programming, Rants, StatisticsTags , , , 4 Comments on Factors are not first-class citizens in R

Factors are not first-class citizens in R

The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R that scalar values such as the number 5 are actually represented as length-1 vectors. We commonly think about working over vectors of “logical”, “integer”, “numeric”, “complex”, “character”, and “factor” types. However, a “factor” is not a R vector. In fact “factor” is not a first-class citizen in R, which can lead to some ugly bugs.

For example, consider the following R code.

levels <- c('a','b','c')
f <- factor(c('c','a','a',NA,'b','a'),levels=levels)
print(f)
## [1] c    a    a    <NA> b    a   
## Levels: a b c
print(class(f))
## [1] "factor"

This example encoding a series of 6 observations into a known set of factor-levels ('a', 'b', and 'c'). As is the case with real data some of the positions might be missing/invalid values such as NA. One of the strengths of R is we have a uniform explicit representation of bad values, so with appropriate domain knowledge we can find and fix such problems. Suppose we knew (by policy or domain experience) that the level 'a' was a suitable default value to use when the actual data is missing/invalid. You would think the following code would be the reasonable way to build a new revised data column.

fRevised <- ifelse(is.na(f),'a',f)
print(fRevised)
##  [1] "3" "1" "1" "a" "2" "1"
print(class(fRevised))
## [1] "character"

Notice the new column fRevised is an absolute mess (and not even of class/type factor). This sort of fix would have worked if f had been a vector of characters or even a vector of integers, but for factors we get gibberish.

We are going to work through some more examples of this problem. Continue reading Factors are not first-class citizens in R

Posted on Categories Coding, Computer Science, Programming, Rants, TutorialsTags , ,

Unit tests as penance

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. Continue reading Unit tests as penance

Posted on Categories Computer Science, Expository Writing, RantsTags , , 3 Comments on A randomized algorithm that fails with near certainty

A randomized algorithm that fails with near certainty

Recently Heroku was accused of using random queue routing while claiming to supply something similar to shortest queue routing (see: James Somers – Heroku’s Ugly Secret and more discussion at hacker news: Heroku’s Ugly Secret). If this is true it is pretty bad. I like randomized algorithms and I like queueing theory, but you need to work through proofs or at least simulations when playing with queues. You don’t want to pick an arbitrary algorithm and claim it works “due to randomness.” We will show a very quick example where randomized routing is very bad with near certainty. Just because things are “random” doesn’t mean you can’t or shouldn’t characterize them. Continue reading A randomized algorithm that fails with near certainty

Posted on Categories Computer Science, math programming, Mathematics, ProgrammingTags , ,

Yet Another Java Linear Programming Library

From time to time we work on projects that would benefit from a free lightweight pure Java linear programming library. That is a library unencumbered by a bad license, available cheaply, without an infinite amount of file format and interop cruft and available in Java (without binary blobs and JNI linkages). There are a few such libraries, but none have repeatably, efficiently and reliably met our needs. So we have re-packaged an older one of our own for release under the Apache 2.0 license. This code will have its own rough edges (not having been used widely in production), but I still feel fills an important gap. This article is brief introduction to our WVLPSolver Java library. Continue reading Yet Another Java Linear Programming Library

Posted on Categories Coding, Computer Science, data science, Mathematics, StatisticsTags , , , , , ,

Added worked example to logistic regression project

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. Continue reading Added worked example to logistic regression project