Computer Science – Win-Vector Blog

February 1, 2018John MountComputer Science, Mathematics, Statisticspermutation groups, permutation tests, permutations, R, significance

Why No Exact Permutation Tests at Scale?

Here at Win-Vector LLC we like permutation tests. Our team has written on them (for example: How Do You Know if Your Data Has Signal?) and they are used to estimate significances in our sigr and WVPlots R packages. For example permutation methods are used to estimate the significance reported in the following ROC plot.

Permutation tests have their own literature and issues (examples: Permutation, Parametric and Bootstrap Tests of Hypotheses, Springer-Verlag, NY, 1994 (3rd edition, 2005), 2, 3, and 4).

In our R packages the permutation tests are estimated by a sampling procedure, and not computed exactly (or deterministically). It turns out this is likely a necessary concession; a complete exact permutation test procedure at scale would be big news. Please read on for my comments on this issue.

Continue reading Why No Exact Permutation Tests at Scale?

January 15, 2018January 18, 2018John MountCoding, Computer Science, data science, Opinion, Programming, Statistics, Tutorialsbenchmarking, data.table, dplyr, R, rquery14 Comments

Base R can be Fast

“Base R” (call it “Pure R”, “Good Old R”, just don’t call it “Old R” or late for dinner) can be fast for in-memory tasks. This is despite the commonly repeated claim that: “packages written in C/C++ are (edit: “always”) faster than R code.”

The benchmark results of “rquery: Fast Data Manipulation in R” really called out for follow-up timing experiments. This note is one such set of experiments, this time concentrating on in-memory (non-database) solutions.

Below is a graph summarizing our new results for a number of in-memory implementations, a range of data sizes, and two different machine types.

Unnamed chunk 2 1 Continue reading Base R can be Fast

January 9, 2018January 16, 2018John MountComputer Science, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, ProgrammingBig Data, data wrangling, data wrangling at scale, R, R and big data, rquery, SQL3 Comments

rquery: Fast Data Manipulation in R

Win-Vector LLC recently announced the rquery R package, an operator based query generator.

In this note I want to share some exciting and favorable initial rquery benchmark timings.

Continue reading rquery: Fast Data Manipulation in R

May 18, 2017May 18, 2017John MountComputer Science, Expository Writing, Programming, Tutorialsalgebra, function composition, group theory, permutation groups, programming patterns, R, theory of programming1 Comment

On indexing operators and composition

In this article I will discuss array indexing, operators, and composition in depth. If you work through this article you should end up with a very deep understanding of array indexing and the deep interpretation available when we realize indexing is an instance of function composition (or an example of permutation groups or semigroups: some very deep yet accessible pure mathematics).

A permutation of indices

In this article I will be working hard to convince you a very fundamental true statement is in fact true: array indexing is associative; and to simultaneously convince you that you should still consider this amazing (as it is a very strong claim with very many consequences). Array indexing respecting associative transformations should not be a-priori intuitive to the general programmer, as array indexing code is rarely re-factored or transformed, so programmers tend to have little experience with the effect. Consider this article an exercise to build the experience to make this statement a posteriori obvious, and hence something you are more comfortable using and relying on.

R‘s array indexing notation is really powerful, so we will use it for our examples. This is going to be long (because I am trying to slow the exposition down enough to see all the steps and relations) and hard to follow without working examples (say with R), and working through the logic with pencil and a printout (math is not a spectator sport). I can’t keep all the steps in my head without paper, so I don’t really expect readers to keep all the steps in their heads without paper (though I have tried to organize the flow of this article and signal intent often enough to make this readable). Continue reading On indexing operators and composition

December 19, 2016Nina ZumelCoding, Computer Science, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Programming, StatisticsR, replyr

A Simple Example of Using replyr::gapply

It’s a common situation to have data from multiple processes in a “long” data format, for example a table with columns measurement and process_that_produced_measurement. It’s also natural to split that data apart to analyze or transform it, per-process — and then to bring the results of that data processing together, for comparison. Such a work pattern is called “Split-Apply-Combine,” and we discuss several R implementations of this pattern here. In this article we show a simple example of one such implementation, replyr::gapply, from our latest package, replyr.

Illustration by Boris Artzybasheff. Image: James Vaughn, some rights reserved.

The example task is to evaluate how several different models perform on the same classification problem, in terms of deviance, accuracy, precision and recall. We will use the “default of credit card clients” data set from the UCI Machine Learning Repository.

Continue reading A Simple Example of Using replyr::gapply

December 6, 2016December 19, 2016Nina ZumelCoding, Computer Science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Programming, Statisticsdplyr, R, replyr2 Comments

Using replyr::let to Parameterize dplyr Expressions

Rplot

Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want the summary broken down with respect to groupings in the data. In other words, you want a table of values, something like this:

dist_intervals(iris, "Sepal.Length", "Species")

# A tibble: 3 × 7
     Species  sdlower  mean  sdupper iqrlower median iqrupper
                         
1     setosa 4.653510 5.006 5.358490   4.8000    5.0   5.2000
2 versicolor 5.419829 5.936 6.452171   5.5500    5.9   6.2500
3  virginica 5.952120 6.588 7.223880   6.1625    6.5   6.8375

For a specific data frame, with known column names, such a table is easy to construct using dplyr::group_by and dplyr::summarize. But what if you want a function to calculate this table on an arbitrary data frame, with arbitrary quantity and grouping columns? To write such a function in dplyr can get quite hairy, quite quickly. Try it yourself, and see.

Enter let, from our new package replyr.

Continue reading Using replyr::let to Parameterize dplyr Expressions

August 27, 2016John MountComputer Science, math programming, Statistics, Tutorialsmonotone functions, significance, submodular functions, variable selection2 Comments

Variable pruning is NP hard

I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression. One thing I noticed while preparing some examples is that summaries such as model quality (especially out of sample quality) and variable significances are not quite as simple as one would hope (they in fact lack a lot of the monotone structure or submodular structure that would make things easy).

That being said we have a lot of powerful and effective heuristics to discuss in upcoming articles. I am going to leave such positive results for my later articles and here concentrate on an instructive technical negative result: picking a good subset of variables is theoretically quite hard. Continue reading Variable pruning is NP hard

August 6, 2016February 19, 2017John MountComputer Scienceprogramming patterns, R2 Comments

The magrittr monad

Monads are a formal theory of composition where programmers get to invoke some very abstract mathematics (category theory) to argue the minutia of annotating, scheduling, sequencing operations, and side effects. On the positive side the monad axioms are a guarantee that related ways of writing code are in fact substitutable and equivalent; so you want your supplied libraries to obey such axioms to make your life easy. On the negative side, the theory is complicated.

In this article we will consider the latest entry of our mad “programming theory in R series” (see Some programming language theory in R, You don’t need to understand pointers to program using R, Using closures as objects in R, and How and why to return functions in R): category theory!

Continue reading The magrittr monad

January 1, 2016August 22, 2017John MountCoding, Computer Science, Expository Writing, Programming, Tutorialsfixed point, functional programming, lambda calculus, R, R as it is, recursion, theory of programming1 Comment

Some programming language theory in R

Let’s take a break from statistics and data science to think a bit about programming language theory, and how the theory relates to the programming language used in the R analysis platform (the language is technically called “S”, but we are going to just call the whole analysis system “R”).

Our reasoning is: if you want to work as a modern data scientist you have to program (this is not optional for reasons of documentation, sharing and scientific repeatability). If you do program you are going to have to eventually think a bit about programming theory (hopefully not too early in your studies, but it will happen). Let’s use R’s powerful programming language (and implementation) to dive into some deep issues in programming language theory:

References versus values
Function abstraction
Equational reasoning
Recursion
Substitution and evaluation
Fixed point theory

To do this we will translate some common ideas from a theory called “the lambda calculus” into R (where we can actually execute them). This translation largely involves changing the word “lambda” to “function” and introducing some parenthesis (which I think greatly improve readability, part of the mystery of the lambda calculus is how unreadable its preferred notation actually is).

Recursive Opus (on a Hyperbolic disk) Continue reading

November 1, 2015November 12, 2015Nina ZumelAdministrativia, Computer Science, data science, Exciting Techniques, Statistics, Uncategorizeddifferential privacy, R, Serial writing on data science, Statistics as it should be, writing

Our Differential Privacy Mini-series

We’ve just finished off a series of articles on some recent research results applying differential privacy to improve machine learning. Some of these results are pretty technical, so we thought it was worth working through concrete examples. And some of the original results are locked behind academic journal paywalls, so we’ve tried to touch on the highlights of the papers, and to play around with variations of our own.

Blurry snowflakes stock by cosmicgallifrey d3inho1

A Simpler Explanation of Differential Privacy: Quick explanation of epsilon-differential privacy, and an introduction to an algorithm for safely reusing holdout data, recently published in Science (Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth, “The reusable holdout: Preserving validity in adaptive data analysis”, Science, vol 349, no. 6248, pp. 636-638, August 2015).
Note that Cynthia Dwork is one of the inventors of differential privacy, originally used in the analysis of sensitive information.
Using differential privacy to reuse training data: Specifically, how differential privacy helps you build efficient encodings of categorical variables with many levels from your training data without introducing undue bias into downstream modeling.
A simple differentially private-ish procedure: The bootstrap as an alternative to Laplace noise to introduce privacy.

Our R code and experiments are available on Github here, so you can try some experiments and variations yourself.

Image Credit