1. Kemeny-Young Optimal Rank Aggregation in Python

Rank aggregation is a problem with many important applications and naive approaches to it go wrong in subtle ways.

Let’s say that your national Quidditch league is dominated by five major wizard sports newspapers. Yes, the ones with moving images and everything. Every week after the games, each of them publishes a ranking of the star players. For now, let’s suppose that the set of players under investigation is always the same, as the problem becomes a bit more complicated otherwise.

2. Scikit-learn-speed: An overview on the final day

This summer, I was granted the project called scikit-learn-speed, consisting of developing a benchmarking platform for scikit-learn and using it to find potential speedups, and in the end, make the library go faster wherever I can.

On the official closing day of this work, I’d like to take a …

3. Inverses and pseudoinverses. Numerical issues, speed, symmetry.

The matrix inverse is a cornerstone of linear algebra, taught, along with its applications, since high school. The inverse of a matrix \$latex A\$, if it exists, is the matrix \$latex A\^{-1}\$ such that \$latex AA\^{-1} = A\^{-1}A = I_n\$. Based on the requirement that the left and …

4. The scikit-learn-speed ship has set sail! Faster than ever, with multi-step benchmarks!

I am pleased to announce that last night at 2:03 AM, the first fully automated run of the scikit-learn-speed test suite has run on our Jenkins instance! You can admire it at its temporary home for now. As soon as we verify that everything is good, we will move …

5. Profiler output, benchmark standard deviation and other goodies in scikit-learn-speed

This post is about the scikit-learnbenchmarking project that I am working on, called scikit-learn-speed. This is a continuous benchmarking suite that runs and generates HTML reports using Wes McKinney’s vbench framework, to which I had to make some (useful, I hope) additions.

You …

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