My latest contribution for scikit-learn is
an implementation of the isotonic regression model that I coded with
Nelle Varoquaux and
Alexandre Gramfort. This model
finds the best least squares fit to a set of points, given the
constraint that the fit must be a non-decreasing
function. The example
on the …

Besides performing a line-by-line analysis of memory consumption,
`memory_profiler`

exposes some functions that allow to retrieve the memory consumption
of a function in real-time, allowing e.g. to visualize the memory
consumption of a given function over time.

The function to be used is `memory_usage`

. The first argument
specifies what …

SciPy contains two methods to compute the singular value decomposition (SVD) of a matrix: `scipy.linalg.svd`

and `scipy.sparse.linalg.svds`

. In this post I'll compare both methods for the task of computing the full SVD of a large dense matrix.

The first method, `scipy.linalg.svd`

, is perhaps …

This tutorial introduces the concept of pairwise preference used in most ranking problems. I'll use scikit-learn and for learning and matplotlib for visualization.

In the ranking setting, training data consists of lists of items with some order specified between items in each list. This order is typically induced by giving …

My newest project is a Python library for monitoring memory consumption
of arbitrary process, and one of its most useful features is the
line-by-line analysis of memory usage for Python code. I wrote a basic
prototype six months ago after being surprised by the lack of related
tools. I wanted …

A little experiment to see what low rank approximation looks like. These
are the best rank-k approximations (in the Frobenius norm) to the a
natural image for increasing values of k and an original image of rank
512.

Python code can be found here. GIF animation made
using ImageMagic's convert …

In scipy's development version there's a new function closely related to
the QR-decomposition of a matrix and to the least-squares solution of
a linear system. What this function does is to compute the
QR-decomposition of a matrix and then multiply the resulting orthogonal
factor by another arbitrary matrix. In pseudocode …

As a warm-up for the upcoming EuroScipy-conference, some of the
scikit-learn developers decided to gather and work together for a
couple of days. Today was the first day and there was only a handfull of
us, as the real kickoff is expected tomorrow. Some interesting coding
happened, although most of …

I was last weekend in FOSDEM presenting scikits.learn (here are
the slides I used at the Data Analytics Devroom). Kudos to Olivier
Grisel and all the people who organized such a fun and authentic
meeting!