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Lightning v0.1

Category: misc
#Python #scikit-learn #machine learning #lightning

Announce: first public release of lightning!, a library for large-scale linear classification, regression and ranking in Python. The library was started a couple of years ago by Mathieu Blondel who also contributed the vast majority of source code. I joined recently its development and decided it was about time for …

scikit-learn-contrib, an umbrella for scikit-learn related projects.

Category: misc
#Python #scikit-learn #machine learning #lightning

Together with other scikit-learn developers we've created an umbrella organization for scikit-learn-related projects named scikit-learn-contrib. The idea is for this organization to host projects that are deemed too specific or too experimental to be included in the scikit-learn codebase but still offer an API which is compatible with scikit-learn and …

SAGA algorithm in the lightning library

Category: misc
#Python #scikit-learn #machine learning #lightning

Recently I've implemented, together with Arnaud Rachez, the SAGA[1] algorithm in the lightning machine learning library (which by the way, has been recently moved to the new scikit-learn-contrib project). The lightning library uses the same API as scikit-learn but is particularly adapted to online learning. As for the SAGA …

On the consistency of ordinal regression methods

Category: misc
#consistency #machine learning

My latests work (with Francis Bach and Alexandre Gramfort) is on the consistency of ordinal regression methods. It has the wildly imaginative title of "On the Consistency of Ordinal Regression Methods" and is currently under review but you can read the draft of it on ArXiv. If you have any …

Holdout cross-validation generator

Category: misc
#Python #scikit-learn #machine learning #model selection

Cross-validation iterators in scikit-learn are simply generator objects, that is, Python objects that implement the __iter__ method and that for each call to this method return (or more precisely, yield) the indices or a boolean mask for the train and test set. Hence, implementing new cross-validation iterators that behave as …

IPython/Jupyter notebook gallery

Category: misc
#Python #Jupyter

TL;DR I created a gallery for IPython/Jupyter notebooks. Check it out :-)

Notebook gallery

A couple of months ago I put online a website that displays a collection of IPython/Jupyter notebooks. The is a website that collects user-submitted and publicly available notebooks and displays them with a nice screenshot. The …

PyData Paris - April 2015

Category: misc
#Python #Paris #NumPy #Numba

Last Friday was PyData Paris, in words of the organizers, ''a gathering of users and developers of data analysis tools in Python''.

The organizers did a great job in putting together and the event started already with a full room for Gael's keynote

Gael's keynote

My take-away message from the talks is …

Data-driven hemodynamic response function estimation

Category: misc
#fMRI #GLM #python

My latest research paper[1] deals with the estimation of the hemodynamic response function (HRF) from fMRI data.

This is an important topic since the knowledge of a hemodynamic response function is what makes it possible to extract the brain activation maps that are used in most of the impressive …

Plot memory usage as a function of time

Category: misc
#memory_profiler #mprof #profile

One of the lesser known features of the memory_profiler package is its ability to plot memory consumption as a function of time. This was implemented by my friend Philippe Gervais, previously a colleague at INRIA and now at Google.

With this feature it is possible to generate very easily a …

Surrogate Loss Functions in Machine Learning

Category: misc
#machine learning #consistency #calibration

TL; DR These are some notes on calibration of surrogate loss functions in the context of machine learning. But mostly it is an excuse to post some images I made.

In the binary-class classification setting we are given $n$ training samples $\{(X_1, Y_1), \ldots, (X_n, Y_n)\}$, where $X_i$ belongs to …