Keep the gradient flowing

SAGA algorithm in the lightning library

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 …

Holdout cross-validation generator

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

Due to lack of time and interest, I'm no longer maintaining this project. Feel free to grab the sources from https://github.com/fabianp/nbgallery and fork the project.

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

Notebook gallery

A couple of months ago I put online …

PyData Paris - April 2015

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

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

:og_image: http://fa.bianp.net/blog/images/2014/mprof_example.png

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 …

Surrogate Loss Functions in Machine Learning

TL; DR These are some notes on calibration of surrogate loss functions in the context of machine learning. But mostly it is …

Different ways to get memory consumption or lessons learned from ``memory_profiler``

As part of the development of memory_profiler I've tried several ways to get memory usage of a program from within Python. In this post I'll describe the different alternatives I've tested.

The psutil library

psutil is a python library that provides an interface for retrieving information on running processes. It …

Numerical optimizers for Logistic Regression

In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization …

Logistic Ordinal Regression

TL;DR: I've implemented a logistic ordinal regression or proportional odds model. Here is the Python code

The logistic ordinal regression model …

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