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1. Plot memory usage as a function of time

   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 working for Google.

   With this feature it is possible to generate very easily ...

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2. 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 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 ...

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3. 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 ...

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4. Numerical optimizers for Logistic Regression

   Following a challenge proposed by Gael to my group I compared several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization tools from scipy.optimize and compare them against state of the art implementations such as LIBLINEAR.

   In this blog post I'll ...

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5. 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, also known as the proportional odds was introduced in the early 80s by McCullagh [¹, ²] and is a generalized linear model specially tailored for the case ...

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6. Isotonic Regression

   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 ...

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7. Householder matrices

   Householder matrices are square matrices of the form

   $$ P = I - \beta v v^T$$

   where $\beta$ is a scalar and $v$ is a vector. It has the useful property that for suitable chosen $v$ and $\beta$ it makes the product $P x$ to zero out all of the coordinates but ...

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8. Loss Functions for Ordinal regression

   Note: this post contains a fair amount of LaTeX, if you don't visualize the math correctly come to its original location

   In machine learning it is common to formulate the classification task as a minimization problem over a given loss function. Given data input data $(x_1, ..., x_n)$ and associated ...

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9. Memory plots with memory_profiler

   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 ...

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