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LLE comes in different flavours
I haven't worked in the manifold module since last time, yet thanks to Jake VanderPlas there are some cool features I can talk about. First of, the ARPACK backend is finally working and gives factor...
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Ridge regression path
Ridge coefficients for multiple values of the regularization parameter can be elegantly computed by updating the thin SVD decomposition of the design matrix: import numpy as np from scipy import linalg...
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scikit-learn EuroScipy 2011 coding sprint -- day one
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...
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scikit-learn’s EuroScipy 2011 coding sprint -- day two
Today's coding sprint was a bit more crowded, with some notable scipy hackers such as Ralph Gommers, Stefan van der Walt, David Cournapeau or Fernando Perez from Ipython joining in. On what got done:...
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Reworked example gallery for scikit-learn
I've been working lately in improving the scikit-learn example gallery to show also a small thumbnail of the plotted result. Here is what the gallery looks like now: And the real thing should be...
View Articlescikit-learn 0.9
Last week we released a new version of scikit-learn. The Changelog is particularly impressive, yet personally this release is important for other reasons. This will probably be my last release as a...
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qr_multiply function in scipy.linalg
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...
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Low rank approximation
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...
View Articleline-by-line memory usage of a Python program
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...
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Learning to rank with scikit-learn: the pairwise transform
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...
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Singular Value Decomposition in SciPy
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...
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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...
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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...
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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...
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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...
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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...
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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 tools from scipy.optimize and compare them...
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Different ways to get memory consumption or lessons learned from...
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...
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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...
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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...
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