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...
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 ...
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 generali...
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 f...
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 coordinat...
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...
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 ove...
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 me...
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 specifi...
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 surpris...
