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.
psutil is a python library that provides an interface for retrieving information on ...
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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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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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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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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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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 method, scipy.linalg.svd, is ...
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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 specified between items in each list. This order is typically induced by ...
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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 surprised by the lack of related tools. I wanted ...
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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 image of rank 512.
Python code can be found here. GIF animation made using ImageMagic's ...
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