Tag Archives: Scikit

Book review: Learning scikit-learn – Machine Learning in Python

It seems that Packt Publishing is on a publishing spree on Machine Learning in Python. After Building Machine Learning Systems In Python for which I was technical reviewer, Packt published Learning Scikit-Learn In Python last November.

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Annoucement: scikits.optimization 0.3

I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.

Changelog

  • Fixes on the Simplex state implementation
  • Added several Quasi-Newton steps (BFGS, rank 1 update…)

The scikit can be installed with pip/easy_install or downloaded from PyPI

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Optimization scikit: Polytope (Simplex/Nelder-Mead) optimization

Now that version 0.2 of scikit.optimization is out, here is a tutorial on the gradient-free optimizer based on the simplex algorithm.

When the only thing you have is the cost function and when you don’t have dozens of parameters, the first thing that can be tried is a simplex algorithm.

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Annoucement: scikits.optimization 0.2

It has been a while, too long for sure, since my last update on this scikit. I’m pleased to announce that some algorithms are finally fixed as well as some tests.

Changelog:

  • Fixed Polytope/Simplex/Nelder-Mead
  • Fixed the Quadratic Hessian helper class

Additional tutorials will be available in the next weeks.

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Optimization scikit: separation of orthogonally convoluted signals

My last blog post on optimization helped me generate orthogonal sequences. Now, I will use those sequences to separate two signals. The basic use case is a linear system with two inputs, one output, and instead of recording the response of one input at a time, one plays both inputs simultaneously with specific sequences so that they can be separated in another process.
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Dimensionality reduction: Refactoring the manifold module

It’s been a while since I last blogged about manifold learning. I don’t think I’ll add much in terms of algorithms to the scikit, but now that a clear API is being defined (http://sourceforge.net/apps/trac/scikit-learn/wiki/ApiDiscussion), it’s time for the manifold module to comply to it. Also, documentation will be enhanced and some dependencies will be removed.

I’ve started a branch available on github.com, and I will some examples in the scikit as well. I may explain them here, but I won’t rewrite what is already published. A future post will explain the changes, and I hope that interested people will understand the modifications and apply them to my former posts. It’s just that I don’t have much time to change everything…

Optimization scikit: a conjugate-gradient optimization

In my last post about optimization, I’ve derived my function analytically. Sometimes, it’s not as easy. Sometimes also, a simple gradient optimization is not enough.

scikits.optimization has a special class for handling numerical differentiation, and several tools for conjugate gradients.
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Optimization scikit: a gradient-based optimization

Last time, I’ve made a simple example of a gradient-free optimization. Now, I’d like to use the gradient of my function (analytical gradient I’ve computed) to be able to get the global minimum in less iterations.
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