One thing I'd really like to see done in this Friday's scikit-learn sprint is to have full support for Python 3. There's a branch were the hard word has been done (porting C extensions, automatic 2to3 conversion, etc.), although joblib still has some bugs and no one ...

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Update: a fast and stable norm was added to scipy.linalg in August 2011 and will be available in scipy 0.10 Last week I discussed with Gael how we should compute the euclidean norm of a vector a using SciPy. Two approaches suggest themselves, either calling scipy.linalg.norm ...

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I was last weekend in FOSDEM presenting scikits.learn (here are the slides I used at the Data Analytics Devroom). Kudos to Olivier Grisel and all the people who organized such a fun and authentic meeting!

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Latest release of scikits.learn comes with an awesome collection of examples. These are some of my favorites:

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Based on the work of libsvm-dense by Ming-Wei Chang, Hsuan-Tien Lin, Ming-Hen Tsai, Chia-Hua Ho and Hsiang-Fu Yu I patched the libsvm distribution shipped with scikits.learn to allow setting weights for individual instances. The motivation behind this is to be able force a classifier to focus its attention in ...

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Highlights for this release: * New stochastic gradient descent module by Peter Prettenhofer * Improved svm module: memory efficiency, automatic class weights. * Wrap for liblinear's Multi-class SVC (option multi_class in LinearSVC) * New features and performance improvements of text feature extraction. * Improved sparse matrix support, both in main classes (GridSearch) as in ...

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scikits.learn.svm now uses LibSVM-dense instead of LibSVM for some support vector machine related algorithms when input is a dense matrix. As a result most of the copies associated with argument passing are avoided, giving 50% less memory footprint and several times less than the python bindings that ship ...

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For some time now I've been missing a function in scipy that exploits the triangular structure of a matrix to efficiently solve the associated system, so I decided to implement it by binding the LAPACK method "trtrs", which also checks for singularities and is capable handling several right-hand sides ...

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I've been working lately with Alexandre Gramfort coding the LARS algorithm in scikits.learn. This algorithm computes the solution to several general linear models used in machine learning: LAR, Lasso, Elasticnet and Forward Stagewise. Unlike the implementation by coordinate descent, the LARS algorithm gives the full coefficient path along ...

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Las week took place in Paris the second scikits.learn sprint. It was two days of insane activity (115 commits, 6 branches, 33 coffees) in which we did a lot of work, both implementing new algorithms and fixing or improving old ones. This includes: * sparse version of Lasso by coordinate ...

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