Hi, Granfort, Currently I am implementing the BSBL-EM and BSBL-l1 algorithm by Zhang . The effort is to maintain compatibility with the original calling conversion of Zhang's TSP2012 paper. I am totally new to python and struggling in the matrix/array conversion, matrix/array concatenate and insert etc., but I enchanting at this programming language.
The insight of scikit-Learn was by an article on kaggle.com which interview the winners of Merck visualization challenge by lvdm. I am not familiar with the regression routines of sklearn, but I think once the code is thoughtfully tested, maybe a wrapper on each function might do the job? There is many thing todo for me to get familiar with scikit-learn. Thanks for take interest in our algorithm. Liu benyuan 在 2012-11-29，16:47，Alexandre Gramfort <[email protected]> 写道： > hi, > > I briefly looked at your code and it would need a bit of effort to make it > compatible with sklearn and the way estimators work. For better usability > it would be great it you could make the solvers be exposed as proper sklearn > estimators i.e. with a fit, predict and coef_. It you do this it means you > will > benefit for free of the scikit-learn machinery (cross validation, grid search, > pipelines etc.). It will also make it easier to compare to solvers available > in sklearn.linear_model > > Best, > Alex > > On Thu, Nov 29, 2012 at 1:03 AM, by liu <[email protected]> wrote: >> Dear, sklearn community, >> >> 1. The source code of the Block SBL algorithm is now available at bitbucket: >> https://bitbucket.org/liubenyuan/pybsbl >> any suggestion, optimization and test on the code are all welcome! as well >> as your success stories on applying our methods. >> >> 2. Block-OMP is an extension to the original OMP algorithm which can handle >> the block sparsity model. Proposed by Eldar in 2010. >> >> 3. Group-lasso in python is also very important. As introduced in the >> TSP2012 paper by Zhang, the block SBL can be viewed as an iterative >> re-weighted group lasso algorithm, which is called BSBL-L1 in the original >> paper. The implementation of BSBL-L1 thus relies on some group-lasso >> solvers. I will contact Fabian (@fabianp on github) ASAP. >> >> 4. The BSBL, easily outperforms Block-OMP, group-lasso, Model-CoSaMP (by >> Baraniuk) with a simple, intuitive Bayesian framework. The BSBL algorithm >> has superior phase-transition performance, and plus, in additional to the >> ability of recovering block sparse signals with high sparsity levels and low >> indeterminacy levels, the proposed algorithm BSBL-BO can also recover >> non-sparse signals. The website of the author Zhilin Zhang: >> http://dsp.ucsd.edu/~zhilin/BSBL.html >> provide many real-life examples, you can check it out. >> >> 5. The algorithm is not far away from real applications. Apply the algorithm >> on the bio-medical signal processing has witness many success. By the way, >> in our real life applications, many signals tend to be block sparse and are >> rich of intra-block structures. The algorithm has a promising future. >> >> 6. I will continue commit to the python society of implementing further, >> many more SBL algorithms, first on the bitbucket and if any of you get >> interested, contact me! >> >> Best wishes to the sklearn community. >> >> liu benyuan >> >> >> >> On Thu, Nov 29, 2012 at 1:22 AM, Gael Varoquaux >> <[email protected]> wrote: >>> >>> Hi Liu, >>> >>> This work is really nice and very fancy, but it is also very recent and >>> needs a bit more insight and benchmarking before it can enter >>> scikit-learn: we have a rule not to integrate any new approach that is >>> more than 2 years old. The reason is that if the approach is to be a >>> massive success, it will be well-studied and thus it pays to wait a bit >>> to understand the trade-offs better. A good approach is to put the >>> implementation that you have on github in a separate repo and advertise >>> it so that people use it and improve it. As it becomes more and more >>> used, you'll get feedback on it, and we'll gather insight, so that we can >>> planify a merge or not, depending on the community pick up. >>> >>> There are well-known and fundamental algorithms to deal with similar >>> problems that are not in the scikit-learn yet, such as group lasso. From >>> what I understand of the block sparse bayesian learning, could benefit >>> from a group lasso solver. Thus integrating a group lasso would be a >>> useful step to deal with block-sparse problems. Fabian (@fabianp on >>> github) has started work on a group lasso solver that is on his github >>> account, but we never could find time to do the integration. I am not >>> sure if it does overlapping groups, and thus if you can use it, but it >>> would be nice to work on its integration in the scikit-learn. >>> Documentation and examples need to be written. >>> >>> Thanks a lot for your interest. >>> >>> Gaël >>> >>> On Wed, Nov 28, 2012 at 03:08:23PM +0800, [email protected] wrote: >>>> Dear scikit-learn community: >>> >>>> Block Sparse Bayesian Learning is a powerful CS algorithm for recovering >>>> block sparse signals with structures, and shows the additional benefits of >>>> reconstruct non-sparse signals, see Dr. zhilin zhang's websites: >>>> http://dsp.ucsd.edu/~zhilin/BSBL.html >>> >>>> I currently implement the BSBL-BO algorithm by Zhang and a fast version >>>> of BSBL algorithm recently proposed by us, called BSBL-FM, in python. Plus >>>> many demos using these two codes. Does scikit-learn community welcome such >>>> type of code ? >>>> what is the procedure to submit the code in the mainstream of scikit >>>> learn? >>> >>>> Thanks for the great project! >>> >>>> Liu benyuan >>> >>>> >>>> ------------------------------------------------------------------------------ >>>> Keep yourself connected to Go Parallel: >>>> INSIGHTS What's next for parallel hardware, programming and related >>>> areas? >>>> Interviews and blogs by thought leaders keep you ahead of the curve. >>>> http://goparallel.sourceforge.net >>> >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> [email protected] >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> -- >>> Gael Varoquaux >>> Researcher, INRIA Parietal >>> Laboratoire de Neuro-Imagerie Assistee par Ordinateur >>> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France >>> Phone: ++ 33-1-69-08-79-68 >>> http://gael-varoquaux.info http://twitter.com/GaelVaroquaux >>> >>> >>> ------------------------------------------------------------------------------ >>> Keep yourself connected to Go Parallel: >>> INSIGHTS What's next for parallel hardware, programming and related areas? >>> Interviews and blogs by thought leaders keep you ahead of the curve. >>> http://goparallel.sourceforge.net >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> ------------------------------------------------------------------------------ >> Keep yourself connected to Go Parallel: >> VERIFY Test and improve your parallel project with help from experts >> and peers. http://goparallel.sourceforge.net >> _______________________________________________ >> Scikit-learn-general mailing list >> [email protected] >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > Keep yourself connected to Go Parallel: > VERIFY Test and improve your parallel project with help from experts > and peers. http://goparallel.sourceforge.net > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Keep yourself connected to Go Parallel: VERIFY Test and improve your parallel project with help from experts and peers. http://goparallel.sourceforge.net _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
