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        "author_email": "will@pedalwrencher.com",
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        "description": "Scikit-Learn Extensions\n=======================\n\nv0.0.1\n\nScikit-Learn Extensions (sklearn_extensions) is a single source repository for extensions to [scikit-learn](https://github.com/sklearn/sklearn). It is intended\nto compliment the slower more cautious approach of scikit-learn with regard to adding new predictors and modules, with a \nseparate pip-installable source for sklearn-compatible modules that may not meet those standards. \n\nIn particular, this project is interested in smaller one-off projects, particularly even gists, rather than larger more \nestablished ones (such as pylearn2, lifeline, or lightning). Other than larger projects, we will shy away from projects \nwith significant external dependencies (i.e. wrappers around vowpal wabbit or xgboost), and rather prefer more \npython/numpy/scipy based projects. \n\nDue to these guiding goals, the modules included here may not be as well tested, production ready, or stable as those included \ndirectly in sklearn.  This is pretty much the wild west, test anything that uses this package heavily.\n\nDocs to be at: \n\n[http://wdm0006.github.io/sklearn-extensions](http://wdm0006.github.io/sklearn-extensions)\n\nInstallation / Usage\n--------------------\n\nWe aim to first support python 3, and are hosted on pypi, so to install just:\n \n    pip install sklearn-extensions\n\nNote that the install here will install all underlying packages, and is therefore pretty big.  It is recommended that \nyou do this in a virtualenv.\n\nExtensions Included So Far\n--------------------------\n\n * [Kernel Regression](https://github.com/jmetzen/kernel_regression)\n * [Fuzzy K-Means](https://gist.github.com/mblondel/1451300)\n * [Sparse Filtering](https://github.com/jmetzen/sparse-filtering)\n * [Extreme Learning Machines](https://github.com/dclambert/Python-ELM)\n * [Non-negative Garotte](https://gist.github.com/agramfort/2351057)\n \nTODO\n----\n\nA number of packages have been identified but not been added yet. As a general rule for identifying potential projects \nto add to sklearn-extensions, if it cannot be pip-installed: it may be a candidate here\n\n * [Optimal Path Forest Classifiers](https://github.com/LibOPF/LibOPF)\n * [Random Output Trees](https://github.com/arjoly/random-output-trees)\n * [Simple MLP](https://gist.github.com/amueller/2061456)\n * [Kernel SGD](https://gist.github.com/mblondel/2573392)\n * [Kernel K-Means](https://gist.github.com/mblondel/6230787)\n * [Non-Negative Least Squares](https://gist.github.com/mblondel/4421380)\n * [Non-Negative Matrix Factorization](https://gist.github.com/omangin/8801846)\n * [K-means Feature Mapper](https://gist.github.com/larsmans/5996074)\n * [NMF via Coordinate Descent](https://gist.github.com/mblondel/09648344984565f9477a)\n * [Pegasos](https://github.com/ejlb/pegasos)\n * [Gaussian Processes](https://github.com/jmetzen/skgp)\n * [Pinch Ratio Clustering](https://github.com/rsbowman/sklearn-prc)\n * [py-soft-impute](https://github.com/travisbrady/py-soft-impute)\n \nIf you have any more suggestions, please feel free to add them, or let me know and I will try to. \n\nContributing\n------------\n\nIf you have an extension that you'd like to add, please submit a pull request and we can throw it in.  A major benefit of\nthis package is that we will aim to consolidate requirements among the disparate projects, therefore, for the sake of \nmanagement, the code for the projects will be replicated here. In the spirit of OSS, we will also aim to contribute any\nmeaningful changes back to the original projects as well.\n\nA complete addition of a new package has a few components:\n\n * Actual addition of package into sklearn\\_extensions directory\n * Documentation of the included transformers/predictors in the sklearn\\_extensions docs\n * An example or two (included in the aforementioned docs as well) in the examples directory\n * A test or two, more if the source package has poor testing coverage\n\nLicense\n-------\n\nIn most cases, all that sklearn\\_extensions does with external projects is include them. All of the projects will remain\nsegregated into their own subdirectory, and will carry their original licenses in those subdirectories.\n\nAll material specific to this project (specifically any docs, tests, examples or original code) is released under the \nBSD 3-clause license. Any packages included in the bundle retain their original licences (as included in their subdirectories)",
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