Installation / Usage

We aim to first support python 3, and are hosted on pypi, so to install just:

pip install sklearn-extensions

Note that the install here will install all underlying packages, and is therefore pretty big. It is recommended that you do this in a virtualenv.

Extensions Included So Far

• [Kernel Regression](https://github.com/jmetzen/kernel_regression)

• [Fuzzy K-Means](https://gist.github.com/mblondel/1451300)

• [Sparse Filtering](https://github.com/jmetzen/sparse-filtering)

• [Extreme Learning Machines](https://github.com/dclambert/Python-ELM)

• [Non-negative Garotte](https://gist.github.com/agramfort/2351057)

TODO

A number of packages have been identified but not been added yet. As a general rule for identifying potential projects to add to sklearn-extensions, if it cannot be pip-installed: it may be a candidate here

• [Optimal Path Forest Classifiers](https://github.com/LibOPF/LibOPF)

• [Random Output Trees](https://github.com/arjoly/random-output-trees)

• [Simple MLP](https://gist.github.com/amueller/2061456)

• [Kernel SGD](https://gist.github.com/mblondel/2573392)

• [Kernel K-Means](https://gist.github.com/mblondel/6230787)

• [Non-Negative Least Squares](https://gist.github.com/mblondel/4421380)

• [Non-Negative Matrix Factorization](https://gist.github.com/omangin/8801846)

• [K-means Feature Mapper](https://gist.github.com/larsmans/5996074)

• [NMF via Coordinate Descent](https://gist.github.com/mblondel/09648344984565f9477a)

• [Pegasos](https://github.com/ejlb/pegasos)

• [Gaussian Processes](https://github.com/jmetzen/skgp)

• [Pinch Ratio Clustering](https://github.com/rsbowman/sklearn-prc)

• [py-soft-impute](https://github.com/travisbrady/py-soft-impute)

If you have any more suggestions, please feel free to add them, or let me know and I will try to.

Contributing

If 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 this package is that we will aim to consolidate requirements among the disparate projects, therefore, for the sake of management, the code for the projects will be replicated here. In the spirit of OSS, we will also aim to contribute any meaningful changes back to the original projects as well.

A complete addition of a new package has a few components:

• Actual addition of package into sklearn_extensions directory

• Documentation of the included transformers/predictors in the sklearn_extensions docs

• An example or two (included in the aforementioned docs as well) in the examples directory

• A test or two, more if the source package has poor testing coverage

License

In most cases, all that sklearn_extensions does with external projects is include them. All of the projects will remain segregated into their own subdirectory, and will carry their original licenses in those subdirectories.

All material specific to this project (specifically any docs, tests, examples or original code) is released under the BSD 3-clause license. Any packages included in the bundle retain their original licences (as included in their subdirectories)