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112 | 113 | 114 |If you use scikit-rebate or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):
124 |Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).
125 |Alternatively a complete review of Relief-based algorithms is available at:
126 |Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).
127 |To cite the original Relief paper:
128 |Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.
129 |To cite the original ReliefF paper:
130 |Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.
131 |To cite the original SURF paper:
132 |Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.
133 |To cite the original SURF* paper:
134 |Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.
135 |To cite the original MultiSURF* paper:
136 |Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.
137 |To cite the original TuRF paper:
138 |Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.
139 | 140 |We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to scikit-rebate, please file a new issue so we can discuss it.
139 |The latest stable release of scikit-rebate is on the master branch, whereas the latest version of scikit-rebate in development is on the development branch. Make sure you are looking at and working on the correct branch if you're looking to contribute code.
141 |In terms of directory structure:
142 |skrebate directorydocs_sources directorydocs directorytests.py fileMake sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the development branch.
The preferred way to contribute to scikit-rebate is to fork the 151 | main repository on 152 | GitHub:
153 |Fork the project repository: 156 | click on the 'Fork' button near the top of the page. This creates 157 | a copy of the code under your account on the GitHub server.
158 |Clone this copy to your local disk:
161 | $ git clone git@github.com:YourLogin/scikit-rebate.git
162 | $ cd scikit-rebate
163 | Create a branch to hold your changes:
167 | $ git checkout -b my-contribution
168 | Make sure your local environment is setup correctly for development. Installation instructions are almost identical to the user instructions except that scikit-rebate should not be installed. If you have scikit-rebate installed on your computer, then make sure you are using a virtual environment that does not have scikit-rebate installed. Furthermore, you should make sure you have installed the nose package into your development environment so that you can test changes locally.
$ conda install nose
173 | Start making changes on your newly created branch, remembering to never work on the master branch! Work on this copy on your computer using Git to do the version control.
Once some changes are saved locally, you can use your tweaked version of scikit-rebate by navigating to the project's base directory and running scikit-rebate in a script.
180 |To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the nose package installed within your dev environment for this to work):
$ nosetests -s -v
184 | When you're done editing and local testing, run:
188 | $ git add modified_files
189 | $ git commit
190 | to record your changes in Git, then push them to GitHub with:
194 | $ git push -u origin my-contribution
195 | Finally, go to the web page of your fork of the scikit-rebate repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the development branch, as the master branch is reserved for the latest stable release. This will start the CI server to check all the project's unit tests run and send an email to the maintainers.
(For details on the above look up the Git documentation on the web.)
198 |Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes.
200 |If your contribution changes scikit-rebate in any way:
201 |Update the documentation so all of your changes are reflected there.
204 |Update the README if anything there has changed.
207 |If your contribution involves any code changes:
210 |Update the project unit tests to test your code changes.
213 |Make sure that your code is properly commented with docstrings and comments explaining your rationale behind non-obvious coding practices.
216 |If your contribution requires a new library dependency:
219 |Double-check that the new dependency is easy to install via pip or Anaconda and supports both Python 2 and 3. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep scikit-rebate easy to install.
Add a line to pip install the library to .travis_install.sh
225 |Add a line to print the version of the library to .travis_install.sh
228 |Similarly add a line to print the version of the library to .travis_test.sh
231 |We use mkdocs to manage our documentation. This allows us to write the docs in Markdown and compile them to HTML as needed. Below are a few useful commands to know when updating the documentation. Make sure that you are running them in the base repository directory.
235 |mkdocs serve: Hosts of a local version of the documentation that you can access at the provided URL. The local version will update automatically as you save changes to the documentation.
mkdocs build --clean: Creates a fresh build of the documentation in HTML. Always run this before deploying the documentation to GitHub.
mkdocs gh-deploy: Deploys the documentation to GitHub. If you're deploying on your fork of scikit-rebate, the online documentation should be accessible at http://<YOUR GITHUB USERNAME>.github.io/scikit-rebate/. Generally, you shouldn't need to run this command because you can view your changes with mkdocs serve.
After submitting your pull request, Travis-CI will automatically run unit tests on your changes and make sure that your updated code builds and runs on Python 2 and 3. We also use services that automatically check code quality and test coverage.
248 |Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors.
249 | 250 |scikit-rebate is a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. As of 5/7/18, this project is still under active development and we encourage you to check back on this repository regularly for updates.
124 |These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection methods.
125 |The main benefit of Relief-based algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search.
126 |Relief-based algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common. However, the algorithms implemented in this package can be applied to almost any supervised classification data set and supports:
127 |A mix of categorical and/or continuous features
130 |Data with missing values
133 |Binary endpoints (i.e., classification)
136 |Multi-class endpoints (i.e., classification)
139 |Continuous endpoints (i.e., regression)
142 |scikit-rebate is built on top of the following existing Python packages:
124 |NumPy
127 |SciPy
130 |scikit-learn
133 |All of the necessary Python packages can be installed via the Anaconda Python distribution, which we strongly recommend that you use. We also strongly recommend that you use Python 3 over Python 2 if you're given the choice.
136 |NumPy, SciPy, and scikit-learn can be installed in Anaconda via the command:
137 |conda install numpy scipy scikit-learn
138 | Once the prerequisites are installed, you should be able to install scikit-rebate with a pip command:
141 |pip install skrebate
142 | You can retrieve basic information about your installed version of skrebate with the following pip command:
145 |pip show skrebate
146 | You can check that you have the most up to date pypi release of skrebate with the following pip command:
149 |pip install skrebate -U
150 | Please file a new issue if you run into installation problems.
153 | 154 |Fixed internal TuRF implementation so that it outputs scores for all features. Those that make it to the last iteration get true core algorithm scoring, while those that were removed along the way are assigned token scores (lower than the lowest true scoring feature) that indicate when the respective feature(s) were removed. This also alows for greater flexibility in the user specifying the number for features to return.
145 |Updated the usage documentation to demonstrate how to use RFE as well as the newly updated internal TuRF implementation.
148 |Fixed the pct paramter of TuRF to properly determine the percent of features removed each iteration as well as the total number of iterations as described in the original TuRF paper. Also managed the edge case to ensure that at least one feature would be removed each TuRF iteration.
151 |Fixed ability to parallelize run of core algorithm while using TuRF.
154 |Updated the unit testing file to remove some excess unite tests, add other relevant ones, speed up testing overall, and make the testing better organized.
157 |Added a preliminary implementation of VLSRelief to scikit-rebate, along with associated unit tests. Documentation and code examples not yet supported.
160 |Removed some unused code from TuRF implementation.
163 |Added check in the transform method required by scikit-learn in both relieff.py and turf.py to ensure that the number of selected features requested by the user was not larger than the number of features in the dataset.
166 |Reduced the default value for number of features selected
169 |Added fixes to score normalizations that should ensure that feature scores for all algorithms fall between -1 and 1.
175 |Added multi-class endpoint functionality. (now discriminates between binary and multiclass endpoints) Includes new methods for multi-class score update normalization.
178 |Fixed normalization for missing data.
181 |Fixed inconsistent pre-normalization for continuous feature data.
184 |Added a custom ramp function to improve performance of all algorithms on data with a mix of discrete and continuous features. Based on the standard deviation of a given continuous feature.
187 |Updated the implementation of TuRF as an internal custom component of ReBATE.
190 |Added support for multicore processing to all Relief algorithms. Multiprocessing is now also supported in Python 2.
196 |The ReliefF algorithm now accepts float values in the range (0, 1.0] for the n_neighbors parameter. Float values will be interpreted as a fraction of the training set sample size.
Refined the MultiSURF and MultiSURF* algorithms. From our internal research, MultiSURF is now one of our best-performing feature selection algorithms.
202 |Added a parallelization parameter, n_jobs, to ReliefF, SURF, SURF*, and MultiSURF via joblib.
Renamed the dlimit parameter to discrete_limit to better reflect the purpose of the parameter.
Minor code optimizations.
214 |Added documentation.
220 |Minor code optimizations.
223 |{{summary}}
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