[WIP] Benchmarks

.coveragerc

@@ -2,10 +2,11 @@
 
 [run]
 branch = True
-source = unbalanced_dataset
-include = */unbalanced_dataset/*
+source = imblearn
+include = */imblearn/*
 omit =
     */setup.py
+    */benchmarks/*
 
 [report]
 exclude_lines =
@@ -16,4 +17,6 @@ exclude_lines =
     raise AssertionError
     raise NotImplementedError
     if 0:
-    if __name__ == .__main__.:
+    if __name__ == .__main__.:
+    if self.verbose:
+show_missing = True

.gitignore

@@ -56,4 +56,14 @@ docs/_build/
 # PyBuilder
 target/
 
-*~
+# vim
+*.swp
+
+# emacs
+*~
+
+# Visual Studio
+*.sln
+*.pyproj
+*.suo
+*.vs

.travis.yml

@@ -23,7 +23,7 @@ env:
   global:
     # Directory where tests are run from
     - TEST_DIR=/tmp/test_dir
-    - MODULE=unbalanced_dataset
+    - MODULE=imblearn
     - OMP_NUM_THREADS=4
     - OPENBLAS_NUM_THREADS=4
   matrix:
@@ -36,3 +36,11 @@ env:
 install: source build_tools/travis/install.sh
 script: bash build_tools/travis/test_script.sh
 after_success: source build_tools/travis/after_success.sh
+
+notifications:
+  webhooks:
+    urls:
+      - https://webhooks.gitter.im/e/188e3c7a5180fd4f2120
+    on_success: always  # options: [always|never|change] default: always
+    on_failure: always  # options: [always|never|change] default: always
+    on_start: never     # options: [always|never|change] default: always

AUTHORS.rst

@@ -0,0 +1,16 @@
+History
+-------
+
+Development lead
+~~~~~~~~~~~~~~~~
+
+The project started in August 2014 by Fernando Nogueira and focused on SMOTE implementation.
+Together with Guillaume Lemaitre, Dayvid Victor, and Christos Aridas, additional under-sampling and over-sampling methods have been implemented as well as major changes in the API to be fully compatible with scikit-learn_.
+
+Contributors
+------------
+
+Refers to GitHub contributors page_.
+
+.. _scikit-learn: http://scikit-learn.org
+.. _page: https://github.com/scikit-learn-contrib/imbalanced-learn/graphs/contributors

CONTRIBUTING.md

@@ -0,0 +1,179 @@
+
+Contributing code
+=================
+
+This guide is adapted from (scikit-learn)[https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md]
+
+How to contribute
+-----------------
+
+The preferred way to contribute to imbalanced-learn is to fork the 
+[main repository](https://github.com/scikit-learn-contrib/imbalanced-learn) on
+GitHub:
+
+1. Fork the [project repository](https://github.com/scikit-learn-contrib/imbalanced-learn):
+   click on the 'Fork' button near the top of the page. This creates
+   a copy of the code under your account on the GitHub server.
+
+2. Clone this copy to your local disk:
+
+        $ git clone git@github.com:YourLogin/imbalanced-learn.git
+        $ cd UnbalancedDataset
+
+3. Create a branch to hold your changes:
+
+        $ git checkout -b my-feature
+
+   and start making changes. Never work in the ``master`` branch!
+
+4. Work on this copy on your computer using Git to do the version
+   control. When you're done editing, do:
+
+        $ git add modified_files
+        $ git commit
+
+   to record your changes in Git, then push them to GitHub with:
+
+        $ git push -u origin my-feature
+
+Finally, go to the web page of your fork of the imbalanced-learn repo,
+and click 'Pull request' to send your changes to the maintainers for
+review. This will send an email to the committers.
+
+(If any of the above seems like magic to you, then look up the 
+[Git documentation](http://git-scm.com/documentation) on the web.)
+
+Contributing Pull Requests
+--------------------------
+
+It is recommended to check that your contribution complies with the
+following rules before submitting a pull request:
+
+-  Follow the
+   [coding-guidelines](http://scikit-learn.org/dev/developers/contributing.html#coding-guidelines)
+   as for scikit-learn.
+
+-  When applicable, use the validation tools and other code in the
+   `sklearn.utils` submodule.  A list of utility routines available
+   for developers can be found in the
+   [Utilities for Developers](http://scikit-learn.org/dev/developers/utilities.html#developers-utils)
+   page.
+
+-  If your pull request addresses an issue, please use the title to describe
+   the issue and mention the issue number in the pull request description to
+   ensure a link is created to the original issue.
+
+-  All public methods should have informative docstrings with sample
+   usage presented as doctests when appropriate.
+
+-  Please prefix the title of your pull request with `[MRG]` if the
+   contribution is complete and should be subjected to a detailed review.
+   Incomplete contributions should be prefixed `[WIP]` to indicate a work
+   in progress (and changed to `[MRG]` when it matures). WIPs may be useful
+   to: indicate you are working on something to avoid duplicated work,
+   request broad review of functionality or API, or seek collaborators.
+   WIPs often benefit from the inclusion of a
+   [task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
+   in the PR description.
+
+-  All other tests pass when everything is rebuilt from scratch. On
+   Unix-like systems, check with (from the toplevel source folder):
+
+        $ make
+
+-  When adding additional functionality, provide at least one
+   example script in the ``examples/`` folder. Have a look at other
+   examples for reference. Examples should demonstrate why the new
+   functionality is useful in practice and, if possible, compare it
+   to other methods available in scikit-learn.
+
+-  Documentation and high-coverage tests are necessary for enhancements
+   to be accepted.
+
+-  At least one paragraph of narrative documentation with links to
+   references in the literature (with PDF links when possible) and
+   the example.
+
+You can also check for common programming errors with the following
+tools:
+
+-  Code with good unittest coverage (at least 80%), check with:
+
+        $ pip install nose coverage
+        $ nosetests --with-coverage path/to/tests_for_package
+
+-  No pyflakes warnings, check with:
+
+        $ pip install pyflakes
+        $ pyflakes path/to/module.py
+
+-  No PEP8 warnings, check with:
+
+        $ pip install pep8
+        $ pep8 path/to/module.py
+
+-  AutoPEP8 can help you fix some of the easy redundant errors:
+
+        $ pip install autopep8
+        $ autopep8 path/to/pep8.py
+
+Filing bugs
+-----------
+We use Github issues to track all bugs and feature requests; feel free to
+open an issue if you have found a bug or wish to see a feature implemented.
+
+It is recommended to check that your issue complies with the
+following rules before submitting:
+
+-  Verify that your issue is not being currently addressed by other
+   [issues](https://github.com/scikit-learn-contrib/imbalanced-learn/issues?q=)
+   or [pull requests](https://github.com/scikit-learn-contrib/imbalanced-learn/pulls?q=).
+
+-  Please ensure all code snippets and error messages are formatted in
+   appropriate code blocks.
+   See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
+
+-  Please include your operating system type and version number, as well
+   as your Python, scikit-learn, numpy, and scipy versions. This information
+   can be found by runnning the following code snippet:
+
+   ```python
+   import platform; print(platform.platform())
+   import sys; print("Python", sys.version)
+   import numpy; print("NumPy", numpy.__version__)
+   import scipy; print("SciPy", scipy.__version__)
+   import sklearn; print("Scikit-Learn", sklearn.__version__)
+   ```
+
+-  Please be specific about what estimators and/or functions are involved
+   and the shape of the data, as appropriate; please include a
+   [reproducible](http://stackoverflow.com/help/mcve) code snippet
+   or link to a [gist](https://gist.github.com). If an exception is raised,
+   please provide the traceback.
+
+Documentation
+-------------
+
+We are glad to accept any sort of documentation: function docstrings,
+reStructuredText documents (like this one), tutorials, etc.
+reStructuredText documents live in the source code repository under the
+doc/ directory.
+
+You can edit the documentation using any text editor and then generate
+the HTML output by typing ``make html`` from the doc/ directory.
+Alternatively, ``make`` can be used to quickly generate the
+documentation without the example gallery. The resulting HTML files will
+be placed in _build/html/ and are viewable in a web browser. See the
+README file in the doc/ directory for more information.
+
+For building the documentation, you will need
+[sphinx](http://sphinx.pocoo.org/),
+[matplotlib](http://matplotlib.sourceforge.net/), and
+[pillow](http://pillow.readthedocs.org/en/latest/).
+
+When you are writing documentation, it is important to keep a good
+compromise between mathematical and algorithmic details, and give
+intuition to the reader on what the algorithm does. It is best to always
+start with a small paragraph with a hand-waving explanation of what the
+method does to the data and a figure (coming from an example)
+illustrating it.

LICENSE.md → LICENSE

@@ -1,6 +1,8 @@
 The MIT License (MIT)
 
-Copyright (c) 2014 Fernando M. F. Nogueira
+Copyright (c) 2014 Fernando M. F. Nogueira,
+                   Guillaume Lemaitre,
+                   Dayvid Victor
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

MANIFEST.in

@@ -0,0 +1,7 @@
+
+recursive-include doc *
+recursive-include examples *
+include AUTHORS.rst
+include CONTRIBUTING.ms
+include LICENSE
+include README.rst

Makefile

@@ -11,16 +11,20 @@ clean:
 	rm -rf coverage
 	rm -rf dist
 	rm -rf build
+	rm -rf doc/auto_examples
+	rm -rf doc/generated
+	rm -rf doc/modules
+	rm -rf examples/.ipynb_checkpoints
 
 test:
-	$(NOSETESTS) -s -v unbalanced_dataset
-
-# doctest:
-# 	$(PYTHON) -c "import unbalanced_dataset, sys, io; sys.exit(unbalanced_dataset.doctest_verbose())"
+	$(NOSETESTS) -s -v imblearn
 
 coverage:
-	$(NOSETESTS) unbalanced_dataset -s -v --with-coverage --cover-package=unbalanced_dataset
+	$(NOSETESTS) imblearn -s -v --with-coverage --cover-package=imblearn
 
 html:
 	conda install -y sphinx sphinx_rtd_theme numpydoc
 	export SPHINXOPTS=-W; make -C doc html
+
+conda:
+	conda-build conda-recipe