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A set of python modules for machine learning and data mining

Project description

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SGD-based online learning with scikit-learn with the ability to add or remove new classes.

This branch is forked off of scikit-learn. The original SGD and Passive Aggressive classifier is changed to support addition and deletion of new classes. The changes are kinda hacky to YMMV.

Here is a description of the changes:

  1. We now allow the partial_fit call to contain new classes in every round of online training.
  2. Also added the ability to remove classes.
  3. As a consequence, we have removed the ability to pre-specify classes and class_weights.
  4. Also removed all the regressors.
  5. As a consequence of this change, we now treat the binary classification problem also a multi class problem with two different label sets.
  6. Removed all tests with class weights and binary classification.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 2.7 or >= 3.3)
  • NumPy (>= 1.6.1)
  • SciPy (>= 0.9)

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries for known issues.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn

or conda:

conda install scikit-learn

The documentation includes more detailed installation instructions.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):

nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing:

C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

Communication

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn

Project details


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