============ Sparseklearn

Dimensionality reduction for unsupervised machine learning.

Documentation

Documentation is available at https://erickightley.github.io/sparseklearn/.

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Overview

Sparseklearn is a Python package of machine learning algorithms based on dimensionality reduction via random projections. By working on compressed data, Sparseklearn performs standard machine learning tasks more efficiently and uses less memory. Its algorithms are all one-pass, meaning that they only need to access the raw data once. Sparseklearn implements algorithms described in our papers on sparsified k-means and PCA <https://arxiv.org/pdf/1511.00152.pdf>_ and on Gaussian mixtures <https://arxiv.org/abs/1903.04056v2>_.

Installation

It is highly recommended that you install this package in a virtual environment <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/>_. With the virtual environment active, build the C extensions and install the package:

.. code-block:: bash

python setup.py build_ext --inplace
pip install .

To test the installation, run the unit tests:

.. code-block:: bash

pytest

Usage

See :code:examples/ for notebooks of usage examples. You will need Jupyterlab:

.. code-block:: bash

cd examples
pip install -r requirements.txt
jupyter lab