| Name | coreax JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | Jax coreset algorithms. |
| upload_time | 2025-02-12 13:59:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license |
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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To apply the Apache License to your work, attach the following
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|
| keywords |
coreset
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<img alt="Coreax logo" src="https://raw.githubusercontent.com/gchq/coreax/main/documentation/assets/Logo.svg">
</div>
# Coreax
[](https://github.com/gchq/coreax/actions/workflows/unittests.yml)
[](https://github.com/gchq/coreax/actions/workflows/coverage.yml)
[](https://github.com/gchq/coreax/actions/workflows/pre_commit_checks.yml)
[](https://github.com/pylint-dev/pylint)
[](https://pypi.org/project/coreax)
[](https://pypi.org/project/coreax)
_© Crown Copyright GCHQ_
Coreax is a library for **coreset algorithms**, written in <a href="https://jax.readthedocs.io/en/latest/notebooks/quickstart.html" target="_blank">JAX</a> for fast execution and GPU support.
## About Coresets
For $n$ points in $d$ dimensions, a coreset algorithm takes an $n \times d$ data set and
reduces it to $m \ll n$ points whilst attempting to preserve the statistical properties
of the full data set. The algorithm maintains the dimension of the original data set.
Thus the $m$ points, referred to as the **coreset**, are also $d$-dimensional.
The $m$ points need not be in the original data set. We refer to the special case where
all selected points are in the original data set as a **coresubset**.
Some algorithms return the $m$ points with weights, so that importance can be
attributed to each point in the coreset. The weights, $w_i$ for $i=1,...,m$, are often
chosen from the simplex. In this case, they are non-negative and sum to 1:
$w_i >0$ $\forall i$ and $\sum_{i} w_i =1$.
Please see [the documentation](https://coreax.readthedocs.io/en/latest/quickstart.html) for some in-depth examples.
## Example applications
### Choosing pixels from an image
In the example below, we reduce the original 180x215
pixel image (38,700 pixels in total) to a coreset approximately 20% of this size.
(Left) original image.
(Centre) 8,000 coreset points chosen using Stein kernel herding, with point size a
function of weight.
(Right) 8,000 points chosen randomly.
Run `examples/david_map_reduce_weighted.py` to replicate.
### Video event detection
Here we identify representative frames such that most of the
useful information in a video is preserved.
Run `examples/pounce.py` to replicate.
| Original | Coreset |
|:------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|
|  |  |
# Setup
Install Coreax from PyPI by adding `coreax` to your project dependencies or running
```shell
pip install coreax
```
Coreax uses JAX. It installs the CPU version by default, but if you have a GPU or TPU,
see the
[JAX installation instructions](https://jax.readthedocs.io/en/latest/installation.html)
for options available to take advantage of the power of your system. For example, if you
have an NVIDIA GPU on Linux, add `jax[cuda12]` to your project dependencies or run
```shell
pip install jax[cuda12]
```
There are optional sets of additional dependencies:
* `coreax[test]` is required to run the tests;
* `coreax[example]` contains all dependencies for the example scripts;
* `coreax[benchmark]` is required to run benchmarking;
* `coreax[doc]` is for compiling the Sphinx documentation;
* `coreax[dev]` includes all tools and packages a developer of Coreax might need.
Note that the `test` and `dev` dependencies include `opencv-python-headless`, which is
the headless version of OpenCV and is incompatible with other versions of OpenCV. If you
wish to use an alternative version, remove `opencv-python-headless` and select an
alternative from the
[OpenCV documentation](https://pypi.org/project/opencv-python-headless/).
Should the installation of Coreax fail, you can see the versions used by the Coreax
development team in `uv.lock`. You can transfer these to your own project as follows.
First, [install UV](https://docs.astral.sh/uv/getting-started/installation/). Then,
clone the repo from [GitHub](https://github.com/gchq/coreax). Next, run
```shell
uv export --format requirements-txt
```
which will generate a `requirements.txt`. Install this in your own project before trying
to install Coreax itself,
```shell
pip install -r requirements.txt
pip install coreax
```
# Release cycle
We anticipate two release types: feature releases and security releases. Security
releases will be issued as needed in accordance with the
[security policy](https://github.com/gchq/coreax/security/policy). Feature releases will
be issued as appropriate, dependent on the feature pipeline and development priorities.
# Coming soon
Some features coming soon include:
* Coordinate bootstrapping for high-dimensional data.
* Other coreset-style algorithms, including recombination, as means
to reducing a large dataset whilst maintaining properties of the underlying distribution.
Raw data
{
"_id": null,
"home_page": null,
"name": "coreax",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "coreset",
"author": null,
"author_email": "GCHQ <oss@gchq.gov.uk>",
"download_url": "https://files.pythonhosted.org/packages/b8/d2/4f91826274233fc60a398be55c966b5534110b53e8eb3aabf02f5201b420/coreax-0.4.0.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n <img alt=\"Coreax logo\" src=\"https://raw.githubusercontent.com/gchq/coreax/main/documentation/assets/Logo.svg\">\n</div>\n\n# Coreax\n\n[](https://github.com/gchq/coreax/actions/workflows/unittests.yml)\n[](https://github.com/gchq/coreax/actions/workflows/coverage.yml)\n[](https://github.com/gchq/coreax/actions/workflows/pre_commit_checks.yml)\n[](https://github.com/pylint-dev/pylint)\n[](https://pypi.org/project/coreax)\n[](https://pypi.org/project/coreax)\n\n\n_\u00a9 Crown Copyright GCHQ_\n\nCoreax is a library for **coreset algorithms**, written in <a href=\"https://jax.readthedocs.io/en/latest/notebooks/quickstart.html\" target=\"_blank\">JAX</a> for fast execution and GPU support.\n\n## About Coresets\n\nFor $n$ points in $d$ dimensions, a coreset algorithm takes an $n \\times d$ data set and\nreduces it to $m \\ll n$ points whilst attempting to preserve the statistical properties\nof the full data set. The algorithm maintains the dimension of the original data set.\nThus the $m$ points, referred to as the **coreset**, are also $d$-dimensional.\n\nThe $m$ points need not be in the original data set. We refer to the special case where\nall selected points are in the original data set as a **coresubset**.\n\nSome algorithms return the $m$ points with weights, so that importance can be\nattributed to each point in the coreset. The weights, $w_i$ for $i=1,...,m$, are often\nchosen from the simplex. In this case, they are non-negative and sum to 1:\n$w_i >0$ $\\forall i$ and $\\sum_{i} w_i =1$.\n\nPlease see [the documentation](https://coreax.readthedocs.io/en/latest/quickstart.html) for some in-depth examples.\n\n\n## Example applications\n\n### Choosing pixels from an image\n\nIn the example below, we reduce the original 180x215\npixel image (38,700 pixels in total) to a coreset approximately 20% of this size.\n(Left) original image.\n(Centre) 8,000 coreset points chosen using Stein kernel herding, with point size a\nfunction of weight.\n(Right) 8,000 points chosen randomly.\nRun `examples/david_map_reduce_weighted.py` to replicate.\n\n\n\n\n### Video event detection\n\nHere we identify representative frames such that most of the\nuseful information in a video is preserved.\nRun `examples/pounce.py` to replicate.\n\n| Original | Coreset |\n|:------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|\n|  |  |\n\n\n# Setup\n\nInstall Coreax from PyPI by adding `coreax` to your project dependencies or running\n```shell\npip install coreax\n```\n\nCoreax uses JAX. It installs the CPU version by default, but if you have a GPU or TPU,\nsee the\n[JAX installation instructions](https://jax.readthedocs.io/en/latest/installation.html)\nfor options available to take advantage of the power of your system. For example, if you\nhave an NVIDIA GPU on Linux, add `jax[cuda12]` to your project dependencies or run\n```shell\npip install jax[cuda12]\n```\n\nThere are optional sets of additional dependencies:\n* `coreax[test]` is required to run the tests;\n* `coreax[example]` contains all dependencies for the example scripts;\n* `coreax[benchmark]` is required to run benchmarking;\n* `coreax[doc]` is for compiling the Sphinx documentation;\n* `coreax[dev]` includes all tools and packages a developer of Coreax might need.\n\nNote that the `test` and `dev` dependencies include `opencv-python-headless`, which is\nthe headless version of OpenCV and is incompatible with other versions of OpenCV. If you\nwish to use an alternative version, remove `opencv-python-headless` and select an\nalternative from the\n[OpenCV documentation](https://pypi.org/project/opencv-python-headless/).\n\nShould the installation of Coreax fail, you can see the versions used by the Coreax\ndevelopment team in `uv.lock`. You can transfer these to your own project as follows.\nFirst, [install UV](https://docs.astral.sh/uv/getting-started/installation/). Then,\nclone the repo from [GitHub](https://github.com/gchq/coreax). Next, run\n```shell\nuv export --format requirements-txt\n```\nwhich will generate a `requirements.txt`. Install this in your own project before trying\nto install Coreax itself,\n```shell\npip install -r requirements.txt\npip install coreax\n```\n\n# Release cycle\n\nWe anticipate two release types: feature releases and security releases. Security\nreleases will be issued as needed in accordance with the\n[security policy](https://github.com/gchq/coreax/security/policy). Feature releases will\nbe issued as appropriate, dependent on the feature pipeline and development priorities.\n\n# Coming soon\n\nSome features coming soon include:\n* Coordinate bootstrapping for high-dimensional data.\n* Other coreset-style algorithms, including recombination, as means\nto reducing a large dataset whilst maintaining properties of the underlying distribution.\n",
"bugtrack_url": null,
"license": "\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n \n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n \n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n \n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n \n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n \n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n \n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n \n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n \n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n \n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n \n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n \n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n \n END OF TERMS AND CONDITIONS\n \n APPENDIX: How to apply the Apache License to your work.\n \n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n \n Copyright 2023 GCHQ\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n ",
"summary": "Jax coreset algorithms.",
"version": "0.4.0",
"project_urls": {
"Changelog": "https://github.com/gchq/coreax/blob/main/CHANGELOG.md",
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