| Name | attribench JSON |
| Version |
0.1.9
JSON |
| download |
| home_page | |
| Summary | A benchmark for feature attribution techniques |
| upload_time | 2024-03-06 09:36:36 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) [year] [fullname] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
attributions
benchmark
explanation
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
AttriBench: Metrics for Feature Attribution Techniques
======================================================
**AttriBench** is a `Pytorch <https://pytorch.org/>`_-based implementation of
several metrics for the evaluation of feature attribution maps and methods.
AttriBench provides a functional and an object-oriented API for the computation
of these metrics, along with a set of utility functions for the necessary
preparations (e.g. computing attribution maps) as well as for the visualization
of the results.
The **functional API** is generally easier to use, and can be used to get
started quickly if the scale of the evaluation is not too large. The
**object-oriented API** is more flexible and can use multiple GPUs for
evaluation of large datasets.
For more information, see the `documentation <https://attribench.readthedocs.io/>`_.
Installation
------------
AttriBench can be installed from PyPI using pip:
.. code-block:: bash
pip install attribench
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