| Name | cytobench JSON |
| Version |
0.1.27
JSON |
| download |
| home_page | None |
| Summary | Benchmarking library for generative algorithms |
| upload_time | 2024-09-20 14:48:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License Copyright (c) 2024 Nicolo' Lazzaro 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 |
benchmarking
generative algorithms
omics data
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
pandas
scipy
scikit-learn
seaborn
matplotlib
tensorflow
tensorflow-probability
python-igraph
leidenalg
anndata
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Cytobench
<img src="https://github.com/lazzaronico/cytobench/blob/main/logo.png" alt="Cytobench Logo" width="400" align="left" style="margin-right: 10px;" />
Welcome to Cytobench, the friendliest benchmarking library in the village!
This repository is meant as a demo implementation of a scoring pipeline to evaluate generative models capabilities of recapitulating target empirical distributions via Pointwise Empirical Distance estimation, as presented in (paper_link). Most of the functionalities are presented in the Jupyter notebooks, which can be run interactively via Colab without having to create a local enviornment.
The code is not meant as a performant implementation, nor has it been protected by proper safety checks: do not use this in production environments!
You can install the latest version via pip:
```bash
pip install cytobench
```
If you use this library in your work please cite: (citation)
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