Benchmarking library for generative algorithms
Project description
Cytobench
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:
pip install cytobench
If you use this library in your work please cite: (citation)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file cytobench-0.1.27.tar.gz.
File metadata
- Download URL: cytobench-0.1.27.tar.gz
- Upload date:
- Size: 10.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
660f6bdf29393fe22394405c654429c1af3445740b3eb51ec9c77ce1a5ff6550
|
|
| MD5 |
e8ad00f726879973c54678bdca7d3b81
|
|
| BLAKE2b-256 |
3e576e7ffdfe737743cae1245f4b2d598526e30d18455ff5c879f370b56f461a
|
File details
Details for the file cytobench-0.1.27-py3-none-any.whl.
File metadata
- Download URL: cytobench-0.1.27-py3-none-any.whl
- Upload date:
- Size: 10.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2ad9168faa1542dc9237ce88abfc4b7c50cdb092765918a717f4e4c8f24b9875
|
|
| MD5 |
7e6b74341c5b9e889544f34f15b9f678
|
|
| BLAKE2b-256 |
2bc945f3335a198b95e27318e44cc7b1ecd2af1e1db1a3fb61bc7ad2e74c8055
|