Skip to main content

cobsurv : Cobra Ensemble for Conditional Survival

Documentation Status

cobsurv

Cobra Ensemble for Conditional Survival are algorithms, designed for survival prediction using proximity information. The k-NN survival, Random Survival Forest, Kernel Survival are some examples of Cobra Ensemble for Conditional Survival. While this package tends to provide those algorithms later, currently the package provides the following algorithms:

  • COBRA Survival

For now other algorithms are taken from scikit-survival and np_survival to provide as a base learner for the ensemble algorithms.

installation

pip install cobsurv

The documentation is available at https://cobsurv.readthedocs.io/en/latest/

Citation

@misc{goswami2023areanorm,
      title={Area-norm COBRA on Conditional Survival Prediction}, 
      author={Rahul Goswami and Arabin Kr. Dey},
      year={2023},
      eprint={2309.00417},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Metadata

Release files for cobsurv 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cobsurv 0.0.1
File Size Uploaded
cobsurv-0.0.1.tar.gz 16.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for cobsurv 0.0.1
File Interpreter ABI Platform
cobsurv-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 33.4 kB

Release files / cobsurv-0.0.1.tar.gz

Download URL cobsurv-0.0.1.tar.gz
Size 16.8 kB
Tags Source
SHA-256 checksum
How to use checksums
24fcd53f14103b4c6cc455e3ae32af477779998106de91ad7457f4a2332ec25e
BLAKE2b-256 checksum
How to use checksums
20885262b32f16aa1a118ad9d11ab858ae7aac42196df6153b076c05adfcb6a1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.18

Release files / cobsurv-0.0.1-py3-none-any.whl

Download URL cobsurv-0.0.1-py3-none-any.whl
Size 16.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8588c240d50314d14658909932d5e95177d52b77ebee02a840e6f22814e2a9ec
BLAKE2b-256 checksum
How to use checksums
97af4bbb10940c728756a11cfa0ae0d2ffd3ab0124496f187731beba56ce0512
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.18

Release history Release notifications | RSS feed

This release

0.0.1 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page