Metadata-Version: 2.1
Name: survival-datasets
Version: 0.1.4
Summary: Data loader for common datasets in Survival Analysis.
Home-page: https://github.com/thecml/survival-datasets
Author: Christian Marius Lillelund
Author-email: chr1000@gmail.com
Keywords: Survival Analysis,Datasets
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

# survival-datasets

A simple data loader for the most common datasets in Survival Analysis. Currently the following are included: 
* Veterans Lung Cancer (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_veterans_lung_cancer.html)
* German Breast Cancer Study Group (GBSG2) (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_gbsg2.html)
* AIDS dataset (https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.datasets.load_aids.html)
* NHANES (https://shap.readthedocs.io/en/latest/generated/shap.datasets.nhanesi.html)
* SUPPORT Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (from DeepSurv paper, https://arxiv.org/abs/1606.00931)
* METABRIC The Molecular Taxonomy of Breast Cancer International Consortium (from DeepSurv paper, https://arxiv.org/abs/1606.00931)
* WHAS500 Worcester Heart Attack Study (https://scikit-survival.readthedocs.io/en/stable/api/datasets.html)
* FLCHAIN (https://scikit-survival.readthedocs.io/en/stable/api/datasets.html)
* SEER (from Kaggle, https://www.kaggle.com/code/jnegrini/breast-cancer-dataset)

## Requirements

* Python 3.8 or later
* scikit-survival 0.17.2 or later
* pandas 1.4.3 or later
* numpy 1.22.4 or later
* shap 0.41 or later
* pyarrow 11.0 or later

## Installation

Simply install via pip:
```
pip install survival-datasets
```

## Examples

Import the datasets module from the package and load your dataset of choice:
```
from survdata import datasets

if __name__ == "__main__":
    X, y = datasets.load_seer_dataset()
```
