guanrank 0.1.1

GuanRank algorithm in Python

guanrank

Python implementation of the GuanRank hazard ranking algorithm. Assigns a continuous hazard rank to each subject in a survival dataset — higher rank means higher risk of earlier event. Ranks can be used as regression targets for machine learning models in place of raw survival times.

Citation

Huang et al. (2017). Complete hazard ranking to analyze right-censored data: An ALS survival study. PLOS Comp Bio, 15(1), 41–51. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005887

Installation

pip install guanrank
# or with uv
uv add guanrank

Usage

Functional API

from guanrank import guanrank

T = [5.0, 3.0, 8.0, 3.0]  # time-to-event or censoring times
E = [1,   1,   0,   0  ]  # 1 = event, 0 = censored

ranks = guanrank(T, E)

Sklearn-style API (train/test split)

from guanrank import GuanRank

gr = GuanRank()
train_ranks = gr.fit_transform(T_train, E_train)
test_ranks = gr.transform(T_test, E_test)

fit stores the training cohort's Kaplan-Meier curve; transform scores new subjects by comparing them against the training cohort.