tausurv
Survival analysis for Python: nonparametric estimators, regression models, tree ensembles and neural networks behind one interface, with support for competing risks and minimal dependencies.
Documentation: https://scai-bio.github.io/tausurv/
Installation
pip install tausurv
Requires Python 3.12 or newer. Optional extras:
| extra | adds |
|---|---|
tausurv[plot] |
plotting with matplotlib |
tausurv[boost] |
SurvivalBoost, via scikit-learn |
tausurv[tune] |
hyperparameter search and nested cross-validation, via Optuna |
tausurv[deepsurv] |
the four DeepSurv benchmark tables stored as HDF5 |
The neural models in tausurv.nn need PyTorch, which is not installed
automatically because the right build depends on your hardware. Install it
first by following https://pytorch.org/get-started/locally/.
Example
import tausurv as ts
X, time, event = ts.datasets.load_dataset("pbc")
time = time / 365.25 # days -> years
km = ts.nonparametric.kaplan_meier(time, event)
km(5.0) # 5-year survival, about 0.70
covariates = X.select("age", "bili", "albumin").to_numpy()
cox = ts.linear.CoxPH().fit(covariates, time, event)
cox.predict_survival_function(covariates[:3], [1.0, 5.0, 10.0])
What's included
- Nonparametric: Kaplan-Meier, Nelson-Aalen, Aalen-Johansen, censoring distribution.
- Regression: Cox proportional hazards, Fine-Gray, Weibull, log-normal and log-logistic AFT.
- Trees: survival tree, random survival forest, gradient-boosted
SurvivalBoost. - Neural: DeepSurv, DeepHit, DSM, logistic hazard, copula-based and HACSurv models.
- Metrics: Harrell's, Uno's and Antolini's C-index, Brier score and integrated Brier score, time-dependent AUC, calibration.
- Model selection: survival-aware splits, cross-validation, hyperparameter tuning and nested cross-validation.
- Plotting: survival and cumulative-incidence curves with at-risk tables, forest plots, calibration, metrics over time.
- Datasets: about 100 published cohorts that download on first use and are verified against a pinned checksum.
Development
The repository is a uv workspace. Install PyTorch into it with the build for your hardware, then sync:
uv pip install torch --index-url https://download.pytorch.org/whl/cpu
uv sync --all-packages --all-groups
uv run pytest
License
MIT. Developed at Fraunhofer SCAI.
1 maintainer
Metadata
1 maintainer
Release files for tausurv 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tausurv-0.1.0.tar.gz | 216.9 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| tausurv-0.1.0-cp312-abi3-win_amd64.whl | CPython 3.12 | abi3 | Windows x86-64 | Details |
| tausurv-0.1.0-cp312-abi3-musllinux_1_2_x86_64.whl | CPython 3.12 | abi3 | Linux musl 1.2+ x86-64 | Details |
| tausurv-0.1.0-cp312-abi3-musllinux_1_2_aarch64.whl | CPython 3.12 | abi3 | Linux musl 1.2+ ARM64 | Details |
| tausurv-0.1.0-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.12 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| tausurv-0.1.0-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.12 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| tausurv-0.1.0-cp312-abi3-macosx_11_0_arm64.whl | CPython 3.12 | abi3 | macOS 11.0+ ARM64 | Details |
| tausurv-0.1.0-cp312-abi3-macosx_10_12_x86_64.whl | CPython 3.12 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 8.4 MB
Release files / tausurv-0.1.0.tar.gz
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