TabICL-Survival: A Tabular Foundation Model for Survival In-Context Learning
This repo is an adaptation of "TabICL: A Tabular Foundation Model for In-Context Learning on Large Data" for survival analysis. It is trained using Cox negative log likelihood.
Installation
Option 1: Installing tabicl-survival from PyPI
pip install tabicl-survival
Option 2: Installing tabicl-survival from the local clone
cd tabicl
pip install -e .
Option 3: Installing tabicl-survival directly from the git remote
pip install git+https://github.com/taltstidl/tabicl-survival.git
Basic Usage
from tabicl import TabICLSurver
surv = TabICLSurver()
surv.fit(X_train, y_train) # this is cheap
surv.predict(X_test) # in-context learning happens here
The code above will automatically download the pre-trained checkpoint (~325MB) from Hugging Face Hub on first use and choose a GPU if available. It supports datasets with up to 1,024 samples and 100 features at the moment.
Contributors
For the original TabICL implementation
For the adapted TabICL-Surival implementation
1 maintainer
Metadata
1 maintainer
Release files for tabicl-survival 0.1.0
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Source distribution (sdist)
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| tabicl_survival-0.1.0.tar.gz | 93.5 kB | Details |
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|---|---|---|---|---|
| tabicl_survival-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 207.2 kB
Release files / tabicl_survival-0.1.0.tar.gz
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