This repo provides the implementation of SurvTRACE for survival analysis. It is easy to use with only the following codes:
from survtrace.dataset import load_data
from survtrace.model import SurvTraceSingle
from survtrace import Evaluator
from survtrace import Trainer
from survtrace import STConfig
# use METABRIC dataset
STConfig['data'] = 'metabric'
df, df_train, df_y_train, df_test, df_y_test, df_val, df_y_val = load_data(STConfig)
# initialize model
model = SurvTraceSingle(STConfig)
# execute training
trainer = Trainer(model)
trainer.fit((df_train, df_y_train), (df_val, df_y_val))
# evaluating
evaluator = Evaluator(df, df_train.index)
evaluator.eval(model, (df_test, df_y_test))
print("done!")Please refer to experiment_metabric.ipynb and experiment_support.ipynb !
Use our pre-saved conda environment!
conda env create --name survtrace --file=survtrace.yml
conda activate survtraceor try to install from the requirement.txt
pip3 install -r requirements.txt-
Go to https://seer.cancer.gov/data/ to ask for data request from SEER following the guide there.
-
After complete the step one, we should have the following seerstat software for data access. Open it and sign in with the username and password sent by seer.
- Use seerstat to open the ./data/seer.sl file, we shall see the following.
Click on the 'excute' icon to request from the seer database. We will obtain a csv file.
-
move the csv file to ./data/seer_raw.csv, then run the python script process_seer.py, as
python process_seer.py
we will obtain the processed seer data named seer_processed.csv.
- single event survival analysis
- competing events survival analysis
- multi-task learning
- automatic hyperparameter grid-search
@article{wang2021survtrace,
title={Surv{TRACE}: Transformers for Survival Analysis with Competing Events},
author={Zifeng Wang and Jimeng Sun},
year={2021},
eprint={2110.00855},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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