FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
Authors: Jiachang Liu, Rui Zhang, Cynthia Rudin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we verify the computational efficiency of our methods. As a direct application, we show how our optimization methods can be used to solve the cardinality-constrained CPH problem, producing very sparse high-quality models that were not previously practical to construct. ... We test the effectiveness of our optimization methods on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Jiachang Liu1, Rui Zhang2, Cynthia Rudin2 1Cornell University, 2Duke University |
| Pseudocode | No | The paper describes its methods verbally in text and mathematical derivations in the appendix, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementations of Fast Survival discussed in this paper are available at https://github.com/jiachangliu/Fast Survival. |
| Open Datasets | Yes | Flchain: We use the dataset from the Scikit-Survival [56] package. The Git Hub link to this dataset is https://github.com/sebp/scikit-survival/tree/master/sksurv/datasets/data. The license of this package is GPL-3. ... We have a summary of datasets for experiments in Table 1. |
| Dataset Splits | Yes | We perform 5-fold cross validation and report the mean and standard deviation of different metrics on both the training and test sets. ... We ran 5-fold cross-validation (random seed 0) on the following datasets: Dialysis, Flchain, Kickstarter1, Employee Attrition, Synthetic High Corr High Dim1, Synthetic High Corr High Dim2, Synthetic High Corr High Dim3. |
| Hardware Specification | Yes | All experiments were run on the Intel(R) Xeon(R) CPU E5-2680 v3 Processor, 2.50GHz. We set the memory limit to be 100GB. |
| Software Dependencies | Yes | Abess: ... We used the Cox model in abess python package Version 0.4.6. ... Sksurv Coxnet: ... scikit-survival version-0.20.0 (https: //scikit-survival.readthedocs.io/en/stable/index.html). |
| Experiment Setup | Yes | For each dataset, we ran algorithms with different configurations and evaluated fitted models with metrics described in Appendix C.2: Abess: We ran this algorithm with 30 different configurations: support size, k, ranging from 1 to 30, forcing the number of non-zero coefficients in the Cox model to be exact k. We set primary_model_fit_max_iter to be 20, approximate_Newton to be False. All other parameters were set to the default. |