README.md

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license: cc-by-4.0
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TITLE: "Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study"

AUTHORS:
- PETR PHILONENKO, Ph.D. in Computer Science;
- SERGEY POSTOVALOV, D.Sc. in Computer Science.

This dataset is a supplement to the github-project published in the https://github.com/pfilonenko/ML_for_TwoSampleTesting. This dataset contains following files:
1) **two_sample_problem_dataset.tsv.gz** is a raw data. This file must be located in the "data/1_raw/";
2) **sample_train.tsv.gz** and **sample_simulation.tsv.gz** are train and test samples splited from the **two_sample_problem_dataset.tsv.gz**. These files must be located in the "data/2_samples/";
3) **dataset_with_ML_pred.tsv.gz** is the test sample supplemented by the predictions of the proposed ML-methods. This file must be located in "data/3_dataset_with_ML_pred/".

In these files there are following fields:
- **sample** is a sample type (train, val, test);
- **H0_H1** is a true hypothesis (H0 or H1);
- **Hi** is an alternative hypothesis (H01-H09, H11-H19 or H21-H29);
- **n1** is the size of sample 1;
- **n2** is the size of sample 2;
- **real_perc1** is an actual censoring rate of sample 1;
- **real_perc2** is an actual censoring rate of sample 2;
- **perc** is the set censoring rate for the samples 1 and 2;

Test statistics of classical two-sample tests under right-censored data:
- **Peto_test** 
- **Gehan_test** 
- **logrank_test** 
- **CoxMantel_test** 
- **BN_GPH_test** 
- **BN_MCE_test** 
- **BN_SCE_test** 
- **Q_test** 
- **MAX_Value_test** 
- **MIN3_test** 
- **WLg_logrank_test** 
- **WLg_TaroneWare_test** 
- **WLg_Breslow_test** 
- **WLg_PetoPrentice_test** 
- **WLg_Prentice_test** 
- **WKM_test**

Test statistics of the proposed ML-based methods:
- **CatBoost_test** 
- **XGBoost_test** 
- **LightAutoML_test** 
- **SKLEARN_RF_test** 
- **SKLEARN_LogReg_test** 
- **SKLEARN_GB_test**