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license: cc-by-4.0
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# Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
- [Petr PHILONENKO](https://orcid.org/0000-0002-6295-4470), Ph.D. in Computer Science;
- [Sergey POSTOVALOV](https://orcid.org/0000-0003-3718-1936), D.Sc. in Computer Science.

# About
This dataset is a supplement to the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting) and paper addressed to solve the two-sample problem under right-censored observations using Machine Learning. 
The problem statement can be formualted as H0: S1(t)=S2(t) versus H: S1(t)≠S_2(t) where S1(t) and S2(t) are survival functions of samples X1 and X2.

This dataset contains the synthetic data simulated by the Monte Carlo method and Inverse Transform Sampling.

# Repository

The files of this dataset has following structure:
~~~
data
├── 1_raw
│   └── two_sample_problem_dataset.tsv.gz
├── 2_samples
│   ├── sample_train.tsv.gz
│   └── sample_simulation.tsv.gz
└── 3_dataset_with_ML_pred
    └── dataset_with_ML_pred.tsv.gz
~~~

- **two_sample_problem_dataset.tsv.gz** is a raw simulated data. In the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting), this file must be located in the _ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing/data/1_raw/_;
- **sample_train.tsv.gz** and **sample_simulation.tsv.gz** are train and test samples splited from the **two_sample_problem_dataset.tsv.gz**. In the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting), these files must be located in the _ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing/data/2_samples/_;
- **dataset_with_ML_pred.tsv.gz** is the test sample supplemented by the predictions of the proposed ML-methods. In the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting), this file must be located in the _ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing/data/3_dataset_with_ML_pred/_.

# Dataset & Samples
In these files there are following fields:
- **sample** is a type the sample for machine learning process (train, val, test);
- **H0_H1** is a true hypothesis: **H0** when test statistics simulated have S1(t)=S2(t) and **H1** when test statistics simulated have S1(t)≠S2(t);
- **Hi** is an alternative hypothesis (H01-H09, H11-H19, or H21-H29). Detailed description of these alternatives can be find in the paper;
- **n1** is the size of the sample 1;
- **n2** is the size of the sample 2;
- **perc** is a set (expected) censoring rate for the samples 1 and 2;
- **real_perc1** is an actual censoring rate of sample 1;
- **real_perc2** is an actual censoring rate of sample 2;
- **Peto_test** is a statistic of the Peto and Peto’s Generalized Wilcoxon Test which is computed on two samples under parameters described above;
- **Gehan_test** is a statistic of the ,
- **logrank_test** is a statistic of the ,
- **CoxMantel_test** is a statistic of the ,
- **BN_GPH_test** is a statistic of the ,
- **BN_MCE_test** is a statistic of the ,
- **BN_SCE_test** is a statistic of the ,
- **Q_test** is a statistic of the ,
- **MAX_Value_test** is a statistic of the ,
- **MIN3_test** is a statistic of the ,
- **WLg_logrank_test** is a statistic of the ,
- **WLg_TaroneWare_test** is a statistic of the ,
- **WLg_Breslow_test** is a statistic of the ,
- **WLg_PetoPrentice_test** is a statistic of the ,
- **WLg_Prentice_test** is a statistic of the ,
- **WKM_test** are test statistics of classical two-sample tests under right-censored data;
- **CatBoost_test**,
- **XGBoost_test**,
- **LightAutoML_test**,
- **SKLEARN_RF_test**,
- **SKLEARN_LogReg_test**,
- **SKLEARN_GB_test** are test statistics of the proposed ML-based methods.