Datasets:

pfilonenko
/

ML_for_TwoSampleTesting

This dataset is a supplement to the github repositiry 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, 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, 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, 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.