README.md

---
license: apache-2.0
---

# 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.

The paper can be downloaded [here](https://arxiv.org/abs/2409.08201).

# 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.

**Contents**
- [About](#about)
- [Citing](#citing)
- [Repository](#repository)
- [Fields](#fields)
- [Simulation](#simulation)
    - [main.cpp](#maincpp)
    - [simulation_for_machine_learning.h](#simulation_for_machine_learningh)

# Citing
~~~
@misc {petr_philonenko_2024,
	author       = { {Petr Philonenko} },
	title        = { ML_for_TwoSampleTesting (Revision a4ae672) },
	year         = 2024,
	url          = { https://huggingface.co/datasets/pfilonenko/ML_for_TwoSampleTesting },
	doi          = { 10.57967/hf/2978 },
	publisher    = { Hugging Face }
}
~~~

# Repository

The files of this dataset have following structure:
~~~
data
├── 1_raw
│   └── two_sample_problem_dataset.tsv.gz    (121,986,000 rows)
├── 2_samples
│   ├── sample_train.tsv.gz                   (24,786,000 rows)
│   └── sample_simulation.tsv.gz              (97,200,000 rows)
└── 3_dataset_with_ML_pred
    └── dataset_with_ML_pred.tsv.gz           (97,200,000 rows)
~~~

- **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/_

# Fields
In these files, there are following fields:

1) PARAMETERS OF SAMPLE SIMULATION
- **iter** is an iteration number of the Monte Carlo replication (in total, 37650);
- **sample** is a type of the sample (train, val, test). This field is used to split dataset into train-validate-test samples for ML-model training;
- **H0_H1** is a true hypothesis: if **H0**, then samples X1 and X2 were simulated under S1(t)=S2(t); if **H1**, then samples X1 and X2 were simulated under S1(t)≠S2(t);
- **Hi** is an alternative (H01-H09, H11-H19, or H21-H29) with competing hypotheses S1(t) and S2(t). Detailed description of these alternatives can be found 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 the sample 1;
- **real_perc2** is an actual censoring rate of the sample 2;

2) STATISTICS OF CLASSICAL TWO-SAMPLE TESTS
- **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 Gehan’s Generalized Wilcoxon test;
- **logrank_test** is a statistic of the logrank test;
- **CoxMantel_test** is a statistic of the Cox-Mantel test;
- **BN_GPH_test** is a statistic of the Bagdonavičius-Nikulin test (Generalized PH model);
- **BN_MCE_test** is a statistic of the Bagdonavičius-Nikulin test (Multiple Crossing-Effect model);
- **BN_SCE_test** is a statistic of the Bagdonavičius-Nikulin test (Single Crossing-Effect model);
- **Q_test** is a statistic of the Q-test;
- **MAX_Value_test** is a statistic of the Maximum Value test;
- **MIN3_test** is a statistic of the MIN3 test;
- **WLg_logrank_test** is a statistic of the Weighted Logrank test (weighted function: 'logrank');
- **WLg_TaroneWare_test** is a statistic of the Weighted Logrank test (weighted function: 'Tarone-Ware');
- **WLg_Breslow_test** is a statistic of the Weighted Logrank test (weighted function: 'Breslow');
- **WLg_PetoPrentice_test** is a statistic of the Weighted Logrank test (weighted function: 'Peto-Prentice');
- **WLg_Prentice_test** is a statistic of the Weighted Logrank test (weighted function: 'Prentice');
- **WKM_test** is a statistic of the Weighted Kaplan-Meier test;

3) STATISTICS OF THE PROPOSED ML-METHODS FOR TWO-SAMPLE PROBLEM
- **CatBoost_test** is a statistic of the proposed ML-method based on the CatBoost framework;
- **XGBoost_test** is a statistic of the proposed ML-method based on the XGBoost framework;
- **LightAutoML_test** is a statistic of the proposed ML-method based on the LightAutoML (LAMA) framework;
- **SKLEARN_RF_test** is a statistic of the proposed ML-method based on Random Forest (implemented in sklearn);
- **SKLEARN_LogReg_test** is a statistic of the proposed ML-method based on Logistic Regression (implemented in sklearn);
- **SKLEARN_GB_test** is a statistic of the proposed ML-method based on Gradient Boosting Machine (implemented in sklearn).

# Simulation

For this dataset, the full source code (C++) is available [here](https://github.com/pfilonenko/ML_for_TwoSampleTesting/tree/main/dataset/simulation). 
It makes possible to reproduce and extend the simulation by the Monte Carlo method. Here, we present two fragments of the source code (**main.cpp** and **simulation_for_machine_learning.h**) which can help to understand the main steps of the simulation process.
### main.cpp
```C++
#include"simulation_for_machine_learning.h"

// Select two-sample tests
vector<HomogeneityTest*> AllTests()
{
	vector<HomogeneityTest*> D;
	
	// ---- Classical Two-Sample tests for Uncensored Case ----
	//D.push_back( new HT_AndersonDarlingPetitt );
	//D.push_back( new HT_KolmogorovSmirnovTest );
	//D.push_back( new HT_LehmannRosenblatt );
	
	// ---- Two-Sample tests for Right-Censored Case ----
	D.push_back( new HT_Peto );
	D.push_back( new HT_Gehan );
	D.push_back( new HT_Logrank );
	
	D.push_back( new HT_BagdonaviciusNikulinGeneralizedCox );
	D.push_back( new HT_BagdonaviciusNikulinMultiple );
	D.push_back( new HT_BagdonaviciusNikulinSingle );

	D.push_back( new HT_QTest );			//Q-test
	D.push_back( new HT_MAX );				//Maximum Value test
	D.push_back( new HT_SynthesisTest );	//MIN3 test
	
	D.push_back( new HT_WeightedLogrank("logrank") );
	D.push_back( new HT_WeightedLogrank("Tarone–Ware") );
	D.push_back( new HT_WeightedLogrank("Breslow") );
	D.push_back( new HT_WeightedLogrank("Peto–Prentice") );
	D.push_back( new HT_WeightedLogrank("Prentice") );
	
	D.push_back( new HT_WeightedKaplanMeyer );
		
	return D;
}

// Example of two-sample testing using this code
void EXAMPLE_1(vector<HomogeneityTest*> &D)
{
	// load the samples
	Sample T1(".//samples//1Chemotherapy.txt");
	Sample T2(".//samples//2Radiotherapy.txt");

	// two-sample testing through selected tests
	for(int j=0; j<D.size(); j++)
	{
		char test_name[512];
		D[j]->TitleTest(test_name);
		

		double Sn = D[j]->CalculateStatistic(T1, T2);
		double pvalue = D[j]->p_value(T1, T2, 27000);  // 27k in accodring to the Kolmogorov's theorem => simulation error MAX||G(S|H0)-Gn(S|H0)|| <= 0.01

		printf("%s\n", &test_name);
		printf("\t Sn: %lf\n", Sn);
		printf("\t pv: %lf\n", pvalue);
		printf("--------------------------------");
	}
}

// Example of the dataset simulation for the proposed ML-method
void EXAMPLE_2(vector<HomogeneityTest*> &D)
{
	// Run dataset (train or test sample) simulation (results in ".//to_machine_learning_2024//")
	simulation_for_machine_learning sm(D);
}

// init point
int main()
{
	// Set the number of threads
	int k = omp_get_max_threads() - 1;
	omp_set_num_threads( k );

	// Select two-sample tests
	auto D = AllTests();
	
	// Example of two-sample testing using this code
	EXAMPLE_1(D);

	// Example of the dataset simulation for the proposed ML-method
	EXAMPLE_2(D);

	// Freeing memory
	ClearMemory(D);
	
	printf("The mission is completed.\n");
	return 0;
}
```
### simulation_for_machine_learning.h
```C++
#ifndef simulation_for_machine_learning_H
#define simulation_for_machine_learning_H

#include"HelpFucntions.h"

// Object of the data simulation for training of the proposed ML-method
class simulation_for_machine_learning{
	private:
		// p-value computation using the Test and Test Statistic (Sn)
		double pvalue(double Sn, HomogeneityTest* Test)
		{
			auto f = Test->F( Sn );
			double pv = 0;
			if( Test->TestType().c_str() == "right" )
				pv = 1.0 - f;
			else
				if( Test->TestType().c_str() == "left" )
					pv = f;
				else    // "double"
					pv = 2.0*min( f, 1-f );
			return pv;
		}

		// Process of simulation
		void Simulation(int iter, vector<HomogeneityTest*> &D, int rank, mt19937boost Gw)
		{
			// preparation the file to save
			char file_to_save[512];
			sprintf(file_to_save,".//to_machine_learning_2024//to_machine_learning[rank=%d].csv", rank);

			// if it is the first iteration, the head of the table must be read
			if( iter == 0 )
			{
				FILE *ou = fopen(file_to_save,"w");
				fprintf(ou, "num;H0/H1;model;n1;n2;perc;real_perc1;real_perc2;");
				for(int i=0; i<D.size(); i++)
				{
					char title_of_test[512];
					D[i]->TitleTest(title_of_test);
					fprintf(ou, "Sn [%s];p-value [%s];", title_of_test, title_of_test);
				}
				fprintf(ou, "\n");
				fclose(ou);
			}

			// Getting list of the Alternative Hypotheses (H01 - H27)
			vector<int> H;
			int l = 1;
			for(int i=100; i<940; i+=100)			// Groups of Alternative Hypotheses (I, II, III, IV, V, VI, VII, VIII, IX)
			{
				for(int j=10; j<40; j+=10)			// Alternative Hypotheses in the Group (e.g., H01, H02, H03 into the I and so on)
					//for(int l=1; l<4; l++)		// various families of distribution of censoring time F^C(t)
						H.push_back( 1000+i+j+l );
			}

			// Sample sizes
			vector<int> sample_sizes;
			sample_sizes.push_back( 20 );	// n1 = n2 = 20
			sample_sizes.push_back( 30 );	// n1 = n2 = 30
			sample_sizes.push_back( 50 );	// n1 = n2 = 50
			sample_sizes.push_back( 75 );	// n1 = n2 = 75
			sample_sizes.push_back( 100 );	// n1 = n2 = 100
			sample_sizes.push_back( 150 );	// n1 = n2 = 150
			sample_sizes.push_back( 200 );	// n1 = n2 = 200
			sample_sizes.push_back( 300 );	// n1 = n2 = 300
			sample_sizes.push_back( 500 );	// n1 = n2 = 500
			sample_sizes.push_back( 1000 );	// n1 = n2 = 1000

			// Simulation (Getting H, Simulation samples, Computation of the test statistics & Save to file)
			for(int i = 0; i<H.size(); i++)
			{
				int Hyp = H[i];
		
				if(rank == 0)
					printf("\tH = %d\n",Hyp);

				for(int per = 0; per<51; per+=10)
				{
					// ---- Getting Hi ----
					AlternativeHypotheses H0_1(Hyp,1,0), H0_2(Hyp,2,0);
					AlternativeHypotheses H1_1(Hyp,1,per), H1_2(Hyp,2,per);

					for(int jj=0; jj<sample_sizes.size(); jj++)
					{
						int n = sample_sizes[jj];

						// ---- Simulation samples ----
						//competing hypothesis H0
						Sample A0(*H0_1.D,n,Gw);
						Sample B0(*H0_1.D,n,Gw);
						if( per > 0 )
						{
							A0.CensoredTypeThird(*H1_1.D,Gw);
							B0.CensoredTypeThird(*H1_1.D,Gw);
						}

						//competing hypothesis H1
						Sample A1(*H0_1.D,n,Gw);
						Sample B1(*H0_2.D,n,Gw);
						if( per > 0 )
						{
							A1.CensoredTypeThird(*H1_1.D,Gw);
							B1.CensoredTypeThird(*H1_2.D,Gw);
						}

						// ---- Computation of the test statistics & Save to file ----
						//Sn and p-value computation under H0
						FILE *ou = fopen(file_to_save, "a");
						auto perc1 = A0.RealCensoredPercent();
						auto perc2 = B0.RealCensoredPercent();
						fprintf(ou,"%d;", iter);
						fprintf(ou,"H0;");
						fprintf(ou,"%d;", Hyp);
						fprintf(ou,"%d;%d;", n,n);
						fprintf(ou,"%d;%lf;%lf", per, perc1, perc2);
						for(int j=0; j<D.size(); j++)
						{
							auto Sn_H0 = D[j]->CalculateStatistic(A0, B0);
							auto pv_H0 = 0.0;	// skip computation (it prepares in ML-framework)
							fprintf(ou, ";%lf;0", Sn_H0);
						}
						fprintf(ou, "\n");

						//Sn and p-value computation under H1
						perc1 = A1.RealCensoredPercent();
						perc2 = B1.RealCensoredPercent();
						fprintf(ou,"%d;", iter);
						fprintf(ou,"H1;");
						fprintf(ou,"%d;", Hyp);
						fprintf(ou,"%d;%d;", n,n);
						fprintf(ou,"%d;%lf;%lf", per, perc1, perc2);
						for(int j=0; j<D.size(); j++)
						{
							auto Sn_H1 = D[j]->CalculateStatistic(A1, B1);
							auto pv_H1 = 0.0;  // skip computation (it prepares in ML-framework)
							fprintf(ou, ";%lf;0", Sn_H1);
						}
						fprintf(ou, "\n");
						fclose( ou );
					}
				}
			}
		}

	public:
		// Constructor of the class
		simulation_for_machine_learning(vector<HomogeneityTest*> &D)
		{
			int N = 37650;	// number of the Monte-Carlo replications
			#pragma omp parallel for
			for(int k=0; k<N; k++)
			{
				int rank = omp_get_thread_num();
				auto gen = GwMT19937[rank];
		
				if(rank == 0)
					printf("\r%d", k);

				Simulation(k, D, rank, gen);
			}
		}
};

#endif
```