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/*
* Long running tests that ensure that scrm produces correct results
* in various settings.
*/
#pragma GCC diagnostic ignored "-Wwrite-strings"
#include <cppunit/TestCase.h>
#include <cppunit/extensions/HelperMacros.h>
#include <valarray>
#include <cmath>
#include "../../src/forest.h"
#include "../../src/param.h"
#include "../../src/random/mersenne_twister.h"
class TestAlgorithm : public CppUnit::TestCase {
CPPUNIT_TEST_SUITE( TestAlgorithm );
CPPUNIT_TEST( testInitialTree );
CPPUNIT_TEST( testARG );
CPPUNIT_TEST( testPruning );
CPPUNIT_TEST( testMigration );
CPPUNIT_TEST( testSizeChange );
CPPUNIT_TEST( testGrowth );
CPPUNIT_TEST( testSplit );
CPPUNIT_TEST( testMerge );
CPPUNIT_TEST_SUITE_END();
private:
MersenneTwister *rg;
void testTree(Model &model, size_t replicates,
double tmrca_mean, double tmrca_sd,
double tree_length_mean, double tree_length_sd) {
double tmrca[6] = { 0 }, tree_length[6] = { 0 };
std::cout << "." << std::flush;
for (size_t i = 0; i < replicates; ++i) {
// Check initial tree
Forest forest = Forest(&model, this->rg);
forest.buildInitialTree();
tmrca[0] += forest.getTMRCA(true);
tree_length[0] += forest.getLocalTreeLength(true);
// Check after recombinations
if (model.recombination_rate() > 0) {
for (size_t j = 1; j <= 5; j++) {
while (forest.next_base() < j*5) {
forest.sampleNextGenealogy();
}
tmrca[j] += forest.getTMRCA(true);
tree_length[j] += forest.getLocalTreeLength(true);
}
}
}
// Allow an relative error of 2.5%. It would be nice to calculate
// standard errors, but there's nothing in the std and I'm to lazy to
// implement very it myself...
for (int i = 0; i <= 5; ++i) {
if (i > 0 && tmrca[i] == 0 && tree_length[i] == 0) continue;
tmrca[i] /= replicates;
double SE = tmrca_sd / sqrt(replicates);
if (tmrca[i] < tmrca_mean - 4 * SE || tmrca_mean + 4 * SE < tmrca[i]) {
std::cout << std::endl
<< "TMRCA outside expected range. Observed: " << tmrca[i]
<< " Expected: " << tmrca_mean << std::endl;
CPPUNIT_ASSERT( false );
}
tree_length[i] /= replicates;
SE = tree_length_sd / sqrt(replicates);
if (tree_length[i] < tree_length_mean - 4 * SE || tree_length_mean + 4 * SE < tree_length[i]) {
std::cout << std::endl
<< "Local Tree Length outside expected range. Observed: " << tree_length[i]
<< " Expected: " << tree_length_mean << std::endl;
CPPUNIT_ASSERT( false );
}
}
}
public:
void setUp() {
rg = new MersenneTwister(78361);
}
void tearDown() {
delete rg;
}
void testInitialTree() {
Model model = Model(5);
model.setRecombinationRate(0);
testTree(model, 10000, 0.8, 0.53, 2.08, 1.19);
model = Model(10);
model.setRecombinationRate(0);
testTree(model, 10000, 0.9, 0.53, 2.83, 1.24);
model = Model(20);
model.setRecombinationRate(0);
testTree(model, 10000, 0.95, 0.54, 3.55, 1.26);
}
void testARG() {
Model model = Model(5);
model.setRecombinationRate(1, false, true);
testTree(model, 10000, 0.8, 0.53, 2.08, 1.19);
model = Model(10);
model.setRecombinationRate(1, false, true);
testTree(model, 10000, 0.9, 0.53, 2.83, 1.24);
model = Model(20);
model.setRecombinationRate(1, false, true);
testTree(model, 10000, 0.95, 0.54, 3.55, 1.26);
}
void testPruning() {
Model model = Model(10);
model.setRecombinationRate(1, false, true);
model.set_window_length_seq(0);
testTree(model, 10000, 0.9, 0.53, 2.83, 1.24);
model.set_window_length_seq(5);
testTree(model, 5000, 0.9, 0.53, 2.83, 1.24);
model.set_window_length_seq(10);
testTree(model, 5000, 0.9, 0.53, 2.83, 1.24);
model.disable_approximation();
model.set_window_length_rec(0);
testTree(model, 10000, 0.9, 0.53, 2.83, 1.24);
model.set_window_length_rec(5);
testTree(model, 5000, 0.9, 0.53, 2.83, 1.24);
model.set_window_length_rec(10);
testTree(model, 5000, 0.9, 0.53, 2.83, 1.24);
}
void testMigration() {
Model model = Model(0);
model.setRecombinationRate(1, false, true);
model.set_population_number(2);
std::vector<size_t> sample_size;
sample_size.push_back(7);
sample_size.push_back(3);
model.addSampleSizes(0.0, sample_size);
model.addSymmetricMigration(0.0, 0.5, true, true);
model.finalize();
testTree(model, 2000, 2.76, 1.79, 7.82, 3.86);
model = Model(0);
model.setRecombinationRate(1, false, true);
model.set_population_number(2);
model.addSampleSizes(0.0, sample_size);
model.addSymmetricMigration(0.0, 0.5, true, true);
model.addSymmetricMigration(0.3, 1.1, true, true);
model.set_window_length_seq(5);
model.finalize();
testTree(model, 2000, 2.24, 1.36, 6.73, 3.04);
model = Model(0);
char *argv[] = { "scrm", "20", "10", "-I", "2", "10", "10", "-ma", "x", "5", "7", "x" };
model = Param(12, argv).parse();
testTree(model, 2000, 1.93, 1.09, 7.24, 2.56);
}
void testSizeChange() {
Model model = Model(0);
model = Param("10 1 -r 1 100 -I 3 3 3 4 0.5 -eN 0.1 0.05 -eN 0.2 0.5").parse();
testTree(model, 1000, 3.75, 2.68, 9.31, 5.67);
model.set_window_length_seq(5);
testTree(model, 1000, 3.75, 2.68, 9.31, 5.67);
}
void testGrowth() {
Model model = Param("10 30 -r 50 50 -G 5").parse();
testTree(model, 2500, 0.321, 0.089, 1.31, 0.28);
model = Param("10 30 -r 50 50 -G -0.5 -eG 0.75 2 -l 5").parse();
testTree(model, 2500, 0.918, 0.38, 2.95, 1.00);
model = Param("4 30 -r 50 50 -G -2.5 -eN 1 0.25 -eG 2 0.0 -eN 2.5 0.25 -l 5").parse();
testTree(model, 1000, 0.964, 0.34, 2.48, 0.97);
}
void testSplit() {
Model model = Model(0);
model.setLocusLength(100);
model.setRecombinationRate(1, false, true);
model.set_population_number(2);
std::vector<size_t> sample_size;
sample_size.push_back(7);
sample_size.push_back(3);
model.addSampleSizes(0.0, sample_size);
model.addSymmetricMigration(0.0, 0.5, true, true);
model.addSingleMigrationEvent(1.0, 1, 0, 1.0, true);
model.addSymmetricMigration(1.0, 0.0, true, true);
model.finalize();
testTree(model, 2500, 1.51, 0.55, 5.20, 1.44);
model = Param("15 5 -r 50 50 -I 3 7 3 5 0.5 -ej 0.5 3 2 -ej 1.0 2 1").parse();
testTree(model, 2500, 1.60, 0.54, 7.29, 1.48);
model.set_window_length_seq(5);
testTree(model, 2500, 1.60, 0.54, 7.29, 1.48);
}
void testMerge() {
Model model = Param("20 10 -r 50 50 -I 2 10 10 1.5 -es 1.6 2 0.5 -eM 2.0 1").parse();
testTree(model, 1000, 2.88, 2.26, 9.36, 4.87);
model.set_window_length_seq(5);
testTree(model, 1000, 2.88, 2.26, 9.36, 4.87);
model = Param("20 10 -r 50 50 -I 2 10 10 1.5 -es 0.9 1 0.8 -es 1.6 2 0.5 -eM 2.0 1").parse();
testTree(model, 1000, 3.82, 3.32, 11.39, 7.09);
model.set_window_length_seq(5);
testTree(model, 1000, 3.82, 3.32, 11.39, 7.09);
}
};
//Uncomment this to activate the test
CPPUNIT_TEST_SUITE_REGISTRATION( TestAlgorithm );
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