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Add tests to reach 90% coverage

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-08 00:13:59 +02:00
parent 46cb8d30eb
commit 0d6a081d01
13 changed files with 424 additions and 56 deletions
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3
tests/CMakeLists.txt
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@@ -8,10 +8,11 @@ if(ENABLE_TESTING)
${CMAKE_BINARY_DIR}/configured_files/include
)
file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestUtils.cc ${BayesNet_SOURCES})
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestUtils.cc ${BayesNet_SOURCES})
target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain )
add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
add_test(NAME Network COMMAND TestBayesNet "[Network]")
add_test(NAME Node COMMAND TestBayesNet "[Node]")
add_test(NAME Metrics COMMAND TestBayesNet "[Metrics]")
add_test(NAME FeatureSelection COMMAND TestBayesNet "[FeatureSelection]")
add_test(NAME Models COMMAND TestBayesNet "[Models]")

10
tests/TestBayesMetrics.cc
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@@ -32,31 +32,41 @@ TEST_CASE("Metrics Test", "[Metrics]")
};
auto raw = RawDatasets(file_name, true);
bayesnet::Metrics metrics(raw.dataset, raw.featurest, raw.classNamet, raw.classNumStates);
bayesnet::Metrics metricsv(raw.Xv, raw.yv, raw.featurest, raw.classNamet, raw.classNumStates);
SECTION("Test Constructor")
{
REQUIRE(metrics.getScoresKBest().size() == 0);
REQUIRE(metricsv.getScoresKBest().size() == 0);
}
SECTION("Test SelectKBestWeighted")
{
std::vector<int> kBest = metrics.SelectKBestWeighted(raw.weights, true, resultsKBest.at(file_name).first);
std::vector<int> kBestv = metricsv.SelectKBestWeighted(raw.weights, true, resultsKBest.at(file_name).first);
REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
REQUIRE(kBestv.size() == resultsKBest.at(file_name).first);
REQUIRE(kBest == resultsKBest.at(file_name).second);
REQUIRE(kBestv == resultsKBest.at(file_name).second);
}
SECTION("Test Mutual Information")
{
auto result = metrics.mutualInformation(raw.dataset.index({ 1, "..." }), raw.dataset.index({ 2, "..." }), raw.weights);
auto resultv = metricsv.mutualInformation(raw.dataset.index({ 1, "..." }), raw.dataset.index({ 2, "..." }), raw.weights);
REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(raw.epsilon));
REQUIRE(resultv == Catch::Approx(resultsMI.at(file_name)).epsilon(raw.epsilon));
}
SECTION("Test Maximum Spanning Tree")
{
auto weights_matrix = metrics.conditionalEdge(raw.weights);
auto weights_matrixv = metricsv.conditionalEdge(raw.weights);
for (int i = 0; i < 2; ++i) {
auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, i);
auto resultv = metricsv.maximumSpanningTree(raw.featurest, weights_matrixv, i);
REQUIRE(result == resultsMST.at({ file_name, i }));
REQUIRE(resultv == resultsMST.at({ file_name, i }));
}
}
}

27
tests/TestBayesModels.cc
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@@ -1,3 +1,4 @@
#include <type_traits>
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
@@ -98,6 +99,30 @@ TEST_CASE("BoostAODE feature_select CFS", "[Models]")
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE feature_select IWSS", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE feature_select FCBF", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
TEST_CASE("BoostAODE test used features in train note and score", "[Models]")
{
auto raw = RawDatasets("diabetes", true);
@@ -246,7 +271,7 @@ TEST_CASE("SPODELd dataset", "[Models]")
{
auto raw = RawDatasets("iris", false);
auto clf = bayesnet::SPODELd(0);
raw.dataset.to(torch::kFloat32);
// raw.dataset.to(torch::kFloat32);
clf.fit(raw.dataset, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xt, raw.yt);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);

247
tests/TestBayesNetwork.cc
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@@ -2,9 +2,11 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include <string>
#include "TestUtils.h"
#include "bayesnet/network/Network.h"
#include "bayesnet/utils/bayesnetUtils.h"
void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
{
@@ -111,6 +113,22 @@ TEST_CASE("Test Bayesian Network", "[Network]")
net3.fit(raw.Xt, raw.yt, raw.weights, raw.featurest, raw.classNamet, raw.statest);
REQUIRE(net.getStates() == net2.getStates());
REQUIRE(net.getStates() == net3.getStates());
REQUIRE(net.getFeatures() == net2.getFeatures());
REQUIRE(net.getFeatures() == net3.getFeatures());
REQUIRE(net.getClassName() == net2.getClassName());
REQUIRE(net.getClassName() == net3.getClassName());
REQUIRE(net.getNodes().size() == net2.getNodes().size());
REQUIRE(net.getNodes().size() == net3.getNodes().size());
REQUIRE(net.getEdges() == net2.getEdges());
REQUIRE(net.getEdges() == net3.getEdges());
REQUIRE(net.getNumEdges() == net2.getNumEdges());
REQUIRE(net.getNumEdges() == net3.getNumEdges());
REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
REQUIRE(net.getClassNumStates() == net3.getClassNumStates());
REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
REQUIRE(net.getSamples().size(0) == net3.getSamples().size(0));
REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
REQUIRE(net.getSamples().size(1) == net3.getSamples().size(1));
// Check Conditional Probabilities tables
for (int i = 0; i < features.size(); ++i) {
auto feature = features.at(i);
@@ -125,7 +143,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test show")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
net.addNode("C");
@@ -139,7 +156,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test topological_sort")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
net.addNode("C");
@@ -153,7 +169,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test graph")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
net.addNode("C");
@@ -171,7 +186,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test predict")
{
auto net = bayesnet::Network();
buildModel(net, raw.featuresv, raw.classNamev);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
@@ -181,7 +195,6 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test predict_proba")
{
auto net = bayesnet::Network();
buildModel(net, raw.featuresv, raw.classNamev);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
@@ -203,10 +216,230 @@ TEST_CASE("Test Bayesian Network", "[Network]")
}
SECTION("Test score")
{
auto net = bayesnet::Network();
buildModel(net, raw.featuresv, raw.classNamev);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = net.score(raw.Xv, raw.yv);
REQUIRE(score == Catch::Approx(0.97333333).margin(threshold));
}
}
SECTION("Copy constructor")
{
buildModel(net, raw.featuresv, raw.classNamev);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
auto net2 = bayesnet::Network(net);
REQUIRE(net.getFeatures() == net2.getFeatures());
REQUIRE(net.getEdges() == net2.getEdges());
REQUIRE(net.getNumEdges() == net2.getNumEdges());
REQUIRE(net.getStates() == net2.getStates());
REQUIRE(net.getClassName() == net2.getClassName());
REQUIRE(net.getClassNumStates() == net2.getClassNumStates());
REQUIRE(net.getSamples().size(0) == net2.getSamples().size(0));
REQUIRE(net.getSamples().size(1) == net2.getSamples().size(1));
REQUIRE(net.getNodes().size() == net2.getNodes().size());
for (const auto& feature : net.getFeatures()) {
auto& node = net.getNodes().at(feature);
auto& node2 = net2.getNodes().at(feature);
REQUIRE(node->getName() == node2->getName());
REQUIRE(node->getChildren().size() == node2->getChildren().size());
REQUIRE(node->getParents().size() == node2->getParents().size());
REQUIRE(node->getCPT().equal(node2->getCPT()));
}
}
SECTION("Test oddities")
{
buildModel(net, raw.featuresv, raw.classNamev);
// predict without fitting
std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
auto test_tensor = bayesnet::vectorToTensor(test);
REQUIRE_THROWS_AS(net.predict(test), std::logic_error);
REQUIRE_THROWS_WITH(net.predict(test), "You must call fit() before calling predict()");
REQUIRE_THROWS_AS(net.predict(test_tensor), std::logic_error);
REQUIRE_THROWS_WITH(net.predict(test_tensor), "You must call fit() before calling predict()");
REQUIRE_THROWS_AS(net.predict_proba(test), std::logic_error);
REQUIRE_THROWS_WITH(net.predict_proba(test), "You must call fit() before calling predict_proba()");
REQUIRE_THROWS_AS(net.score(raw.Xv, raw.yv), std::logic_error);
REQUIRE_THROWS_WITH(net.score(raw.Xv, raw.yv), "You must call fit() before calling predict()");
// predict with wrong data
auto netx = bayesnet::Network();
buildModel(netx, raw.featuresv, raw.classNamev);
netx.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
std::vector<std::vector<int>> test2 = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1} };
auto test_tensor2 = bayesnet::vectorToTensor(test2, false);
REQUIRE_THROWS_AS(netx.predict(test2), std::logic_error);
REQUIRE_THROWS_WITH(netx.predict(test2), "Sample size (3) does not match the number of features (4)");
REQUIRE_THROWS_AS(netx.predict(test_tensor2), std::logic_error);
REQUIRE_THROWS_WITH(netx.predict(test_tensor2), "Sample size (3) does not match the number of features (4)");
// fit with wrong data
// Weights
auto net2 = bayesnet::Network();
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
std::string invalid_weights = "Weights (0) must have the same number of elements as samples (150) in Network::fit";
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), invalid_weights);
// X & y
std::string invalid_labels = "X and y must have the same number of samples in Network::fit (150 != 0)";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), invalid_labels);
// Features
std::string invalid_features = "X and features must have the same number of features in Network::fit (4 != 0)";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), invalid_features);
// Different number of features
auto net3 = bayesnet::Network();
auto test2y = { 1, 2, 3, 4, 5 };
buildModel(net3, raw.featuresv, raw.classNamev);
auto features3 = raw.featuresv;
features3.pop_back();
std::string invalid_features2 = "X and local features must have the same number of features in Network::fit (3 != 4)";
REQUIRE_THROWS_AS(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), invalid_features2);
// Uninitialized network
std::string network_invalid = "The network has not been initialized. You must call addNode() before calling fit()";
REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), network_invalid);
// Classname
std::string invalid_classname = "Class Name not found in Network::features";
REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), invalid_classname);
// Invalid feature
auto features2 = raw.featuresv;
features2.pop_back();
features2.push_back("duck");
std::string invalid_feature = "Feature duck not found in Network::features";
REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), std::invalid_argument);
REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), invalid_feature);
}
}
TEST_CASE("Test and empty Node", "[Network]")
{
auto net = bayesnet::Network();
REQUIRE_THROWS_AS(net.addNode(""), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addNode(""), "Node name cannot be empty");
}
TEST_CASE("Cicle in Network", "[Network]")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
net.addNode("C");
net.addEdge("A", "B");
net.addEdge("B", "C");
REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Adding this edge forms a cycle in the graph.");
}
TEST_CASE("Test max threads constructor", "[Network]")
{
auto net = bayesnet::Network();
REQUIRE(net.getMaxThreads() == 0.95f);
auto net2 = bayesnet::Network(4);
REQUIRE(net2.getMaxThreads() == 4);
auto net3 = bayesnet::Network(1.75);
REQUIRE(net3.getMaxThreads() == 1.75);
}
TEST_CASE("Edges troubles", "[Network]")
{
auto net = bayesnet::Network();
net.addNode("A");
net.addNode("B");
REQUIRE_THROWS_AS(net.addEdge("A", "C"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("A", "C"), "Child node C does not exist");
REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Parent node C does not exist");
}
TEST_CASE("Dump CPT", "[Network]")
{
auto net = bayesnet::Network();
auto raw = RawDatasets("iris", true);
buildModel(net, raw.featuresv, raw.classNamev);
net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
auto res = net.dump_cpt();
std::string expected = R"(* class: (3) : [3]
0.3333
0.3333
0.3333
[ CPUFloatType{3} ]
* petallength: (4) : [4, 3, 3]
(1,.,.) =
0.9388 0.1000 0.2000
0.6250 0.0526 0.1667
0.4000 0.0303 0.0196
(2,.,.) =
0.0204 0.7000 0.4000
0.1250 0.8421 0.1667
0.2000 0.7273 0.0196
(3,.,.) =
0.0204 0.1000 0.2000
0.1250 0.0526 0.5000
0.2000 0.1818 0.1373
(4,.,.) =
0.0204 0.1000 0.2000
0.1250 0.0526 0.1667
0.2000 0.0606 0.8235
[ CPUFloatType{4,3,3} ]
* petalwidth: (3) : [3, 6, 3]
(1,.,.) =
0.5000 0.0417 0.0714
0.3333 0.1111 0.0909
0.5000 0.1000 0.2000
0.7778 0.0909 0.0667
0.8667 0.1000 0.0667
0.9394 0.2500 0.1250
(2,.,.) =
0.2500 0.9167 0.2857
0.3333 0.7778 0.1818
0.2500 0.8000 0.2000
0.1111 0.8182 0.1333
0.0667 0.7000 0.0667
0.0303 0.5000 0.1250
(3,.,.) =
0.2500 0.0417 0.6429
0.3333 0.1111 0.7273
0.2500 0.1000 0.6000
0.1111 0.0909 0.8000
0.0667 0.2000 0.8667
0.0303 0.2500 0.7500
[ CPUFloatType{3,6,3} ]
* sepallength: (3) : [3, 3]
0.8679 0.1321 0.0377
0.0943 0.3019 0.0566
0.0377 0.5660 0.9057
[ CPUFloatType{3,3} ]
* sepalwidth: (6) : [6, 3, 3]
(1,.,.) =
0.0392 0.5000 0.2857
0.1000 0.4286 0.2500
0.1429 0.2571 0.1887
(2,.,.) =
0.0196 0.0833 0.1429
0.1000 0.1429 0.2500
0.1429 0.1429 0.1509
(3,.,.) =
0.0392 0.0833 0.1429
0.1000 0.1429 0.1250
0.1429 0.1714 0.0566
(4,.,.) =
0.1373 0.1667 0.1429
0.1000 0.1905 0.1250
0.1429 0.1429 0.2453
(5,.,.) =
0.2549 0.0833 0.1429
0.1000 0.0476 0.1250
0.1429 0.2286 0.2453
(6,.,.) =
0.5098 0.0833 0.1429
0.5000 0.0476 0.1250
0.2857 0.0571 0.1132
[ CPUFloatType{6,3,3} ]
)";
REQUIRE(res == expected);
}

84
tests/TestBayesNode.cc Normal file
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@@ -0,0 +1,84 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <string>
#include "TestUtils.h"
#include "bayesnet/network/Network.h"
TEST_CASE("Test Node children and parents", "[Node]")
{
auto node = bayesnet::Node("Node");
REQUIRE(node.getName() == "Node");
auto parent_1 = bayesnet::Node("P1");
auto parent_2 = bayesnet::Node("P2");
auto child_1 = bayesnet::Node("H1");
auto child_2 = bayesnet::Node("H2");
auto child_3 = bayesnet::Node("H3");
node.addParent(&parent_1);
node.addParent(&parent_2);
node.addChild(&child_1);
node.addChild(&child_2);
node.addChild(&child_3);
auto parents = node.getParents();
auto children = node.getChildren();
REQUIRE(parents.size() == 2);
REQUIRE(children.size() == 3);
REQUIRE(parents[0]->getName() == "P1");
REQUIRE(parents[1]->getName() == "P2");
REQUIRE(children[0]->getName() == "H1");
REQUIRE(children[1]->getName() == "H2");
REQUIRE(children[2]->getName() == "H3");
node.removeParent(&parent_1);
node.removeChild(&child_1);
parents = node.getParents();
children = node.getChildren();
REQUIRE(parents.size() == 1);
REQUIRE(children.size() == 2);
node.clear();
parents = node.getParents();
children = node.getChildren();
REQUIRE(parents.size() == 0);
REQUIRE(children.size() == 0);
}
TEST_CASE("TEST MinFill method", "[Node]")
{
// Generate a test to test the minFill method of the Node class
// Create a graph with 5 nodes
// The graph is a chain with some additional edges
// 0 -> 1,2,3
// 1 -> 2,4
// 2 -> 3
// 3 -> 4
auto node_0 = bayesnet::Node("0");
auto node_1 = bayesnet::Node("1");
auto node_2 = bayesnet::Node("2");
auto node_3 = bayesnet::Node("3");
auto node_4 = bayesnet::Node("4");
// node 0
node_0.addChild(&node_1);
node_0.addChild(&node_2);
node_0.addChild(&node_3);
// node 1
node_1.addChild(&node_2);
node_1.addChild(&node_4);
node_1.addParent(&node_0);
// node 2
node_2.addChild(&node_3);
node_2.addChild(&node_4);
node_2.addParent(&node_0);
node_2.addParent(&node_1);
// node 3
node_3.addChild(&node_4);
node_3.addParent(&node_0);
node_3.addParent(&node_2);
// node 4
node_4.addParent(&node_1);
node_4.addParent(&node_3);
REQUIRE(node_0.minFill() == 3);
REQUIRE(node_1.minFill() == 3);
REQUIRE(node_2.minFill() == 6);
REQUIRE(node_3.minFill() == 3);
REQUIRE(node_4.minFill() == 1);
}