Add Maximum Spanning Tree test

Makefile

@@ -49,11 +49,12 @@ release: ## Build a Release version of the project
 	@cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; 
 	@echo ">>> Done";	
 
-test: ## Run tests
+opt = ""
+test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running tests...";
 	$(MAKE) clean
 	@cmake --build build --target unit_tests ; 
-	@if [ -f build/tests/unit_tests ]; then cd build/tests ; ./unit_tests ; fi ; 
+	@if [ -f build/tests/unit_tests ]; then cd build/tests ; ./unit_tests $(opt) ; fi ; 
 	@echo ">>> Done";	
 
 coverage: ## Run tests and generate coverage report (build/index.html)

tests/TestBayesMetrics.cc

@@ -6,41 +6,58 @@
 
 using namespace std;
 
-TEST_CASE("Metrics Test", "[Metrics]")
+TEST_CASE("Metrics Test", "[BayesNet]")
 {
     string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
     map<string, pair<int, vector<int>>> resultsKBest = {
-        {"glass", {7, { 3, 2, 0, 1, 6, 7, 5 }}},
+        {"glass", {7, { 0, 1, 7, 6, 3, 5, 2 }}},
-        {"iris", {3, { 1, 0, 2 }} },
+        {"iris", {3, { 0, 3, 2 }} },
-        {"ecoli", {6, { 2, 3, 1, 0, 4, 5 }}},
+        {"ecoli", {6, { 2, 4, 1, 0, 6, 5 }}},
-        {"diabetes", {2, { 2, 0 }}}
+        {"diabetes", {2, { 7, 1 }}}
+    };
+    map<string, double> resultsMI = {
+        {"glass", 0.12805398},
+        {"iris", 0.3158139948},
+        {"ecoli", 0.0089431099},
+        {"diabetes", 0.0345470614}
+    };
+    map<string, vector<pair<int, int>>> resultsMST = {
+        {"glass", {{0,6}, {0,5}, {0,3}, {6,2}, {6,7}, {5,1}, {5,8}, {5,4}}},
+        {"iris", {{0,1},{0,2},{1,3}}},
+        {"ecoli", {{0,1}, {0,2}, {1,5}, {1,3}, {5,6}, {5,4}}},
+        {"diabetes", {{0,7}, {0,2}, {0,6}, {2,3}, {3,4}, {3,5}, {4,1}}}
     };
     auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadDataset(file_name, true, true);
     int classNumStates = statesDisc.at(classNameDisc).size();
     auto yresized = torch::transpose(yDisc.view({ yDisc.size(0), 1 }), 0, 1);
     torch::Tensor dataset = torch::cat({ XDisc, yresized }, 0);
     int nSamples = dataset.size(1);
+    double epsilon = 1e-5;
+    torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
+    bayesnet::Metrics metrics(dataset, featuresDisc, classNameDisc, classNumStates);
 
     SECTION("Test Constructor")
     {
-        bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
         REQUIRE(metrics.getScoresKBest().size() == 0);
     }
 
     SECTION("Test SelectKBestWeighted")
     {
-        bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
-        torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
         vector<int> kBest = metrics.SelectKBestWeighted(weights, true, resultsKBest.at(file_name).first);
         REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
         REQUIRE(kBest == resultsKBest.at(file_name).second);
     }
 
-    SECTION("Test mutualInformation")
+    SECTION("Test Mutual Information")
     {
-        bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
-        torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
         auto result = metrics.mutualInformation(dataset.index({ 1, "..." }), dataset.index({ 2, "..." }), weights);
-        REQUIRE(result == 0.87);
+        REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(epsilon));
+    }
+
+    SECTION("Test Maximum Spanning Tree")
+    {
+        auto weights_matrix = metrics.conditionalEdge(weights);
+        auto result = metrics.maximumSpanningTree(featuresDisc, weights_matrix, 0);
+        REQUIRE(result == resultsMST.at(file_name));
     }
 }

tests/TestBayesModels.cc

@@ -116,7 +116,7 @@ TEST_CASE("Test Bayesian Classifiers score", "[BayesNet]")
     //     cout << "{{\"" << scores.first.first << "\", \"" << scores.first.second << "\"}, " << scores.second << "}, ";
     // }
 }
-TEST_CASE("Models featuresDisc")
+TEST_CASE("Models features", "[BayesNet]")
 {
     auto graph = vector<string>({ "digraph BayesNet {\nlabel=<BayesNet Test>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n",
         "class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
@@ -134,7 +134,7 @@ TEST_CASE("Models featuresDisc")
     REQUIRE(clf.show() == vector<string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
     REQUIRE(clf.graph("Test") == graph);
 }
-TEST_CASE("Get num featuresDisc & num edges")
+TEST_CASE("Get num features & num edges", "[BayesNet]")
 {
     auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadFile("iris");
     auto clf = bayesnet::KDB(2);

tests/TestBayesNetwork.cc

@@ -5,7 +5,7 @@
 #include "TestUtils.h"
 #include "KDB.h"
 
-TEST_CASE("Test Bayesian Network")
+TEST_CASE("Test Bayesian Network", "[BayesNet]")
 {
     auto [Xd, y, features, className, states] = loadFile("iris");