Add First BayesMetrics Tests

tests/TestBayesMetrics.cc

@@ -2,54 +2,55 @@
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
 #include "BayesMetrics.h"
+#include "TestUtils.h"
 
 using namespace std;
 
 TEST_CASE("Metrics Test", "[Metrics]")
 {
+    string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
+    map<string, pair<int, vector<int>>> results = {
+        {"glass", {7, { 3, 2, 0, 1, 6, 7, 5 }}},
+        {"iris", {3, { 1, 0, 2 }} },
+        {"ecoli", {6, { 2, 3, 1, 0, 4, 5 }}},
+        {"diabetes", {2, { 2, 0 }}}
+    };
+    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);
+
     SECTION("Test Constructor")
     {
-        torch::Tensor samples = torch::rand({ 10, 5 });
-        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
-        string className = "class1";
-        int classNumStates = 2;
-
-        bayesnet::Metrics obj(samples, features, className, classNumStates);
-
-        REQUIRE(obj.getScoresKBest().size() == 0);
+        bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
+        REQUIRE(metrics.getScoresKBest().size() == 0);
     }
 
     SECTION("Test SelectKBestWeighted")
     {
-        torch::Tensor samples = torch::rand({ 10, 5 });
-        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
-        string className = "class1";
-        int classNumStates = 2;
-
-        bayesnet::Metrics obj(samples, features, className, classNumStates);
-
-        torch::Tensor weights = torch::ones({ 5 });
-
-        vector<int> kBest = obj.SelectKBestWeighted(weights, true, 3);
-
-        REQUIRE(kBest.size() == 3);
+        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, results.at(file_name).first);
+        REQUIRE(kBest.size() == results.at(file_name).first);
+        REQUIRE(kBest == results.at(file_name).second);
     }
 
     SECTION("Test mutualInformation")
     {
-        torch::Tensor samples = torch::rand({ 10, 5 });
-        vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
-        string className = "class1";
-        int classNumStates = 2;
+        // torch::Tensor samples = torch::rand({ 10, 5 });
+        // vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
+        // string className = "class1";
+        // int classNumStates = 2;
 
-        bayesnet::Metrics obj(samples, features, className, classNumStates);
+        // bayesnet::Metrics obj(samples, features, className, classNumStates);
 
-        torch::Tensor firstFeature = samples.select(1, 0);
-        torch::Tensor secondFeature = samples.select(1, 1);
-        torch::Tensor weights = torch::ones({ 10 });
+        // torch::Tensor firstFeature = samples.select(1, 0);
+        // torch::Tensor secondFeature = samples.select(1, 1);
+        // torch::Tensor weights = torch::ones({ 10 });
 
-        double mi = obj.mutualInformation(firstFeature, secondFeature, weights);
+        // double mi = obj.mutualInformation(firstFeature, secondFeature, weights);
 
-        REQUIRE(mi >= 0);
+        // REQUIRE(mi >= 0);
     }
 }