Refactor coverage report generation

Add some tests to reach 99%

tests/TestBayesNode.cc

@@ -7,7 +7,9 @@
 #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 <vector>
 #include "TestUtils.h"
 #include "bayesnet/network/Network.h"
 
@@ -48,6 +50,73 @@ TEST_CASE("Test Node children and parents", "[Node]")
     REQUIRE(parents.size() == 0);
     REQUIRE(children.size() == 0);
 }
+TEST_CASE("Test Node computeCPT", "[Node]")
+{
+    // Generate a test to test the computeCPT method of the Node class
+    // Create a dataset with 3 features and 4 samples
+    // The dataset is a 2D tensor with 4 rows and 4 columns
+    auto dataset = torch::tensor({ {1, 0, 0, 1}, {1, 1, 2, 0}, {0, 1, 2, 1}, {0, 1, 0, 1} });
+    auto states = std::vector<int>({ 2, 3, 3 });
+    // Create a vector with the names of the features
+    auto features = std::vector<std::string>{ "F1", "F2", "F3" };
+    // Create a vector with the names of the classes
+    auto className = std::string("Class");
+    // weights
+    auto weights = torch::tensor({ 1.0, 1.0, 1.0, 1.0 });
+    std::vector<bayesnet::Node> nodes;
+    for (int i = 0; i < features.size(); i++) {
+        auto node = bayesnet::Node(features[i]);
+        node.setNumStates(states[i]);
+        nodes.push_back(node);
+    }
+    nodes.push_back(bayesnet::Node(className));
+    nodes[features.size()].setNumStates(2);
+    for (int i = 0; i < features.size(); i++) {
+        // Add class node as parent of all feature nodes
+        nodes[i].addParent(&nodes[features.size()]);
+        // Node[0] -> Node[1], Node[2]
+        if (i > 0)
+            nodes[i].addParent(&nodes[0]);
+    }
+    features.push_back(className);
+    // Compute the conditional probability table
+    nodes[1].computeCPT(dataset, features, 0.0, weights);
+    // Get the conditional probability table
+    auto cpTable = nodes[1].getCPT();
+    // Get the dimensions of the conditional probability table
+    auto dimensions = cpTable.sizes();
+    // Check the dimensions of the conditional probability table
+    REQUIRE(dimensions.size() == 3);
+    REQUIRE(dimensions[0] == 3);
+    REQUIRE(dimensions[1] == 2);
+    REQUIRE(dimensions[2] == 2);
+    // Check the values of the conditional probability table
+    REQUIRE(cpTable[0][0][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][0][1].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][1][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][1][1].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][0][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[1][0][1].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][1][0].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][1][1].item<float>() == Catch::Approx(0));
+    // Compute evidence
+    for (auto& node : nodes) {
+        node.computeCPT(dataset, features, 0.0, weights);
+    }
+    auto evidence = std::map<std::string, int>{ { "F1", 0 },  { "F2", 1 }, { "F3", 1 } };
+    REQUIRE(nodes[3].getFactorValue(evidence) == 0.5);
+    // Oddities
+    auto features_back = features;
+    // Remove a parent from features
+    features.pop_back();
+    REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
+    REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature parent Class not found in dataset");
+    // Remove a feature from features
+    features = features_back;
+    features.erase(features.begin());
+    REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
+    REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature F1 not found in dataset");
+}
 TEST_CASE("TEST MinFill method", "[Node]")
 {
     // Generate a test to test the minFill method of the Node class