Fix tests to 97%

README.md

@@ -7,7 +7,7 @@
 [![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
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-[![Coverage Badge](https://img.shields.io/badge/Coverage-96,8%25-green)](html/index.html)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-97,0%25-green)](html/index.html)
 
 Bayesian Network Classifiers using libtorch from scratch
 

tests/TestA2DE.cc

@@ -45,5 +45,5 @@ TEST_CASE("Test graph", "[A2DE]")
     auto graph = clf.graph();
     REQUIRE(graph.size() == 78);
     REQUIRE(graph[0] == "digraph BayesNet {\nlabel=<BayesNet A2DE_0>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
-    REQUIRE(graph[1] == "class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n");
+    REQUIRE(graph[1] == "\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n");
 }

tests/TestBayesClassifier.cc

@@ -85,7 +85,7 @@ TEST_CASE("Dump_cpt", "[Classifier]")
     auto raw = RawDatasets("iris", true);
     model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     auto cpt = model.dump_cpt();
-    REQUIRE(cpt.size() == 1713);
+    REQUIRE(cpt.size() == 1718);
 }
 TEST_CASE("Not fitted model", "[Classifier]")
 {

tests/TestBayesModels.cc

@@ -27,13 +27,13 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
     map <pair<std::string, std::string>, float> scores{
         // Diabetes
         {{"diabetes", "AODE"}, 0.82161}, {{"diabetes", "KDB"}, 0.852865}, {{"diabetes", "SPODE"}, 0.802083}, {{"diabetes", "TAN"}, 0.821615},
-        {{"diabetes", "AODELd"}, 0.8138f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.78646f}, {{"diabetes", "TANLd"}, 0.8099f},  {{"diabetes", "BoostAODE"}, 0.83984f},
+        {{"diabetes", "AODELd"}, 0.8125f}, {{"diabetes", "KDBLd"}, 0.80208f}, {{"diabetes", "SPODELd"}, 0.7890625f}, {{"diabetes", "TANLd"}, 0.803385437f},  {{"diabetes", "BoostAODE"}, 0.83984f},
         // Ecoli
         {{"ecoli", "AODE"}, 0.889881}, {{"ecoli", "KDB"}, 0.889881}, {{"ecoli", "SPODE"}, 0.880952}, {{"ecoli", "TAN"}, 0.892857},
-        {{"ecoli", "AODELd"}, 0.8869f}, {{"ecoli", "KDBLd"}, 0.875f}, {{"ecoli", "SPODELd"}, 0.84226f}, {{"ecoli", "TANLd"}, 0.86905f}, {{"ecoli", "BoostAODE"}, 0.89583f},
+        {{"ecoli", "AODELd"}, 0.875f}, {{"ecoli", "KDBLd"}, 0.880952358f}, {{"ecoli", "SPODELd"}, 0.839285731f}, {{"ecoli", "TANLd"}, 0.848214269f}, {{"ecoli", "BoostAODE"}, 0.89583f},
         // Glass
         {{"glass", "AODE"}, 0.79439}, {{"glass", "KDB"}, 0.827103}, {{"glass", "SPODE"}, 0.775701}, {{"glass", "TAN"}, 0.827103},
-        {{"glass", "AODELd"}, 0.79439f}, {{"glass", "KDBLd"}, 0.85047f}, {{"glass", "SPODELd"}, 0.79439f}, {{"glass", "TANLd"}, 0.86449f}, {{"glass", "BoostAODE"}, 0.84579f},
+        {{"glass", "AODELd"}, 0.799065411f}, {{"glass", "KDBLd"}, 0.82710278f}, {{"glass", "SPODELd"}, 0.780373812f}, {{"glass", "TANLd"}, 0.869158864f}, {{"glass", "BoostAODE"}, 0.84579f},
         // Iris
         {{"iris", "AODE"}, 0.973333}, {{"iris", "KDB"}, 0.973333}, {{"iris", "SPODE"}, 0.973333}, {{"iris", "TAN"}, 0.973333},
         {{"iris", "AODELd"}, 0.973333}, {{"iris", "KDBLd"}, 0.973333}, {{"iris", "SPODELd"}, 0.96f}, {{"iris", "TANLd"}, 0.97333f}, {{"iris", "BoostAODE"}, 0.98f}
@@ -71,10 +71,10 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
 TEST_CASE("Models features & Graph", "[Models]")
 {
     auto graph = std::vector<std::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",
+        "\"class\" [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n",
-        "class -> sepallength", "class -> sepalwidth", "class -> petallength", "class -> petalwidth", "petallength [shape=circle] \n",
+        "\"class\" -> \"sepallength\"", "\"class\" -> \"sepalwidth\"", "\"class\" -> \"petallength\"", "\"class\" -> \"petalwidth\"", "\"petallength\" [shape=circle] \n",
-        "petallength -> sepallength", "petalwidth [shape=circle] \n", "sepallength [shape=circle] \n",
+        "\"petallength\" -> \"sepallength\"", "\"petalwidth\" [shape=circle] \n", "\"sepallength\" [shape=circle] \n",
-        "sepallength -> sepalwidth", "sepalwidth [shape=circle] \n", "sepalwidth -> petalwidth", "}\n"
+        "\"sepallength\" -> \"sepalwidth\"", "\"sepalwidth\" [shape=circle] \n", "\"sepalwidth\" -> \"petalwidth\"", "}\n"
         }
     );
     SECTION("Test TAN")
@@ -96,7 +96,7 @@ TEST_CASE("Models features & Graph", "[Models]")
         clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
         REQUIRE(clf.getNumberOfNodes() == 5);
         REQUIRE(clf.getNumberOfEdges() == 7);
-        REQUIRE(clf.getNumberOfStates() == 19);
+        REQUIRE(clf.getNumberOfStates() == 27);
         REQUIRE(clf.getClassNumStates() == 3);
         REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ", "petallength -> sepallength, ", "petalwidth -> ", "sepallength -> sepalwidth, ", "sepalwidth -> petalwidth, "});
         REQUIRE(clf.graph("Test") == graph);

tests/TestBayesNetwork.cc

@@ -186,11 +186,11 @@ TEST_CASE("Test Bayesian Network", "[Network]")
         auto str = net.graph("Test Graph");
         REQUIRE(str.size() == 7);
         REQUIRE(str[0] == "digraph BayesNet {\nlabel=<BayesNet Test Graph>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
-        REQUIRE(str[1] == "A [shape=circle] \n");
+        REQUIRE(str[1] == "\"A\" [shape=circle] \n");
-        REQUIRE(str[2] == "A -> B");
+        REQUIRE(str[2] == "\"A\" -> \"B\"");
-        REQUIRE(str[3] == "A -> C");
+        REQUIRE(str[3] == "\"A\" -> \"C\"");
-        REQUIRE(str[4] == "B [shape=circle] \n");
+        REQUIRE(str[4] == "\"B\" [shape=circle] \n");
-        REQUIRE(str[5] == "C [shape=circle] \n");
+        REQUIRE(str[5] == "\"C\" [shape=circle] \n");
         REQUIRE(str[6] == "}\n");
     }
     SECTION("Test predict")
@@ -373,7 +373,7 @@ TEST_CASE("Dump CPT", "[Network]")
  0.3333
  0.3333
  0.3333
-[ CPUFloatType{3} ]
+[ CPUDoubleType{3} ]
 * petallength: (4) : [4, 3, 3]
 (1,.,.) = 
   0.9388  0.1000  0.2000
@@ -394,7 +394,7 @@ TEST_CASE("Dump CPT", "[Network]")
   0.0204  0.1000  0.2000
   0.1250  0.0526  0.1667
   0.2000  0.0606  0.8235
-[ CPUFloatType{4,3,3} ]
+[ CPUDoubleType{4,3,3} ]
 * petalwidth: (3) : [3, 6, 3]
 (1,.,.) = 
   0.5000  0.0417  0.0714
@@ -419,12 +419,12 @@ TEST_CASE("Dump CPT", "[Network]")
   0.1111  0.0909  0.8000
   0.0667  0.2000  0.8667
   0.0303  0.2500  0.7500
-[ CPUFloatType{3,6,3} ]
+[ CPUDoubleType{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} ]
+[ CPUDoubleType{3,3} ]
 * sepalwidth: (6) : [6, 3, 3]
 (1,.,.) = 
   0.0392  0.5000  0.2857
@@ -455,7 +455,7 @@ TEST_CASE("Dump CPT", "[Network]")
   0.5098  0.0833  0.1429
   0.5000  0.0476  0.1250
   0.2857  0.0571  0.1132
-[ CPUFloatType{6,3,3} ]
+[ CPUDoubleType{6,3,3} ]
 )";
     REQUIRE(res == expected);
 }
@@ -549,19 +549,32 @@ TEST_CASE("Test Smoothing B", "[Network]")
         { "C", {0, 1} }
     };
     auto weights = std::vector<double>(C.size(), 1);
-    // Simple
+    // See https://www.overleaf.com/read/tfnhpfysfkfx#2d576c example for calculations
-    std::cout << "LAPLACE\n";
+    INFO("Test Smoothing B - Laplace");
     net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
-    std::cout << net.dump_cpt();
+    auto laplace_values = std::vector<std::vector<float>>({ {0.377418, 0.622582}, {0.217821, 0.782179} });
-    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+    auto laplace_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
-    std::cout << "ORIGINAL\n";
+    for (auto i = 0; i < 2; ++i) {
+        for (auto j = 0; j < 2; ++j) {
+            REQUIRE(laplace_score.at(i).at(j) == Catch::Approx(laplace_values.at(i).at(j)).margin(threshold));
+        }
+    }
+    INFO("Test Smoothing B - Original");
     net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::ORIGINAL);
-    std::cout << net.dump_cpt();
+    auto original_values = std::vector<std::vector<float>>({ {0.344769, 0.655231}, {0.0421263, 0.957874} });
-    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+    auto original_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
-    std::cout << "CESTNIK\n";
+    for (auto i = 0; i < 2; ++i) {
+        for (auto j = 0; j < 2; ++j) {
+            REQUIRE(original_score.at(i).at(j) == Catch::Approx(original_values.at(i).at(j)).margin(threshold));
+        }
+    }
+    INFO("Test Smoothing B - Cestnik");
     net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
-    std::cout << net.dump_cpt();
+    auto cestnik_values = std::vector<std::vector<float>>({ {0.353422, 0.646578}, {0.12364, 0.87636} });
-    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+    auto cestnik_score = net.predict_proba({ {0, 1}, {1, 2}, {2, 3} });
-
+    for (auto i = 0; i < 2; ++i) {
-
+        for (auto j = 0; j < 2; ++j) {
-}
+            REQUIRE(cestnik_score.at(i).at(j) == Catch::Approx(cestnik_values.at(i).at(j)).margin(threshold));
+        }
+    }
+}

tests/TestBayesNode.cc

@@ -62,15 +62,17 @@ TEST_CASE("Test Node computeCPT", "[Node]")
     // 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 });
+    auto weights = torch::tensor({ 1.0, 1.0, 1.0, 1.0 }, torch::kDouble);
     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);
     }
+    // Create node class with 2 states
     nodes.push_back(bayesnet::Node(className));
     nodes[features.size()].setNumStates(2);
+    // The network is c->f1, f2, f3 y f1->f2, f3 
     for (int i = 0; i < features.size(); i++) {
         // Add class node as parent of all feature nodes
         nodes[i].addParent(&nodes[features.size()]);

tests/TestModulesVersions.cc

@@ -16,7 +16,7 @@
 #include "TestUtils.h"
 
 std::map<std::string, std::string> modules = {
-    { "mdlp", "2.0.0" },
+    { "mdlp", "2.0.1" },
     { "Folding", "1.1.0" },
     { "json", "3.11" },
     { "ArffFiles", "1.1.0" }