Implement SPnDE and A2DE

bayesnet/classifiers/SPnDE.cc

@@ -0,0 +1,37 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPnDE.h"
+
+namespace bayesnet {
+
+    SPnDE::SPnDE(std::vector<int> parents) : Classifier(Network()), parents(parents) {}
+
+    void SPnDE::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        std::vector<int> attributes;
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            if (std::find(parents.begin(), parents.end(), i) != parents.end()) {
+                attributes.push_back(i);
+            }
+        }
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the parents nodes to all other nodes
+        for (const auto& attribute : attributes) {
+            model.addEdge(className, features[attribute]);
+            for (const auto& root : parents) {
+                model.addEdge(features[root], features[attribute]);
+            }
+        }
+    }
+    std::vector<std::string> SPnDE::graph(const std::string& name) const
+    {
+        return model.graph(name);
+    }
+
+}

bayesnet/classifiers/SPnDE.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPnDE_H
+#define SPnDE_H
+#include <vector>
+#include "Classifier.h"
+
+namespace bayesnet {
+    class SPnDE : public Classifier {
+    public:
+        explicit SPnDE(std::vector<int> parents);
+        virtual ~SPnDE() = default;
+        std::vector<std::string> graph(const std::string& name = "SPnDE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        std::vector<int> parents;
+
+
+    };
+}
+#endif

bayesnet/ensembles/A2DE.cc

@@ -0,0 +1,40 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "A2DE.h"
+
+namespace bayesnet {
+    A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+
+    }
+    void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void A2DE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size() - 1; ++i) {
+            for (int j = i + 1; j < features.size(); ++j) {
+                models.push_back(std::make_unique<SPnDE>(std::vector<int>({ i, j })));
+            }
+        }
+        n_models = models.size();
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> A2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/A2DE.h

@@ -0,0 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef A2DE_H
+#define A2DE_H
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Ensemble.h"
+namespace bayesnet {
+    class A2DE : public Ensemble {
+    public:
+        A2DE(bool predict_voting = false);
+        virtual ~A2DE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "A2DE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

lib/catch2

@@ -0,0 +1 @@
+Subproject commit 029fe3b4609dd84cd939b73357f37bbb75bcf82f

tests/CMakeLists.txt

@@ -21,4 +21,5 @@ if(ENABLE_TESTING)
     add_test(NAME Ensemble COMMAND TestBayesNet "[Ensemble]")
     add_test(NAME Models COMMAND TestBayesNet "[Models]")
     add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
+    add_test(NAME A2DE COMMAND TestBayesNet "[A2DE]")
 endif(ENABLE_TESTING)

tests/TestA2DE.cc

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <type_traits>
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include "bayesnet/ensembles/A2DE.h"
+#include "TestUtils.h"
+
+
+TEST_CASE("Fit and Score", "[A2DE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::A2DE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    std::cout << "Score A2DE: " << clf.score(raw.Xv, raw.yv) << std::endl;
+    // REQUIRE(clf.getNumberOfNodes() == 90);
+    // REQUIRE(clf.getNumberOfEdges() == 153);
+    // REQUIRE(clf.getNotes().size() == 2);
+    // REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
+    // REQUIRE(clf.getNotes()[1] == "Number of models: 9");
+}