Merge branch 'main' into TANNew

.vscode/launch.json

@@ -25,14 +25,14 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "TANNew",
+                "AODELd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
                 "-d",
                 "iris"
             ],
-            "cwd": "${workspaceFolder}/build/src/Platform",
+            "cwd": "/Users/rmontanana/Code/discretizbench",
         },
         {
             "name": "Build & debug active file",

Makefile

@@ -14,6 +14,14 @@ setup: ## Install dependencies for tests and coverage
 dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
 	cd build && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
+build: ## Build the main and BayesNetSample
+	cmake --build build -t main -t BayesNetSample -j 32
+
+clean: ## Clean the debug info
+	@echo ">>> Cleaning Debug BayesNet ...";
+	find . -name "*.gcda" -print0 | xargs -0 rm
+	@echo ">>> Done";
+
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet ...";
 	@if [ -d ./build ]; then rm -rf ./build; fi

sample/sample.cc

@@ -178,61 +178,59 @@ int main(int argc, char** argv)
     cout << "end." << endl;
     auto score = clf->score(Xd, y);
     cout << "Score: " << score << endl;
-    auto graph = clf->graph();
-    auto dot_file = model_name + "_" + file_name;
-    ofstream file(dot_file + ".dot");
-    file << graph;
-    file.close();
-    cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
-    cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
-    string stratified_string = stratified ? " Stratified" : "";
-    cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
-    cout << "==========================================" << endl;
-    torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
-    torch::Tensor yt = torch::tensor(y, torch::kInt32);
-    for (int i = 0; i < features.size(); ++i) {
-        Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
-    }
-    float total_score = 0, total_score_train = 0, score_train, score_test;
-    Fold* fold;
-    if (stratified)
-        fold = new StratifiedKFold(nFolds, y, seed);
-    else
-        fold = new KFold(nFolds, y.size(), seed);
-    for (auto i = 0; i < nFolds; ++i) {
-        auto [train, test] = fold->getFold(i);
-        cout << "Fold: " << i + 1 << endl;
-        if (tensors) {
-            auto ttrain = torch::tensor(train, torch::kInt64);
-            auto ttest = torch::tensor(test, torch::kInt64);
-            torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
-            torch::Tensor ytraint = yt.index({ ttrain });
-            torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
-            torch::Tensor ytestt = yt.index({ ttest });
-            clf->fit(Xtraint, ytraint, features, className, states);
-            auto temp = clf->predict(Xtraint);
-            score_train = clf->score(Xtraint, ytraint);
-            score_test = clf->score(Xtestt, ytestt);
-        } else {
-            auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
-            auto [Xtest, ytest] = extract_indices(test, Xd, y);
-            clf->fit(Xtrain, ytrain, features, className, states);
-
-            score_train = clf->score(Xtrain, ytrain);
-            score_test = clf->score(Xtest, ytest);
-        }
-        if (dump_cpt) {
-            cout << "--- CPT Tables ---" << endl;
-            clf->dump_cpt();
-        }
-        total_score_train += score_train;
-        total_score += score_test;
-        cout << "Score Train: " << score_train << endl;
-        cout << "Score Test : " << score_test << endl;
-        cout << "-------------------------------------------------------------------------------" << endl;
-    }
-    cout << "**********************************************************************************" << endl;
-    cout << "Average Score Train: " << total_score_train / nFolds << endl;
-    cout << "Average Score Test : " << total_score / nFolds << endl;
-    return 0;
+    // auto graph = clf->graph();
+    // auto dot_file = model_name + "_" + file_name;
+    // ofstream file(dot_file + ".dot");
+    // file << graph;
+    // file.close();
+    // cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
+    // cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
+    // string stratified_string = stratified ? " Stratified" : "";
+    // cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
+    // cout << "==========================================" << endl;
+    // torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
+    // torch::Tensor yt = torch::tensor(y, torch::kInt32);
+    // for (int i = 0; i < features.size(); ++i) {
+    //     Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
+    // }
+    // float total_score = 0, total_score_train = 0, score_train, score_test;
+    // Fold* fold;
+    // if (stratified)
+    //     fold = new StratifiedKFold(nFolds, y, seed);
+    // else
+    //     fold = new KFold(nFolds, y.size(), seed);
+    // for (auto i = 0; i < nFolds; ++i) {
+    //     auto [train, test] = fold->getFold(i);
+    //     cout << "Fold: " << i + 1 << endl;
+    //     if (tensors) {
+    //         auto ttrain = torch::tensor(train, torch::kInt64);
+    //         auto ttest = torch::tensor(test, torch::kInt64);
+    //         torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
+    //         torch::Tensor ytraint = yt.index({ ttrain });
+    //         torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
+    //         torch::Tensor ytestt = yt.index({ ttest });
+    //         clf->fit(Xtraint, ytraint, features, className, states);
+    //         auto temp = clf->predict(Xtraint);
+    //         score_train = clf->score(Xtraint, ytraint);
+    //         score_test = clf->score(Xtestt, ytestt);
+    //     } else {
+    //         auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
+    //         auto [Xtest, ytest] = extract_indices(test, Xd, y);
+    //         clf->fit(Xtrain, ytrain, features, className, states);
+    //         score_train = clf->score(Xtrain, ytrain);
+    //         score_test = clf->score(Xtest, ytest);
+    //     }
+    //     if (dump_cpt) {
+    //         cout << "--- CPT Tables ---" << endl;
+    //         clf->dump_cpt();
+    //     }
+    //     total_score_train += score_train;
+    //     total_score += score_test;
+    //     cout << "Score Train: " << score_train << endl;
+    //     cout << "Score Test : " << score_test << endl;
+    //     cout << "-------------------------------------------------------------------------------" << endl;
+    // }
+    // cout << "**********************************************************************************" << endl;
+    // cout << "Average Score Train: " << total_score_train / nFolds << endl;
+    // cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
 }

src/BayesNet/AODELd.cc

@@ -0,0 +1,34 @@
+#include "AODELd.h"
+
+namespace bayesnet {
+    using namespace std;
+    AODELd::AODELd() : Ensemble(), Proposal(Ensemble::Xv, Ensemble::yv, features, className) {}
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        features = features_;
+        className = className_;
+        states = states_;
+        train();
+        for (const auto& model : models) {
+            model->fit(X_, y_, features_, className_, states_);
+        }
+        n_models = models.size();
+        fitted = true;
+        return *this;
+    }
+    void AODELd::train()
+    {
+        models.clear();
+        for (int i = 0; i < features.size(); ++i) {
+            models.push_back(std::make_unique<SPODELd>(i));
+        }
+    }
+    Tensor AODELd::predict(Tensor& X)
+    {
+        return Ensemble::predict(X);
+    }
+    vector<string> AODELd::graph(const string& name)
+    {
+        return Ensemble::graph(name);
+    }
+}

src/BayesNet/AODELd.h

@@ -0,0 +1,20 @@
+#ifndef AODELD_H
+#define AODELD_H
+#include "Ensemble.h"
+#include "Proposal.h"
+#include "SPODELd.h"
+
+namespace bayesnet {
+    using namespace std;
+    class AODELd : public Ensemble, public Proposal {
+    public:
+        AODELd();
+        virtual ~AODELd() = default;
+        AODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "AODE") override;
+        Tensor predict(Tensor& X) override;
+        void train() override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !AODELD_H

src/BayesNet/BaseClassifier.h

@@ -10,6 +10,7 @@ namespace bayesnet {
         virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
         virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;
         float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
@@ -19,7 +20,6 @@ namespace bayesnet {
         int virtual getNumberOfStates() = 0;
         vector<string> virtual show() = 0;
         vector<string> virtual graph(const string& title = "") = 0;
-        virtual ~BaseClassifier() = default;
         const string inline getVersion() const { return "0.1.0"; };
         vector<string> virtual topological_order() = 0;
         void virtual dump_cpt() = 0;

src/BayesNet/CMakeLists.txt

@@ -1,4 +1,5 @@
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
-add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANNew.cc Mst.cc)
+add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc)
 target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -1,6 +1,5 @@
 #include "Classifier.h"
 #include "bayesnetUtils.h"
-#include "ArffFiles.h"
 
 namespace bayesnet {
     using namespace torch;
@@ -37,15 +36,18 @@ namespace bayesnet {
         yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
         return build(features, className, states);
     }
+    void Classifier::generateTensorXFromVector()
+    {
+        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
+        for (int i = 0; i < Xv.size(); ++i) {
+            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
+        }
+    }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-
-        this->X = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
         Xv = X;
-        for (int i = 0; i < X.size(); ++i) {
-            this->X.index_put_({ i, "..." }, torch::tensor(X[i], kInt32));
-        }
+        generateTensorXFromVector();
         this->y = torch::tensor(y, kInt32);
         yv = y;
         return build(features, className, states);
@@ -114,11 +116,9 @@ namespace bayesnet {
     {
         // Add all nodes to the network
         for (const auto& feature : features) {
-            model.addNode(feature, states[feature].size());
-            cout << "-Adding node " << feature << " with " << states[feature].size() << " states" << endl;
+            model.addNode(feature);
         }
-        model.addNode(className, states[className].size());
-        cout << "*Adding class " << className << " with " << states[className].size() << " states" << endl;
+        model.addNode(className);
     }
     int Classifier::getNumberOfNodes()
     {
@@ -141,57 +141,5 @@ namespace bayesnet {
     {
         model.dump_cpt();
     }
-    void Classifier::localDiscretizationProposal(map<string, mdlp::CPPFImdlp*>& discretizers, Tensor& Xf)
-    {
-        // order of local discretization is important. no good 0, 1, 2...
-        auto order = model.topological_sort();
-        auto& nodes = model.getNodes();
-        vector<int> indicesToReDiscretize;
-        auto n_samples = Xf.size(1);
-        bool upgrade = false; // Flag to check if we need to upgrade the model
-        for (auto feature : order) {
-            auto nodeParents = nodes[feature]->getParents();
-            int index = find(features.begin(), features.end(), feature) - features.begin();
-            vector<string> parents;
-            transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) {return p->getName(); });
-            if (parents.size() == 1) continue; // Only has class as parent
-            upgrade = true;
-            // Remove class as parent as it will be added later
-            parents.erase(remove(parents.begin(), parents.end(), className), parents.end());
-            // Get the indices of the parents
-            vector<int> indices;
-            transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(features.begin(), features.end(), p) - features.begin(); });
-            // Now we fit the discretizer of the feature conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
-            vector<string> yJoinParents;
-            transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
-            for (auto idx : indices) {
-                for (int i = 0; i < n_samples; ++i) {
-                    yJoinParents[i] += to_string(Xv[idx][i]);
-                }
-            }
-            auto arff = ArffFiles();
-            auto yxv = arff.factorize(yJoinParents);
-            auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
-            auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
-            discretizers[feature]->fit(xvf, yxv);
-            indicesToReDiscretize.push_back(index);
-        }
-        if (upgrade) {
-            // Discretize again X (only the affected indices) with the new fitted discretizers
-            for (auto index : indicesToReDiscretize) {
-                auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
-                auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-                Xv[index] = discretizers[features[index]]->transform(Xt);
-                auto xStates = vector<int>(discretizers[features[index]]->getCutPoints().size() + 1);
-                iota(xStates.begin(), xStates.end(), 0);
-                states[features[index]] = xStates;
-            }
-            // Now we fit the model again with the new values
-            cout << "Classifier: Upgrading model" << endl;
-            // To update the nodes states
-            addNodes();
-            model.fit(Xv, yv, features, className);
-            cout << "Classifier: Model upgraded" << endl;
-        }
-    }
+
 }

src/BayesNet/Classifier.h

@@ -4,7 +4,6 @@
 #include "BaseClassifier.h"
 #include "Network.h"
 #include "BayesMetrics.h"
-#include "CPPFImdlp.h"
 using namespace std;
 using namespace torch;
 
@@ -26,8 +25,8 @@ namespace bayesnet {
         string className;
         map<string, vector<int>> states;
         void checkFitParameters();
+        void generateTensorXFromVector();
         virtual void train() = 0;
-        void localDiscretizationProposal(map<string, mdlp::CPPFImdlp*>& discretizers, Tensor& Xf);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;

src/BayesNet/Ensemble.cc

@@ -3,24 +3,24 @@
 namespace bayesnet {
     using namespace torch;
 
-    Ensemble::Ensemble() : m(0), n(0), n_models(0), metrics(Metrics()), fitted(false) {}
+    Ensemble::Ensemble() : n_models(0), metrics(Metrics()), fitted(false) {}
     Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        dataset = cat({ X, y.view({y.size(0), 1}) }, 1);
+        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        samples = torch::cat({ X, ytmp }, 0);
         this->features = features;
         this->className = className;
         this->states = states;
         auto n_classes = states[className].size();
-        metrics = Metrics(dataset, features, className, n_classes);
+        metrics = Metrics(samples, features, className, n_classes);
         // Build models
         train();
         // Train models
         n_models = models.size();
-        auto Xt = torch::transpose(X, 0, 1);
         for (auto i = 0; i < n_models; ++i) {
-            if (Xv == vector<vector<int>>()) {
+            if (Xv.empty()) {
                 // fit with tensors
-                models[i]->fit(Xt, y, features, className, states);
+                models[i]->fit(X, y, features, className, states);
             } else {
                 // fit with vectors
                 models[i]->fit(Xv, yv, features, className, states);
@@ -29,9 +29,16 @@ namespace bayesnet {
         fitted = true;
         return *this;
     }
+    void Ensemble::generateTensorXFromVector()
+    {
+        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
+        for (int i = 0; i < Xv.size(); ++i) {
+            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
+        }
+    }
     Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        this->X = torch::transpose(X, 0, 1);
+        this->X = X;
         this->y = y;
         Xv = vector<vector<int>>();
         yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
@@ -39,11 +46,8 @@ namespace bayesnet {
     }
     Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        this->X = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, kInt32);
         Xv = X;
-        for (int i = 0; i < X.size(); ++i) {
-            this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt32));
-        }
+        generateTensorXFromVector();
         this->y = torch::tensor(y, kInt32);
         yv = y;
         return build(features, className, states);
@@ -53,10 +57,11 @@ namespace bayesnet {
         auto y_pred_ = y_pred.accessor<int, 2>();
         vector<int> y_pred_final;
         for (int i = 0; i < y_pred.size(0); ++i) {
-            vector<float> votes(states[className].size(), 0);
+            vector<float> votes(y_pred.size(1), 0);
             for (int j = 0; j < y_pred.size(1); ++j) {
                 votes[y_pred_[i][j]] += 1;
             }
+            // argsort in descending order
             auto indices = argsort(votes);
             y_pred_final.push_back(indices[0]);
         }
@@ -70,13 +75,12 @@ namespace bayesnet {
         Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
         //Create a threadpool
         auto threads{ vector<thread>() };
-        auto lock = mutex();
+        mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
             threads.push_back(thread([&, i]() {
                 auto ypredict = models[i]->predict(X);
-                lock.lock();
+                lock_guard<mutex> lock(mtx);
                 y_pred.index_put_({ "...", i }, ypredict);
-                lock.unlock();
                 }));
         }
         for (auto& thread : threads) {

src/BayesNet/Ensemble.h

@@ -10,23 +10,23 @@ using namespace torch;
 namespace bayesnet {
     class Ensemble : public BaseClassifier {
     private:
-        bool fitted;
-        long n_models;
         Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
+        unsigned n_models;
+        bool fitted;
         vector<unique_ptr<Classifier>> models;
-        int m, n; // m: number of samples, n: number of features
         Tensor X;
         vector<vector<int>> Xv;
         Tensor y;
         vector<int> yv;
-        Tensor dataset;
+        Tensor samples;
         Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
         void virtual train() = 0;
         vector<int> voting(Tensor& y_pred);
+        void generateTensorXFromVector();
     public:
         Ensemble();
         virtual ~Ensemble() = default;

src/BayesNet/KDBLd.cc

@@ -0,0 +1,35 @@
+#include "KDBLd.h"
+
+namespace bayesnet {
+    using namespace std;
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        fit_local_discretization(states, y);
+        generateTensorXFromVector();
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
+        KDB::fit(KDB::Xv, KDB::yv, features, className, states);
+        localDiscretizationProposal(states, model);
+        generateTensorXFromVector();
+        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        samples = torch::cat({ X, ytmp }, 0);
+        model.fit(KDB::Xv, KDB::yv, features, className);
+        return *this;
+    }
+    Tensor KDBLd::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return KDB::predict(Xt);
+    }
+    vector<string> KDBLd::graph(const string& name)
+    {
+        return KDB::graph(name);
+    }
+}

src/BayesNet/KDBLd.h

@@ -0,0 +1,19 @@
+#ifndef KDBLD_H
+#define KDBLD_H
+#include "KDB.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class KDBLd : public KDB, public Proposal {
+    private:
+    public:
+        explicit KDBLd(int k);
+        virtual ~KDBLd() = default;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "KDB") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !KDBLD_H

src/BayesNet/Network.cc

@@ -31,21 +31,18 @@ namespace bayesnet {
     {
         return samples;
     }
-    void Network::addNode(const string& name, int numStates)
+    void Network::addNode(const string& name)
     {
         if (name == "") {
             throw invalid_argument("Node name cannot be empty");
         }
+        if (nodes.find(name) != nodes.end()) {
+            return;
+        }
         if (find(features.begin(), features.end(), name) == features.end()) {
             features.push_back(name);
         }
-        if (nodes.find(name) != nodes.end()) {
-            // if node exists update its number of states and remove parents, children and CPT
-            nodes[name]->clear();
-            nodes[name]->setNumStates(numStates);
-            return;
-        }
-        nodes[name] = std::make_unique<Node>(name, numStates);
+        nodes[name] = std::make_unique<Node>(name);
     }
     vector<string> Network::getFeatures()
     {
@@ -128,14 +125,20 @@ namespace bayesnet {
             }
         }
     }
+    void Network::setStates()
+    {
+        // Set states to every Node in the network
+        for (int i = 0; i < features.size(); ++i) {
+            nodes[features[i]]->setNumStates(static_cast<int>(torch::max(samples.index({ i, "..." })).item<int>()) + 1);
+        }
+        classNumStates = nodes[className]->getNumStates();
+    }
     // X comes in nxm, where n is the number of features and m the number of samples
     void Network::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& featureNames, const string& className)
     {
         checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
         this->className = className;
         dataset.clear();
-        // Specific part
-        classNumStates = torch::max(y).item<int>() + 1;
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
         samples = torch::cat({ X , ytmp }, 0);
         for (int i = 0; i < featureNames.size(); ++i) {
@@ -151,8 +154,6 @@ namespace bayesnet {
         checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
         this->className = className;
         dataset.clear();
-        // Specific part
-        classNumStates = *max_element(labels.begin(), labels.end()) + 1;
         // Build dataset & tensor of samples (nxm) (n+1 because of the class)
         samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
         for (int i = 0; i < featureNames.size(); ++i) {
@@ -165,6 +166,7 @@ namespace bayesnet {
     }
     void Network::completeFit()
     {
+        setStates();
         int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
         if (maxThreadsRunning < 1) {
             maxThreadsRunning = 1;
@@ -211,7 +213,10 @@ namespace bayesnet {
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         for (int i = 0; i < samples.size(1); ++i) {
             auto sample = samples.index({ "...", i });
-            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
+            auto psample = predict_sample(sample);
+            auto temp = torch::tensor(psample, torch::kFloat64);
+            //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
+            result.index_put_({ i, "..." }, temp);
         }
         if (proba)
             return result;
@@ -323,17 +328,16 @@ namespace bayesnet {
         mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
             threads.emplace_back([this, &result, &evidence, i, &mtx]() {
-                auto completeEvidence = map<string, int>(evidence);
-                completeEvidence[getClassName()] = i;
+            auto completeEvidence = map<string, int>(evidence);
+            completeEvidence[getClassName()] = i;
                 double factor = computeFactor(completeEvidence);
                 lock_guard<mutex> lock(mtx);
                 result[i] = factor;
-                });
+            });
         }
         for (auto& thread : threads) {
             thread.join();
         }
-
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
         transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
@@ -419,7 +423,7 @@ namespace bayesnet {
     void Network::dump_cpt()
     {
         for (auto& node : nodes) {
-            cout << "* " << node.first << ": " << node.second->getCPT() << endl;
+            cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << endl;
         }
     }
 }

src/BayesNet/Network.h

@@ -27,6 +27,7 @@ namespace bayesnet {
         double mutualInformation(torch::Tensor&, torch::Tensor&);
         void completeFit();
         void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className);
+        void setStates();
     public:
         Network();
         explicit Network(float, int);
@@ -34,7 +35,7 @@ namespace bayesnet {
         explicit Network(Network&);
         torch::Tensor& getSamples();
         float getmaxThreads();
-        void addNode(const string&, int);
+        void addNode(const string&);
         void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
         vector<string> getFeatures();

src/BayesNet/Node.cc

@@ -2,8 +2,8 @@
 
 namespace bayesnet {
 
-    Node::Node(const std::string& name, int numStates)
-        : name(name), numStates(numStates), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
+    Node::Node(const std::string& name)
+        : name(name), numStates(0), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
     {
     }
     void Node::clear()
@@ -86,6 +86,7 @@ namespace bayesnet {
     }
     void Node::computeCPT(map<string, vector<int>>& dataset, const int laplaceSmoothing)
     {
+        dimensions.clear();
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
         transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });

src/BayesNet/Node.h

@@ -16,7 +16,7 @@ namespace bayesnet {
         vector<int64_t> dimensions; // dimensions of the cpTable
     public:
         vector<pair<string, string>> combinations(const vector<string>&);
-        Node(const string&, int);
+        explicit Node(const string&);
         void clear();
         void addParent(Node*);
         void addChild(Node*);

src/BayesNet/Proposal.cc

@@ -0,0 +1,102 @@
+#include "Proposal.h"
+#include "ArffFiles.h"
+
+namespace bayesnet {
+    Proposal::Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_) : Xv(Xv_), yv(yv_), pFeatures(features_), pClassName(className_) {}
+    Proposal::~Proposal()
+    {
+        for (auto& [key, value] : discretizers) {
+            delete value;
+        }
+    }
+    void Proposal::localDiscretizationProposal(map<string, vector<int>>& states, Network& model)
+    {
+        // order of local discretization is important. no good 0, 1, 2...
+        // although we rediscretize features after the local discretization of every feature
+        auto order = model.topological_sort();
+        auto& nodes = model.getNodes();
+        vector<int> indicesToReDiscretize;
+        auto n_samples = Xf.size(1);
+        bool upgrade = false; // Flag to check if we need to upgrade the model
+        for (auto feature : order) {
+            auto nodeParents = nodes[feature]->getParents();
+            if (nodeParents.size() < 2) continue; // Only has class as parent
+            upgrade = true;
+            int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
+            indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
+            vector<string> parents;
+            transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
+            // Remove class as parent as it will be added later
+            parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
+            // Get the indices of the parents
+            vector<int> indices;
+            transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
+            // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
+            vector<string> yJoinParents;
+            transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
+            for (auto idx : indices) {
+                for (int i = 0; i < n_samples; ++i) {
+                    yJoinParents[i] += to_string(Xv[idx][i]);
+                }
+            }
+            auto arff = ArffFiles();
+            auto yxv = arff.factorize(yJoinParents);
+            auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
+            auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
+            discretizers[feature]->fit(xvf, yxv);
+            //
+            //
+            //
+            // auto tmp = discretizers[feature]->transform(xvf);
+            // Xv[index] = tmp;
+            // auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+            // iota(xStates.begin(), xStates.end(), 0);
+            // //Update new states of the feature/node
+            // states[feature] = xStates;
+        }
+        if (upgrade) {
+            // Discretize again X (only the affected indices) with the new fitted discretizers
+            for (auto index : indicesToReDiscretize) {
+                auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
+                auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+                Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
+                auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+                iota(xStates.begin(), xStates.end(), 0);
+                //Update new states of the feature/node
+                states[pFeatures[index]] = xStates;
+            }
+        }
+    }
+    void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
+    {
+        // Sharing Xv and yv with Classifier
+        Xv = vector<vector<int>>();
+        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        // discretize input data by feature(row)
+        for (int i = 0; i < pFeatures.size(); ++i) {
+            auto* discretizer = new mdlp::CPPFImdlp();
+            auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
+            auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+            discretizer->fit(Xt, yv);
+            Xv.push_back(discretizer->transform(Xt));
+            auto xStates = vector<int>(discretizer->getCutPoints().size() + 1);
+            iota(xStates.begin(), xStates.end(), 0);
+            states[pFeatures[i]] = xStates;
+            discretizers[pFeatures[i]] = discretizer;
+        }
+        int n_classes = torch::max(y).item<int>() + 1;
+        auto yStates = vector<int>(n_classes);
+        iota(yStates.begin(), yStates.end(), 0);
+        states[pClassName] = yStates;
+    }
+    torch::Tensor Proposal::prepareX(torch::Tensor& X)
+    {
+        auto Xtd = torch::zeros_like(X, torch::kInt32);
+        for (int i = 0; i < X.size(0); ++i) {
+            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
+            auto Xd = discretizers[pFeatures[i]]->transform(Xt);
+            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
+        }
+        return Xtd;
+    }
+}

src/BayesNet/Proposal.h

@@ -0,0 +1,29 @@
+#ifndef PROPOSAL_H
+#define PROPOSAL_H
+#include <string>
+#include <map>
+#include <torch/torch.h>
+#include "Network.h"
+#include "CPPFImdlp.h"
+#include "Classifier.h"
+
+namespace bayesnet {
+    class Proposal {
+    public:
+        Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
+        virtual ~Proposal();
+    protected:
+        torch::Tensor prepareX(torch::Tensor& X);
+        void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
+        void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
+        torch::Tensor Xf; // X continuous nxm tensor
+        map<string, mdlp::CPPFImdlp*> discretizers;
+    private:
+        vector<string>& pFeatures;
+        string& pClassName;
+        vector<vector<int>>& Xv; // X discrete nxm vector
+        vector<int>& yv;
+    };
+}
+
+#endif

src/BayesNet/SPODELd.cc

@@ -0,0 +1,35 @@
+#include "SPODELd.h"
+
+namespace bayesnet {
+    using namespace std;
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        fit_local_discretization(states, y);
+        generateTensorXFromVector();
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
+        SPODE::fit(SPODE::Xv, SPODE::yv, features, className, states);
+        localDiscretizationProposal(states, model);
+        generateTensorXFromVector();
+        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        samples = torch::cat({ X, ytmp }, 0);
+        model.fit(SPODE::Xv, SPODE::yv, features, className);
+        return *this;
+    }
+    Tensor SPODELd::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return SPODE::predict(Xt);
+    }
+    vector<string> SPODELd::graph(const string& name)
+    {
+        return SPODE::graph(name);
+    }
+}

src/BayesNet/SPODELd.h

@@ -0,0 +1,19 @@
+#ifndef SPODELD_H
+#define SPODELD_H
+#include "SPODE.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class SPODELd : public SPODE, public Proposal {
+    private:
+    public:
+        explicit SPODELd(int root);
+        virtual ~SPODELd() = default;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "SPODE") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !SPODELD_H

src/BayesNet/TANLd.cc

@@ -0,0 +1,35 @@
+#include "TANLd.h"
+
+namespace bayesnet {
+    using namespace std;
+    TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        fit_local_discretization(states, y);
+        generateTensorXFromVector();
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
+        TAN::fit(TAN::Xv, TAN::yv, features, className, states);
+        localDiscretizationProposal(states, model);
+        generateTensorXFromVector();
+        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
+        samples = torch::cat({ X, ytmp }, 0);
+        model.fit(TAN::Xv, TAN::yv, features, className);
+        return *this;
+    }
+    Tensor TANLd::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
+    }
+    vector<string> TANLd::graph(const string& name)
+    {
+        return TAN::graph(name);
+    }
+}

src/BayesNet/TANLd.h

@@ -0,0 +1,19 @@
+#ifndef TANLD_H
+#define TANLD_H
+#include "TAN.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class TANLd : public TAN, public Proposal {
+    private:
+    public:
+        TANLd();
+        virtual ~TANLd() = default;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "TAN") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !TANLD_H

src/BayesNet/TANNew.cc

@@ -1,55 +0,0 @@
-#include "TANNew.h"
-
-namespace bayesnet {
-    using namespace std;
-    TANNew::TANNew() : TAN() {}
-    TANNew::~TANNew() {}
-    TANNew& TANNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
-    {
-        Xf = X_;
-        y = y_;
-        features = features_;
-        className = className_;
-        Xv = vector<vector<int>>();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-        // discretize input data by feature(row)
-        for (int i = 0; i < features.size(); ++i) {
-            auto* discretizer = new mdlp::CPPFImdlp();
-            auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
-            auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-            discretizer->fit(Xt, yv);
-            Xv.push_back(discretizer->transform(Xt));
-            auto xStates = vector<int>(discretizer->getCutPoints().size() + 1);
-            iota(xStates.begin(), xStates.end(), 0);
-            states[features[i]] = xStates;
-            discretizers[features[i]] = discretizer;
-        }
-        int n_classes = torch::max(y).item<int>() + 1;
-        auto yStates = vector<int>(n_classes);
-        iota(yStates.begin(), yStates.end(), 0);
-        states[className] = yStates;
-        // Now we have standard TAN and now we implement the proposal
-        // 1st we need to fit the model to build the TAN structure
-        cout << "TANNew: Fitting model" << endl;
-        TAN::fit(Xv, yv, features, className, states);
-        cout << "TANNew: Model fitted" << endl;
-        localDiscretizationProposal(discretizers, Xf);
-        return *this;
-    }
-
-    Tensor TANNew::predict(Tensor& X)
-    {
-        auto Xtd = torch::zeros_like(X, torch::kInt32);
-        for (int i = 0; i < X.size(0); ++i) {
-            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
-            auto Xd = discretizers[features[i]]->transform(Xt);
-            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
-        }
-        cout << "TANNew Xtd: " << Xtd.sizes() << endl;
-        return TAN::predict(Xtd);
-    }
-    vector<string> TANNew::graph(const string& name)
-    {
-        return TAN::graph(name);
-    }
-}

src/BayesNet/TANNew.h

@@ -1,21 +0,0 @@
-#ifndef TANNEW_H
-#define TANNEW_H
-#include "TAN.h"
-#include "CPPFImdlp.h"
-
-namespace bayesnet {
-    using namespace std;
-    class TANNew : public TAN {
-    private:
-        map<string, mdlp::CPPFImdlp*> discretizers;
-        torch::Tensor Xf; // X continuous nxm tensor
-    public:
-        TANNew();
-        virtual ~TANNew();
-        TANNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "TAN") override;
-        Tensor predict(Tensor& X) override;
-        static inline string version() { return "0.0.1"; };
-    };
-}
-#endif // !TANNEW_H

src/BayesNet/bayesnetUtils.cc

@@ -3,6 +3,7 @@
 namespace bayesnet {
     using namespace std;
     using namespace torch;
+    // Return the indices in descending order
     vector<int> argsort(vector<float>& nums)
     {
         int n = nums.size();

src/Platform/CMakeLists.txt

@@ -4,5 +4,5 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
-add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc)
+add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc Report.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/Experiment.cc

@@ -1,6 +1,7 @@
 #include "Experiment.h"
 #include "Datasets.h"
 #include "Models.h"
+#include "Report.h"
 
 namespace platform {
     using json = nlohmann::json;
@@ -86,6 +87,13 @@ namespace platform {
         file.close();
     }
 
+    void Experiment::report()
+    {
+        json data = build_json();
+        Report report(data);
+        report.show();
+    }
+
     void Experiment::show()
     {
         json data = build_json();

src/Platform/Experiment.h

@@ -108,6 +108,7 @@ namespace platform {
         void cross_validation(const string& path, const string& fileName);
         void go(vector<string> filesToProcess, const string& path);
         void show();
+        void report();
     };
 }
 #endif

src/Platform/Models.h

@@ -6,7 +6,10 @@
 #include "TAN.h"
 #include "KDB.h"
 #include "SPODE.h"
-#include "TANNew.h"
+#include "TANLd.h"
+#include "KDBLd.h"
+#include "SPODELd.h"
+#include "AODELd.h"
 namespace platform {
     class Models {
     private:

src/Platform/Report.cc

@@ -0,0 +1,66 @@
+#include "Report.h"
+
+namespace platform {
+    string headerLine(const string& text)
+    {
+        int n = MAXL - text.length() - 3;
+        return "* " + text + string(n, ' ') + "*\n";
+    }
+    string Report::fromVector(const string& key)
+    {
+        string result = "";
+
+        for (auto& item : data[key]) {
+            result += to_string(item) + ", ";
+        }
+        return "[" + result.substr(0, result.length() - 2) + "]";
+    }
+    string fVector(const json& data)
+    {
+        string result = "";
+        for (const auto& item : data) {
+            result += to_string(item) + ", ";
+        }
+        return "[" + result.substr(0, result.length() - 2) + "]";
+    }
+    void Report::show()
+    {
+        header();
+        body();
+    }
+    void Report::header()
+    {
+        cout << string(MAXL, '*') << endl;
+        cout << headerLine("Report " + data["model"].get<string>() + " ver. " + data["version"].get<string>() + " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) + " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>());
+        cout << headerLine(data["title"].get<string>());
+        cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
+        cout << headerLine("Execution took  " + to_string(data["duration"].get<float>()) + " seconds,   " + to_string(data["duration"].get<float>() / 3600) + " hours, on " + data["platform"].get<string>());
+        cout << headerLine("Score is " + data["score_name"].get<string>());
+        cout << string(MAXL, '*') << endl;
+        cout << endl;
+    }
+    void Report::body()
+    {
+        cout << "Dataset                        Sampl. Feat. Cls Nodes   Edges   States  Score           Time              Hyperparameters" << endl;
+        cout << "============================== ====== ===== === ======= ======= ======= =============== ================= ===============" << endl;
+        for (const auto& r : data["results"]) {
+            cout << setw(30) << left << r["dataset"].get<string>() << " ";
+            cout << setw(6) << right << r["samples"].get<int>() << " ";
+            cout << setw(5) << right << r["features"].get<int>() << " ";
+            cout << setw(3) << right << r["classes"].get<int>() << " ";
+            cout << setw(7) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
+            cout << setw(7) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
+            cout << setw(7) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
+            cout << setw(8) << right << setprecision(6) << fixed << r["score_test"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_test_std"].get<double>() << " ";
+            cout << setw(10) << right << setprecision(6) << fixed << r["test_time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["test_time_std"].get<double>() << " ";
+            cout << " " << r["hyperparameters"].get<string>();
+            cout << endl;
+            cout << string(MAXL, '*') << endl;
+            cout << headerLine("Train scores: " + fVector(r["scores_train"]));
+            cout << headerLine("Test  scores: " + fVector(r["scores_test"]));
+            cout << headerLine("Train  times: " + fVector(r["times_train"]));
+            cout << headerLine("Test   times: " + fVector(r["times_test"]));
+            cout << string(MAXL, '*') << endl;
+        }
+    }
+}

src/Platform/Report.h

@@ -0,0 +1,23 @@
+#ifndef REPORT_H
+#define REPORT_H
+#include <string>
+#include <iostream>
+#include <nlohmann/json.hpp>
+
+using json = nlohmann::json;
+const int MAXL = 121;
+namespace platform {
+    using namespace std;
+    class Report {
+    public:
+        explicit Report(json data_) { data = data_; };
+        virtual ~Report() = default;
+        void show();
+    private:
+        void header();
+        void body();
+        string fromVector(const string& key);
+        json data;
+    };
+};
+#endif

src/Platform/main.cc

@@ -102,9 +102,10 @@ int main(int argc, char** argv)
     /*
     * Begin Processing
     */
+    auto env = platform::DotEnv();
     auto experiment = platform::Experiment();
     experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
-    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform("BayesNet");
+    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
     experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
     for (auto seed : seeds) {
         experiment.addRandomSeed(seed);
@@ -116,7 +117,7 @@ int main(int argc, char** argv)
     if (saveResults)
         experiment.save(PATH_RESULTS);
     else
-        experiment.show();
+        experiment.report();
     cout << "Done!" << endl;
     return 0;
 }

src/Platform/modelRegister.h

@@ -2,12 +2,18 @@
 #define MODEL_REGISTER_H
 static platform::Registrar registrarT("TAN",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
-static platform::Registrar registrarTN("TANNew",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANNew();});
+static platform::Registrar registrarTLD("TANLd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
 static platform::Registrar registrarS("SPODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
+static platform::Registrar registrarSLD("SPODELd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
 static platform::Registrar registrarK("KDB",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
+static platform::Registrar registrarKLD("KDBLd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
 static platform::Registrar registrarA("AODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
+static platform::Registrar registrarALD("AODELd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
 #endif

tests/BayesNetwork.cc

@@ -9,29 +9,21 @@ TEST_CASE("Test Bayesian Network")
 {
     auto [Xd, y, features, className, states] = loadFile("iris");
 
-    SECTION("Test Update Nodes")
-    {
-        auto net = bayesnet::Network();
-        net.addNode("A", 3);
-        REQUIRE(net.getStates() == 3);
-        net.addNode("A", 5);
-        REQUIRE(net.getStates() == 5);
-    }
     SECTION("Test get features")
     {
         auto net = bayesnet::Network();
-        net.addNode("A", 3);
-        net.addNode("B", 5);
+        net.addNode("A");
+        net.addNode("B");
         REQUIRE(net.getFeatures() == vector<string>{"A", "B"});
-        net.addNode("C", 2);
+        net.addNode("C");
         REQUIRE(net.getFeatures() == vector<string>{"A", "B", "C"});
     }
     SECTION("Test get edges")
     {
         auto net = bayesnet::Network();
-        net.addNode("A", 3);
-        net.addNode("B", 5);
-        net.addNode("C", 2);
+        net.addNode("A");
+        net.addNode("B");
+        net.addNode("C");
         net.addEdge("A", "B");
         net.addEdge("B", "C");
         REQUIRE(net.getEdges() == vector<pair<string, string>>{ {"A", "B"}, { "B", "C" } });