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

@@ -17,6 +17,11 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
 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

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/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 KDBNew.cc Mst.cc Proposal.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/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/KDB.cc

@@ -35,7 +35,6 @@ namespace bayesnet {
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
         auto conditionalEdgeWeights = metrics.conditionalEdge();
-        cout << "Conditional edge weights: " << conditionalEdgeWeights << endl;
         // 3. Let the used variable list, S, be empty.
         vector<int> S;
         // 4. Let the DAG network being constructed, BN, begin with a single

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/KDBNew.cc

@@ -1,48 +0,0 @@
-#include "KDBNew.h"
-
-namespace bayesnet {
-    using namespace std;
-    KDBNew::KDBNew(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
-    KDBNew& KDBNew::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_;
-        model.initialize();
-        // 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
-        cout << "KDBNew: Fitting model" << endl;
-        KDB::fit(KDB::Xv, KDB::yv, features, className, states);
-        cout << "KDBNew: Model fitted" << endl;
-        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;
-    }
-    void KDBNew::train()
-    {
-        KDB::train();
-    }
-    Tensor KDBNew::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 << "KDBNew Xtd: " << Xtd.sizes() << endl;
-        return KDB::predict(Xtd);
-    }
-    vector<string> KDBNew::graph(const string& name)
-    {
-        return KDB::graph(name);
-    }
-}

src/BayesNet/KDBNew.h

@@ -1,20 +0,0 @@
-#ifndef KDBNEW_H
-#define KDBNEW_H
-#include "KDB.h"
-#include "Proposal.h"
-
-namespace bayesnet {
-    using namespace std;
-    class KDBNew : public KDB, public Proposal {
-    private:
-    public:
-        KDBNew(int k);
-        virtual ~KDBNew() = default;
-        KDBNew& 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;
-        void train() override;
-        static inline string version() { return "0.0.1"; };
-    };
-}
-#endif // !KDBNew_H

src/BayesNet/Proposal.cc

@@ -47,25 +47,25 @@ namespace bayesnet {
             //
             //
             //
-            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;
+            // 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;
+            }
         }
-        // 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)
     {
@@ -89,4 +89,14 @@ namespace bayesnet {
         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

@@ -5,6 +5,7 @@
 #include <torch/torch.h>
 #include "Network.h"
 #include "CPPFImdlp.h"
+#include "Classifier.h"
 
 namespace bayesnet {
     class Proposal {
@@ -12,6 +13,7 @@ namespace bayesnet {
         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

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

@@ -1,9 +1,9 @@
-#include "TANNew.h"
+#include "TANLd.h"
 
 namespace bayesnet {
     using namespace std;
-    TANNew::TANNew() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
-    TANNew& TANNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    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_;
@@ -15,9 +15,7 @@ namespace bayesnet {
         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
-        cout << "TANNew: Fitting model" << endl;
         TAN::fit(TAN::Xv, TAN::yv, features, className, states);
-        cout << "TANNew: Model fitted" << endl;
         localDiscretizationProposal(states, model);
         generateTensorXFromVector();
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
@@ -25,18 +23,12 @@ namespace bayesnet {
         model.fit(TAN::Xv, TAN::yv, features, className);
         return *this;
     }
-    Tensor TANNew::predict(Tensor& X)
+    Tensor TANLd::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);
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
     }
-    vector<string> TANNew::graph(const string& name)
+    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.h

@@ -1,19 +0,0 @@
-#ifndef TANNEW_H
-#define TANNEW_H
-#include "TAN.h"
-#include "Proposal.h"
-
-namespace bayesnet {
-    using namespace std;
-    class TANNew : public TAN, public Proposal {
-    private:
-    public:
-        TANNew();
-        virtual ~TANNew() = default;
-        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();
@@ -146,11 +154,6 @@ namespace platform {
                 auto y_test = y.index({ test_t });
                 cout << nfold + 1 << ", " << flush;
                 clf->fit(X_train, y_train, features, className, states);
-                cout << endl;
-                auto lines = clf->show();
-                for (auto line : lines) {
-                    cout << line << endl;
-                }
                 nodes[item] = clf->getNumberOfNodes();
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();

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

@@ -6,8 +6,10 @@
 #include "TAN.h"
 #include "KDB.h"
 #include "SPODE.h"
-#include "TANNew.h"
-#include "KDBNew.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

@@ -2,14 +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 registrarKN("KDBNew",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBNew(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