Add SPODENew to models

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 TANNew.cc KDBNew.cc SPODENew.cc Mst.cc Proposal.cc)
 target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")

src/BayesNet/KDBNew.h

@@ -8,7 +8,7 @@ namespace bayesnet {
     class KDBNew : public KDB, public Proposal {
     private:
     public:
-        KDBNew(int k);
+        explicit 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;

src/BayesNet/SPODENew.cc

@@ -0,0 +1,35 @@
+#include "SPODENew.h"
+
+namespace bayesnet {
+    using namespace std;
+    SPODENew::SPODENew(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
+    SPODENew& SPODENew::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 SPODENew::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return SPODE::predict(Xt);
+    }
+    vector<string> SPODENew::graph(const string& name)
+    {
+        return SPODE::graph(name);
+    }
+}

src/BayesNet/SPODENew.h

@@ -0,0 +1,19 @@
+#ifndef SPODENEW_H
+#define SPODENEW_H
+#include "SPODE.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class SPODENew : public SPODE, public Proposal {
+    private:
+    public:
+        explicit SPODENew(int root);
+        virtual ~SPODENew() = default;
+        SPODENew& 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 // !SPODENew_H

src/Platform/Models.h

@@ -8,6 +8,7 @@
 #include "SPODE.h"
 #include "TANNew.h"
 #include "KDBNew.h"
+#include "SPODENew.h"
 namespace platform {
     class Models {
     private:

src/Platform/Report.cc

@@ -48,9 +48,9 @@ namespace platform {
             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) << right << r["nodes"].get<float>() << " ";
-            cout << setw(7) << right << r["leaves"].get<float>() << " ";
-            cout << setw(7) << right << r["depth"].get<float>() << " ";
+            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>();

src/Platform/modelRegister.h

@@ -6,6 +6,8 @@ static platform::Registrar registrarTN("TANNew",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANNew();});
 static platform::Registrar registrarS("SPODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
+static platform::Registrar registrarSN("SPODENew",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODENew(2);});
 static platform::Registrar registrarK("KDB",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
 static platform::Registrar registrarKN("KDBNew",