Add colors and enhace input control

Reviewed-on: https://gitea.rmontanana.es:11000/rmontanana/BayesNet/pulls/3

.vscode/launch.json

@@ -25,8 +25,7 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "AODE",
-                "--discretize",
+                "SPODELd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
@@ -35,6 +34,17 @@
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "manage",
+            "program": "${workspaceFolder}/build/src/Platform/manage",
+            "args": [
+                "-n",
+                "20"
+            ],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
         {
             "name": "Build & debug active file",
             "type": "cppdbg",

src/BayesNet/AODE.cc

@@ -9,7 +9,7 @@ namespace bayesnet {
             models.push_back(std::make_unique<SPODE>(i));
         }
     }
-    vector<string> AODE::graph(const string& title)
+    vector<string> AODE::graph(const string& title) const
     {
         return Ensemble::graph(title);
     }

src/BayesNet/AODE.h

@@ -9,7 +9,7 @@ namespace bayesnet {
     public:
         AODE();
         virtual ~AODE() {};
-        vector<string> graph(const string& title = "AODE") override;
+        vector<string> graph(const string& title = "AODE") const override;
     };
 }
 #endif

src/BayesNet/AODELd.cc

@@ -1,18 +1,23 @@
 #include "AODELd.h"
+#include "Models.h"
 
 namespace bayesnet {
     using namespace std;
     AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
     AODELd& AODELd::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_;
-        states = states_;
-        buildModel();
-        trainModel();
-        n_models = models.size();
-        fitted = true;
+        Xf = X_;
+        y = y_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // 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
+        Ensemble::fit(dataset, features, className, states);
         return *this;
+
     }
     void AODELd::buildModel()
     {
@@ -20,18 +25,15 @@ namespace bayesnet {
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODELd>(i));
         }
+        n_models = models.size();
     }
     void AODELd::trainModel()
     {
         for (const auto& model : models) {
-            model->fit(dataset, features, className, states);
+            model->fit(Xf, y, features, className, states);
         }
     }
-    Tensor AODELd::predict(Tensor& X)
-    {
-        return Ensemble::predict(X);
-    }
-    vector<string> AODELd::graph(const string& name)
+    vector<string> AODELd::graph(const string& name) const
     {
         return Ensemble::graph(name);
     }

src/BayesNet/AODELd.h

@@ -7,15 +7,14 @@
 namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
-    private:
+    protected:
         void trainModel() override;
         void buildModel() override;
     public:
         AODELd();
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override;
         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;
+        vector<string> graph(const string& name = "AODE") const override;
         static inline string version() { return "0.0.1"; };
     };
 }

src/BayesNet/BaseClassifier.h

@@ -5,6 +5,8 @@
 namespace bayesnet {
     using namespace std;
     class BaseClassifier {
+    protected:
+        virtual void trainModel() = 0;
     public:
         // X is nxm vector, y is nx1 vector
         virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
@@ -16,14 +18,14 @@ namespace bayesnet {
         vector<int> virtual predict(vector<vector<int>>& X) = 0;
         float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
         float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
-        int virtual getNumberOfNodes() = 0;
-        int virtual getNumberOfEdges() = 0;
-        int virtual getNumberOfStates() = 0;
-        vector<string> virtual show() = 0;
-        vector<string> virtual graph(const string& title = "") = 0;
+        int virtual getNumberOfNodes()const = 0;
+        int virtual getNumberOfEdges()const = 0;
+        int virtual getNumberOfStates() const = 0;
+        vector<string> virtual show() const = 0;
+        vector<string> virtual graph(const string& title = "") const = 0;
         const string inline getVersion() const { return "0.1.0"; };
         vector<string> virtual topological_order() = 0;
-        void virtual dump_cpt() = 0;
+        void virtual dump_cpt()const = 0;
     };
 }
 #endif

src/BayesNet/CMakeLists.txt

@@ -1,5 +1,7 @@
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
+include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
+include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
 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)
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
 target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -37,7 +37,7 @@ namespace bayesnet {
     }
     void Classifier::trainModel()
     {
-        model.fit(dataset, features, className);
+        model.fit(dataset, features, className, states);
     }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
@@ -112,7 +112,7 @@ namespace bayesnet {
         }
         return model.score(X, y);
     }
-    vector<string> Classifier::show()
+    vector<string> Classifier::show() const
     {
         return model.show();
     }
@@ -124,16 +124,16 @@ namespace bayesnet {
         }
         model.addNode(className);
     }
-    int Classifier::getNumberOfNodes()
+    int Classifier::getNumberOfNodes() const
     {
         // Features does not include class
         return fitted ? model.getFeatures().size() + 1 : 0;
     }
-    int Classifier::getNumberOfEdges()
+    int Classifier::getNumberOfEdges() const
     {
-        return fitted ? model.getEdges().size() : 0;
+        return fitted ? model.getNumEdges() : 0;
     }
-    int Classifier::getNumberOfStates()
+    int Classifier::getNumberOfStates() const
     {
         return fitted ? model.getStates() : 0;
     }
@@ -141,7 +141,7 @@ namespace bayesnet {
     {
         return model.topological_sort();
     }
-    void Classifier::dump_cpt()
+    void Classifier::dump_cpt() const
     {
         model.dump_cpt();
     }

src/BayesNet/Classifier.h

@@ -23,7 +23,7 @@ namespace bayesnet {
         map<string, vector<int>> states;
         void checkFitParameters();
         virtual void buildModel() = 0;
-        virtual void trainModel();
+        void trainModel() override;
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
@@ -31,16 +31,16 @@ namespace bayesnet {
         Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
         void addNodes();
-        int getNumberOfNodes() override;
-        int getNumberOfEdges() override;
-        int getNumberOfStates() override;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
         Tensor predict(Tensor& X) override;
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        vector<string> show() override;
-        vector<string> topological_order() override;
-        void dump_cpt() override;
+        vector<string> show() const override;
+        vector<string> topological_order()  override;
+        void dump_cpt() const override;
     };
 }
 #endif

src/BayesNet/Ensemble.cc

@@ -94,7 +94,7 @@ namespace bayesnet {
         }
         return (double)correct / y_pred.size();
     }
-    vector<string> Ensemble::show()
+    vector<string> Ensemble::show() const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -103,7 +103,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Ensemble::graph(const string& title)
+    vector<string> Ensemble::graph(const string& title) const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -112,7 +112,7 @@ namespace bayesnet {
         }
         return result;
     }
-    int Ensemble::getNumberOfNodes()
+    int Ensemble::getNumberOfNodes() const
     {
         int nodes = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -120,7 +120,7 @@ namespace bayesnet {
         }
         return nodes;
     }
-    int Ensemble::getNumberOfEdges()
+    int Ensemble::getNumberOfEdges() const
     {
         int edges = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -128,7 +128,7 @@ namespace bayesnet {
         }
         return edges;
     }
-    int Ensemble::getNumberOfStates()
+    int Ensemble::getNumberOfStates() const
     {
         int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {

src/BayesNet/Ensemble.h

@@ -23,16 +23,16 @@ namespace bayesnet {
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        int getNumberOfNodes() override;
-        int getNumberOfEdges() override;
-        int getNumberOfStates() override;
-        vector<string> show() override;
-        vector<string> graph(const string& title) override;
-        vector<string> topological_order() override
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        vector<string> show() const override;
+        vector<string> graph(const string& title) const override;
+        vector<string> topological_order()  override
         {
             return vector<string>();
         }
-        void dump_cpt() override
+        void dump_cpt() const override
         {
         }
     };

src/BayesNet/KDB.cc

@@ -79,7 +79,7 @@ namespace bayesnet {
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::graph(const string& title)
+    vector<string> KDB::graph(const string& title) const
     {
         string header{ title };
         if (title == "KDB") {

src/BayesNet/KDB.h

@@ -15,7 +15,7 @@ namespace bayesnet {
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};
-        vector<string> graph(const string& name = "KDB") override;
+        vector<string> graph(const string& name = "KDB") const override;
     };
 }
 #endif

src/BayesNet/KDBLd.cc

@@ -11,11 +11,11 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // 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(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
+        states = localDiscretizationProposal(states, model);
         return *this;
     }
     Tensor KDBLd::predict(Tensor& X)
@@ -23,7 +23,7 @@ namespace bayesnet {
         auto Xt = prepareX(X);
         return KDB::predict(Xt);
     }
-    vector<string> KDBLd::graph(const string& name)
+    vector<string> KDBLd::graph(const string& name) const
     {
         return KDB::graph(name);
     }

src/BayesNet/KDBLd.h

@@ -11,7 +11,7 @@ namespace bayesnet {
         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;
+        vector<string> graph(const string& name = "KDB") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/BayesNet/Network.cc

@@ -43,15 +43,15 @@ namespace bayesnet {
         }
         nodes[name] = std::make_unique<Node>(name);
     }
-    vector<string> Network::getFeatures()
+    vector<string> Network::getFeatures() const
     {
         return features;
     }
-    int Network::getClassNumStates()
+    int Network::getClassNumStates() const
     {
         return classNumStates;
     }
-    int Network::getStates()
+    int Network::getStates() const
     {
         int result = 0;
         for (auto& node : nodes) {
@@ -59,7 +59,7 @@ namespace bayesnet {
         }
         return result;
     }
-    string Network::getClassName()
+    string Network::getClassName() const
     {
         return className;
     }
@@ -104,7 +104,7 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className)
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");
@@ -122,39 +122,42 @@ namespace bayesnet {
             if (find(features.begin(), features.end(), feature) == features.end()) {
                 throw invalid_argument("Feature " + feature + " not found in Network::features");
             }
+            if (states.find(feature) == states.end()) {
+                throw invalid_argument("Feature " + feature + " not found in states");
+            }
         }
     }
-    void Network::setStates()
+    void Network::setStates(const map<string, vector<int>>& states)
     {
         // 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);
+            nodes[features[i]]->setNumStates(states.at(features[i]).size());
         }
         classNumStates = nodes[className]->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states);
         this->className = className;
         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) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit();
+        completeFit(states);
     }
-    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className);
+        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states);
         this->className = className;
         this->samples = samples;
-        completeFit();
+        completeFit(states);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className)
+    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
+        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states);
         this->className = className;
         // Build 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);
@@ -162,11 +165,11 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit();
+        completeFit(states);
     }
-    void Network::completeFit()
+    void Network::completeFit(const map<string, vector<int>>& states)
     {
-        setStates();
+        setStates(states);
         int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
         if (maxThreadsRunning < 1) {
             maxThreadsRunning = 1;
@@ -212,7 +215,7 @@ namespace bayesnet {
         torch::Tensor result;
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         for (int i = 0; i < samples.size(1); ++i) {
-            auto sample = samples.index({ "...", i });
+            const Tensor sample = samples.index({ "...", i });
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
             //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
@@ -343,7 +346,7 @@ namespace bayesnet {
         transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
         return result;
     }
-    vector<string> Network::show()
+    vector<string> Network::show() const
     {
         vector<string> result;
         // Draw the network
@@ -356,7 +359,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(const string& title)
+    vector<string> Network::graph(const string& title) const
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
@@ -370,7 +373,7 @@ namespace bayesnet {
         output.push_back("}\n");
         return output;
     }
-    vector<pair<string, string>> Network::getEdges()
+    vector<pair<string, string>> Network::getEdges() const
     {
         auto edges = vector<pair<string, string>>();
         for (const auto& node : nodes) {
@@ -382,6 +385,10 @@ namespace bayesnet {
         }
         return edges;
     }
+    int Network::getNumEdges() const
+    {
+        return getEdges().size();
+    }
     vector<string> Network::topological_sort()
     {
         /* Check if al the fathers of every node are before the node */
@@ -420,7 +427,7 @@ namespace bayesnet {
         }
         return result;
     }
-    void Network::dump_cpt()
+    void Network::dump_cpt() const
     {
         for (auto& node : nodes) {
             cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << endl;

src/BayesNet/Network.h

@@ -20,13 +20,9 @@ namespace bayesnet {
         vector<double> predict_sample(const torch::Tensor&);
         vector<double> exactInference(map<string, int>&);
         double computeFactor(map<string, int>&);
-        double mutual_info(torch::Tensor&, torch::Tensor&);
-        double entropy(torch::Tensor&);
-        double conditionalEntropy(torch::Tensor&, torch::Tensor&);
-        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();
+        void completeFit(const map<string, vector<int>>&);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>&);
+        void setStates(const map<string, vector<int>>&);
     public:
         Network();
         explicit Network(float, int);
@@ -37,27 +33,26 @@ namespace bayesnet {
         void addNode(const string&);
         void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
-        vector<string> getFeatures();
-        int getStates();
-        vector<pair<string, string>> getEdges();
-        int getClassNumStates();
-        string getClassName();
-        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
-        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&);
-        void fit(const torch::Tensor&, const vector<string>&, const string&);
+        vector<string> getFeatures() const;
+        int getStates() const;
+        vector<pair<string, string>> getEdges() const;
+        int getNumEdges() const;
+        int getClassNumStates() const;
+        string getClassName() const;
+        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
         vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
-        //Computes the conditional edge weight of variable index u and v conditioned on class_node
-        torch::Tensor conditionalEdgeWeight();
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
         vector<vector<double>> predict_proba(const vector<vector<int>>&); // Return mxn vector of probabilities
         torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
         double score(const vector<vector<int>>&, const vector<int>&);
         vector<string> topological_sort();
-        vector<string> show();
-        vector<string> graph(const string& title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> show() const;
+        vector<string> graph(const string& title) const; // Returns a vector of strings representing the graph in graphviz format
         void initialize();
-        void dump_cpt();
+        void dump_cpt() const;
         inline string version() { return "0.1.0"; }
     };
 }

src/BayesNet/Proposal.cc

@@ -2,19 +2,20 @@
 #include "ArffFiles.h"
 
 namespace bayesnet {
-    Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_), m(dataset_.size(1)), n(dataset_.size(0) - 1) {}
+    Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
     Proposal::~Proposal()
     {
         for (auto& [key, value] : discretizers) {
             delete value;
         }
     }
-    void Proposal::localDiscretizationProposal(map<string, vector<int>>& states, Network& model)
+    map<string, vector<int>> Proposal::localDiscretizationProposal(const map<string, vector<int>>& oldStates, 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();
+        map<string, vector<int>> states = oldStates;
         vector<int> indicesToReDiscretize;
         bool upgrade = false; // Flag to check if we need to upgrade the model
         for (auto feature : order) {
@@ -32,9 +33,9 @@ namespace bayesnet {
             indices.push_back(-1); // Add class index
             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(indices.size());
+            vector<string> yJoinParents(Xf.size(1));
             for (auto idx : indices) {
-                for (int i = 0; i < n; ++i) {
+                for (int i = 0; i < Xf.size(1); ++i) {
                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
                 }
             }
@@ -64,10 +65,16 @@ namespace bayesnet {
                 //Update new states of the feature/node
                 states[pFeatures[index]] = xStates;
             }
+            model.fit(pDataset, pFeatures, pClassName, states);
         }
+        return states;
     }
-    void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
+    map<string, vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
     {
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
+        int m = Xf.size(1);
+        int n = Xf.size(0);
+        map<string, vector<int>> states;
         pDataset = torch::zeros({ n + 1, m }, kInt32);
         auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
         // discretize input data by feature(row)
@@ -86,6 +93,8 @@ namespace bayesnet {
         auto yStates = vector<int>(n_classes);
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
+        pDataset.index_put_({ n, "..." }, y);
+        return states;
     }
     torch::Tensor Proposal::prepareX(torch::Tensor& X)
     {

src/BayesNet/Proposal.h

@@ -14,12 +14,11 @@ namespace bayesnet {
         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);
+        map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
+        map<string, vector<int>> fit_local_discretization(const torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
         torch::Tensor y; // y discrete nx1 tensor
         map<string, mdlp::CPPFImdlp*> discretizers;
-        int m, n;
     private:
         torch::Tensor& pDataset; // (n+1)xm tensor
         vector<string>& pFeatures;

src/BayesNet/SPODE.cc

@@ -17,7 +17,7 @@ namespace bayesnet {
             }
         }
     }
-    vector<string> SPODE::graph(const string& name)
+    vector<string> SPODE::graph(const string& name) const
     {
         return model.graph(name);
     }

src/BayesNet/SPODE.h

@@ -11,7 +11,7 @@ namespace bayesnet {
     public:
         explicit SPODE(int root);
         virtual ~SPODE() {};
-        vector<string> graph(const string& name = "SPODE") override;
+        vector<string> graph(const string& name = "SPODE") const override;
     };
 }
 #endif

src/BayesNet/SPODELd.cc

@@ -11,20 +11,36 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // 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(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
-        //model.fit(SPODE::Xv, SPODE::yv, features, className);
+        states = localDiscretizationProposal(states, model);
         return *this;
     }
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        cout << "Xf " << Xf.sizes() << " dtype: " << Xf.dtype() << endl;
+        y = dataset.index({ -1, "..." }).clone();
+        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        features = features_;
+        className = className_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // 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(dataset, features, className, states);
+        states = localDiscretizationProposal(states, model);
+        return *this;
+    }
+
     Tensor SPODELd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return SPODE::predict(Xt);
     }
-    vector<string> SPODELd::graph(const string& name)
+    vector<string> SPODELd::graph(const string& name) const
     {
         return SPODE::graph(name);
     }

src/BayesNet/SPODELd.h

@@ -6,12 +6,12 @@
 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;
+        SPODELd& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "SPODE") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/BayesNet/TAN.cc

@@ -34,7 +34,7 @@ namespace bayesnet {
             model.addEdge(className, feature);
         }
     }
-    vector<string> TAN::graph(const string& title)
+    vector<string> TAN::graph(const string& title) const
     {
         return model.graph(title);
     }

src/BayesNet/TAN.h

@@ -11,7 +11,7 @@ namespace bayesnet {
     public:
         TAN();
         virtual ~TAN() {};
-        vector<string> graph(const string& name = "TAN") override;
+        vector<string> graph(const string& name = "TAN") const override;
     };
 }
 #endif

src/BayesNet/TANLd.cc

@@ -11,20 +11,20 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // 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(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
-        //model.fit(dataset, features, className);
+        states = localDiscretizationProposal(states, model);
         return *this;
+
     }
     Tensor TANLd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return TAN::predict(Xt);
     }
-    vector<string> TANLd::graph(const string& name)
+    vector<string> TANLd::graph(const string& name) const
     {
         return TAN::graph(name);
     }

src/BayesNet/TANLd.h

@@ -11,7 +11,7 @@ namespace bayesnet {
         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;
+        vector<string> graph(const string& name = "TAN") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/Platform/BestResult.h

@@ -0,0 +1,10 @@
+#ifndef BESTRESULT_H
+#define BESTRESULT_H
+#include <string>
+class BestResult {
+public:
+    static std::string title() { return "STree_default (linear-ovo)"; }
+    static double score() { return 22.109799; }
+    static std::string scoreName() { return "accuracy"; }
+};
+#endif

src/Platform/CMakeLists.txt

@@ -5,4 +5,6 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
 add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc Report.cc)
+add_executable(manage manage.cc Results.cc Report.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(manage "${TORCH_LIBRARIES}")

src/Platform/Colors.h

@@ -0,0 +1,14 @@
+#ifndef COLORS_H
+#define COLORS_H
+class Colors {
+public:
+    static std::string MAGENTA() { return "\033[1;35m"; }
+    static std::string BLUE() { return "\033[1;34m"; }
+    static std::string CYAN() { return "\033[1;36m"; }
+    static std::string GREEN() { return "\033[1;32m"; }
+    static std::string YELLOW() { return "\033[1;33m"; }
+    static std::string RED() { return "\033[1;31m"; }
+    static std::string WHITE() { return "\033[1;37m"; }
+    static std::string RESET() { return "\033[0m"; }
+};
+#endif // COLORS_H

src/Platform/Paths.h

@@ -0,0 +1,10 @@
+#ifndef PATHS_H
+#define PATHS_H
+namespace platform {
+    class Paths {
+    public:
+        static std::string datasets() { return "datasets/"; }
+        static std::string results() { return "results/"; }
+    };
+}
+#endif

src/Platform/Report.cc

@@ -1,9 +1,11 @@
 #include "Report.h"
+#include "BestResult.h"
 
 namespace platform {
     string headerLine(const string& text)
     {
         int n = MAXL - text.length() - 3;
+        n = n < 0 ? 0 : n;
         return "* " + text + string(n, ' ') + "*\n";
     }
     string Report::fromVector(const string& key)
@@ -13,7 +15,7 @@ namespace platform {
         for (auto& item : data[key]) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     string fVector(const json& data)
     {
@@ -21,16 +23,17 @@ namespace platform {
         for (const auto& item : data) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     void Report::show()
     {
         header();
         body();
+        footer();
     }
     void Report::header()
     {
-        cout << string(MAXL, '*') << endl;
+        cout << Colors::MAGENTA() << 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"));
@@ -41,26 +44,50 @@ namespace platform {
     }
     void Report::body()
     {
-        cout << "Dataset                        Sampl. Feat. Cls Nodes   Edges   States  Score           Time              Hyperparameters" << endl;
-        cout << "============================== ====== ===== === ======= ======= ======= =============== ================= ===============" << endl;
+        cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls Nodes   Edges   States  Score           Time               Hyperparameters" << endl;
+        cout << "============================== ====== ===== === ======= ======= ======= =============== ================== ===============" << endl;
+        json lastResult;
+        totalScore = 0;
+        bool odd = true;
         for (const auto& r : data["results"]) {
-            cout << setw(30) << left << r["dataset"].get<string>() << " ";
+            auto color = odd ? Colors::CYAN() : Colors::BLUE();
+            cout << color << 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 << setw(8) << right << setprecision(6) << fixed << r["score"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_std"].get<double>() << " ";
+            cout << setw(11) << right << setprecision(6) << fixed << r["time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["time_std"].get<double>() << " ";
+            try {
+                cout << r["hyperparameters"].get<string>();
+            }
+            catch (const exception& err) {
+                cout << r["hyperparameters"];
+            }
             cout << endl;
+            lastResult = r;
+            totalScore += r["score"].get<double>();
+            odd = !odd;
+        }
+        if (data["results"].size() == 1) {
             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 << headerLine("Train scores: " + fVector(lastResult["scores_train"]));
+            cout << headerLine("Test  scores: " + fVector(lastResult["scores_test"]));
+            cout << headerLine("Train  times: " + fVector(lastResult["times_train"]));
+            cout << headerLine("Test   times: " + fVector(lastResult["times_test"]));
             cout << string(MAXL, '*') << endl;
         }
     }
+    void Report::footer()
+    {
+        cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
+        auto score = data["score_name"].get<string>();
+        if (score == BestResult::scoreName()) {
+            cout << headerLine(score + " compared to " + BestResult::title() + " .:  " + to_string(totalScore / BestResult::score()));
+        }
+        cout << string(MAXL, '*') << endl << Colors::RESET();
+
+    }
 }

src/Platform/Report.h

@@ -3,6 +3,7 @@
 #include <string>
 #include <iostream>
 #include <nlohmann/json.hpp>
+#include "Colors.h"
 
 using json = nlohmann::json;
 const int MAXL = 121;
@@ -16,8 +17,10 @@ namespace platform {
     private:
         void header();
         void body();
+        void footer();
         string fromVector(const string& key);
         json data;
+        double totalScore; // Total score of all results in a report
     };
 };
 #endif

src/Platform/Results.cc

@@ -0,0 +1,239 @@
+#include <filesystem>
+#include "platformUtils.h"
+#include "Results.h"
+#include "Report.h"
+#include "BestResult.h"
+#include "Colors.h"
+namespace platform {
+    Result::Result(const string& path, const string& filename)
+        : path(path)
+        , filename(filename)
+    {
+        auto data = load();
+        date = data["date"];
+        score = 0;
+        for (const auto& result : data["results"]) {
+            score += result["score"].get<double>();
+        }
+        scoreName = data["score_name"];
+        if (scoreName == BestResult::scoreName()) {
+            score /= BestResult::score();
+        }
+        title = data["title"];
+        duration = data["duration"];
+        model = data["model"];
+    }
+    json Result::load() const
+    {
+        ifstream resultData(path + "/" + filename);
+        if (resultData.is_open()) {
+            json data = json::parse(resultData);
+            return data;
+        }
+        throw invalid_argument("Unable to open result file. [" + path + "/" + filename + "]");
+    }
+    void Results::load()
+    {
+        using std::filesystem::directory_iterator;
+        for (const auto& file : directory_iterator(path)) {
+            auto filename = file.path().filename().string();
+            if (filename.find(".json") != string::npos && filename.find("results_") == 0) {
+                auto result = Result(path, filename);
+                bool addResult = true;
+                if (model != "any" && result.getModel() != model || scoreName != "any" && scoreName != result.getScoreName())
+                    addResult = false;
+                if (addResult)
+                    files.push_back(result);
+            }
+        }
+    }
+    string Result::to_string() const
+    {
+        stringstream oss;
+        oss << date << " ";
+        oss << setw(12) << left << model << " ";
+        oss << setw(11) << left << scoreName << " ";
+        oss << right << setw(11) << setprecision(7) << fixed << score << " ";
+        oss << setw(9) << setprecision(3) << fixed << duration << " ";
+        oss << setw(50) << left << title << " ";
+        return  oss.str();
+    }
+    void Results::show() const
+    {
+        cout << Colors::GREEN() << "Results found: " << files.size() << endl;
+        cout << "-------------------" << endl;
+        auto i = 0;
+        cout << " #  Date       Model        Score Name  Score       Duration  Title" << endl;
+        cout << "=== ========== ============ =========== =========== ========= =============================================================" << endl;
+        bool odd = true;
+        for (const auto& result : files) {
+            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            cout << color << setw(3) << fixed << right << i++ << " ";
+            cout << result.to_string() << endl;
+            if (i == max && max != 0) {
+                break;
+            }
+            odd = !odd;
+        }
+    }
+    int Results::getIndex(const string& intent) const
+    {
+        string color;
+        if (intent == "delete") {
+            color = Colors::RED();
+        } else {
+            color = Colors::YELLOW();
+        }
+        cout << color << "Choose result to " << intent << " (cancel=-1): ";
+        string line;
+        getline(cin, line);
+        int index = stoi(line);
+        if (index >= -1 && index < static_cast<int>(files.size())) {
+            return index;
+        }
+        cout << "Invalid index" << endl;
+        return -1;
+    }
+    void Results::report(const int index) const
+    {
+        cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl;
+        auto data = files.at(index).load();
+        Report report(data);
+        report.show();
+    }
+    void Results::menu()
+    {
+        char option;
+        int index;
+        bool finished = false;
+        string filename, line, options = "qldhsr";
+        while (!finished) {
+            cout << Colors::RESET() << "Choose option (quit='q', list='l', delete='d', hide='h', sort='s', report='r'): ";
+            getline(cin, line);
+            if (line.size() == 0)
+                continue;
+            if (options.find(line[0]) != string::npos) {
+                if (line.size() > 1) {
+                    cout << "Invalid option" << endl;
+                    continue;
+                }
+                option = line[0];
+            } else {
+                index = stoi(line);
+                if (index >= 0 && index < files.size()) {
+                    report(index);
+                } else {
+                    cout << "Invalid option" << endl;
+                }
+                continue;
+            }
+            switch (option) {
+                case 'q':
+                    finished = true;
+                    break;
+                case 'l':
+                    show();
+                    break;
+                case 'd':
+                    index = getIndex("delete");
+                    if (index == -1)
+                        break;
+                    filename = files[index].getFilename();
+                    cout << "Deleting " << filename << endl;
+                    remove((path + "/" + filename).c_str());
+                    files.erase(files.begin() + index);
+                    cout << "File: " + filename + " deleted!" << endl;
+                    show();
+                    break;
+                case 'h':
+                    index = getIndex("hide");
+                    if (index == -1)
+                        break;
+                    filename = files[index].getFilename();
+                    cout << "Hiding " << filename << endl;
+                    rename((path + "/" + filename).c_str(), (path + "/." + filename).c_str());
+                    files.erase(files.begin() + index);
+                    show();
+                    menu();
+                    break;
+                case 's':
+                    sortList();
+                    show();
+                    break;
+                case 'r':
+                    index = getIndex("report");
+                    if (index == -1)
+                        break;
+                    report(index);
+                    break;
+                default:
+                    cout << "Invalid option" << endl;
+            }
+        }
+    }
+    void Results::sortList()
+    {
+        cout << Colors::YELLOW() << "Choose sorting field (date='d', score='s', duration='u', model='m'): ";
+        string line;
+        char option;
+        getline(cin, line);
+        if (line.size() == 0)
+            return;
+        if (line.size() > 1) {
+            cout << "Invalid option" << endl;
+            return;
+        }
+        option = line[0];
+        switch (option) {
+            case 'd':
+                sortDate();
+                break;
+            case 's':
+                sortScore();
+                break;
+            case 'u':
+                sortDuration();
+                break;
+            case 'm':
+                sortModel();
+                break;
+            default:
+                cout << "Invalid option" << endl;
+        }
+    }
+    void Results::sortDate()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getDate() > b.getDate();
+            });
+    }
+    void Results::sortModel()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getModel() > b.getModel();
+            });
+    }
+    void Results::sortDuration()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getDuration() > b.getDuration();
+            });
+    }
+    void Results::sortScore()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getScore() > b.getScore();
+            });
+    }
+    void Results::manage()
+    {
+        if (files.size() == 0) {
+            cout << "No results found!" << endl;
+            exit(0);
+        }
+        show();
+        menu();
+        cout << "Done!" << endl;
+    }
+
+}

src/Platform/Results.h

@@ -0,0 +1,56 @@
+#ifndef RESULTS_H
+#define RESULTS_H
+#include <map>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+namespace platform {
+    using namespace std;
+    using json = nlohmann::json;
+
+    class Result {
+    public:
+        Result(const string& path, const string& filename);
+        json load() const;
+        string to_string() const;
+        string getFilename() const { return filename; };
+        string getDate() const { return date; };
+        double getScore() const { return score; };
+        string getTitle() const { return title; };
+        double getDuration() const { return duration; };
+        string getModel() const { return model; };
+        string getScoreName() const { return scoreName; };
+    private:
+        string path;
+        string filename;
+        string date;
+        double score;
+        string title;
+        double duration;
+        string model;
+        string scoreName;
+    };
+    class Results {
+    public:
+        Results(const string& path, const int max, const string& model, const string& score) : path(path), max(max), model(model), scoreName(score) { load(); };
+        void manage();
+    private:
+        string path;
+        int max;
+        string model;
+        string scoreName;
+        vector<Result> files;
+        void load(); // Loads the list of results
+        void show() const;
+        void report(const int index) const;
+        int getIndex(const string& intent) const;
+        void menu();
+        void sortList();
+        void sortDate();
+        void sortScore();
+        void sortModel();
+        void sortDuration();
+    };
+};
+
+#endif

src/Platform/main.cc

@@ -6,20 +6,19 @@
 #include "DotEnv.h"
 #include "Models.h"
 #include "modelRegister.h"
+#include "Paths.h"
+
 
 using namespace std;
-const string PATH_RESULTS = "results";
-const string PATH_DATASETS = "datasets";
 
 argparse::ArgumentParser manageArguments(int argc, char** argv)
 {
     auto env = platform::DotEnv();
-    argparse::ArgumentParser program("BayesNetSample");
+    argparse::ArgumentParser program("main");
     program.add_argument("-d", "--dataset").default_value("").help("Dataset file name");
     program.add_argument("-p", "--path")
         .help("folder where the data files are located, default")
-        .default_value(string{ PATH_DATASETS }
-    );
+        .default_value(string{ platform::Paths::datasets() });
     program.add_argument("-m", "--model")
         .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
@@ -115,7 +114,7 @@ int main(int argc, char** argv)
     experiment.go(filesToTest, path);
     experiment.setDuration(timer.getDuration());
     if (saveResults)
-        experiment.save(PATH_RESULTS);
+        experiment.save(platform::Paths::results());
     else
         experiment.report();
     cout << "Done!" << endl;

src/Platform/manage.cc

@@ -0,0 +1,41 @@
+#include <iostream>
+#include <argparse/argparse.hpp>
+#include "platformUtils.h"
+#include "Paths.h"
+#include "Results.h"
+
+using namespace std;
+
+argparse::ArgumentParser manageArguments(int argc, char** argv)
+{
+    argparse::ArgumentParser program("manage");
+    program.add_argument("-n", "--number").default_value(0).help("Number of results to show (0 = all)").scan<'i', int>();
+    program.add_argument("-m", "--model").default_value("any").help("Filter results of the selected model)");
+    program.add_argument("-s", "--score").default_value("any").help("Filter results of the score name supplied");
+    try {
+        program.parse_args(argc, argv);
+        auto number = program.get<int>("number");
+        if (number < 0) {
+            throw runtime_error("Number of results must be greater than or equal to 0");
+        }
+        auto model = program.get<string>("model");
+        auto score = program.get<string>("score");
+    }
+    catch (const exception& err) {
+        cerr << err.what() << endl;
+        cerr << program;
+        exit(1);
+    }
+    return program;
+}
+
+int main(int argc, char** argv)
+{
+    auto program = manageArguments(argc, argv);
+    auto number = program.get<int>("number");
+    auto model = program.get<string>("model");
+    auto score = program.get<string>("score");
+    auto results = platform::Results(platform::Paths::results(), number, model, score);
+    results.manage();
+    return 0;
+}

src/Platform/platformUtils.cc

@@ -1,4 +1,5 @@
 #include "platformUtils.h"
+#include "Paths.h"
 
 using namespace torch;
 
@@ -85,7 +86,7 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
 tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
 {
     auto handler = ArffFiles();
-    handler.load(PATH + static_cast<string>(name) + ".arff");
+    handler.load(platform::Paths::datasets() + static_cast<string>(name) + ".arff");
     // Get Dataset X, y
     vector<mdlp::samples_t>& X = handler.getX();
     mdlp::labels_t& y = handler.getY();