Complete fixing the linter warnings

sample/CMakeLists.txt

@@ -3,5 +3,5 @@ include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
-add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc) 
+add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc) 
 target_link_libraries(BayesNetSample BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

sample/sample.cc

@@ -4,16 +4,12 @@
 #include <thread>
 #include <map>
 #include <argparse/argparse.hpp>
-#include "BaseClassifier.h"
 #include "ArffFiles.h"
-#include "Network.h"
 #include "BayesMetrics.h"
 #include "CPPFImdlp.h"
-#include "KDB.h"
-#include "SPODE.h"
-#include "AODE.h"
-#include "TAN.h"
 #include "Folding.h"
+#include "Models.h"
+#include "modelRegister.h"
 
 
 using namespace std;
@@ -73,9 +69,8 @@ int main(int argc, char** argv)
             {"mfeat-factors",      true},
     };
     auto valid_datasets = vector<string>();
-    for (auto dataset : datasets) {
-        valid_datasets.push_back(dataset.first);
-    }
+    transform(datasets.begin(), datasets.end(), back_inserter(valid_datasets),
+        [](const pair<string, bool>& pair) { return pair.first; });
     argparse::ArgumentParser program("BayesNetSample");
     program.add_argument("-d", "--dataset")
         .help("Dataset file name")
@@ -91,13 +86,13 @@ int main(int argc, char** argv)
         .default_value(string{ PATH }
     );
     program.add_argument("-m", "--model")
-        .help("Model to use {AODE, KDB, SPODE, TAN}")
+        .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
-        static const vector<string> choices = { "AODE", "KDB", "SPODE", "TAN" };
+        static const vector<string> choices = platform::Models::instance()->getNames();
         if (find(choices.begin(), choices.end(), value) != choices.end()) {
             return value;
         }
-        throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
+        throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
             }
     );
     program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
@@ -153,9 +148,9 @@ int main(int argc, char** argv)
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
-        features.push_back(feature.first);
-    }
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features),
+        [](const pair<string, string>& item) { return item.first; });
     // Discretize Dataset
     auto [Xd, maxes] = discretize(X, y, features);
     maxes[className] = *max_element(y.begin(), y.end()) + 1;
@@ -164,12 +159,7 @@ int main(int argc, char** argv)
         states[feature] = vector<int>(maxes[feature]);
     }
     states[className] = vector<int>(maxes[className]);
-    auto classifiers = map<string, bayesnet::BaseClassifier*>({
-        { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
-        { "SPODE",  new bayesnet::SPODE(2) }, { "TAN",  new bayesnet::TAN() }
-        }
-    );
-    bayesnet::BaseClassifier* clf = classifiers[model_name];
+    auto clf = platform::Models::instance()->create(model_name);
     clf->fit(Xd, y, features, className, states);
     auto score = clf->score(Xd, y);
     auto lines = clf->show();

src/BayesNet/BayesMetrics.cc

@@ -12,8 +12,8 @@ namespace bayesnet {
         : features(features)
         , className(className)
         , classNumStates(classNumStates)
+        , samples(torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
     {
-        samples = torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32);
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ "...", i }, torch::tensor(vsamples[i], torch::kInt32));
         }
@@ -123,7 +123,6 @@ namespace bayesnet {
     */
     vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
     {
-        auto result = vector<pair<int, int>>();
         auto mst = MST(features, weights, root);
         return mst.maximumSpanningTree();
     }

src/BayesNet/BayesMetrics.h

@@ -11,7 +11,7 @@ namespace bayesnet {
         Tensor samples;
         vector<string> features;
         string className;
-        int classNumStates;
+        int classNumStates = 0;
     public:
         Metrics() = default;
         Metrics(Tensor&, vector<string>&, string&, int);

src/BayesNet/Classifier.cc

@@ -125,7 +125,6 @@ namespace bayesnet {
     }
     void Classifier::addNodes()
     {
-        auto test = model.getEdges();
         // Add all nodes to the network
         for (auto feature : features) {
             model.addNode(feature, states[feature].size());

src/BayesNet/Ensemble.cc

@@ -148,10 +148,10 @@ namespace bayesnet {
     }
     int Ensemble::getNumberOfStates()
     {
-        int states = 0;
+        int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {
-            states += models[i]->getNumberOfStates();
+            nstates += models[i]->getNumberOfStates();
         }
-        return states;
+        return nstates;
     }
 }

src/BayesNet/KDB.h

@@ -13,7 +13,7 @@ namespace bayesnet {
     protected:
         void train() override;
     public:
-        KDB(int k, float theta = 0.03);
+        explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};
         vector<string> graph(string name = "KDB") override;
     };

src/BayesNet/Mst.cc

@@ -7,9 +7,8 @@
 
 namespace bayesnet {
     using namespace std;
-    Graph::Graph(int V)
+    Graph::Graph(int V) : V(V), parent(vector<int>(V))
     {
-        parent = vector<int>(V);
         for (int i = 0; i < V; i++)
             parent[i] = i;
         G.clear();
@@ -34,10 +33,10 @@ namespace bayesnet {
     }
     void Graph::kruskal_algorithm()
     {
-        int i, uSt, vEd;
         // sort the edges ordered on decreasing weight
-        sort(G.begin(), G.end(), [](auto& left, auto& right) {return left.first > right.first;});
-        for (i = 0; i < G.size(); i++) {
+        sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
+        for (int i = 0; i < G.size(); i++) {
+            int uSt, vEd;
             uSt = find_set(G[i].second.first);
             vEd = find_set(G[i].second.second);
             if (uSt != vEd) {

src/BayesNet/Mst.h

@@ -10,7 +10,7 @@ namespace bayesnet {
     private:
         Tensor weights;
         vector<string> features;
-        int root;
+        int root = 0;
     public:
         MST() = default;
         MST(vector<string>& features, Tensor& weights, int root);
@@ -23,7 +23,7 @@ namespace bayesnet {
         vector <pair<float, pair<int, int>>> T; // vector for mst
         vector<int> parent;
     public:
-        Graph(int V);
+        explicit Graph(int V);
         void addEdge(int u, int v, float wt);
         int find_set(int i);
         void union_set(int u, int v);

src/BayesNet/Network.cc

@@ -8,7 +8,7 @@ namespace bayesnet {
     Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
     Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads()), fitted(other.fitted)
     {
-        for (auto& pair : other.nodes) {
+        for (const auto& pair : other.nodes) {
             nodes[pair.first] = std::make_unique<Node>(*pair.second);
         }
     }
@@ -20,7 +20,7 @@ namespace bayesnet {
     {
         return samples;
     }
-    void Network::addNode(string name, int numStates)
+    void Network::addNode(const string& name, int numStates)
     {
         if (find(features.begin(), features.end(), name) == features.end()) {
             features.push_back(name);
@@ -69,7 +69,7 @@ namespace bayesnet {
         recStack.erase(nodeId); // remove node from recursion stack before function ends
         return false;
     }
-    void Network::addEdge(const string parent, const string child)
+    void Network::addEdge(const string& parent, const string& child)
     {
         if (nodes.find(parent) == nodes.end()) {
             throw invalid_argument("Parent node " + parent + " does not exist");
@@ -105,8 +105,8 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto column = torch::flatten(X.index({ "...", i }));
             auto k = vector<int>();
-            for (auto i = 0; i < X.size(0); ++i) {
-                k.push_back(column[i].item<int>());
+            for (auto z = 0; z < X.size(0); ++z) {
+                k.push_back(column[z].item<int>());
             }
             dataset[featureNames[i]] = k;
         }
@@ -145,9 +145,6 @@ namespace bayesnet {
         while (nextNodeIndex < nodes.size()) {
             unique_lock<mutex> lock(mtx);
             cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
-            if (nextNodeIndex >= nodes.size()) {
-                break;  // No more work remaining
-            }
             threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads]() {
                 while (true) {
                     unique_lock<mutex> lock(mtx);
@@ -262,9 +259,7 @@ namespace bayesnet {
 
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
-        for (double& value : result) {
-            value /= sum;
-        }
+        transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
         return result;
     }
     vector<string> Network::show()
@@ -280,7 +275,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(string title)
+    vector<string> Network::graph(const string& title)
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";

src/BayesNet/Network.h

@@ -27,13 +27,13 @@ namespace bayesnet {
         void completeFit();
     public:
         Network();
-        Network(float, int);
-        Network(float);
-        Network(Network&);
+        explicit Network(float, int);
+        explicit Network(float);
+        explicit Network(Network&);
         torch::Tensor& getSamples();
         float getmaxThreads();
-        void addNode(string, int);
-        void addEdge(const string, const string);
+        void addNode(const string&, int);
+        void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
         vector<string> getFeatures();
         int getStates();
@@ -48,7 +48,7 @@ namespace bayesnet {
         vector<vector<double>> predict_proba(const vector<vector<int>>&);
         double score(const vector<vector<int>>&, const vector<int>&);
         vector<string> show();
-        vector<string> graph(string title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& title); // Returns a vector of strings representing the graph in graphviz format
         inline string version() { return "0.1.0"; }
     };
 }

src/BayesNet/Node.cc

@@ -88,18 +88,15 @@ namespace bayesnet {
     {
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        for (auto father : getParents()) {
-            dimensions.push_back(father->getNumStates());
-        }
+        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts
         for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
             torch::List<c10::optional<torch::Tensor>> coordinates;
             coordinates.push_back(torch::tensor(dataset[name][n_sample]));
-            for (auto father : getParents()) {
-                coordinates.push_back(torch::tensor(dataset[father->getName()][n_sample]));
-            }
+            transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](const auto& parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
             // Increment the count of the corresponding coordinate
             cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
         }
@@ -111,19 +108,15 @@ namespace bayesnet {
         torch::List<c10::optional<torch::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(torch::tensor(evidence[name]));
-        for (auto parent : getParents()) {
-            coordinates.push_back(torch::tensor(evidence[parent->getName()]));
-        }
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return torch::tensor(evidence[parent->getName()]); });
         return cpTable.index({ coordinates }).item<float>();
     }
-    vector<string> Node::graph(string className)
+    vector<string> Node::graph(const string& className)
     {
         auto output = vector<string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
         output.push_back(name + " [shape=circle" + suffix + "] \n");
-        for (auto& child : children) {
-            output.push_back(name + " -> " + child->getName());
-        }
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
         return output;
     }
 }

src/BayesNet/Node.h

@@ -16,7 +16,7 @@ namespace bayesnet {
         vector<int64_t> dimensions; // dimensions of the cpTable
     public:
         vector<pair<string, string>> combinations(const vector<string>&);
-        Node(const std::string&, int);
+        Node(const string&, int);
         void clear();
         void addParent(Node*);
         void addChild(Node*);
@@ -30,7 +30,7 @@ namespace bayesnet {
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
-        vector<string> graph(string clasName); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& clasName); // Returns a vector of strings representing the graph in graphviz format
         float getFactorValue(map<string, int>&);
     };
 }

src/BayesNet/SPODE.h

@@ -9,7 +9,7 @@ namespace bayesnet {
     protected:
         void train() override;
     public:
-        SPODE(int root);
+        explicit SPODE(int root);
         virtual ~SPODE() {};
         vector<string> graph(string name = "SPODE") override;
     };

src/BayesNet/TAN.cc

@@ -18,7 +18,7 @@ namespace bayesnet {
             auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
             mi.push_back({ i, mi_value });
         }
-        sort(mi.begin(), mi.end(), [](auto& left, auto& right) {return left.second < right.second;});
+        sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
         auto root = mi[mi.size() - 1].first;
         // 2. Compute mutual information between each feature and the class
         auto weights = metrics.conditionalEdge();

src/Platform/Datasets.cc

@@ -4,9 +4,9 @@
 namespace platform {
     void Datasets::load()
     {
-        string line;
         ifstream catalog(path + "/all.txt");
         if (catalog.is_open()) {
+            string line;
             while (getline(catalog, line)) {
                 vector<string> tokens = split(line, ',');
                 string name = tokens[0];
@@ -21,9 +21,7 @@ namespace platform {
     vector<string> Datasets::getNames()
     {
         vector<string> result;
-        for (auto& d : datasets) {
-            result.push_back(d.first);
-        }
+        transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
         return result;
     }
     vector<string> Datasets::getFeatures(string name)
@@ -79,27 +77,12 @@ namespace platform {
         }
         return datasets[name]->getTensors();
     }
-    bool Datasets::isDataset(string name)
+    bool Datasets::isDataset(const string& name)
     {
         return datasets.find(name) != datasets.end();
     }
-    Dataset::Dataset(Dataset& dataset)
+    Dataset::Dataset(const Dataset& dataset) : path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples), n_features(dataset.n_features), features(dataset.features), states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y), Xv(dataset.Xv), Xd(dataset.Xd), yv(dataset.yv), fileType(dataset.fileType)
     {
-        path = dataset.path;
-        name = dataset.name;
-        className = dataset.className;
-        n_samples = dataset.n_samples;
-        n_features = dataset.n_features;
-        features = dataset.features;
-        states = dataset.states;
-        loaded = dataset.loaded;
-        discretize = dataset.discretize;
-        X = dataset.X;
-        y = dataset.y;
-        Xv = dataset.Xv;
-        Xd = dataset.Xd;
-        yv = dataset.yv;
-        fileType = dataset.fileType;
     }
     string Dataset::getName()
     {
@@ -168,9 +151,9 @@ namespace platform {
     }
     void Dataset::load_csv()
     {
-        string line;
         ifstream file(path + "/" + name + ".csv");
         if (file.is_open()) {
+            string line;
             getline(file, line);
             vector<string> tokens = split(line, ',');
             features = vector<string>(tokens.begin(), tokens.end() - 1);
@@ -208,9 +191,8 @@ namespace platform {
         yv = arff.getY();
         // Get className & Features
         className = arff.getClassName();
-        for (auto feature : arff.getAttributes()) {
-            features.push_back(feature.first);
-        }
+        auto attributes = arff.getAttributes();
+        transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& attribute) { return attribute.first; });
     }
     void Dataset::load()
     {

src/Platform/Datasets.h

@@ -13,7 +13,7 @@ namespace platform {
         string name;
         fileType_t fileType;
         string className;
-        int n_samples, n_features;
+        int n_samples{ 0 }, n_features{ 0 };
         vector<string> features;
         map<string, vector<int>> states;
         bool loaded;
@@ -27,8 +27,8 @@ namespace platform {
         void load_arff();
         void computeStates();
     public:
-        Dataset(string path, string name, string className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
-        Dataset(Dataset&);
+        Dataset(const string& path, const string& name, const string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
+        explicit Dataset(const Dataset&);
         string getName();
         string getClassName();
         vector<string> getFeatures();
@@ -49,7 +49,7 @@ namespace platform {
         bool discretize;
         void load(); // Loads the list of datasets
     public:
-        Datasets(string path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
+        explicit Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
         vector<string> getNames();
         vector<string> getFeatures(string name);
         int getNSamples(string name);
@@ -58,7 +58,7 @@ namespace platform {
         pair<vector<vector<float>>&, vector<int>&> getVectors(string name);
         pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(string name);
         pair<torch::Tensor&, torch::Tensor&> getTensors(string name);
-        bool isDataset(string name);
+        bool isDataset(const string& name);
     };
 };
 

src/Platform/DotEnv.h

@@ -52,9 +52,9 @@ namespace platform {
             seeds_str = trim(seeds_str);
             seeds_str = seeds_str.substr(1, seeds_str.size() - 2);
             auto seeds_str_split = split(seeds_str, ',');
-            for (auto seed_str : seeds_str_split) {
-                seeds.push_back(stoi(seed_str));
-            }
+            transform(seeds_str_split.begin(), seeds_str_split.end(), back_inserter(seeds), [](const std::string& str) {
+                return stoi(str);
+                });
             return seeds;
         }
     };

src/Platform/Experiment.cc

@@ -48,7 +48,7 @@ namespace platform {
         result["seeds"] = randomSeeds;
         result["duration"] = duration;
         result["results"] = json::array();
-        for (auto& r : results) {
+        for (const auto& r : results) {
             json j;
             j["dataset"] = r.getDataset();
             j["hyperparameters"] = r.getHyperparameters();
@@ -78,19 +78,31 @@ namespace platform {
         }
         return result;
     }
-    void Experiment::save(string path)
+    void Experiment::save(const string& path)
     {
         json data = build_json();
         ofstream file(path + "/" + get_file_name());
         file << data;
         file.close();
     }
+
     void Experiment::show()
     {
         json data = build_json();
         cout << data.dump(4) << endl;
     }
-    Result Experiment::cross_validation(const string& path, const string& fileName)
+
+    void Experiment::go(vector<string> filesToProcess, const string& path)
+    {
+        cout << "*** Starting experiment: " << title << " ***" << endl;
+        for (auto fileName : filesToProcess) {
+            cout << "- " << setw(20) << left << fileName << " " << right << flush;
+            cross_validation(path, fileName);
+            cout << endl;
+        }
+    }
+
+    void Experiment::cross_validation(const string& path, const string& fileName)
     {
         auto datasets = platform::Datasets(path, true, platform::ARFF);
         // Get dataset
@@ -158,6 +170,7 @@ namespace platform {
         result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
         result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
         result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
-        return result;
+        result.setDataset(fileName);
+        addResult(result);
     }
 }

src/Platform/Experiment.h

@@ -30,14 +30,14 @@ namespace platform {
     class Result {
     private:
         string dataset, hyperparameters, model_version;
-        int samples, features, classes;
-        double score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
-        float nodes, leaves, depth;
+        int samples{ 0 }, features{ 0 }, classes{ 0 };
+        double score_train{ 0 }, score_test{ 0 }, score_train_std{ 0 }, score_test_std{ 0 }, train_time{ 0 }, train_time_std{ 0 }, test_time{ 0 }, test_time_std{ 0 };
+        float nodes{ 0 }, leaves{ 0 }, depth{ 0 };
         vector<double> scores_train, scores_test, times_train, times_test;
     public:
         Result() = default;
-        Result& setDataset(string dataset) { this->dataset = dataset; return *this; }
-        Result& setHyperparameters(string hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
+        Result& setDataset(const string& dataset) { this->dataset = dataset; return *this; }
+        Result& setHyperparameters(const string& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
         Result& setSamples(int samples) { this->samples = samples; return *this; }
         Result& setFeatures(int features) { this->features = features; return *this; }
         Result& setClasses(int classes) { this->classes = classes; return *this; }
@@ -82,21 +82,21 @@ namespace platform {
     class Experiment {
     private:
         string title, model, platform, score_name, model_version, language_version, language;
-        bool discretized, stratified;
+        bool discretized{ false }, stratified{ false };
         vector<Result> results;
         vector<int> randomSeeds;
-        int nfolds;
-        float duration;
+        int nfolds{ 0 };
+        float duration{ 0 };
         json build_json();
     public:
         Experiment() = default;
-        Experiment& setTitle(string title) { this->title = title; return *this; }
-        Experiment& setModel(string model) { this->model = model; return *this; }
-        Experiment& setPlatform(string platform) { this->platform = platform; return *this; }
-        Experiment& setScoreName(string score_name) { this->score_name = score_name; return *this; }
-        Experiment& setModelVersion(string model_version) { this->model_version = model_version; return *this; }
-        Experiment& setLanguage(string language) { this->language = language; return *this; }
-        Experiment& setLanguageVersion(string language_version) { this->language_version = language_version; return *this; }
+        Experiment& setTitle(const string& title) { this->title = title; return *this; }
+        Experiment& setModel(const string& model) { this->model = model; return *this; }
+        Experiment& setPlatform(const string& platform) { this->platform = platform; return *this; }
+        Experiment& setScoreName(const string& score_name) { this->score_name = score_name; return *this; }
+        Experiment& setModelVersion(const string& model_version) { this->model_version = model_version; return *this; }
+        Experiment& setLanguage(const string& language) { this->language = language; return *this; }
+        Experiment& setLanguageVersion(const string& language_version) { this->language_version = language_version; return *this; }
         Experiment& setDiscretized(bool discretized) { this->discretized = discretized; return *this; }
         Experiment& setStratified(bool stratified) { this->stratified = stratified; return *this; }
         Experiment& setNFolds(int nfolds) { this->nfolds = nfolds; return *this; }
@@ -104,8 +104,9 @@ namespace platform {
         Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); return *this; }
         Experiment& setDuration(float duration) { this->duration = duration; return *this; }
         string get_file_name();
-        void save(string path);
-        Result cross_validation(const string& path, const string& fileName);
+        void save(const string& path);
+        void cross_validation(const string& path, const string& fileName);
+        void go(vector<string> filesToProcess, const string& path);
         void show();
     };
 }

src/Platform/Folding.cc

@@ -7,9 +7,8 @@ Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
     random_seed = default_random_engine(seed == -1 ? rd() : seed);
     srand(seed == -1 ? time(0) : seed);
 }
-KFold::KFold(int k, int n, int seed) : Fold(k, n, seed)
+KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
 {
-    indices = vector<int>(n);
     iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
     shuffle(indices.begin(), indices.end(), random_seed);
 }

src/Platform/Folding.h

@@ -22,7 +22,7 @@ private:
     vector<int> indices;
 public:
     KFold(int k, int n, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold);
+    pair<vector<int>, vector<int>> getFold(int nFold) override;
 };
 class StratifiedKFold : public Fold {
 private:
@@ -32,6 +32,6 @@ private:
 public:
     StratifiedKFold(int k, const vector<int>& y, int seed = -1);
     StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold);
+    pair<vector<int>, vector<int>> getFold(int nFold) override;
 };
 #endif

src/Platform/Models.cc

@@ -2,25 +2,6 @@
 namespace platform {
     using namespace std;
     // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
-    // shared_ptr<bayesnet::BaseClassifier> Models::createInstance(const string& name)
-    // {
-    //     bayesnet::BaseClassifier* instance = nullptr;
-    //     if (name == "AODE") {
-    //         instance = new bayesnet::AODE();
-    //     } else if (name == "KDB") {
-    //         instance = new bayesnet::KDB(2);
-    //     } else if (name == "SPODE") {
-    //         instance = new bayesnet::SPODE(2);
-    //     } else if (name == "TAN") {
-    //         instance = new bayesnet::TAN();
-    //     } else {
-    //         throw runtime_error("Model " + name + " not found");
-    //     }
-    //     if (instance != nullptr)
-    //         return shared_ptr<bayesnet::BaseClassifier>(instance);
-    //     else
-    //         return nullptr;
-    // }
     Models* Models::factory = nullptr;;
     Models* Models::instance()
     {
@@ -59,7 +40,7 @@ namespace platform {
     string Models::toString()
     {
         string result = "";
-        for (auto& pair : functionRegistry) {
+        for (const auto& pair : functionRegistry) {
             result += pair.first + ", ";
         }
         return "{" + result.substr(0, result.size() - 2) + "}";

src/Platform/main.cc

@@ -5,7 +5,7 @@
 #include "Datasets.h"
 #include "DotEnv.h"
 #include "Models.h"
-
+#include "modelRegister.h"
 
 using namespace std;
 const string PATH_RESULTS = "results";
@@ -49,22 +49,17 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         }});
     auto seed_values = env.getSeeds();
     program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
-    bool class_last, discretize_dataset, stratified;
-    int n_folds;
-    vector<int> seeds;
-    string model_name, file_name, path, complete_file_name, title;
     try {
         program.parse_args(argc, argv);
-        file_name = program.get<string>("dataset");
-        path = program.get<string>("path");
-        model_name = program.get<string>("model");
-        discretize_dataset = program.get<bool>("discretize");
-        stratified = program.get<bool>("stratified");
-        n_folds = program.get<int>("folds");
-        seeds = program.get<vector<int>>("seeds");
-        complete_file_name = path + file_name + ".arff";
-        class_last = false;//datasets[file_name];
-        title = program.get<string>("title");
+        auto file_name = program.get<string>("dataset");
+        auto path = program.get<string>("path");
+        auto model_name = program.get<string>("model");
+        auto discretize_dataset = program.get<bool>("discretize");
+        auto stratified = program.get<bool>("stratified");
+        auto n_folds = program.get<int>("folds");
+        auto seeds = program.get<vector<int>>("seeds");
+        auto complete_file_name = path + file_name + ".arff";
+        auto title = program.get<string>("title");
         if (title == "" && file_name == "") {
             throw runtime_error("title is mandatory if dataset is not provided");
         }
@@ -76,21 +71,9 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
     }
     return program;
 }
-void registerModels()
-{
-    static platform::Registrar registrarT("TAN",
-        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
-    static platform::Registrar registrarS("SPODE",
-        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
-    static platform::Registrar registrarK("KDB",
-        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
-    static platform::Registrar registrarA("AODE",
-        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
-}
 
 int main(int argc, char** argv)
 {
-    registerModels();
     auto program = manageArguments(argc, argv);
     bool saveResults = false;
     auto file_name = program.get<string>("dataset");
@@ -100,7 +83,7 @@ int main(int argc, char** argv)
     auto stratified = program.get<bool>("stratified");
     auto n_folds = program.get<int>("folds");
     auto seeds = program.get<vector<int>>("seeds");
-    vector<string> filesToProcess;
+    vector<string> filesToTest;
     auto datasets = platform::Datasets(path, true, platform::ARFF);
     auto title = program.get<string>("title");
     if (file_name != "") {
@@ -111,9 +94,9 @@ int main(int argc, char** argv)
         if (title == "") {
             title = "Test " + file_name + " " + model_name + " " + to_string(n_folds) + " folds";
         }
-        filesToProcess.push_back(file_name);
+        filesToTest.push_back(file_name);
     } else {
-        filesToProcess = platform::Datasets(path, true, platform::ARFF).getNames();
+        filesToTest = platform::Datasets(path, true, platform::ARFF).getNames();
         saveResults = true;
     }
 
@@ -128,15 +111,8 @@ int main(int argc, char** argv)
         experiment.addRandomSeed(seed);
     }
     platform::Timer timer;
-    cout << "*** Starting experiment: " << title << " ***" << endl;
     timer.start();
-    for (auto fileName : filesToProcess) {
-        cout << "- " << setw(20) << left << fileName << " " << right << flush;
-        auto result = experiment.cross_validation(path, fileName);
-        result.setDataset(fileName);
-        experiment.addResult(result);
-        cout << endl;
-    }
+    experiment.go(filesToTest, path);
     experiment.setDuration(timer.getDuration());
     if (saveResults)
         experiment.save(PATH_RESULTS);

src/Platform/modelRegister.h

@@ -0,0 +1,11 @@
+#ifndef MODEL_REGISTER_H
+#define MODEL_REGISTER_H
+static platform::Registrar registrarT("TAN",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
+static platform::Registrar registrarS("SPODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
+static platform::Registrar registrarK("KDB",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
+static platform::Registrar registrarA("AODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
+#endif

src/Platform/platformUtils.cc

@@ -2,7 +2,7 @@
 
 using namespace torch;
 
-vector<string> split(string text, char delimiter)
+vector<string> split(const string& text, char delimiter)
 {
     vector<string> result;
     stringstream ss(text);
@@ -39,7 +39,7 @@ vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::label
     return Xd;
 }
 
-bool file_exists(const std::string& name)
+bool file_exists(const string& name)
 {
     if (FILE* file = fopen(name.c_str(), "r")) {
         fclose(file);
@@ -49,7 +49,7 @@ bool file_exists(const std::string& name)
     }
 }
 
-tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset)
+tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset)
 {
     auto handler = ArffFiles();
     handler.load(path + static_cast<string>(name) + ".arff", class_last);
@@ -59,9 +59,8 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
-        features.push_back(feature.first);
-    }
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
     Tensor Xd;
     auto states = map<string, vector<int>>();
     if (discretize_dataset) {
@@ -83,7 +82,7 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
 }
 
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name)
+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");
@@ -93,9 +92,8 @@ tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vect
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
-        features.push_back(feature.first);
-    }
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
     // Discretize Dataset
     vector<mdlp::labels_t> Xd;
     map<string, int> maxes;

src/Platform/platformUtils.h

@@ -11,11 +11,11 @@ using namespace std;
 const string PATH = "../../data/";
 
 bool file_exists(const std::string& name);
-vector<string> split(string text, char delimiter);
+vector<string> split(const string& text, char delimiter);
 pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features);
 vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className);
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name);
-tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset);
+pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, const string& className);
+tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name);
+tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset);
 map<string, vector<int>> get_states(vector<string>& features, string className, map<string, int>& maxes);
 #endif //PLATFORM_UTILS_H