Fix CMakeList manage build with Linux

Reviewed-on: #7

.clang-uml

@@ -0,0 +1,31 @@
+compilation_database_dir: build
+output_directory: puml
+diagrams:
+  BayesNet:
+    type: class
+    glob:
+      - src/BayesNet/*.cc
+      - src/Platform/*.cc
+    using_namespace: bayesnet
+    include:
+      namespaces:
+        - bayesnet
+        - platform
+    plantuml:
+      after:
+        - "note left of {{ alias(\"MyProjectMain\") }}: Main class of myproject library."
+  sequence:
+    type: sequence
+    glob:
+      - src/Platform/main.cc
+    combine_free_functions_into_file_participants: true
+    using_namespace:
+      - std
+      - bayesnet
+      - platform
+    include:
+      paths:
+        - src/BayesNet
+        - src/Platform
+    start_from:
+      - function: main(int,const char **)

.gitignore

@@ -35,3 +35,4 @@ build/
 *.dSYM/**
 cmake-build*/**
 .idea
+puml/**

.gitmodules

@@ -10,3 +10,6 @@
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
+[submodule "lib/openXLSX"]
+	path = lib/openXLSX
+	url = https://github.com/troldal/OpenXLSX.git

.vscode/launch.json

@@ -10,7 +10,7 @@
                 "-d",
                 "iris",
                 "-m",
-                "KDB",
+                "TANLd",
                 "-s",
                 "271",
                 "-p",
@@ -28,12 +28,12 @@
                 "BoostAODE",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
-                "--discretize",
                 "--stratified",
                 "-d",
-                "glass",
+                "mfeat-morphological",
-                "--hyperparameters",
+                "--discretize"
-                "{\"repeatSparent\": true, \"maxModels\": 12}"
+                // "--hyperparameters",
+                // "{\"repeatSparent\": true, \"maxModels\": 12}"
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 0.1.0
+  VERSION 0.2.0
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -30,7 +30,7 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
 option(ENABLE_TESTING "Unit testing build"                        OFF)
 option(CODE_COVERAGE "Collect coverage from test library"         OFF)
-
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 # CMakes modules
 # --------------
 set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
@@ -40,8 +40,7 @@ if (CODE_COVERAGE)
     enable_testing()
     include(CodeCoverage)
     MESSAGE("Code coverage enabled")
-    set(CMAKE_C_FLAGS " ${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
+    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
-    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
     SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -75,7 +74,6 @@ file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/Platform
 if (ENABLE_TESTING)
   MESSAGE("Testing enabled")
   add_git_submodule("lib/catch2")  
-  
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)

Makefile

@@ -32,12 +32,15 @@ clean: ## Clean the debug info
 	find . -name "*.gcda" -print0 | xargs -0 rm
 	@echo ">>> Done";
 
+clang-uml: ## Create uml class and sequence diagrams
+	clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/
+
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet ...";
 	@if [ -d ./build ]; then rm -rf ./build; fi
 	@mkdir build; 
 	cmake -S . -B build -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON; \
-	cmake --build build -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list  unit_tests -j 32;
 	@echo ">>> Done";
 
 release: ## Build a Release version of the project

TAN_iris.dot

@@ -1,12 +0,0 @@
-digraph BayesNet {
-label=<BayesNet >
-fontsize=30
-fontcolor=blue
-labelloc=t
-layout=circo
- class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] 
- class -> sepallength class -> sepalwidth class -> petallength class -> petalwidth petallength [shape=circle] 
- petallength -> sepallength petalwidth [shape=circle] 
- sepallength [shape=circle] 
- sepallength -> sepalwidth sepalwidth [shape=circle] 
- sepalwidth -> petalwidth }

data/_TAN_cpp_accuracy__.json

@@ -1 +0,0 @@
-null

sample/sample.cc

@@ -10,7 +10,7 @@
 #include "Folding.h"
 #include "Models.h"
 #include "modelRegister.h"
-
+#include <fstream>
 
 using namespace std;
 
@@ -58,180 +58,226 @@ pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vect
 
 int main(int argc, char** argv)
 {
-    map<string, bool> datasets = {
+    torch::Tensor weights_ = torch::full({ 10 }, 1.0 / 10, torch::kFloat64);
-            {"diabetes",           true},
+    torch::Tensor y_ = torch::tensor({ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, torch::kInt32);
-            {"ecoli",              true},
+    torch::Tensor ypred = torch::tensor({ 1, 1, 1, 0, 0, 1, 1, 1, 1, 0 }, torch::kInt32);
-            {"glass",              true},
+    cout << "Initial weights_: " << endl;
-            {"iris",               true},
+    for (int i = 0; i < 10; i++) {
-            {"kdd_JapaneseVowels", false},
+        cout << weights_.index({ i }).item<double>() << ", ";
-            {"letter",             true},
-            {"liver-disorders",    true},
-            {"mfeat-factors",      true},
-    };
-    auto valid_datasets = vector<string>();
-    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")
-        .action([valid_datasets](const std::string& value) {
-        if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
-            return value;
-        }
-        throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
-            }
-    );
-    program.add_argument("-p", "--path")
-        .help(" folder where the data files are located, default")
-        .default_value(string{ PATH }
-    );
-    program.add_argument("-m", "--model")
-        .help("Model to use " + platform::Models::instance()->toString())
-        .action([](const std::string& value) {
-        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 " + platform::Models::instance()->toString());
-            }
-    );
-    program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
-    program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
-    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
-    program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
-    program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
-        try {
-            auto k = stoi(value);
-            if (k < 2) {
-                throw runtime_error("Number of folds must be greater than 1");
-            }
-            return k;
-        }
-        catch (const runtime_error& err) {
-            throw runtime_error(err.what());
-        }
-        catch (...) {
-            throw runtime_error("Number of folds must be an integer");
-        }});
-    program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
-    bool class_last, stratified, tensors, dump_cpt;
-    string model_name, file_name, path, complete_file_name;
-    int nFolds, seed;
-    try {
-        program.parse_args(argc, argv);
-        file_name = program.get<string>("dataset");
-        path = program.get<string>("path");
-        model_name = program.get<string>("model");
-        complete_file_name = path + file_name + ".arff";
-        stratified = program.get<bool>("stratified");
-        tensors = program.get<bool>("tensors");
-        nFolds = program.get<int>("folds");
-        seed = program.get<int>("seed");
-        dump_cpt = program.get<bool>("dumpcpt");
-        class_last = datasets[file_name];
-        if (!file_exists(complete_file_name)) {
-            throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
-        }
     }
-    catch (const exception& err) {
+    cout << "end." << endl;
-        cerr << err.what() << endl;
+    cout << "y_: " << endl;
-        cerr << program;
+    for (int i = 0; i < 10; i++) {
-        exit(1);
+        cout << y_.index({ i }).item<int>() << ", ";
     }
+    cout << "end." << endl;
+    cout << "ypred: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << ypred.index({ i }).item<int>() << ", ";
+    }
+    cout << "end." << endl;
+    auto mask_wrong = ypred != y_;
+    auto mask_right = ypred == y_;
+    auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
+    double epsilon_t = masked_weights.sum().item<double>();
+    cout << "epsilon_t: " << epsilon_t << endl;
+    double wt = (1 - epsilon_t) / epsilon_t;
+    cout << "wt: " << wt << endl;
+    double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+    cout << "alpha_t: " << alpha_t << endl;
+    // Step 3.2: Update weights for next classifier
+    // Step 3.2.1: Update weights of wrong samples
+    cout << "exp(alpha_t): " << exp(alpha_t) << endl;
+    cout << "exp(-alpha_t): " << exp(-alpha_t) << endl;
+    weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
+    // Step 3.2.2: Update weights of right samples
+    weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
+    // Step 3.3: Normalise the weights
+    double totalWeights = torch::sum(weights_).item<double>();
+    cout << "totalWeights: " << totalWeights << endl;
+    cout << "Before normalization: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << weights_.index({ i }).item<double>() << endl;
+    }
+    weights_ = weights_ / totalWeights;
+    cout << "After normalization: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << weights_.index({ i }).item<double>() << endl;
+    }
+    // map<string, bool> datasets = {
+    //         {"diabetes",           true},
+    //         {"ecoli",              true},
+    //         {"glass",              true},
+    //         {"iris",               true},
+    //         {"kdd_JapaneseVowels", false},
+    //         {"letter",             true},
+    //         {"liver-disorders",    true},
+    //         {"mfeat-factors",      true},
+    // };
+    // auto valid_datasets = vector<string>();
+    // 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")
+    //     .action([valid_datasets](const std::string& value) {
+    //     if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
+    //         return value;
+    //     }
+    //     throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
+    //         }
+    // );
+    // program.add_argument("-p", "--path")
+    //     .help(" folder where the data files are located, default")
+    //     .default_value(string{ PATH }
+    // );
+    // program.add_argument("-m", "--model")
+    //     .help("Model to use " + platform::Models::instance()->toString())
+    //     .action([](const std::string& value) {
+    //     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 " + platform::Models::instance()->toString());
+    //         }
+    // );
+    // program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
+    // program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
+    // program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
+    // program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
+    // program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
+    //     try {
+    //         auto k = stoi(value);
+    //         if (k < 2) {
+    //             throw runtime_error("Number of folds must be greater than 1");
+    //         }
+    //         return k;
+    //     }
+    //     catch (const runtime_error& err) {
+    //         throw runtime_error(err.what());
+    //     }
+    //     catch (...) {
+    //         throw runtime_error("Number of folds must be an integer");
+    //     }});
+    // program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
+    // bool class_last, stratified, tensors, dump_cpt;
+    // string model_name, file_name, path, complete_file_name;
+    // int nFolds, seed;
+    // try {
+    //     program.parse_args(argc, argv);
+    //     file_name = program.get<string>("dataset");
+    //     path = program.get<string>("path");
+    //     model_name = program.get<string>("model");
+    //     complete_file_name = path + file_name + ".arff";
+    //     stratified = program.get<bool>("stratified");
+    //     tensors = program.get<bool>("tensors");
+    //     nFolds = program.get<int>("folds");
+    //     seed = program.get<int>("seed");
+    //     dump_cpt = program.get<bool>("dumpcpt");
+    //     class_last = datasets[file_name];
+    //     if (!file_exists(complete_file_name)) {
+    //         throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
+    //     }
+    // }
+    // catch (const exception& err) {
+    //     cerr << err.what() << endl;
+    //     cerr << program;
+    //     exit(1);
+    // }
 
     /*
     * Begin Processing
     */
-    auto handler = ArffFiles();
+    // auto handler = ArffFiles();
-    handler.load(complete_file_name, class_last);
+    // handler.load(complete_file_name, class_last);
-    // Get Dataset X, y
+    // // Get Dataset X, y
-    vector<mdlp::samples_t>& X = handler.getX();
+    // vector<mdlp::samples_t>& X = handler.getX();
-    mdlp::labels_t& y = handler.getY();
+    // mdlp::labels_t& y = handler.getY();
-    // Get className & Features
+    // // Get className & Features
-    auto className = handler.getClassName();
+    // auto className = handler.getClassName();
-    vector<string> features;
+    // vector<string> features;
-    auto attributes = handler.getAttributes();
+    // auto attributes = handler.getAttributes();
-    transform(attributes.begin(), attributes.end(), back_inserter(features),
+    // transform(attributes.begin(), attributes.end(), back_inserter(features),
-        [](const pair<string, string>& item) { return item.first; });
+    //     [](const pair<string, string>& item) { return item.first; });
-    // Discretize Dataset
+    // // Discretize Dataset
-    auto [Xd, maxes] = discretize(X, y, features);
+    // auto [Xd, maxes] = discretize(X, y, features);
-    maxes[className] = *max_element(y.begin(), y.end()) + 1;
+    // maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    map<string, vector<int>> states;
+    // map<string, vector<int>> states;
-    for (auto feature : features) {
+    // for (auto feature : features) {
-        states[feature] = vector<int>(maxes[feature]);
+    //     states[feature] = vector<int>(maxes[feature]);
-    }
+    // }
-    states[className] = vector<int>(maxes[className]);
+    // states[className] = vector<int>(maxes[className]);
-    auto clf = platform::Models::instance()->create(model_name);
+    // auto clf = platform::Models::instance()->create(model_name);
-    clf->fit(Xd, y, features, className, states);
+    // clf->fit(Xd, y, features, className, states);
-    if (dump_cpt) {
+    // if (dump_cpt) {
-        cout << "--- CPT Tables ---" << endl;
+    //     cout << "--- CPT Tables ---" << endl;
-        clf->dump_cpt();
+    //     clf->dump_cpt();
-    }
+    // }
-    auto lines = clf->show();
+    // auto lines = clf->show();
-    for (auto line : lines) {
+    // for (auto line : lines) {
-        cout << line << endl;
+    //     cout << line << endl;
-    }
+    // }
-    cout << "--- Topological Order ---" << endl;
+    // cout << "--- Topological Order ---" << endl;
-    auto order = clf->topological_order();
+    // auto order = clf->topological_order();
-    for (auto name : order) {
+    // for (auto name : order) {
-        cout << name << ", ";
+    //     cout << name << ", ";
-    }
+    // }
-    cout << "end." << endl;
+    // cout << "end." << endl;
-    auto score = clf->score(Xd, y);
+    // auto score = clf->score(Xd, y);
-    cout << "Score: " << score << endl;
+    // cout << "Score: " << score << endl;
-    auto graph = clf->graph();
+    // auto graph = clf->graph();
-    auto dot_file = model_name + "_" + file_name;
+    // auto dot_file = model_name + "_" + file_name;
-    ofstream file(dot_file + ".dot");
+    // ofstream file(dot_file + ".dot");
-    file << graph;
+    // file << graph;
-    file.close();
+    // file.close();
-    cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
+    // cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
-    cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
+    // cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
-    string stratified_string = stratified ? " Stratified" : "";
+    // string stratified_string = stratified ? " Stratified" : "";
-    cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
+    // cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
-    cout << "==========================================" << endl;
+    // cout << "==========================================" << endl;
-    torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
+    // torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
-    torch::Tensor yt = torch::tensor(y, torch::kInt32);
+    // torch::Tensor yt = torch::tensor(y, torch::kInt32);
-    for (int i = 0; i < features.size(); ++i) {
+    // for (int i = 0; i < features.size(); ++i) {
-        Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
+    //     Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
-    }
+    // }
-    float total_score = 0, total_score_train = 0, score_train, score_test;
+    // float total_score = 0, total_score_train = 0, score_train, score_test;
-    Fold* fold;
+    // platform::Fold* fold;
-    if (stratified)
+    // if (stratified)
-        fold = new StratifiedKFold(nFolds, y, seed);
+    //     fold = new platform::StratifiedKFold(nFolds, y, seed);
-    else
+    // else
-        fold = new KFold(nFolds, y.size(), seed);
+    //     fold = new platform::KFold(nFolds, y.size(), seed);
-    for (auto i = 0; i < nFolds; ++i) {
+    // for (auto i = 0; i < nFolds; ++i) {
-        auto [train, test] = fold->getFold(i);
+    //     auto [train, test] = fold->getFold(i);
-        cout << "Fold: " << i + 1 << endl;
+    //     cout << "Fold: " << i + 1 << endl;
-        if (tensors) {
+    //     if (tensors) {
-            auto ttrain = torch::tensor(train, torch::kInt64);
+    //         auto ttrain = torch::tensor(train, torch::kInt64);
-            auto ttest = torch::tensor(test, torch::kInt64);
+    //         auto ttest = torch::tensor(test, torch::kInt64);
-            torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
+    //         torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
-            torch::Tensor ytraint = yt.index({ ttrain });
+    //         torch::Tensor ytraint = yt.index({ ttrain });
-            torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
+    //         torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
-            torch::Tensor ytestt = yt.index({ ttest });
+    //         torch::Tensor ytestt = yt.index({ ttest });
-            clf->fit(Xtraint, ytraint, features, className, states);
+    //         clf->fit(Xtraint, ytraint, features, className, states);
-            auto temp = clf->predict(Xtraint);
+    //         auto temp = clf->predict(Xtraint);
-            score_train = clf->score(Xtraint, ytraint);
+    //         score_train = clf->score(Xtraint, ytraint);
-            score_test = clf->score(Xtestt, ytestt);
+    //         score_test = clf->score(Xtestt, ytestt);
-        } else {
+    //     } else {
-            auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
+    //         auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
-            auto [Xtest, ytest] = extract_indices(test, Xd, y);
+    //         auto [Xtest, ytest] = extract_indices(test, Xd, y);
-            clf->fit(Xtrain, ytrain, features, className, states);
+    //         clf->fit(Xtrain, ytrain, features, className, states);
-            score_train = clf->score(Xtrain, ytrain);
+    //         score_train = clf->score(Xtrain, ytrain);
-            score_test = clf->score(Xtest, ytest);
+    //         score_test = clf->score(Xtest, ytest);
-        }
+    //     }
-        if (dump_cpt) {
+    //     if (dump_cpt) {
-            cout << "--- CPT Tables ---" << endl;
+    //         cout << "--- CPT Tables ---" << endl;
-            clf->dump_cpt();
+    //         clf->dump_cpt();
-        }
+    //     }
-        total_score_train += score_train;
+    //     total_score_train += score_train;
-        total_score += score_test;
+    //     total_score += score_test;
-        cout << "Score Train: " << score_train << endl;
+    //     cout << "Score Train: " << score_train << endl;
-        cout << "Score Test : " << score_test << endl;
+    //     cout << "Score Test : " << score_test << endl;
-        cout << "-------------------------------------------------------------------------------" << endl;
+    //     cout << "-------------------------------------------------------------------------------" << endl;
-    }
+    // }
-    cout << "**********************************************************************************" << endl;
+    // cout << "**********************************************************************************" << endl;
-    cout << "Average Score Train: " << total_score_train / nFolds << endl;
+    // cout << "Average Score Train: " << total_score_train / nFolds << endl;
-    cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
+    // cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
 }

src/BayesNet/AODE.h

@@ -10,7 +10,6 @@ namespace bayesnet {
         AODE();
         virtual ~AODE() {};
         vector<string> graph(const string& title = "AODE") const override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
     };
 }
 #endif

src/BayesNet/AODELd.cc

@@ -4,9 +4,9 @@
 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_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;
@@ -26,6 +26,7 @@ namespace bayesnet {
             models.push_back(std::make_unique<SPODELd>(i));
         }
         n_models = models.size();
+        significanceModels = vector<double>(n_models, 1.0);
     }
     void AODELd::trainModel(const torch::Tensor& weights)
     {

src/BayesNet/AODELd.h

@@ -12,11 +12,10 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
     public:
         AODELd();
-        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_) override;
         virtual ~AODELd() = default;
-        vector<string> graph(const string& name = "AODE") const override;
+        vector<string> graph(const string& name = "AODELd") const override;
         static inline string version() { return "0.0.1"; };
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
     };
 }
 #endif // !AODELD_H

src/BayesNet/BaseClassifier.h

@@ -5,19 +5,21 @@
 #include <vector>
 namespace bayesnet {
     using namespace std;
+    enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     protected:
         virtual void trainModel(const torch::Tensor& weights) = 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;
+        virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;
+        status_t virtual getStatus() const = 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()const = 0;
@@ -25,7 +27,7 @@ namespace bayesnet {
         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"; };
+        const string inline getVersion() const { return "0.2.0"; };
         vector<string> virtual topological_order() = 0;
         void virtual dump_cpt()const = 0;
         virtual void setHyperparameters(nlohmann::json& hyperparameters) = 0;

src/BayesNet/BayesMetrics.cc

@@ -77,7 +77,6 @@ namespace bayesnet {
         auto source = vector<string>(features);
         source.push_back(className);
         auto combinations = doCombinations(source);
-        double totalWeight = weights.sum().item<double>();
         // Compute class prior
         auto margin = torch::zeros({ classNumStates }, torch::kFloat);
         for (int value = 0; value < classNumStates; ++value) {

src/BayesNet/BoostAODE.cc

@@ -1,6 +1,9 @@
 #include "BoostAODE.h"
 #include <set>
 #include "BayesMetrics.h"
+#include "Colors.h"
+#include "Folding.h"
+#include <limits.h>
 
 namespace bayesnet {
     BoostAODE::BoostAODE() : Ensemble() {}
@@ -11,7 +14,7 @@ namespace bayesnet {
     void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
     {
         // Check if hyperparameters are valid
-        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending" };
+        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence" };
         checkHyperparameters(validKeys, hyperparameters);
         if (hyperparameters.contains("repeatSparent")) {
             repeatSparent = hyperparameters["repeatSparent"];
@@ -22,6 +25,38 @@ namespace bayesnet {
         if (hyperparameters.contains("ascending")) {
             ascending = hyperparameters["ascending"];
         }
+        if (hyperparameters.contains("convergence")) {
+            convergence = hyperparameters["convergence"];
+        }
+    }
+    void BoostAODE::validationInit()
+    {
+        auto y_ = dataset.index({ -1, "..." });
+        if (convergence) {
+            // Prepare train & validation sets from train data
+            auto fold = platform::StratifiedKFold(5, y_, 271);
+            dataset_ = torch::clone(dataset);
+            // save input dataset
+            auto [train, test] = fold.getFold(0);
+            auto train_t = torch::tensor(train);
+            auto test_t = torch::tensor(test);
+            // Get train and validation sets
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+            y_train = dataset.index({ -1, train_t });
+            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+            y_test = dataset.index({ -1, test_t });
+            dataset = X_train;
+            m = X_train.size(1);
+            auto n_classes = states.at(className).size();
+            metrics = Metrics(dataset, features, className, n_classes);
+            // Build dataset with train data
+            buildDataset(y_train);
+        } else {
+            // Use all data to train
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+            y_train = y_;
+        }
+
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
@@ -29,15 +64,22 @@ namespace bayesnet {
         n_models = 0;
         if (maxModels == 0)
             maxModels = .1 * n > 10 ? .1 * n : n;
+        validationInit();
         Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-        auto X_ = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-        auto y_ = dataset.index({ -1, "..." });
         bool exitCondition = false;
         unordered_set<int> featuresUsed;
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double delta = 1.0;
+        double threshold = 1e-4;
+        int tolerance = 5; // number of times the accuracy can be lower than the threshold
+        int count = 0; // number of times the accuracy is lower than the threshold
+        fitted = true; // to enable predict
         // Step 0: Set the finish condition
         // if not repeatSparent a finish condition is run out of features
         // n_models == maxModels
-        int numClasses = states[className].size();
+        // epsiolon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
         while (!exitCondition) {
             // Step 1: Build ranking with mutual information
             auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
@@ -60,29 +102,44 @@ namespace bayesnet {
             }
             featuresUsed.insert(feature);
             model = std::make_unique<SPODE>(feature);
-            n_models++;
             model->fit(dataset, features, className, states, weights_);
-            auto ypred = model->predict(X_);
+            auto ypred = model->predict(X_train);
             // Step 3.1: Compute the classifier amout of say
-            auto mask_wrong = ypred != y_;
+            auto mask_wrong = ypred != y_train;
+            auto mask_right = ypred == y_train;
             auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
-            double wrongWeights = masked_weights.sum().item<double>();
+            double epsilon_t = masked_weights.sum().item<double>();
-            double significance = wrongWeights == 0 ? 1 : 0.5 * log((1 - wrongWeights) / wrongWeights);
+            double wt = (1 - epsilon_t) / epsilon_t;
+            double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
             // Step 3.2: Update weights for next classifier
             // Step 3.2.1: Update weights of wrong samples
-            weights_ += mask_wrong.to(weights_.dtype()) * exp(significance) * weights_;
+            weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
+            // Step 3.2.2: Update weights of right samples
+            weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
             // Step 3.3: Normalise the weights
             double totalWeights = torch::sum(weights_).item<double>();
             weights_ = weights_ / totalWeights;
             // Step 3.4: Store classifier and its accuracy to weigh its future vote
             models.push_back(std::move(model));
-            significanceModels.push_back(significance);
+            significanceModels.push_back(alpha_t);
-            exitCondition = n_models == maxModels && repeatSparent;
+            n_models++;
+            if (convergence) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    delta = accuracy - priorAccuracy;
+                }
+                if (delta < threshold) {
+                    count++;
+                }
+            }
+            exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
         }
         if (featuresUsed.size() != features.size()) {
-            cout << "Warning: BoostAODE did not use all the features" << endl;
+            status = WARNING;
         }
-        weights.copy_(weights_);
     }
     vector<string> BoostAODE::graph(const string& title) const
     {

src/BayesNet/BoostAODE.h

@@ -13,9 +13,13 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights) override;
     private:
-        bool repeatSparent=false;
+        torch::Tensor dataset_;
-        int maxModels=0;
+        torch::Tensor X_train, y_train, X_test, y_test;
-        bool ascending=false; //Process KBest features ascending or descending order
+        void validationInit();
+        bool repeatSparent = false;
+        int maxModels = 0;
+        bool ascending = false; //Process KBest features ascending or descending order
+        bool convergence = false; //if true, stop when the model does not improve
     };
 }
 #endif

src/BayesNet/Classifier.cc

@@ -5,7 +5,7 @@ namespace bayesnet {
     using namespace torch;
 
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         this->features = features;
         this->className = className;
@@ -13,7 +13,7 @@ namespace bayesnet {
         m = dataset.size(1);
         n = dataset.size(0) - 1;
         checkFitParameters();
-        auto n_classes = states[className].size();
+        auto n_classes = states.at(className).size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
         buildModel(weights);
@@ -39,7 +39,7 @@ namespace bayesnet {
         model.fit(dataset, weights, 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)
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
     {
         dataset = X;
         buildDataset(y);
@@ -47,7 +47,7 @@ namespace bayesnet {
         return build(features, className, states, weights);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
     {
         dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
         for (int i = 0; i < X.size(); ++i) {
@@ -58,21 +58,24 @@ namespace bayesnet {
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
         return build(features, className, states, weights);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states)
     {
         this->dataset = dataset;
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
         return build(features, className, states, weights);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
     {
         this->dataset = dataset;
         return build(features, className, states, weights);
     }
     void Classifier::checkFitParameters()
     {
+        if (torch::is_floating_point(dataset)) {
+            throw invalid_argument("dataset (X, y) must be of type Integer");
+        }
         if (n != features.size()) {
-            throw invalid_argument("X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
+            throw invalid_argument("Classifier: X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
             throw invalid_argument("className not found in states");
@@ -160,4 +163,10 @@ namespace bayesnet {
             }
         }
     }
+    void Classifier::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid, default is no hyperparameters
+        const vector<string> validKeys = { };
+        checkHyperparameters(validKeys, hyperparameters);
+    }
 }

src/BayesNet/Classifier.h

@@ -10,8 +10,7 @@ using namespace torch;
 namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
-        void buildDataset(torch::Tensor& y);
+        Classifier& build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights);
-        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights);
     protected:
         bool fitted;
         int m, n; // m: number of samples, n: number of features
@@ -21,28 +20,32 @@ namespace bayesnet {
         string className;
         map<string, vector<int>> states;
         Tensor dataset; // (n+1)xm tensor
+        status_t status = NORMAL;
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
         void trainModel(const torch::Tensor& weights) override;
         void checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters);
+        void buildDataset(torch::Tensor& y);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
+        Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
         int getNumberOfStates() const override;
         Tensor predict(Tensor& X) override;
+        status_t getStatus() const override { return status; }
         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() const override;
         vector<string> topological_order()  override;
         void dump_cpt() const override;
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 }
 #endif

src/BayesNet/Ensemble.cc

@@ -3,7 +3,7 @@
 namespace bayesnet {
     using namespace torch;
 
-    Ensemble::Ensemble() : Classifier(Network()) {}
+    Ensemble::Ensemble() : Classifier(Network()), n_models(0) {}
 
     void Ensemble::trainModel(const torch::Tensor& weights)
     {
@@ -17,10 +17,14 @@ namespace bayesnet {
     {
         auto y_pred_ = y_pred.accessor<int, 2>();
         vector<int> y_pred_final;
+        int numClasses = states.at(className).size();
+        // y_pred is m x n_models with the prediction of every model for each sample
         for (int i = 0; i < y_pred.size(0); ++i) {
-            vector<double> votes(y_pred.size(1), 0);
+            // votes store in each index (value of class) the significance added by each model
-            for (int j = 0; j < y_pred.size(1); ++j) {
+            // i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
-                votes[y_pred_[i][j]] += significanceModels[j];
+            vector<double> votes(numClasses, 0.0);
+            for (int j = 0; j < n_models; ++j) {
+                votes[y_pred_[i][j]] += significanceModels.at(j);
             }
             // argsort in descending order
             auto indices = argsort(votes);
@@ -34,7 +38,6 @@ namespace bayesnet {
             throw logic_error("Ensemble has not been fitted");
         }
         Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
-        //Create a threadpool
         auto threads{ vector<thread>() };
         mutex mtx;
         for (auto i = 0; i < n_models; ++i) {

src/BayesNet/KDB.cc

@@ -4,6 +4,18 @@ namespace bayesnet {
     using namespace torch;
 
     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
+    void KDB::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid
+        const vector<string> validKeys = { "k", "theta" };
+        checkHyperparameters(validKeys, hyperparameters);
+        if (hyperparameters.contains("k")) {
+            k = hyperparameters["k"];
+        }
+        if (hyperparameters.contains("theta")) {
+            theta = hyperparameters["theta"];
+        }
+    }
     void KDB::buildModel(const torch::Tensor& weights)
     {
         /*

src/BayesNet/KDB.h

@@ -16,7 +16,7 @@ namespace bayesnet {
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
         vector<string> graph(const string& name = "KDB") const override;
     };
 }

src/BayesNet/KDBLd.cc

@@ -3,9 +3,9 @@
 namespace bayesnet {
     using namespace std;
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;

src/BayesNet/KDBLd.h

@@ -10,10 +10,9 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
         vector<string> graph(const string& name = "KDB") const override;
         Tensor predict(Tensor& X) override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
         static inline string version() { return "0.0.1"; };
     };
 }

src/BayesNet/Network.cc

@@ -3,8 +3,8 @@
 #include "Network.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
-    Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false) {}
+    Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
+    Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}
     Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
         getmaxThreads()), fitted(other.fitted)
     {
@@ -132,10 +132,10 @@ namespace bayesnet {
     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) {
+        for_each(features.begin(), features.end(), [this, &states](const string& feature) {
-            nodes[features[i]]->setNumStates(states.at(features[i]).size());
+            nodes.at(feature)->setNumStates(states.at(feature).size());
-        }
+            });
-        classNumStates = nodes[className]->getNumStates();
+        classNumStates = nodes.at(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 torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
@@ -174,37 +174,11 @@ namespace bayesnet {
     {
         setStates(states);
         laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
-        int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
-        if (maxThreadsRunning < 1) {
-            maxThreadsRunning = 1;
-        }
         vector<thread> threads;
-        mutex mtx;
+        for (auto& node : nodes) {
-        condition_variable cv;
+            threads.emplace_back([this, &node, &weights]() {
-        int activeThreads = 0;
+                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
-        int nextNodeIndex = 0;
-        while (nextNodeIndex < nodes.size()) {
-            unique_lock<mutex> lock(mtx);
-            cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
-            threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads, &weights]() {
-                while (true) {
-                    unique_lock<mutex> lock(mtx);
-                    if (nextNodeIndex >= nodes.size()) {
-                        break;  // No more work remaining
-                    }
-                    auto& pair = *std::next(nodes.begin(), nextNodeIndex);
-                    ++nextNodeIndex;
-                    lock.unlock();
-                    pair.second->computeCPT(samples, features, laplaceSmoothing, weights);
-                    lock.lock();
-                    nodes[pair.first] = std::move(pair.second);
-                    lock.unlock();
-                }
-                lock_guard<mutex> lock(mtx);
-                --activeThreads;
-                cv.notify_one();
                 });
-            ++activeThreads;
         }
         for (auto& thread : threads) {
             thread.join();
@@ -399,7 +373,6 @@ namespace bayesnet {
         auto result = features;
         result.erase(remove(result.begin(), result.end(), className), result.end());
         bool ending{ false };
-        int idx = 0;
         while (!ending) {
             ending = true;
             for (auto feature : features) {

src/BayesNet/Network.h

@@ -27,6 +27,7 @@ namespace bayesnet {
         Network();
         explicit Network(float);
         explicit Network(Network&);
+        ~Network() = default;
         torch::Tensor& getSamples();
         float getmaxThreads();
         void addNode(const string&);
@@ -52,7 +53,7 @@ namespace bayesnet {
         vector<string> graph(const string& title) const; // Returns a vector of strings representing the graph in graphviz format
         void initialize();
         void dump_cpt() const;
-        inline string version() { return "0.1.0"; }
+        inline string version() { return "0.2.0"; }
     };
 }
 #endif

src/BayesNet/Node.cc

@@ -100,7 +100,7 @@ namespace bayesnet {
         }
         int name_index = pos - features.begin();
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            torch::List<c10::optional<torch::Tensor>> coordinates;
+            c10::List<c10::optional<at::Tensor>> coordinates;
             coordinates.push_back(dataset.index({ name_index, n_sample }));
             for (auto parent : parents) {
                 pos = find(features.begin(), features.end(), parent->getName());
@@ -118,10 +118,10 @@ namespace bayesnet {
     }
     float Node::getFactorValue(map<string, int>& evidence)
     {
-        torch::List<c10::optional<torch::Tensor>> coordinates;
+        c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
-        coordinates.push_back(torch::tensor(evidence[name]));
+        coordinates.push_back(at::tensor(evidence[name]));
-        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return torch::tensor(evidence[parent->getName()]); });
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
         return cpTable.index({ coordinates }).item<float>();
     }
     vector<string> Node::graph(const string& className)

src/BayesNet/Proposal.cc

@@ -9,6 +9,15 @@ namespace bayesnet {
             delete value;
         }
     }
+    void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
+    {
+        if (!torch::is_floating_point(X)) {
+            throw std::invalid_argument("X must be a floating point tensor");
+        }
+        if (torch::is_floating_point(y)) {
+            throw std::invalid_argument("y must be an integer tensor");
+        }
+    }
     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...
@@ -44,15 +53,6 @@ namespace bayesnet {
             auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
             auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
             discretizers[feature]->fit(xvf, yxv);
-            //
-            //
-            //
-            // auto tmp = discretizers[feature]->transform(xvf);
-            // Xv[index] = tmp;
-            // auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
-            // iota(xStates.begin(), xStates.end(), 0);
-            // //Update new states of the feature/node
-            // states[feature] = xStates;
         }
         if (upgrade) {
             // Discretize again X (only the affected indices) with the new fitted discretizers

src/BayesNet/Proposal.h

@@ -13,6 +13,7 @@ namespace bayesnet {
         Proposal(torch::Tensor& pDataset, vector<string>& features_, string& className_);
         virtual ~Proposal();
     protected:
+        void checkInput(const torch::Tensor& X, const torch::Tensor& y);
         torch::Tensor prepareX(torch::Tensor& X);
         map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
         map<string, vector<int>> fit_local_discretization(const torch::Tensor& y);

src/BayesNet/SPODE.h

@@ -12,7 +12,6 @@ namespace bayesnet {
         explicit SPODE(int root);
         virtual ~SPODE() {};
         vector<string> graph(const string& name = "SPODE") const override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
     };
 }
 #endif

src/BayesNet/SPODELd.cc

@@ -3,9 +3,9 @@
 namespace bayesnet {
     using namespace std;
     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;
@@ -18,11 +18,13 @@ namespace bayesnet {
         states = localDiscretizationProposal(states, model);
         return *this;
     }
-    SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
+        if (!torch::is_floating_point(dataset)) {
+            throw std::runtime_error("Dataset must be a floating point tensor");
+        }
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
         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

src/BayesNet/SPODELd.h

@@ -9,11 +9,10 @@ namespace bayesnet {
     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;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        SPODELd& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
         vector<string> graph(const string& name = "SPODE") const override;
         Tensor predict(Tensor& X) override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
         static inline string version() { return "0.0.1"; };
     };
 }

src/BayesNet/TAN.h

@@ -11,7 +11,6 @@ namespace bayesnet {
         TAN();
         virtual ~TAN() {};
         vector<string> graph(const string& name = "TAN") const override;
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
     };
 }
 #endif

src/BayesNet/TANLd.cc

@@ -3,9 +3,9 @@
 namespace bayesnet {
     using namespace std;
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;

src/BayesNet/TANLd.h

@@ -10,11 +10,10 @@ namespace bayesnet {
     public:
         TANLd();
         virtual ~TANLd() = default;
-        TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
         vector<string> graph(const string& name = "TAN") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
-        void setHyperparameters(nlohmann::json& hyperparameters) override {};
     };
 }
 #endif // !TANLD_H

src/Platform/CMakeLists.txt

@@ -8,5 +8,9 @@ add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.c
 add_executable(manage manage.cc Results.cc ReportConsole.cc ReportExcel.cc ReportBase.cc)
 add_executable(list list.cc platformUtils Datasets.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
-target_link_libraries(manage "${TORCH_LIBRARIES}" OpenXLSX::OpenXLSX)
+if (${CMAKE_HOST_SYSTEM_NAME} MATCHES "Linux")
+    target_link_libraries(manage "${TORCH_LIBRARIES}" OpenXLSX::OpenXLSX stdc++fs)
+else()
+    target_link_libraries(manage "${TORCH_LIBRARIES}" OpenXLSX::OpenXLSX)
+endif()
 target_link_libraries(list ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/Datasets.cc

@@ -1,6 +1,7 @@
 #include "Datasets.h"
 #include "platformUtils.h"
 #include "ArffFiles.h"
+#include <fstream>
 namespace platform {
     void Datasets::load()
     {
@@ -212,10 +213,11 @@ namespace platform {
     {
         for (int i = 0; i < features.size(); ++i) {
             states[features[i]] = vector<int>(*max_element(Xd[i].begin(), Xd[i].end()) + 1);
-            iota(begin(states[features[i]]), end(states[features[i]]), 0);
+            auto item = states.at(features[i]);
+            iota(begin(item), end(item), 0);
         }
         states[className] = vector<int>(*max_element(yv.begin(), yv.end()) + 1);
-        iota(begin(states[className]), end(states[className]), 0);
+        iota(begin(states.at(className)), end(states.at(className)), 0);
     }
     void Dataset::load_arff()
     {

src/Platform/Experiment.cc

@@ -2,7 +2,7 @@
 #include "Datasets.h"
 #include "Models.h"
 #include "ReportConsole.h"
-
+#include <fstream>
 namespace platform {
     using json = nlohmann::json;
     string get_date()
@@ -111,6 +111,26 @@ namespace platform {
         }
     }
 
+    string getColor(bayesnet::status_t status)
+    {
+        switch (status) {
+            case bayesnet::NORMAL:
+                return Colors::GREEN();
+            case bayesnet::WARNING:
+                return Colors::YELLOW();
+            case bayesnet::ERROR:
+                return Colors::RED();
+            default:
+                return Colors::RESET();
+        }
+    }
+
+    void showProgress(int fold, const string& color, const string& phase)
+    {
+        string prefix = phase == "a" ? "" : "\b\b\b\b";
+        cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
+
+    }
     void Experiment::cross_validation(const string& path, const string& fileName)
     {
         auto datasets = platform::Datasets(path, discretized, platform::ARFF);
@@ -159,28 +179,34 @@ namespace platform {
                 auto y_train = y.index({ train_t });
                 auto X_test = X.index({ "...", test_t });
                 auto y_test = y.index({ test_t });
-                cout << nfold + 1 << ", " << flush;
+                showProgress(nfold + 1, getColor(clf->getStatus()), "a");
                 // Train model
                 clf->fit(X_train, y_train, features, className, states);
+                showProgress(nfold + 1, getColor(clf->getStatus()), "b");
                 nodes[item] = clf->getNumberOfNodes();
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();
                 train_time[item] = train_timer.getDuration();
+                // Score train
                 auto accuracy_train_value = clf->score(X_train, y_train);
                 // Test model
+                showProgress(nfold + 1, getColor(clf->getStatus()), "c");
                 test_timer.start();
                 auto accuracy_test_value = clf->score(X_test, y_test);
                 test_time[item] = test_timer.getDuration();
                 accuracy_train[item] = accuracy_train_value;
                 accuracy_test[item] = accuracy_test_value;
+                cout << "\b\b\b, " << flush;
                 // Store results and times in vector
                 result.addScoreTrain(accuracy_train_value);
                 result.addScoreTest(accuracy_test_value);
                 result.addTimeTrain(train_time[item].item<double>());
                 result.addTimeTest(test_time[item].item<double>());
                 item++;
+                clf.reset();
             }
             cout << "end. " << flush;
+            delete fold;
         }
         result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
         result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());

src/Platform/Folding.cc

@@ -1,95 +1,97 @@
 #include "Folding.h"
 #include <algorithm>
 #include <map>
-Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
+namespace platform {
-{
+    Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
-    random_device rd;
+    {
-    random_seed = default_random_engine(seed == -1 ? rd() : seed);
+        random_device rd;
-    srand(seed == -1 ? time(0) : 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), indices(vector<int>(n))
-{
-    iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
-    shuffle(indices.begin(), indices.end(), random_seed);
-}
-pair<vector<int>, vector<int>> KFold::getFold(int nFold)
-{
-    if (nFold >= k || nFold < 0) {
-        throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
     }
-    int nTest = n / k;
+    KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
-    auto train = vector<int>();
+    {
-    auto test = vector<int>();
+        iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
-    for (int i = 0; i < n; i++) {
-        if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
-            test.push_back(indices[i]);
-        } else {
-            train.push_back(indices[i]);
-        }
-    }
-    return { train, test };
-}
-StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
-{
-    n = y.numel();
-    this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
-    build();
-}
-StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
-    : Fold(k, y.size(), seed)
-{
-    this->y = y;
-    n = y.size();
-    build();
-}
-void StratifiedKFold::build()
-{
-    stratified_indices = vector<vector<int>>(k);
-    int fold_size = n / k;
-    // Compute class counts and indices
-    auto class_indices = map<int, vector<int>>();
-    vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
-    for (auto i = 0; i < n; ++i) {
-        class_counts[y[i]]++;
-        class_indices[y[i]].push_back(i);
-    }
-    // Shuffle class indices
-    for (auto& [cls, indices] : class_indices) {
         shuffle(indices.begin(), indices.end(), random_seed);
     }
-    // Assign indices to folds
+    pair<vector<int>, vector<int>> KFold::getFold(int nFold)
-    for (auto label = 0; label < class_counts.size(); ++label) {
+    {
-        auto num_samples_to_take = class_counts[label] / k;
+        if (nFold >= k || nFold < 0) {
-        if (num_samples_to_take == 0)
+            throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
-            continue;
-        auto remainder_samples_to_take = class_counts[label] % k;
-        for (auto fold = 0; fold < k; ++fold) {
-            auto it = next(class_indices[label].begin(), num_samples_to_take);
-            move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold]));  // ##
-            class_indices[label].erase(class_indices[label].begin(), it);
         }
-        while (remainder_samples_to_take > 0) {
+        int nTest = n / k;
-            int fold = (rand() % static_cast<int>(k));
+        auto train = vector<int>();
-            if (stratified_indices[fold].size() == fold_size + 1) {
+        auto test = vector<int>();
-                continue;
+        for (int i = 0; i < n; i++) {
+            if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
+                test.push_back(indices[i]);
+            } else {
+                train.push_back(indices[i]);
+            }
+        }
+        return { train, test };
+    }
+    StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
+    {
+        n = y.numel();
+        this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
+        build();
+    }
+    StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
+        : Fold(k, y.size(), seed)
+    {
+        this->y = y;
+        n = y.size();
+        build();
+    }
+    void StratifiedKFold::build()
+    {
+        stratified_indices = vector<vector<int>>(k);
+        int fold_size = n / k;
+        // Compute class counts and indices
+        auto class_indices = map<int, vector<int>>();
+        vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
+        for (auto i = 0; i < n; ++i) {
+            class_counts[y[i]]++;
+            class_indices[y[i]].push_back(i);
+        }
+        // Shuffle class indices
+        for (auto& [cls, indices] : class_indices) {
+            shuffle(indices.begin(), indices.end(), random_seed);
+        }
+        // Assign indices to folds
+        for (auto label = 0; label < class_counts.size(); ++label) {
+            auto num_samples_to_take = class_counts[label] / k;
+            if (num_samples_to_take == 0)
+                continue;
+            auto remainder_samples_to_take = class_counts[label] % k;
+            for (auto fold = 0; fold < k; ++fold) {
+                auto it = next(class_indices[label].begin(), num_samples_to_take);
+                move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold]));  // ##
+                class_indices[label].erase(class_indices[label].begin(), it);
+            }
+            while (remainder_samples_to_take > 0) {
+                int fold = (rand() % static_cast<int>(k));
+                if (stratified_indices[fold].size() == fold_size + 1) {
+                    continue;
+                }
+                auto it = next(class_indices[label].begin(), 1);
+                stratified_indices[fold].push_back(*class_indices[label].begin());
+                class_indices[label].erase(class_indices[label].begin(), it);
+                remainder_samples_to_take--;
             }
-            auto it = next(class_indices[label].begin(), 1);
-            stratified_indices[fold].push_back(*class_indices[label].begin());
-            class_indices[label].erase(class_indices[label].begin(), it);
-            remainder_samples_to_take--;
         }
     }
-}
+    pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
-pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
+    {
-{
+        if (nFold >= k || nFold < 0) {
-    if (nFold >= k || nFold < 0) {
+            throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
-        throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
+        }
+        vector<int> test_indices = stratified_indices[nFold];
+        vector<int> train_indices;
+        for (int i = 0; i < k; ++i) {
+            if (i == nFold) continue;
+            train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
+        }
+        return { train_indices, test_indices };
     }
-    vector<int> test_indices = stratified_indices[nFold];
-    vector<int> train_indices;
-    for (int i = 0; i < k; ++i) {
-        if (i == nFold) continue;
-        train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
-    }
-    return { train_indices, test_indices };
 }

src/Platform/Folding.h

@@ -4,34 +4,35 @@
 #include <vector>
 #include <random> 
 using namespace std;
-
+namespace platform {
-class Fold {
+    class Fold {
-protected:
+    protected:
-    int k;
+        int k;
-    int n;
+        int n;
-    int seed;
+        int seed;
-    default_random_engine random_seed;
+        default_random_engine random_seed;
-public:
+    public:
-    Fold(int k, int n, int seed = -1);
+        Fold(int k, int n, int seed = -1);
-    virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
+        virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
-    virtual ~Fold() = default;
+        virtual ~Fold() = default;
-    int getNumberOfFolds() { return k; }
+        int getNumberOfFolds() { return k; }
-};
+    };
-class KFold : public Fold {
+    class KFold : public Fold {
-private:
+    private:
-    vector<int> indices;
+        vector<int> indices;
-public:
+    public:
-    KFold(int k, int n, int seed = -1);
+        KFold(int k, int n, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold) override;
+        pair<vector<int>, vector<int>> getFold(int nFold) override;
-};
+    };
-class StratifiedKFold : public Fold {
+    class StratifiedKFold : public Fold {
-private:
+    private:
-    vector<int> y;
+        vector<int> y;
-    vector<vector<int>> stratified_indices;
+        vector<vector<int>> stratified_indices;
-    void build();
+        void build();
-public:
+    public:
-    StratifiedKFold(int k, const vector<int>& y, int seed = -1);
+        StratifiedKFold(int k, const vector<int>& y, int seed = -1);
-    StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
+        StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold) override;
+        pair<vector<int>, vector<int>> getFold(int nFold) override;
-};
+    };
+}
 #endif

src/Platform/Models.cc

@@ -26,7 +26,7 @@ namespace platform {
             instance = it->second();
         // wrap instance in a shared ptr and return
         if (instance != nullptr)
-            return shared_ptr<bayesnet::BaseClassifier>(instance);
+            return unique_ptr<bayesnet::BaseClassifier>(instance);
         else
             return nullptr;
     }

src/Platform/ReportConsole.cc

@@ -36,14 +36,21 @@ namespace platform {
     }
     void ReportConsole::body()
     {
-        cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls Nodes     Edges     States    Score           Time               Hyperparameters" << endl;
+        cout << Colors::GREEN() << " #  Dataset                        Sampl. Feat. Cls Nodes     Edges     States    Score           Time               Hyperparameters" << endl;
-        cout << "============================== ====== ===== === ========= ========= ========= =============== ================== ===============" << endl;
+        cout << "=== ============================== ====== ===== === ========= ========= ========= =============== ================== ===============" << endl;
         json lastResult;
         double totalScore = 0.0;
         bool odd = true;
+        int index = 0;
         for (const auto& r : data["results"]) {
+            if (selectedIndex != -1 && index != selectedIndex) {
+                index++;
+                continue;
+            }
             auto color = odd ? Colors::CYAN() : Colors::BLUE();
-            cout << color << setw(30) << left << r["dataset"].get<string>() << " ";
+            cout << color;
+            cout << setw(3) << index++ << " ";
+            cout << setw(30) << left << r["dataset"].get<string>() << " ";
             cout << setw(6) << right << r["samples"].get<int>() << " ";
             cout << setw(5) << right << r["features"].get<int>() << " ";
             cout << setw(3) << right << r["classes"].get<int>() << " ";
@@ -63,7 +70,7 @@ namespace platform {
             totalScore += r["score"].get<double>();
             odd = !odd;
         }
-        if (data["results"].size() == 1) {
+        if (data["results"].size() == 1 || selectedIndex != -1) {
             cout << string(MAXL, '*') << endl;
             cout << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
             cout << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));

src/Platform/ReportConsole.h

@@ -7,12 +7,13 @@
 
 namespace platform {
     using namespace std;
-    const int MAXL = 128;
+    const int MAXL = 132;
-    class ReportConsole : public ReportBase{
+    class ReportConsole : public ReportBase {
     public:
-        explicit ReportConsole(json data_) : ReportBase(data_) {};
+        explicit ReportConsole(json data_, int index = -1) : ReportBase(data_), selectedIndex(index) {};
         virtual ~ReportConsole() = default;
     private:
+        int selectedIndex;
         string headerLine(const string& text);
         void header() override;
         void body() override;

src/Platform/ReportExcel.cc

@@ -47,11 +47,11 @@ namespace platform {
 
     void ReportExcel::body()
     {
-        auto header = vector<string>(
+        auto head = vector<string>(
             { "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "Time",
              "Time Std.", "Hyperparameters" });
         int col = 1;
-        for (const auto& item : header) {
+        for (const auto& item : head) {
             wks.cell(8, col++).value() = item;
         }
         int row = 9;

src/Platform/Results.cc

@@ -23,6 +23,7 @@ namespace platform {
         title = data["title"];
         duration = data["duration"];
         model = data["model"];
+        complete = data["results"].size() > 1;
     }
     json Result::load() const
     {
@@ -41,7 +42,7 @@ namespace platform {
             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())
+                if (model != "any" && result.getModel() != model || scoreName != "any" && scoreName != result.getScoreName() || complete && !result.isComplete() || partial && result.isComplete())
                     addResult = false;
                 if (addResult)
                     files.push_back(result);
@@ -55,6 +56,8 @@ namespace platform {
         oss << setw(12) << left << model << " ";
         oss << setw(11) << left << scoreName << " ";
         oss << right << setw(11) << setprecision(7) << fixed << score << " ";
+        auto completeString = isComplete() ? "C" : "P";
+        oss << setw(1) << " " << completeString << "  ";
         oss << setw(9) << setprecision(3) << fixed << duration << " ";
         oss << setw(50) << left << title << " ";
         return  oss.str();
@@ -63,9 +66,15 @@ namespace platform {
     {
         cout << Colors::GREEN() << "Results found: " << files.size() << endl;
         cout << "-------------------" << endl;
+        if (complete) {
+            cout << Colors::MAGENTA() << "Only listing complete results" << endl;
+        }
+        if (partial) {
+            cout << Colors::MAGENTA() << "Only listing partial results" << endl;
+        }
         auto i = 0;
-        cout << " #  Date       Model        Score Name  Score       Duration  Title" << endl;
+        cout << Colors::GREEN() << " #  Date       Model        Score Name  Score       C/P Duration  Title" << endl;
-        cout << "=== ========== ============ =========== =========== ========= =============================================================" << endl;
+        cout << "=== ========== ============ =========== =========== === ========= =============================================================" << endl;
         bool odd = true;
         for (const auto& result : files) {
             auto color = odd ? Colors::BLUE() : Colors::CYAN();
@@ -100,13 +109,24 @@ namespace platform {
         cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl;
         auto data = files.at(index).load();
         if (excelReport) {
-            ReportExcel report(data);
+            ReportExcel reporter(data);
-            report.show();
+            reporter.show();
         } else {
-            ReportConsole report(data);
+            ReportConsole reporter(data);
-            report.show();
+            reporter.show();
         }
     }
+    void Results::showIndex(const int index, const int idx) const
+    {
+        auto data = files.at(index).load();
+        if (idx < 0 or idx >= static_cast<int>(data["results"].size())) {
+            cout << "Invalid index" << endl;
+            return;
+        }
+        cout << Colors::YELLOW() << "Showing " << files.at(index).getFilename() << endl;
+        ReportConsole reporter(data, idx);
+        reporter.show();
+    }
     void Results::menu()
     {
         char option;
@@ -126,9 +146,16 @@ namespace platform {
                 option = line[0];
             } else {
                 if (all_of(line.begin(), line.end(), ::isdigit)) {
-                    index = stoi(line);
+                    int idx = stoi(line);
-                    if (index >= 0 && index < files.size()) {
+                    if (indexList) {
-                        report(index, false);
+                        index = idx;
+                        if (index >= 0 && index < files.size()) {
+                            report(index, false);
+                            indexList = false;
+                            continue;
+                        }
+                    } else {
+                        showIndex(index, idx);
                         continue;
                     }
                 }
@@ -141,6 +168,7 @@ namespace platform {
                     break;
                 case 'l':
                     show();
+                    indexList = true;
                     break;
                 case 'd':
                     index = getIndex("delete");
@@ -152,6 +180,7 @@ namespace platform {
                     files.erase(files.begin() + index);
                     cout << "File: " + filename + " deleted!" << endl;
                     show();
+                    indexList = true;
                     break;
                 case 'h':
                     index = getIndex("hide");
@@ -163,21 +192,25 @@ namespace platform {
                     files.erase(files.begin() + index);
                     show();
                     menu();
+                    indexList = true;
                     break;
                 case 's':
                     sortList();
+                    indexList = true;
                     show();
                     break;
                 case 'r':
                     index = getIndex("report");
                     if (index == -1)
                         break;
+                    indexList = false;
                     report(index, false);
                     break;
                 case 'e':
                     index = getIndex("excel");
                     if (index == -1)
                         break;
+                    indexList = true;
                     report(index, true);
                     break;
                 default:

src/Platform/Results.h

@@ -20,6 +20,7 @@ namespace platform {
         double getDuration() const { return duration; };
         string getModel() const { return model; };
         string getScoreName() const { return scoreName; };
+        bool isComplete() const { return complete; };
     private:
         string path;
         string filename;
@@ -29,20 +30,25 @@ namespace platform {
         double duration;
         string model;
         string scoreName;
+        bool complete;
     };
     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(); };
+        Results(const string& path, const int max, const string& model, const string& score, bool complete, bool partial) : path(path), max(max), model(model), scoreName(score), complete(complete), partial(partial) { load(); };
         void manage();
     private:
         string path;
         int max;
         string model;
         string scoreName;
+        bool complete;
+        bool partial;
+        bool indexList = true;
         vector<Result> files;
         void load(); // Loads the list of results
         void show() const;
         void report(const int index, const bool excelReport) const;
+        void showIndex(const int index, const int idx) const;
         int getIndex(const string& intent) const;
         void menu();
         void sortList();

src/Platform/manage.cc

@@ -12,6 +12,8 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
     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");
+    program.add_argument("--complete").help("Show only results with all datasets").default_value(false).implicit_value(true);
+    program.add_argument("--partial").help("Show only partial results").default_value(false).implicit_value(true);
     try {
         program.parse_args(argc, argv);
         auto number = program.get<int>("number");
@@ -20,6 +22,8 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         }
         auto model = program.get<string>("model");
         auto score = program.get<string>("score");
+        auto complete = program.get<bool>("complete");
+        auto partial = program.get<bool>("partial");
     }
     catch (const exception& err) {
         cerr << err.what() << endl;
@@ -35,7 +39,11 @@ int main(int argc, char** 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);
+    auto complete = program.get<bool>("complete");
+    auto partial = program.get<bool>("partial");
+    if (complete)
+        partial = false;
+    auto results = platform::Results(platform::Paths::results(), number, model, score, complete, partial);
     results.manage();
     return 0;
 }

src/Platform/platformUtils.cc

@@ -69,11 +69,12 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
         Xd = torch::zeros({ static_cast<int>(Xr[0].size()), static_cast<int>(Xr.size()) }, torch::kInt32);
         for (int i = 0; i < features.size(); ++i) {
             states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
-            iota(begin(states[features[i]]), end(states[features[i]]), 0);
+            auto item = states.at(features[i]);
+            iota(begin(item), end(item), 0);
             Xd.index_put_({ "...", i }, torch::tensor(Xr[i], torch::kInt32));
         }
         states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
-        iota(begin(states[className]), end(states[className]), 0);
+        iota(begin(states.at(className)), end(states.at(className)), 0);
     } else {
         Xd = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, torch::kFloat32);
         for (int i = 0; i < features.size(); ++i) {

tests/CMakeLists.txt

@@ -4,6 +4,7 @@ if(ENABLE_TESTING)
     include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
     include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
     include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
+    include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
     set(TEST_SOURCES BayesModels.cc BayesNetwork.cc ${BayesNet_SOURCE_DIR}/src/Platform/platformUtils.cc ${BayesNet_SOURCES})
     add_executable(${TEST_MAIN} ${TEST_SOURCES})
     target_link_libraries(${TEST_MAIN} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)