Add boost info to README

Reviewed-on: https://gitea.rmontanana.es:3000/rmontanana/BayesNet/pulls/9

Add best results management, build, report, build all & report all

.vscode/launch.json

@@ -37,6 +37,20 @@
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "best",
+            "program": "${workspaceFolder}/build/src/Platform/best",
+            "args": [
+                "-m",
+                "BoostAODE",
+                "-s",
+                "accuracy",
+                "--build",
+            ],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
         {
             "type": "lldb",
             "request": "launch",

CMakeLists.txt

@@ -30,6 +30,17 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
 option(ENABLE_TESTING "Unit testing build"                        OFF)
 option(CODE_COVERAGE "Collect coverage from test library"         OFF)
+
+# Boost Library
+set(Boost_USE_STATIC_LIBS OFF) 
+set(Boost_USE_MULTITHREADED ON)  
+set(Boost_USE_STATIC_RUNTIME OFF) 
+find_package(Boost 1.78.0 REQUIRED) 
+if(Boost_FOUND)
+    message("Boost_INCLUDE_DIRS=${Boost_INCLUDE_DIRS}")
+    include_directories(${Boost_INCLUDE_DIRS}) 
+endif()
+
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 # CMakes modules
 # --------------
@@ -54,6 +65,7 @@ endif (ENABLE_CLANG_TIDY)
 add_git_submodule("lib/mdlp")
 add_git_submodule("lib/argparse")
 add_git_submodule("lib/json")
+find_library(XLSXWRITER_LIB libxlsxwriter.dylib PATHS /usr/local/lib)
 
 # Subdirectories
 # --------------

Makefile

@@ -19,13 +19,14 @@ copy: ## Copy binary files to selected folder
 	@cp build/src/Platform/main $(dest)
 	@cp build/src/Platform/list $(dest)
 	@cp build/src/Platform/manage $(dest)
+	@cp build/src/Platform/best $(dest)
 	@echo ">>> Done"
 
 dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
 	cd build && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
 build: ## Build the main and BayesNetSample
-	cmake --build build -t main -t BayesNetSample -t manage -t list -j 32
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -j 32
 
 clean: ## Clean the debug info
 	@echo ">>> Cleaning Debug BayesNet ...";
@@ -40,7 +41,7 @@ debug: ## Build a debug version of the project
 	@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 -t main -t BayesNetSample -t manage -t list  unit_tests -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -t unit_tests -j 32;
 	@echo ">>> Done";
 
 release: ## Build a Release version of the project
@@ -48,7 +49,7 @@ release: ## Build a Release version of the project
 	@if [ -d ./build ]; then rm -rf ./build; fi
 	@mkdir build; 
 	cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; \
-	cmake --build build -t main -t BayesNetSample -t manage -t list -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -j 32;
 	@echo ">>> Done";	
 
 test: ## Run tests

README.md

@@ -4,10 +4,14 @@ Bayesian Network Classifier with libtorch from scratch
 
 ## 0. Setup
 
-### libxlswriter
-
 Before compiling BayesNet.
 
+### boost library
+
+[Getting Started](<https://www.boost.org/doc/libs/1_83_0/more/getting_started/index.html>)
+
+### libxlswriter
+
 ```bash
 cd lib/libxlsxwriter
 make

sample/sample.cc

@@ -104,180 +104,180 @@ int main(int argc, char** argv)
     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);
-    // }
+    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();
-    // handler.load(complete_file_name, class_last);
-    // // Get Dataset X, y
-    // vector<mdlp::samples_t>& X = handler.getX();
-    // mdlp::labels_t& y = handler.getY();
-    // // Get className & Features
-    // auto className = handler.getClassName();
-    // vector<string> features;
-    // 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;
-    // map<string, vector<int>> states;
-    // for (auto feature : features) {
-    //     states[feature] = vector<int>(maxes[feature]);
-    // }
-    // states[className] = vector<int>(maxes[className]);
-    // auto clf = platform::Models::instance()->create(model_name);
-    // clf->fit(Xd, y, features, className, states);
-    // if (dump_cpt) {
-    //     cout << "--- CPT Tables ---" << endl;
-    //     clf->dump_cpt();
-    // }
-    // auto lines = clf->show();
-    // for (auto line : lines) {
-    //     cout << line << endl;
-    // }
-    // cout << "--- Topological Order ---" << endl;
-    // auto order = clf->topological_order();
-    // for (auto name : order) {
-    //     cout << name << ", ";
-    // }
-    // cout << "end." << endl;
-    // auto score = clf->score(Xd, y);
-    // cout << "Score: " << score << endl;
-    // auto graph = clf->graph();
-    // auto dot_file = model_name + "_" + file_name;
-    // ofstream file(dot_file + ".dot");
-    // file << graph;
-    // file.close();
-    // cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
-    // cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
-    // string stratified_string = stratified ? " Stratified" : "";
-    // cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
-    // cout << "==========================================" << endl;
-    // 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);
-    // for (int i = 0; i < features.size(); ++i) {
-    //     Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
-    // }
-    // float total_score = 0, total_score_train = 0, score_train, score_test;
-    // platform::Fold* fold;
-    // if (stratified)
-    //     fold = new platform::StratifiedKFold(nFolds, y, seed);
-    // else
-    //     fold = new platform::KFold(nFolds, y.size(), seed);
-    // for (auto i = 0; i < nFolds; ++i) {
-    //     auto [train, test] = fold->getFold(i);
-    //     cout << "Fold: " << i + 1 << endl;
-    //     if (tensors) {
-    //         auto ttrain = torch::tensor(train, torch::kInt64);
-    //         auto ttest = torch::tensor(test, torch::kInt64);
-    //         torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
-    //         torch::Tensor ytraint = yt.index({ ttrain });
-    //         torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
-    //         torch::Tensor ytestt = yt.index({ ttest });
-    //         clf->fit(Xtraint, ytraint, features, className, states);
-    //         auto temp = clf->predict(Xtraint);
-    //         score_train = clf->score(Xtraint, ytraint);
-    //         score_test = clf->score(Xtestt, ytestt);
-    //     } else {
-    //         auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
-    //         auto [Xtest, ytest] = extract_indices(test, Xd, y);
-    //         clf->fit(Xtrain, ytrain, features, className, states);
-    //         score_train = clf->score(Xtrain, ytrain);
-    //         score_test = clf->score(Xtest, ytest);
-    //     }
-    //     if (dump_cpt) {
-    //         cout << "--- CPT Tables ---" << endl;
-    //         clf->dump_cpt();
-    //     }
-    //     total_score_train += score_train;
-    //     total_score += score_test;
-    //     cout << "Score Train: " << score_train << endl;
-    //     cout << "Score Test : " << score_test << endl;
-    //     cout << "-------------------------------------------------------------------------------" << endl;
-    // }
-    // cout << "**********************************************************************************" << endl;
-    // cout << "Average Score Train: " << total_score_train / nFolds << endl;
-    // cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
+    auto handler = ArffFiles();
+    handler.load(complete_file_name, class_last);
+    // Get Dataset X, y
+    vector<mdlp::samples_t>& X = handler.getX();
+    mdlp::labels_t& y = handler.getY();
+    // Get className & Features
+    auto className = handler.getClassName();
+    vector<string> features;
+    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;
+    map<string, vector<int>> states;
+    for (auto feature : features) {
+        states[feature] = vector<int>(maxes[feature]);
+    }
+    states[className] = vector<int>(maxes[className]);
+    auto clf = platform::Models::instance()->create(model_name);
+    clf->fit(Xd, y, features, className, states);
+    if (dump_cpt) {
+        cout << "--- CPT Tables ---" << endl;
+        clf->dump_cpt();
+    }
+    auto lines = clf->show();
+    for (auto line : lines) {
+        cout << line << endl;
+    }
+    cout << "--- Topological Order ---" << endl;
+    auto order = clf->topological_order();
+    for (auto name : order) {
+        cout << name << ", ";
+    }
+    cout << "end." << endl;
+    auto score = clf->score(Xd, y);
+    cout << "Score: " << score << endl;
+    auto graph = clf->graph();
+    auto dot_file = model_name + "_" + file_name;
+    ofstream file(dot_file + ".dot");
+    file << graph;
+    file.close();
+    cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
+    cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
+    string stratified_string = stratified ? " Stratified" : "";
+    cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
+    cout << "==========================================" << endl;
+    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);
+    for (int i = 0; i < features.size(); ++i) {
+        Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
+    }
+    float total_score = 0, total_score_train = 0, score_train, score_test;
+    platform::Fold* fold;
+    if (stratified)
+        fold = new platform::StratifiedKFold(nFolds, y, seed);
+    else
+        fold = new platform::KFold(nFolds, y.size(), seed);
+    for (auto i = 0; i < nFolds; ++i) {
+        auto [train, test] = fold->getFold(i);
+        cout << "Fold: " << i + 1 << endl;
+        if (tensors) {
+            auto ttrain = torch::tensor(train, torch::kInt64);
+            auto ttest = torch::tensor(test, torch::kInt64);
+            torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
+            torch::Tensor ytraint = yt.index({ ttrain });
+            torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
+            torch::Tensor ytestt = yt.index({ ttest });
+            clf->fit(Xtraint, ytraint, features, className, states);
+            auto temp = clf->predict(Xtraint);
+            score_train = clf->score(Xtraint, ytraint);
+            score_test = clf->score(Xtestt, ytestt);
+        } else {
+            auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
+            auto [Xtest, ytest] = extract_indices(test, Xd, y);
+            clf->fit(Xtrain, ytrain, features, className, states);
+            score_train = clf->score(Xtrain, ytrain);
+            score_test = clf->score(Xtest, ytest);
+        }
+        if (dump_cpt) {
+            cout << "--- CPT Tables ---" << endl;
+            clf->dump_cpt();
+        }
+        total_score_train += score_train;
+        total_score += score_test;
+        cout << "Score Train: " << score_train << endl;
+        cout << "Score Test : " << score_test << endl;
+        cout << "-------------------------------------------------------------------------------" << endl;
+    }
+    cout << "**********************************************************************************" << endl;
+    cout << "Average Score Train: " << total_score_train / nFolds << endl;
+    cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
 }

src/Platform/BestResults.cc

@@ -0,0 +1,292 @@
+#include <filesystem>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include "BestResults.h"
+#include "Result.h"
+#include "Colors.h"
+#include "Statistics.h"
+
+
+
+namespace fs = std::filesystem;
+// function ftime_to_string, Code taken from 
+// https://stackoverflow.com/a/58237530/1389271
+template <typename TP>
+std::string ftime_to_string(TP tp)
+{
+    using namespace std::chrono;
+    auto sctp = time_point_cast<system_clock::duration>(tp - TP::clock::now()
+        + system_clock::now());
+    auto tt = system_clock::to_time_t(sctp);
+    std::tm* gmt = std::gmtime(&tt);
+    std::stringstream buffer;
+    buffer << std::put_time(gmt, "%Y-%m-%d %H:%M");
+    return buffer.str();
+}
+namespace platform {
+
+    string BestResults::build()
+    {
+        auto files = loadResultFiles();
+        if (files.size() == 0) {
+            cerr << Colors::MAGENTA() << "No result files were found!" << Colors::RESET() << endl;
+            exit(1);
+        }
+        json bests;
+        for (const auto& file : files) {
+            auto result = Result(path, file);
+            auto data = result.load();
+            for (auto const& item : data.at("results")) {
+                bool update = false;
+                if (bests.contains(item.at("dataset").get<string>())) {
+                    if (item.at("score").get<double>() > bests[item.at("dataset").get<string>()].at(0).get<double>()) {
+                        update = true;
+                    }
+                } else {
+                    update = true;
+                }
+                if (update) {
+                    bests[item.at("dataset").get<string>()] = { item.at("score").get<double>(), item.at("hyperparameters"), file };
+                }
+            }
+        }
+        string bestFileName = path + bestResultFile();
+        if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
+            fclose(fileTest);
+            cout << Colors::MAGENTA() << "File " << bestFileName << " already exists and it shall be overwritten." << Colors::RESET() << endl;
+        }
+        ofstream file(bestFileName);
+        file << bests;
+        file.close();
+        return bestFileName;
+    }
+
+    string BestResults::bestResultFile()
+    {
+        return "best_results_" + score + "_" + model + ".json";
+    }
+
+    pair<string, string> getModelScore(string name)
+    {
+        // results_accuracy_BoostAODE_MacBookpro16_2023-09-06_12:27:00_1.json
+        int i = 0;
+        auto pos = name.find("_");
+        auto pos2 = name.find("_", pos + 1);
+        string score = name.substr(pos + 1, pos2 - pos - 1);
+        pos = name.find("_", pos2 + 1);
+        string model = name.substr(pos2 + 1, pos - pos2 - 1);
+        return { model, score };
+    }
+
+    vector<string> BestResults::loadResultFiles()
+    {
+        vector<string> files;
+        using std::filesystem::directory_iterator;
+        string fileModel, fileScore;
+        for (const auto& file : directory_iterator(path)) {
+            auto fileName = file.path().filename().string();
+            if (fileName.find(".json") != string::npos && fileName.find("results_") == 0) {
+                tie(fileModel, fileScore) = getModelScore(fileName);
+                if (score == fileScore && (model == fileModel || model == "any")) {
+                    files.push_back(fileName);
+                }
+            }
+        }
+        return files;
+    }
+
+    json BestResults::loadFile(const string& fileName)
+    {
+        ifstream resultData(fileName);
+        if (resultData.is_open()) {
+            json data = json::parse(resultData);
+            return data;
+        }
+        throw invalid_argument("Unable to open result file. [" + fileName + "]");
+    }
+    vector<string> BestResults::getModels()
+    {
+        set<string> models;
+        vector<string> result;
+        auto files = loadResultFiles();
+        if (files.size() == 0) {
+            cerr << Colors::MAGENTA() << "No result files were found!" << Colors::RESET() << endl;
+            exit(1);
+        }
+        string fileModel, fileScore;
+        for (const auto& file : files) {
+            // extract the model from the file name
+            tie(fileModel, fileScore) = getModelScore(file);
+            // add the model to the vector of models
+            models.insert(fileModel);
+        }
+        result = vector<string>(models.begin(), models.end());
+        return result;
+    }
+
+    void BestResults::buildAll()
+    {
+        auto models = getModels();
+        for (const auto& model : models) {
+            cout << "Building best results for model: " << model << endl;
+            this->model = model;
+            build();
+        }
+        model = "any";
+    }
+
+    void BestResults::reportSingle()
+    {
+        string bestFileName = path + bestResultFile();
+        if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
+            fclose(fileTest);
+        } else {
+            cerr << Colors::MAGENTA() << "File " << bestFileName << " doesn't exist." << Colors::RESET() << endl;
+            exit(1);
+        }
+        auto date = ftime_to_string(filesystem::last_write_time(bestFileName));
+        auto data = loadFile(bestFileName);
+        cout << Colors::GREEN() << "Best results for " << model << " and " << score << " as of " << date << endl;
+        cout << "--------------------------------------------------------" << endl;
+        cout << Colors::GREEN() << " #  Dataset                   Score       File                                                               Hyperparameters" << endl;
+        cout << "=== ========================= =========== ================================================================== ================================================= " << endl;
+        auto i = 0;
+        bool odd = true;
+        for (auto const& item : data.items()) {
+            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            cout << color << setw(3) << fixed << right << i++ << " ";
+            cout << setw(25) << left << item.key() << " ";
+            cout << setw(11) << setprecision(9) << fixed << item.value().at(0).get<double>() << " ";
+            cout << setw(66) << item.value().at(2).get<string>() << " ";
+            cout << item.value().at(1) << " ";
+            cout << endl;
+            odd = !odd;
+        }
+    }
+    json BestResults::buildTableResults(vector<string> models)
+    {
+        int numberOfDatasets = 0;
+        bool first = true;
+        json origin;
+        json table;
+        auto maxDate = filesystem::file_time_type::max();
+        for (const auto& model : models) {
+            this->model = model;
+            string bestFileName = path + bestResultFile();
+            if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
+                fclose(fileTest);
+            } else {
+                cerr << Colors::MAGENTA() << "File " << bestFileName << " doesn't exist." << Colors::RESET() << endl;
+                exit(1);
+            }
+            auto dateWrite = filesystem::last_write_time(bestFileName);
+            if (dateWrite < maxDate) {
+                maxDate = dateWrite;
+            }
+            auto data = loadFile(bestFileName);
+            if (first) {
+                // Get the number of datasets of the first file and check that is the same for all the models
+                first = false;
+                numberOfDatasets = data.size();
+                origin = data;
+            } else {
+                if (numberOfDatasets != data.size()) {
+                    cerr << Colors::MAGENTA() << "The number of datasets in the best results files is not the same for all the models." << Colors::RESET() << endl;
+                    exit(1);
+                }
+            }
+            table[model] = data;
+        }
+        table["dateTable"] = ftime_to_string(maxDate);
+        return table;
+    }
+
+    void BestResults::printTableResults(vector<string> models, json table)
+    {
+        cout << Colors::GREEN() << "Best results for " << score << " as of " << table.at("dateTable").get<string>() << endl;
+        cout << "------------------------------------------------" << endl;
+        cout << Colors::GREEN() << " #  Dataset                   ";
+        for (const auto& model : models) {
+            cout << setw(12) << left << model << " ";
+        }
+        cout << endl;
+        cout << "=== ========================= ";
+        for (const auto& model : models) {
+            cout << "============ ";
+        }
+        cout << endl;
+        auto i = 0;
+        bool odd = true;
+        map<string, double> totals;
+        int nDatasets = table.begin().value().size();
+        for (const auto& model : models) {
+            totals[model] = 0.0;
+        }
+        json origin = table.begin().value();
+        for (auto const& item : origin.items()) {
+            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            cout << color << setw(3) << fixed << right << i++ << " ";
+            cout << setw(25) << left << item.key() << " ";
+            double maxValue = 0;
+            // Find out the max value for this dataset
+            for (const auto& model : models) {
+                double value = table[model].at(item.key()).at(0).get<double>();
+                if (value > maxValue) {
+                    maxValue = value;
+                }
+            }
+            // Print the row with red colors on max values
+            for (const auto& model : models) {
+                string efectiveColor = color;
+                double value = table[model].at(item.key()).at(0).get<double>();
+                if (value == maxValue) {
+                    efectiveColor = Colors::RED();
+                }
+                totals[model] += value;
+                cout << efectiveColor << setw(12) << setprecision(10) << fixed << value << " ";
+            }
+            cout << endl;
+            odd = !odd;
+        }
+        cout << Colors::GREEN() << "=== ========================= ";
+        for (const auto& model : models) {
+            cout << "============ ";
+        }
+        cout << endl;
+        cout << Colors::GREEN() << setw(30) << "    Totals...................";
+        double max = 0.0;
+        for (const auto& total : totals) {
+            if (total.second > max) {
+                max = total.second;
+            }
+        }
+        for (const auto& model : models) {
+            string efectiveColor = Colors::GREEN();
+            if (totals[model] == max) {
+                efectiveColor = Colors::RED();
+            }
+            cout << efectiveColor << setw(12) << setprecision(9) << fixed << totals[model] << " ";
+        }
+        cout << endl;
+    }
+    void BestResults::reportAll()
+    {
+        auto models = getModels();
+        // Build the table of results
+        json table = buildTableResults(models);
+        // Print the table of results
+        printTableResults(models, table);
+        // Compute the Friedman test
+        if (friedman) {
+            vector<string> datasets;
+            for (const auto& dataset : table.begin().value().items()) {
+                datasets.push_back(dataset.key());
+            }
+            double significance = 0.05;
+            Statistics stats(models, datasets, table, significance);
+            auto result = stats.friedmanTest();
+            stats.postHocHolmTest(result);
+        }
+    }
+}

src/Platform/BestResults.h

@@ -0,0 +1,29 @@
+#ifndef BESTRESULTS_H
+#define BESTRESULTS_H
+#include <string>
+#include <set>
+#include <nlohmann/json.hpp>
+using namespace std;
+using json = nlohmann::json;
+namespace platform {
+    class BestResults {
+    public:
+        explicit BestResults(const string& path, const string& score, const string& model, bool friedman) : path(path), score(score), model(model), friedman(friedman) {}
+        string build();
+        void reportSingle();
+        void reportAll();
+        void buildAll();
+    private:
+        vector<string> getModels();
+        vector<string> loadResultFiles();
+        json buildTableResults(vector<string> models);
+        void printTableResults(vector<string> models, json table);
+        string bestResultFile();
+        json loadFile(const string& fileName);
+        string path;
+        string score;
+        string model;
+        bool friedman;
+    };
+}
+#endif //BESTRESULTS_H

src/Platform/BestScore.h

@@ -1,7 +1,7 @@
-#ifndef BESTRESULT_H
-#define BESTRESULT_H
+#ifndef BESTSCORE_H
+#define BESTSCORE_H
 #include <string>
-class BestResult {
+class BestScore {
 public:
     static std::string title() { return "STree_default (linear-ovo)"; }
     static double score() { return 22.109799; }

src/Platform/CMakeLists.txt

@@ -4,13 +4,17 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
+include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
 add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc ReportConsole.cc ReportBase.cc)
-add_executable(manage manage.cc Results.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc platformUtils.cc)
+add_executable(manage manage.cc Results.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc platformUtils.cc)
 add_executable(list list.cc platformUtils Datasets.cc)
+add_executable(best best.cc BestResults.cc Result.cc Statistics.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
 if (${CMAKE_HOST_SYSTEM_NAME} MATCHES "Linux")
     target_link_libraries(manage "${TORCH_LIBRARIES}" libxlsxwriter.so ArffFiles mdlp stdc++fs)
+    target_link_libraries(best Boost::boost stdc++fs)
 else()
-    target_link_libraries(manage "${TORCH_LIBRARIES}" libxlsxwriter.so ArffFiles mdlp)
+    target_link_libraries(manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
+    target_link_libraries(best Boost::boost)
 endif()
 target_link_libraries(list ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/ReportBase.cc

@@ -2,7 +2,7 @@
 #include <locale>
 #include "Datasets.h"
 #include "ReportBase.h"
-#include "BestResult.h"
+#include "BestScore.h"
 
 
 namespace platform {
@@ -94,6 +94,8 @@ namespace platform {
             ifstream resultData(Paths::results() + "/" + fileName);
             if (resultData.is_open()) {
                 bestResults = json::parse(resultData);
+            } else {
+                existBestFile = false;
             }
         }
         try {
@@ -101,7 +103,12 @@ namespace platform {
         }
         catch (exception) {
             value = 1.0;
+
         }
         return value;
     }
+    bool ReportBase::getExistBestFile()
+    {
+        return existBestFile;
+    }
 }

src/Platform/ReportBase.h

@@ -3,22 +3,13 @@
 #include <string>
 #include <iostream>
 #include "Paths.h"
+#include "Symbols.h"
 #include <nlohmann/json.hpp>
 
 using json = nlohmann::json;
 namespace platform {
     using namespace std;
-    class Symbols {
-    public:
-        inline static const string check_mark{ "\u2714" };
-        inline static const string exclamation{ "\u2757" };
-        inline static const string black_star{ "\u2605" };
-        inline static const string cross{ "\u2717" };
-        inline static const string upward_arrow{ "\u27B6" };
-        inline static const string down_arrow{ "\u27B4" };
-        inline static const string equal_best{ check_mark };
-        inline static const string better_best{ black_star };
-    };
+
     class ReportBase {
     public:
         explicit ReportBase(json data_, bool compare);
@@ -28,6 +19,7 @@ namespace platform {
         json data;
         string fromVector(const string& key);
         string fVector(const string& title, const json& data, const int width, const int precision);
+        bool getExistBestFile();
         virtual void header() = 0;
         virtual void body() = 0;
         virtual void showSummary() = 0;
@@ -35,10 +27,11 @@ namespace platform {
         map<string, int> summary;
         double margin;
         map<string, string> meaning;
+        bool compare;
     private:
         double bestResult(const string& dataset, const string& model);
-        bool compare;
         json bestResults;
+        bool existBestFile = true;
     };
 };
 #endif

src/Platform/ReportConsole.cc

@@ -1,7 +1,7 @@
 #include <sstream>
 #include <locale>
 #include "ReportConsole.h"
-#include "BestResult.h"
+#include "BestScore.h"
 
 
 namespace platform {
@@ -99,11 +99,14 @@ namespace platform {
         cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
         showSummary();
         auto score = data["score_name"].get<string>();
-        if (score == BestResult::scoreName()) {
+        if (score == BestScore::scoreName()) {
             stringstream oss;
-            oss << score << " compared to " << BestResult::title() << " .:  " << totalScore / BestResult::score();
+            oss << score << " compared to " << BestScore::title() << " .:  " << totalScore / BestScore::score();
             cout << headerLine(oss.str());
         }
+        if (!getExistBestFile() && compare) {
+            cout << headerLine("*** Best Results File not found. Couldn't compare any result!");
+        }
         cout << string(MAXL, '*') << endl << Colors::RESET();
     }
 }

src/Platform/ReportConsole.h

@@ -18,7 +18,7 @@ namespace platform {
         void header() override;
         void body() override;
         void footer(double totalScore);
-        void showSummary();
+        void showSummary() override;
     };
 };
 #endif

src/Platform/ReportExcel.cc

@@ -1,7 +1,7 @@
 #include <sstream>
 #include <locale>
 #include "ReportExcel.h"
-#include "BestResult.h"
+#include "BestScore.h"
 
 
 namespace platform {
@@ -162,11 +162,11 @@ namespace platform {
         strcpy(line, data["title"].get<string>().c_str());
         lxw_doc_properties properties = {
             .title = line,
-            .subject = "Machine learning results",
-            .author = "Ricardo Montañana Gómez",
-            .manager = "Dr. J. A. Gámez, Dr. J. M. Puerta",
-            .company = "UCLM",
-            .comments = "Created with libxlsxwriter and c++",
+            .subject = (char*)"Machine learning results",
+            .author = (char*)"Ricardo Montañana Gómez",
+            .manager = (char*)"Dr. J. A. Gámez, Dr. J. M. Puerta",
+            .company = (char*)"UCLM",
+            .comments = (char*)"Created with libxlsxwriter and c++",
         };
         workbook_set_properties(workbook, &properties);
     }
@@ -322,9 +322,12 @@ namespace platform {
         showSummary();
         row += 4 + summary.size();
         auto score = data["score_name"].get<string>();
-        if (score == BestResult::scoreName()) {
-            worksheet_merge_range(worksheet, row, 1, row, 5, (score + " compared to " + BestResult::title() + " .:").c_str(), efectiveStyle("text"));
-            writeDouble(row, 6, totalScore / BestResult::score(), "result");
+        if (score == BestScore::scoreName()) {
+            worksheet_merge_range(worksheet, row, 1, row, 5, (score + " compared to " + BestScore::title() + " .:").c_str(), efectiveStyle("text"));
+            writeDouble(row, 6, totalScore / BestScore::score(), "result");
+        }
+        if (!getExistBestFile() && compare) {
+            worksheet_write_string(worksheet, row + 1, 0, "*** Best Results File not found. Couldn't compare any result!", styles["summaryStyle"]);
         }
     }
 }

src/Platform/ReportExcel.h

@@ -21,7 +21,6 @@ namespace platform {
         void setProperties();
         void createFile();
         void closeFile();
-        void showSummary();
         lxw_workbook* workbook;
         lxw_worksheet* worksheet;
         map<string, lxw_format*> styles;
@@ -33,6 +32,7 @@ namespace platform {
         const string fileName = "some_results.xlsx";
         void header() override;
         void body() override;
+        void showSummary() override;
         void footer(double totalScore, int row);
         void createStyle(const string& name, lxw_format* style, bool odd);
         void addColor(lxw_format* style, bool odd);

src/Platform/Result.cc

@@ -0,0 +1,51 @@
+#include <filesystem>
+#include <fstream>
+#include <sstream>
+#include "Result.h"
+#include "Colors.h"
+#include "BestScore.h"
+namespace platform {
+    Result::Result(const string& path, const string& filename)
+        : path(path)
+        , filename(filename)
+    {
+        auto data = load();
+        date = data["date"];
+        score = 0;
+        for (const auto& result : data["results"]) {
+            score += result["score"].get<double>();
+        }
+        scoreName = data["score_name"];
+        if (scoreName == BestScore::scoreName()) {
+            score /= BestScore::score();
+        }
+        title = data["title"];
+        duration = data["duration"];
+        model = data["model"];
+        complete = data["results"].size() > 1;
+    }
+
+    json Result::load() const
+    {
+        ifstream resultData(path + "/" + filename);
+        if (resultData.is_open()) {
+            json data = json::parse(resultData);
+            return data;
+        }
+        throw invalid_argument("Unable to open result file. [" + path + "/" + filename + "]");
+    }
+
+    string Result::to_string() const
+    {
+        stringstream oss;
+        oss << date << " ";
+        oss << setw(12) << left << model << " ";
+        oss << setw(11) << left << scoreName << " ";
+        oss << right << setw(11) << setprecision(7) << fixed << score << " ";
+        auto completeString = isComplete() ? "C" : "P";
+        oss << setw(1) << " " << completeString << "  ";
+        oss << setw(9) << setprecision(3) << fixed << duration << " ";
+        oss << setw(50) << left << title << " ";
+        return  oss.str();
+    }
+}

src/Platform/Result.h

@@ -0,0 +1,37 @@
+#ifndef RESULT_H
+#define RESULT_H
+#include <map>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+namespace platform {
+    using namespace std;
+    using json = nlohmann::json;
+
+    class Result {
+    public:
+        Result(const string& path, const string& filename);
+        json load() const;
+        string to_string() const;
+        string getFilename() const { return filename; };
+        string getDate() const { return date; };
+        double getScore() const { return score; };
+        string getTitle() const { return title; };
+        double getDuration() const { return duration; };
+        string getModel() const { return model; };
+        string getScoreName() const { return scoreName; };
+        bool isComplete() const { return complete; };
+    private:
+        string path;
+        string filename;
+        string date;
+        double score;
+        string title;
+        double duration;
+        string model;
+        string scoreName;
+        bool complete;
+    };
+};
+
+#endif

src/Platform/Results.cc

@@ -3,37 +3,9 @@
 #include "Results.h"
 #include "ReportConsole.h"
 #include "ReportExcel.h"
-#include "BestResult.h"
+#include "BestScore.h"
 #include "Colors.h"
 namespace platform {
-    Result::Result(const string& path, const string& filename)
-        : path(path)
-        , filename(filename)
-    {
-        auto data = load();
-        date = data["date"];
-        score = 0;
-        for (const auto& result : data["results"]) {
-            score += result["score"].get<double>();
-        }
-        scoreName = data["score_name"];
-        if (scoreName == BestResult::scoreName()) {
-            score /= BestResult::score();
-        }
-        title = data["title"];
-        duration = data["duration"];
-        model = data["model"];
-        complete = data["results"].size() > 1;
-    }
-    json Result::load() const
-    {
-        ifstream resultData(path + "/" + filename);
-        if (resultData.is_open()) {
-            json data = json::parse(resultData);
-            return data;
-        }
-        throw invalid_argument("Unable to open result file. [" + path + "/" + filename + "]");
-    }
     void Results::load()
     {
         using std::filesystem::directory_iterator;
@@ -48,19 +20,9 @@ namespace platform {
                     files.push_back(result);
             }
         }
-    }
-    string Result::to_string() const
-    {
-        stringstream oss;
-        oss << date << " ";
-        oss << setw(12) << left << model << " ";
-        oss << setw(11) << left << scoreName << " ";
-        oss << right << setw(11) << setprecision(7) << fixed << score << " ";
-        auto completeString = isComplete() ? "C" : "P";
-        oss << setw(1) << " " << completeString << "  ";
-        oss << setw(9) << setprecision(3) << fixed << duration << " ";
-        oss << setw(50) << left << title << " ";
-        return  oss.str();
+        if (max == 0) {
+            max = files.size();
+        }
     }
     void Results::show() const
     {
@@ -164,7 +126,7 @@ namespace platform {
                     if (indexList) {
                         // The value is about the files list
                         index = idx;
-                        if (index >= 0 && index < files.size()) {
+                        if (index >= 0 && index < max) {
                             report(index, false);
                             indexList = false;
                             continue;
@@ -300,7 +262,7 @@ namespace platform {
         if (openExcel) {
             workbook_close(workbook);
         }
-        cout << "Done!" << endl;
+        cout << Colors::RESET() << "Done!" << endl;
     }
 
 }

src/Platform/Results.h

@@ -5,34 +5,11 @@
 #include <vector>
 #include <string>
 #include <nlohmann/json.hpp>
+#include "Result.h"
 namespace platform {
     using namespace std;
     using json = nlohmann::json;
 
-    class Result {
-    public:
-        Result(const string& path, const string& filename);
-        json load() const;
-        string to_string() const;
-        string getFilename() const { return filename; };
-        string getDate() const { return date; };
-        double getScore() const { return score; };
-        string getTitle() const { return title; };
-        double getDuration() const { return duration; };
-        string getModel() const { return model; };
-        string getScoreName() const { return scoreName; };
-        bool isComplete() const { return complete; };
-    private:
-        string path;
-        string filename;
-        string date;
-        double score;
-        string title;
-        double duration;
-        string model;
-        string scoreName;
-        bool complete;
-    };
     class Results {
     public:
         Results(const string& path, const int max, const string& model, const string& score, bool complete, bool partial, bool compare) :

src/Platform/Statistics.cc

@@ -0,0 +1,215 @@
+#include "Statistics.h"
+#include "Colors.h"
+#include "Symbols.h"
+#include <boost/math/distributions/chi_squared.hpp>
+#include <boost/math/distributions/normal.hpp>
+
+namespace platform {
+
+    Statistics::Statistics(vector<string>& models, vector<string>& datasets, json data, double significance) : models(models), datasets(datasets), data(data), significance(significance)
+    {
+        nModels = models.size();
+        nDatasets = datasets.size();
+    };
+
+    void Statistics::fit()
+    {
+        if (nModels < 3 || nDatasets < 3) {
+            cerr << "nModels: " << nModels << endl;
+            cerr << "nDatasets: " << nDatasets << endl;
+            throw runtime_error("Can't make the Friedman test with less than 3 models and/or less than 3 datasets.");
+        }
+        computeRanks();
+        // Set the control model as the one with the lowest average rank
+        controlIdx = distance(ranks.begin(), min_element(ranks.begin(), ranks.end(), [](const auto& l, const auto& r) { return l.second < r.second; }));
+        computeWTL();
+        fitted = true;
+    }
+    map<string, float> assignRanks(vector<pair<string, double>>& ranksOrder)
+    {
+        // sort the ranksOrder vector by value
+        sort(ranksOrder.begin(), ranksOrder.end(), [](const pair<string, double>& a, const pair<string, double>& b) {
+            return a.second > b.second;
+            });
+        //Assign ranks to  values and if they are the same they share the same averaged rank
+        map<string, float> ranks;
+        for (int i = 0; i < ranksOrder.size(); i++) {
+            ranks[ranksOrder[i].first] = i + 1.0;
+        }
+        int i = 0;
+        while (i < static_cast<int>(ranksOrder.size())) {
+            int j = i + 1;
+            int sumRanks = ranks[ranksOrder[i].first];
+            while (j < static_cast<int>(ranksOrder.size()) && ranksOrder[i].second == ranksOrder[j].second) {
+                sumRanks += ranks[ranksOrder[j++].first];
+            }
+            if (j > i + 1) {
+                float averageRank = (float)sumRanks / (j - i);
+                for (int k = i; k < j; k++) {
+                    ranks[ranksOrder[k].first] = averageRank;
+                }
+            }
+            i = j;
+        }
+        return ranks;
+    }
+    void Statistics::computeRanks()
+    {
+        map<string, float> ranksLine;
+        for (const auto& dataset : datasets) {
+            vector<pair<string, double>> ranksOrder;
+            for (const auto& model : models) {
+                double value = data[model].at(dataset).at(0).get<double>();
+                ranksOrder.push_back({ model, value });
+            }
+            // Assign the ranks
+            ranksLine = assignRanks(ranksOrder);
+            if (ranks.size() == 0) {
+                ranks = ranksLine;
+            } else {
+                for (const auto& rank : ranksLine) {
+                    ranks[rank.first] += rank.second;
+                }
+            }
+        }
+        // Average the ranks
+        for (const auto& rank : ranks) {
+            ranks[rank.first] /= nDatasets;
+        }
+    }
+    void Statistics::computeWTL()
+    {
+        // Compute the WTL matrix
+        for (int i = 0; i < nModels; ++i) {
+            wtl[i] = { 0, 0, 0 };
+        }
+        json origin = data.begin().value();
+        for (auto const& item : origin.items()) {
+            auto controlModel = models.at(controlIdx);
+            double controlValue = data[controlModel].at(item.key()).at(0).get<double>();
+            for (int i = 0; i < nModels; ++i) {
+                if (i == controlIdx) {
+                    continue;
+                }
+                double value = data[models[i]].at(item.key()).at(0).get<double>();
+                if (value < controlValue) {
+                    wtl[i].win++;
+                } else if (value == controlValue) {
+                    wtl[i].tie++;
+                } else {
+                    wtl[i].loss++;
+                }
+            }
+        }
+    }
+
+    void Statistics::postHocHolmTest(bool friedmanResult)
+    {
+        if (!fitted) {
+            fit();
+        }
+        // Reference https://link.springer.com/article/10.1007/s44196-022-00083-8
+        // Post-hoc Holm test
+        // Calculate the p-value for the models paired with the control model
+        map<int, double> stats; // p-value of each model paired with the control model
+        boost::math::normal dist(0.0, 1.0);
+        double diff = sqrt(nModels * (nModels + 1) / (6.0 * nDatasets));
+        for (int i = 0; i < nModels; i++) {
+            if (i == controlIdx) {
+                stats[i] = 0.0;
+                continue;
+            }
+            double z = abs(ranks.at(models[controlIdx]) - ranks.at(models[i])) / diff;
+            double p_value = (long double)2 * (1 - cdf(dist, z));
+            stats[i] = p_value;
+        }
+        // Sort the models by p-value
+        vector<pair<int, double>> statsOrder;
+        for (const auto& stat : stats) {
+            statsOrder.push_back({ stat.first, stat.second });
+        }
+        sort(statsOrder.begin(), statsOrder.end(), [](const pair<int, double>& a, const pair<int, double>& b) {
+            return a.second < b.second;
+            });
+
+        // Holm adjustment
+        for (int i = 0; i < statsOrder.size(); ++i) {
+            auto item = statsOrder.at(i);
+            double before = i == 0 ? 0.0 : statsOrder.at(i - 1).second;
+            double p_value = min((double)1.0, item.second * (nModels - i));
+            p_value = max(before, p_value);
+            statsOrder[i] = { item.first, p_value };
+        }
+        auto color = friedmanResult ? Colors::CYAN() : Colors::YELLOW();
+        cout << color;
+        cout << "  *************************************************************************************************************" << endl;
+        cout << "  Post-hoc Holm test: H0: 'There is no significant differences between the control model and the other models.'" << endl;
+        cout << "  Control model: " << models[controlIdx] << endl;
+        cout << "  Model        p-value      rank      win tie loss Status" << endl;
+        cout << "  ============ ============ ========= === === ==== =============" << endl;
+        // sort ranks from lowest to highest
+        vector<pair<string, float>> ranksOrder;
+        for (const auto& rank : ranks) {
+            ranksOrder.push_back({ rank.first, rank.second });
+        }
+        sort(ranksOrder.begin(), ranksOrder.end(), [](const pair<string, float>& a, const pair<string, float>& b) {
+            return a.second < b.second;
+            });
+        for (const auto& item : ranksOrder) {
+            if (item.first == models.at(controlIdx)) {
+                continue;
+            }
+            auto idx = distance(models.begin(), find(models.begin(), models.end(), item.first));
+            double pvalue = 0.0;
+            for (const auto& stat : statsOrder) {
+                if (stat.first == idx) {
+                    pvalue = stat.second;
+                }
+            }
+            auto colorStatus = pvalue > significance ? Colors::GREEN() : Colors::MAGENTA();
+            auto status = pvalue > significance ? Symbols::check_mark : Symbols::cross;
+            auto textStatus = pvalue > significance ? " accepted H0" : " rejected H0";
+            cout << "  " << colorStatus << left << setw(12) << item.first << " " << setprecision(6) << scientific << pvalue << setprecision(7) << fixed << " " << item.second;
+            cout << " " << right << setw(3) << wtl.at(idx).win << " " << setw(3) << wtl.at(idx).tie << " " << setw(4) << wtl.at(idx).loss;
+            cout << " " << status << textStatus << endl;
+        }
+        cout << color << "  *************************************************************************************************************" << endl;
+        cout << Colors::RESET();
+    }
+    bool Statistics::friedmanTest()
+    {
+        if (!fitted) {
+            fit();
+        }
+        // Friedman test
+        // Calculate the Friedman statistic
+        cout << Colors::BLUE() << endl;
+        cout << "***************************************************************************************************************" << endl;
+        cout << Colors::GREEN() << "Friedman test: H0: 'There is no significant differences between all the classifiers.'" << Colors::BLUE() << endl;
+        double degreesOfFreedom = nModels - 1.0;
+        double sumSquared = 0;
+        for (const auto& rank : ranks) {
+            sumSquared += pow(rank.second, 2);
+        }
+        // Compute the Friedman statistic as in https://link.springer.com/article/10.1007/s44196-022-00083-8
+        double friedmanQ = 12.0 * nDatasets / (nModels * (nModels + 1)) * (sumSquared - (nModels * pow(nModels + 1, 2)) / 4);
+        cout << "Friedman statistic: " << friedmanQ << endl;
+        // Calculate the critical value
+        boost::math::chi_squared chiSquared(degreesOfFreedom);
+        long double p_value = (long double)1.0 - cdf(chiSquared, friedmanQ);
+        double criticalValue = quantile(chiSquared, 1 - significance);
+        std::cout << "Critical Chi-Square Value for df=" << fixed << (int)degreesOfFreedom
+            << " and alpha=" << setprecision(2) << fixed << significance << ": " << setprecision(7) << scientific << criticalValue << std::endl;
+        cout << "p-value: " << scientific << p_value << " is " << (p_value < significance ? "less" : "greater") << " than " << setprecision(2) << fixed << significance << endl;
+        bool result;
+        if (p_value < significance) {
+            cout << Colors::GREEN() << "The null hypothesis H0 is rejected." << endl;
+            result = true;
+        } else {
+            cout << Colors::YELLOW() << "The null hypothesis H0 is accepted. Computed p-values will not be significant." << endl;
+            result = false;
+        }
+        cout << Colors::BLUE() << "***************************************************************************************************************" << Colors::RESET() << endl;
+        return result;
+    }
+} // namespace platform

src/Platform/Statistics.h

@@ -0,0 +1,37 @@
+#ifndef STATISTICS_H
+#define STATISTICS_H
+#include <iostream>
+#include <vector>
+#include <nlohmann/json.hpp>
+
+using namespace std;
+using json = nlohmann::json;
+
+namespace platform {
+    struct WTL {
+        int win;
+        int tie;
+        int loss;
+    };
+    class Statistics {
+    public:
+        Statistics(vector<string>& models, vector<string>& datasets, json data, double significance = 0.05);
+        bool friedmanTest();
+        void postHocHolmTest(bool friedmanResult);
+    private:
+        void fit();
+        void computeRanks();
+        void computeWTL();
+        vector<string> models;
+        vector<string> datasets;
+        json data;
+        double significance;
+        bool fitted = false;
+        int nModels = 0;
+        int nDatasets = 0;
+        int controlIdx = 0;
+        map<int, WTL> wtl;
+        map<string, float> ranks;
+    };
+}
+#endif // !STATISTICS_H

src/Platform/Symbols.h

@@ -0,0 +1,18 @@
+#ifndef SYMBOLS_H
+#define SYMBOLS_H
+#include <string>
+using namespace std;
+namespace platform {
+    class Symbols {
+    public:
+        inline static const string check_mark{ "\u2714" };
+        inline static const string exclamation{ "\u2757" };
+        inline static const string black_star{ "\u2605" };
+        inline static const string cross{ "\u2717" };
+        inline static const string upward_arrow{ "\u27B6" };
+        inline static const string down_arrow{ "\u27B4" };
+        inline static const string equal_best{ check_mark };
+        inline static const string better_best{ black_star };
+    };
+}
+#endif // !SYMBOLS_H

src/Platform/best.cc

@@ -0,0 +1,71 @@
+#include <iostream>
+#include <argparse/argparse.hpp>
+#include "Paths.h"
+#include "BestResults.h"
+#include "Colors.h"
+
+using namespace std;
+
+argparse::ArgumentParser manageArguments(int argc, char** argv)
+{
+    argparse::ArgumentParser program("best");
+    program.add_argument("-m", "--model").default_value("").help("Filter results of the selected model) (any for all models)");
+    program.add_argument("-s", "--score").default_value("").help("Filter results of the score name supplied");
+    program.add_argument("--build").help("build best score results file").default_value(false).implicit_value(true);
+    program.add_argument("--report").help("report of best score results file").default_value(false).implicit_value(true);
+    program.add_argument("--friedman").help("Friedman test").default_value(false).implicit_value(true);
+    try {
+        program.parse_args(argc, argv);
+        auto model = program.get<string>("model");
+        auto score = program.get<string>("score");
+        auto build = program.get<bool>("build");
+        auto report = program.get<bool>("report");
+        auto friedman = program.get<bool>("friedman");
+        if (model == "" || score == "") {
+            throw runtime_error("Model and score name must be supplied");
+        }
+    }
+    catch (const exception& err) {
+        cerr << err.what() << endl;
+        cerr << program;
+        exit(1);
+    }
+    return program;
+}
+
+int main(int argc, char** argv)
+{
+    auto program = manageArguments(argc, argv);
+    auto model = program.get<string>("model");
+    auto score = program.get<string>("score");
+    auto build = program.get<bool>("build");
+    auto report = program.get<bool>("report");
+    auto friedman = program.get<bool>("friedman");
+    if (friedman && model != "any") {
+        cerr << "Friedman test can only be used with all models" << endl;
+        cerr << program;
+        exit(1);
+    }
+    if (!report && !build) {
+        cerr << "Either build, report or both, have to be selected to do anything!" << endl;
+        cerr << program;
+        exit(1);
+    }
+    auto results = platform::BestResults(platform::Paths::results(), score, model, friedman);
+    if (build) {
+        if (model == "any") {
+            results.buildAll();
+        } else {
+            string fileName = results.build();
+            cout << Colors::GREEN() << fileName << " created!" << Colors::RESET() << endl;
+        }
+    }
+    if (report) {
+        if (model == "any") {
+            results.reportAll();
+        } else {
+            results.reportSingle();
+        }
+    }
+    return 0;
+}

src/Platform/platformUtils.h

@@ -8,7 +8,6 @@
 #include "ArffFiles.h"
 #include "CPPFImdlp.h"
 using namespace std;
-const string PATH = "../../data/";
 
 bool file_exists(const std::string& name);
 vector<string> split(const string& text, char delimiter);