Begin BestResults build

Add multiple sheets to excel file
Add format and color to sheets
Add comparison with ZeroR
Add comparison with Best Results
Separate contextual menu from general in manage

CMakeLists.txt

@@ -54,6 +54,7 @@ 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

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 +1,68 @@
+#include <filesystem>
+#include <fstream>
+#include <iostream>
+#include "platformUtils.h"
+#include "BestResults.h"
+#include "Results.h"
+#include "Colors.h"
+
+namespace platform {
+
+    void BestResults::build()
+    {
+        auto files = loadFiles();
+        if (files.size() == 0) {
+            throw runtime_error("No result files were found!");
+        }
+        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["dataset"].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_exists(bestFileName)) {
+            cout << Colors::MAGENTA() << "File " << bestFileName << " already exists and it shall be overwritten." << Colors::RESET();
+        }
+        ofstream file(bestFileName);
+        file << bests;
+        file.close();
+    }
+
+    string BestResults::bestResultFile()
+    {
+        return "best_results_" + score + "_" + model + ".json";
+    }
+
+    vector<string> BestResults::loadFiles()
+    {
+        vector<string> files;
+        using std::filesystem::directory_iterator;
+        for (const auto& file : directory_iterator(path)) {
+            auto fileName = file.path().filename().string();
+            if (fileName.find(".json") != string::npos && fileName.find("results_") == 0
+                && fileName.find("_" + score + "_") != string::npos
+                && fileName.find("_" + model + "_") != string::npos) {
+                files.push_back(fileName);
+            }
+        }
+        return files;
+    }
+
+    void BestResults::report()
+    {
+
+    }
+}

src/Platform/BestResults.h

@@ -0,0 +1,20 @@
+#ifndef BESTRESULTS_H
+#define BESTRESULTS_H
+#include <string>
+using namespace std;
+
+namespace platform {
+    class BestResults {
+    public:
+        explicit BestResults(const string& path, const string& score, const string& model) : path(path), score(score), model(model) {}
+        void build();
+        void report();
+    private:
+        vector<string> loadFiles();
+        string bestResultFile();
+        string path;
+        string score;
+        string model;
+    };
+}
+#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(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
 add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc ReportConsole.cc ReportBase.cc)
 add_executable(manage manage.cc Results.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc platformUtils.cc)
 add_executable(list list.cc platformUtils Datasets.cc)
+add_executable(best best.cc BestResults.cc Results.cc ReportBase.cc ReportExcel.cc platformUtils.cc)
 target_link_libraries(main BayesNet ArffFiles mdlp "${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 "${TORCH_LIBRARIES}" libxlsxwriter.so stdc++fs)
 else()
-    target_link_libraries(manage "${TORCH_LIBRARIES}" libxlsxwriter.so ArffFiles mdlp)
+    target_link_libraries(manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
+    target_link_libraries(best "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}")
 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

@@ -28,6 +28,7 @@ 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 +36,11 @@ 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/Results.cc

@@ -3,7 +3,7 @@
 #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)
@@ -17,8 +17,8 @@ namespace platform {
             score += result["score"].get<double>();
         }
         scoreName = data["score_name"];
-        if (scoreName == BestResult::scoreName()) {
-            score /= BestResult::score();
+        if (scoreName == BestScore::scoreName()) {
+            score /= BestScore::score();
         }
         title = data["title"];
         duration = data["duration"];
@@ -48,6 +48,9 @@ namespace platform {
                     files.push_back(result);
             }
         }
+        if (max == 0) {
+            max = files.size();
+        }
     }
     string Result::to_string() const
     {
@@ -164,7 +167,7 @@ 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 +303,7 @@ namespace platform {
         if (openExcel) {
             workbook_close(workbook);
         }
-        cout << "Done!" << endl;
+        cout << Colors::RESET() << "Done!" << endl;
     }
 
 }

src/Platform/best.cc

@@ -0,0 +1,49 @@
+#include <iostream>
+#include <argparse/argparse.hpp>
+#include "Paths.h"
+#include "BestResults.h"
+
+using namespace std;
+
+argparse::ArgumentParser manageArguments(int argc, char** argv)
+{
+    argparse::ArgumentParser program("best");
+    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("--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);
+    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");
+    }
+    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");
+    if (!report && !build) {
+        cout << "Either build, report or both, have to be selected to do anything!" << endl;
+        exit(1);
+    }
+    auto results = platform::BestResults(platform::Paths::results(), model, score);
+    if (build) {
+        results.build();
+    }
+    if (report) {
+        results.report();
+    }
+    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);