Update last commit badge in README

Reviewed-on: #5

.gitignore

@@ -41,3 +41,4 @@ puml/**
 *.dot
 diagrams/html/**
 diagrams/latex/**
+.cache

CMakeLists.txt

@@ -15,7 +15,7 @@ endif ()
 
 # Global CMake variables
 # ----------------------
-set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED            ON)
 set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
@@ -90,7 +90,7 @@ cmake_path(SET TEST_DATA_PATH "${CMAKE_CURRENT_SOURCE_DIR}/tests/data")
 configure_file(src/common/SourceData.h.in "${CMAKE_BINARY_DIR}/configured_files/include/SourceData.h")
 add_subdirectory(config)
 add_subdirectory(src)
-# add_subdirectory(sample)
+add_subdirectory(sample)
 file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${Platform_SOURCE_DIR}/src/*.cpp)
 
 # Testing

Makefile

@@ -4,7 +4,7 @@ SHELL := /bin/bash
 
 f_release = build_release
 f_debug = build_debug
-app_targets = b_best b_list b_main b_manage b_grid
+app_targets = b_best b_list b_main b_manage b_grid b_results
 test_targets = unit_tests_platform
 
 define ClearTests
@@ -37,6 +37,11 @@ setup: ## Install dependencies for tests and coverage
 		pip install gcovr; \
 	fi
 
+dest ?= ${HOME}/bin
+main: ## Build only the b_main target
+	@cmake --build $(f_release) -t b_main --parallel
+	@cp $(f_release)/src/b_main $(dest)
+
 dest ?= ${HOME}/bin
 install: ## Copy binary files to bin folder
 	@echo "Destination folder: $(dest)"
@@ -55,10 +60,10 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
 	cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) PlatformSample --parallel
+	@cmake --build $(f_debug) -t $(app_targets) PlatformSample --parallel
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) --parallel
+	@cmake --build $(f_release) -t $(app_targets) --parallel
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug Platform tests...";
@@ -98,8 +103,8 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 fname = iris
 example: ## Build sample
 	@echo ">>> Building Sample...";
-	@cmake --build build_debug -t sample
-	build_debug/sample/PlatformSample --model BoostAODE --dataset $(fname) --discretize --stratified
+	@cmake --build $(f_release) -t sample
+	$(f_release)/sample/PlatformSample --model BoostAODE --dataset $(fname) --discretize --stratified
 	@echo ">>> Done";
 
 

README.md

@@ -2,7 +2,7 @@
 
 ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](<https://opensource.org/licenses/MIT>)
-![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/platform?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
+![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/platform?gitea_url=https://gitea.rmontanana.es&logo=gitea)
 
 Platform to run Bayesian Networks and Machine Learning Classifiers experiments.
 
@@ -40,7 +40,7 @@ export MPI_HOME="/usr/lib64/openmpi"
 In Mac OS X, install mpich with brew and if cmake doesn't find it, edit mpicxx wrapper to remove the ",-commons,use_dylibs" from final_ldflags
 
 ```bash
-vi /opt/homebrew/bin/mpicx
+vi /opt/homebrew/bin/mpicxx
 ```
 
 ### boost library

cmake/modules/CodeCoverage.cmake

@@ -137,7 +137,7 @@
 
 include(CMakeParseArguments)
 
-option(CODE_COVERAGE_VERBOSE "Verbose information" FALSE)
+option(CODE_COVERAGE_VERBOSE "Verbose information" TRUE)
 
 # Check prereqs
 find_program( GCOV_PATH gcov )
@@ -160,7 +160,11 @@ foreach(LANG ${LANGUAGES})
     endif()
   elseif(NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "GNU"
          AND NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(LLVM)?[Ff]lang")
-    message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+         if ("${LANG}" MATCHES "CUDA")
+             message(STATUS "Ignoring CUDA")
+        else()
+            message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+        endif()
   endif()
 endforeach()
 

sample/sample.cpp

@@ -9,6 +9,7 @@
 #include <fimdlp/CPPFImdlp.h>
 #include <folding.hpp>
 #include <bayesnet/utils/BayesMetrics.h>
+#include <bayesnet/classifiers/SPODE.h>
 #include "Models.h"
 #include "modelRegister.h"
 #include "config_platform.h"
@@ -160,82 +161,119 @@ int main(int argc, char** argv)
             states[feature] = std::vector<int>(maxes[feature]);
         }
         states[className] = std::vector<int>(maxes[className]);
-        auto clf = platform::Models::instance()->create(model_name);
+        // Output the states
+        std::cout << std::string(80, '-') << std::endl;
+        std::cout << "States" << std::endl;
+        for (auto feature : features) {
+            std::cout << feature << ": " << states[feature].size() << std::endl;
+        }
+        std::cout << std::string(80, '-') << std::endl;
+        //auto clf = platform::Models::instance()->create("SPODE");
+        auto clf = bayesnet::SPODE(2);
+
         bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::ORIGINAL;
-        clf->fit(Xd, y, features, className, states, smoothing);
+        clf.fit(Xd, y, features, className, states, smoothing);
         if (dump_cpt) {
             std::cout << "--- CPT Tables ---" << std::endl;
-            clf->dump_cpt();
+            std::cout << clf.dump_cpt();
         }
-        auto lines = clf->show();
+        std::cout << "--- Datos predicción ---" << std::endl;
+        std::cout << "Orden de variables: " << std::endl;
+        for (auto feature : features) {
+            std::cout << feature << ", ";
+        }
+        std::cout << std::endl;
+        std::cout << "X[0]: ";
+        for (int i = 0; i < Xd.size(); ++i) {
+            std::cout << Xd[i][0] << ", ";
+        }
+        std::cout << std::endl;
+        std::cout << std::string(80, '-') << std::endl;
+
+        auto lines = clf.show();
         for (auto line : lines) {
             std::cout << line << std::endl;
         }
         std::cout << "--- Topological Order ---" << std::endl;
-        auto order = clf->topological_order();
+        auto order = clf.topological_order();
         for (auto name : order) {
             std::cout << name << ", ";
         }
-        std::cout << "end." << std::endl;
-        auto score = clf->score(Xd, y);
-        std::cout << "Score: " << score << std::endl;
-        auto graph = clf->graph();
-        auto dot_file = model_name + "_" + file_name;
-        ofstream file(dot_file + ".dot");
-        file << graph;
-        file.close();
-        std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
-        std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
-        std::string stratified_string = stratified ? " Stratified" : "";
-        std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
-        std::cout << "==========================================" << std::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;
-        folding::Fold* fold;
-        double nodes = 0.0;
-        if (stratified)
-            fold = new folding::StratifiedKFold(nFolds, y, seed);
-        else
-            fold = new folding::KFold(nFolds, y.size(), seed);
-        for (auto i = 0; i < nFolds; ++i) {
-            auto [train, test] = fold->getFold(i);
-            std::cout << "Fold: " << i + 1 << std::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, smoothing);
-                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, smoothing);
-                std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
-                nodes += clf->getNumberOfNodes();
-                score_train = clf->score(Xtrain, ytrain);
-                score_test = clf->score(Xtest, ytest);
+        auto predict_proba = clf.predict_proba(Xd);
+        std::cout << "Instances predict_proba: ";
+        for (int i = 0; i < predict_proba.size(); i++) {
+            std::cout << "Instance " << i << ": ";
+            for (int j = 0; j < 4; ++j) {
+                std::cout << Xd[j][i] << ", ";
             }
-            if (dump_cpt) {
-                std::cout << "--- CPT Tables ---" << std::endl;
-                clf->dump_cpt();
+            std::cout << ": ";
+            for (auto score : predict_proba[i]) {
+                std::cout << score << ", ";
             }
-            total_score_train += score_train;
-            total_score += score_test;
-            std::cout << "Score Train: " << score_train << std::endl;
-            std::cout << "Score Test : " << score_test << std::endl;
-            std::cout << "-------------------------------------------------------------------------------" << std::endl;
+            std::cout << std::endl;
         }
-        std::cout << "Nodes: " << nodes / nFolds << std::endl;
-        std::cout << "**********************************************************************************" << std::endl;
-        std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
-        std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
+        // std::cout << std::endl;
+        // std::cout << "end." << std::endl;
+        // auto score = clf->score(Xd, y);
+        // std::cout << "Score: " << score << std::endl;
+        // auto graph = clf->graph();
+        // auto dot_file = model_name + "_" + file_name;
+        // ofstream file(dot_file + ".dot");
+        // file << graph;
+        // file.close();
+        // std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
+        // std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
+        // std::string stratified_string = stratified ? " Stratified" : "";
+        // std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
+        // std::cout << "==========================================" << std::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;
+        // folding::Fold* fold;
+        // double nodes = 0.0;
+        // if (stratified)
+        //     fold = new folding::StratifiedKFold(nFolds, y, seed);
+        // else
+        //     fold = new folding::KFold(nFolds, y.size(), seed);
+        // for (auto i = 0; i < nFolds; ++i) {
+        //     auto [train, test] = fold->getFold(i);
+        //     std::cout << "Fold: " << i + 1 << std::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, smoothing);
+        //         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, smoothing);
+        //         std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
+        //         nodes += clf->getNumberOfNodes();
+        //         score_train = clf->score(Xtrain, ytrain);
+        //         score_test = clf->score(Xtest, ytest);
+        //     }
+        //     // if (dump_cpt) {
+        //     //     std::cout << "--- CPT Tables ---" << std::endl;
+        //     //     std::cout << clf->dump_cpt();
+        //     // }
+        //     total_score_train += score_train;
+        //     total_score += score_test;
+        //     std::cout << "Score Train: " << score_train << std::endl;
+        //     std::cout << "Score Test : " << score_test << std::endl;
+        //     std::cout << "-------------------------------------------------------------------------------" << std::endl;
+        // }
+
+        // std::cout << "Nodes: " << nodes / nFolds << std::endl;
+        // std::cout << "**********************************************************************************" << std::endl;
+        // std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
+        // std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
 }

src/CMakeLists.txt

@@ -1,5 +1,6 @@
 include_directories(
     ## Libs
+    ${Platform_SOURCE_DIR}/lib/log
     ${Platform_SOURCE_DIR}/lib/Files
     ${Platform_SOURCE_DIR}/lib/folding
     ${Platform_SOURCE_DIR}/lib/mdlp/src
@@ -25,15 +26,21 @@ add_executable(
     main/Models.cpp main/Scores.cpp
     reports/ReportExcel.cpp reports/ReportBase.cpp reports/ExcelFile.cpp
     results/Result.cpp
+    experimental_clfs/XA1DE.cpp
+    experimental_clfs/ExpClf.cpp
 )
 target_link_libraries(b_best Boost::boost "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
 # b_grid
-set(grid_sources GridSearch.cpp GridData.cpp)
+set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp GridBase.cpp )
 list(TRANSFORM grid_sources PREPEND grid/)
 add_executable(b_grid commands/b_grid.cpp ${grid_sources} 
     common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
-    main/HyperParameters.cpp main/Models.cpp 
+    main/HyperParameters.cpp main/Models.cpp main/Experiment.cpp main/Scores.cpp main/ArgumentsExperiment.cpp
+    reports/ReportConsole.cpp reports/ReportBase.cpp 
+    results/Result.cpp
+    experimental_clfs/XA1DE.cpp
+    experimental_clfs/ExpClf.cpp
 )
 target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
 
@@ -43,16 +50,20 @@ add_executable(b_list commands/b_list.cpp
     main/Models.cpp main/Scores.cpp
     reports/ReportExcel.cpp reports/ExcelFile.cpp reports/ReportBase.cpp reports/DatasetsExcel.cpp reports/DatasetsConsole.cpp reports/ReportsPaged.cpp
     results/Result.cpp results/ResultsDatasetExcel.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
+    experimental_clfs/XA1DE.cpp
+    experimental_clfs/ExpClf.cpp
 )
 target_link_libraries(b_list "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
 # b_main
-set(main_sources Experiment.cpp Models.cpp HyperParameters.cpp Scores.cpp)
+set(main_sources Experiment.cpp Models.cpp HyperParameters.cpp Scores.cpp ArgumentsExperiment.cpp)
 list(TRANSFORM main_sources PREPEND main/)
 add_executable(b_main commands/b_main.cpp ${main_sources} 
     common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
     reports/ReportConsole.cpp reports/ReportBase.cpp 
     results/Result.cpp
+    experimental_clfs/XA1DE.cpp
+    experimental_clfs/ExpClf.cpp
 )
 target_link_libraries(b_main "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
 
@@ -67,3 +78,6 @@ add_executable(
     main/Scores.cpp
 )
 target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" fimdlp "${BayesNet}")
+
+# b_results
+add_executable(b_results commands/b_results.cpp)

src/best/BestResults.cpp

@@ -132,6 +132,7 @@ namespace platform {
         for (const auto& dataset_ : table.items()) {
             datasets.push_back(dataset_.key());
         }
+        std::stable_sort(datasets.begin(), datasets.end());
         maxDatasetName = (*max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
         maxDatasetName = std::max(7, maxDatasetName);
         return datasets;
@@ -214,7 +215,7 @@ namespace platform {
         return table;
     }
 
-    void BestResults::printTableResults(std::vector<std::string> models, json table, bool tex)
+    void BestResults::printTableResults(std::vector<std::string> models, json table, bool tex, bool index)
     {
         std::stringstream oss;
         oss << Colors::GREEN() << "Best results for " << score << " as of " << table.at("dateTable").get<std::string>() << std::endl;
@@ -224,7 +225,7 @@ namespace platform {
         auto bestResultsTex = BestResultsTex();
         auto bestResultsMd = BestResultsMd();
         if (tex) {
-            bestResultsTex.results_header(models, table.at("dateTable").get<std::string>());
+            bestResultsTex.results_header(models, table.at("dateTable").get<std::string>(), index);
             bestResultsMd.results_header(models, table.at("dateTable").get<std::string>());
         }
         for (const auto& model : models) {
@@ -241,7 +242,7 @@ namespace platform {
         int nDatasets = table.begin().value().size();
         auto datasets = getDatasets(table.begin().value());
         if (tex) {
-            bestResultsTex.results_body(datasets, table);
+            bestResultsTex.results_body(datasets, table, index);
             bestResultsMd.results_body(datasets, table);
         }
         for (auto const& dataset_ : datasets) {
@@ -325,14 +326,14 @@ namespace platform {
             messageOutputFile("Excel", excel_report.getFileName());
         }
     }
-    void BestResults::reportAll(bool excel, bool tex)
+    void BestResults::reportAll(bool excel, bool tex, bool index)
     {
         auto models = getModels();
         // Build the table of results
         json table = buildTableResults(models);
         std::vector<std::string> datasets = getDatasets(table.begin().value());
         // Print the table of results
-        printTableResults(models, table, tex);
+        printTableResults(models, table, tex, index);
         // Compute the Friedman test
         std::map<std::string, std::map<std::string, float>> ranksModels;
         if (friedman) {

src/best/BestResults.h

@@ -13,7 +13,7 @@ namespace platform {
         }
         std::string build();
         void reportSingle(bool excel);
-        void reportAll(bool excel, bool tex);
+        void reportAll(bool excel, bool tex, bool index);
         void buildAll();
     private:
         std::vector<std::string> getModels();
@@ -21,7 +21,7 @@ namespace platform {
         std::vector<std::string> loadResultFiles();
         void messageOutputFile(const std::string& title, const std::string& fileName);
         json buildTableResults(std::vector<std::string> models);
-        void printTableResults(std::vector<std::string> models, json table, bool tex);
+        void printTableResults(std::vector<std::string> models, json table, bool tex, bool index);
         json loadFile(const std::string& fileName);
         void listFile();
         std::string path;

src/best/BestResultsTex.cpp

@@ -12,7 +12,7 @@ namespace platform {
             exit(1);
         }
     }
-    void BestResultsTex::results_header(const std::vector<std::string>& models, const std::string& date)
+    void BestResultsTex::results_header(const std::vector<std::string>& models, const std::string& date, bool index)
     {
         this->models = models;
         auto file_name = Paths::tex() + Paths::tex_output();
@@ -29,7 +29,8 @@ namespace platform {
         handler << "\\renewcommand{\\tabcolsep }{0.07cm} " << std::endl;
         handler << "\\caption{Accuracy results(mean $\\pm$ std) for all the algorithms and datasets} " << std::endl;
         handler << "\\label{tab:results_accuracy}" << std::endl;
-        handler << "\\begin{tabular} {{r" << std::string(models.size(), 'c').c_str() << "}}" << std::endl;
+        std::string header_dataset_name = index ? "r" : "l";
+        handler << "\\begin{tabular} {{" << header_dataset_name << std::string(models.size(), 'c').c_str() << "}}" << std::endl;
         handler << "\\hline " << std::endl;
         handler << "" << std::endl;
         for (const auto& model : models) {
@@ -38,13 +39,12 @@ namespace platform {
         handler << "\\\\" << std::endl;
         handler << "\\hline" << std::endl;
     }
-    void BestResultsTex::results_body(const std::vector<std::string>& datasets, json& table)
+    void BestResultsTex::results_body(const std::vector<std::string>& datasets, json& table, bool index)
     {
         int i = 0;
         for (auto const& dataset : datasets) {
             // Find out max value for this dataset
             double max_value = 0;
-            // Find out the max value for this dataset
             for (const auto& model : models) {
                 double value;
                 try {
@@ -57,7 +57,10 @@ namespace platform {
                     max_value = value;
                 }
             }
-            handler << ++i << " ";
+            if (index)
+                handler << ++i << " ";
+            else
+                handler << dataset << " ";
             for (const auto& model : models) {
                 double value = table[model].at(dataset).at(0).get<double>();
                 double std_value = table[model].at(dataset).at(3).get<double>();

src/best/BestResultsTex.h

@@ -9,13 +9,14 @@ namespace platform {
     using json = nlohmann::ordered_json;
     class BestResultsTex {
     public:
-        BestResultsTex() = default;
+        BestResultsTex(bool dataset_name = true) : dataset_name(dataset_name) {};
         ~BestResultsTex() = default;
-        void results_header(const std::vector<std::string>& models, const std::string& date);
-        void results_body(const std::vector<std::string>& datasets, json& table);
+        void results_header(const std::vector<std::string>& models, const std::string& date, bool index);
+        void results_body(const std::vector<std::string>& datasets, json& table, bool index);
         void results_footer(const std::map<std::string, std::vector<double>>& totals, const std::string& best_model);
         void holm_test(struct HolmResult& holmResult, const std::string& date);
     private:
+        bool dataset_name;
         void openTexFile(const std::string& name);
         std::ofstream handler;
         std::vector<std::string> models;

src/commands/b_best.cpp

@@ -16,7 +16,8 @@ void manageArguments(argparse::ArgumentParser& program)
     program.add_argument("-s", "--score").default_value("accuracy").help("Filter results of the score name supplied");
     program.add_argument("--friedman").help("Friedman test").default_value(false).implicit_value(true);
     program.add_argument("--excel").help("Output to excel").default_value(false).implicit_value(true);
-    program.add_argument("--tex").help("Output result table to TeX file").default_value(false).implicit_value(true);
+    program.add_argument("--tex").help("Output results to TeX & Markdown files").default_value(false).implicit_value(true);
+    program.add_argument("--index").help("In tex output show the index of the dataset instead of the name to save space").default_value(false).implicit_value(true);
     program.add_argument("--level").help("significance level").default_value(0.05).scan<'g', double>().action([](const std::string& value) {
         try {
             auto k = std::stod(value);
@@ -38,7 +39,7 @@ int main(int argc, char** argv)
     argparse::ArgumentParser program("b_best", { platform_project_version.begin(), platform_project_version.end() });
     manageArguments(program);
     std::string model, dataset, score;
-    bool build, report, friedman, excel, tex;
+    bool build, report, friedman, excel, tex, index;
     double level;
     try {
         program.parse_args(argc, argv);
@@ -48,6 +49,7 @@ int main(int argc, char** argv)
         friedman = program.get<bool>("friedman");
         excel = program.get<bool>("excel");
         tex = program.get<bool>("tex");
+        index = program.get<bool>("index");
         level = program.get<double>("level");
         if (model == "" || score == "") {
             throw std::runtime_error("Model and score name must be supplied");
@@ -67,7 +69,7 @@ int main(int argc, char** argv)
     auto results = platform::BestResults(platform::Paths::results(), score, model, dataset, friedman, level);
     if (model == "any") {
         results.buildAll();
-        results.reportAll(excel, tex);
+        results.reportAll(excel, tex, index);
     } else {
         std::string fileName = results.build();
         std::cout << Colors::GREEN() << fileName << " created!" << Colors::RESET() << std::endl;

src/commands/b_grid.cpp

@@ -1,16 +1,16 @@
 #include <iostream>
 #include <argparse/argparse.hpp>
 #include <map>
-#include <tuple>
 #include <nlohmann/json.hpp>
 #include <mpi.h>
 #include "main/Models.h"
-#include "main/modelRegister.h"
+#include "main/ArgumentsExperiment.h"
 #include "common/Paths.h"
-#include "common/Timer.h"
+#include "common/Timer.hpp"
 #include "common/Colors.h"
 #include "common/DotEnv.h"
 #include "grid/GridSearch.h"
+#include "grid/GridExperiment.h"
 #include "config_platform.h"
 
 using json = nlohmann::ordered_json;
@@ -31,15 +31,20 @@ void assignModel(argparse::ArgumentParser& parser)
             }
         );
 }
-void add_compute_args(argparse::ArgumentParser& program)
+void add_search_args(argparse::ArgumentParser& program)
 {
     auto env = platform::DotEnv();
     program.add_argument("--discretize").help("Discretize input datasets").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
     program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
     program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
     program.add_argument("--continue").help("Continue computing from that dataset").default_value(platform::GridSearch::NO_CONTINUE());
-    program.add_argument("--only").help("Used with continue to compute that dataset only").default_value(false).implicit_value(true);
+    program.add_argument("--only").help("Used with continue to search with that dataset only").default_value(false).implicit_value(true);
     program.add_argument("--exclude").default_value("[]").help("Datasets to exclude in json format, e.g. [\"dataset1\", \"dataset2\"]");
+    auto valid_choices = env.valid_tokens("smooth_strat");
+    auto& smooth_arg = program.add_argument("--smooth-strat").help("Smooth strategy used in Bayes Network node initialization. Valid values: " + env.valid_values("smooth_strat")).default_value(env.get("smooth_strat"));
+    for (auto choice : valid_choices) {
+        smooth_arg.choices(choice);
+    }
     program.add_argument("--nested").help("Set the double/nested cross validation number of folds").default_value(5).scan<'i', int>().action([](const std::string& value) {
         try {
             auto k = stoi(value);
@@ -133,7 +138,8 @@ void list_results(json& results, std::string& model)
     std::cout << std::string(MAXL, '*') << std::endl;
     int spaces = 7;
     int hyperparameters_spaces = 15;
-    for (const auto& item : results["results"].items()) {
+    nlohmann::json temp = results["results"]; // To show in alphabetical order of the dataset
+    for (const auto& item : temp.items()) {
         auto key = item.key();
         auto value = item.value();
         if (key.size() > spaces) {
@@ -148,7 +154,7 @@ void list_results(json& results, std::string& model)
     std::cout << "=== " << string(spaces, '=') << " " << string(19, '=') << " " << string(8, '=') << " "
         << string(8, '=') << " " << string(hyperparameters_spaces, '=') << std::endl;
     int index = 0;
-    for (const auto& item : results["results"].items()) {
+    for (const auto& item : temp.items()) {
         auto color = (index % 2) ? Colors::CYAN() : Colors::BLUE();
         auto value = item.value();
         std::cout << color;
@@ -181,13 +187,14 @@ void report(argparse::ArgumentParser& program)
         list_results(results, config.model);
     }
 }
-void compute(argparse::ArgumentParser& program)
+void search(argparse::ArgumentParser& program)
 {
     struct platform::ConfigGrid config;
     config.model = program.get<std::string>("model");
     config.score = program.get<std::string>("score");
     config.discretize = program.get<bool>("discretize");
     config.stratified = program.get<bool>("stratified");
+    config.smooth_strategy = program.get<std::string>("smooth-strat");
     config.n_folds = program.get<int>("folds");
     config.quiet = program.get<bool>("quiet");
     config.only = program.get<bool>("only");
@@ -199,9 +206,6 @@ void compute(argparse::ArgumentParser& program)
     }
     auto excluded = program.get<std::string>("exclude");
     config.excluded = json::parse(excluded);
-
-    auto env = platform::DotEnv();
-    config.platform = env.get("platform");
     platform::Paths::createPath(platform::Paths::grid());
     auto grid_search = platform::GridSearch(config);
     platform::Timer timer;
@@ -212,16 +216,47 @@ void compute(argparse::ArgumentParser& program)
     MPI_Comm_rank(MPI_COMM_WORLD, &mpi_config.rank);
     MPI_Comm_size(MPI_COMM_WORLD, &mpi_config.n_procs);
     if (mpi_config.n_procs < 2) {
-        throw std::runtime_error("Cannot use --compute with less than 2 mpi processes, try mpirun -np 2 ...");
+        throw std::runtime_error("Cannot use --search with less than 2 mpi processes, try mpirun -np 2 ...");
     }
     grid_search.go(mpi_config);
     if (mpi_config.rank == mpi_config.manager) {
         auto results = grid_search.loadResults();
+        std::cout << Colors::RESET() << "* Report of the computed hyperparameters" << std::endl;
         list_results(results, config.model);
         std::cout << "Process took " << timer.getDurationString() << std::endl;
     }
     MPI_Finalize();
 }
+void experiment(argparse::ArgumentParser& program)
+{
+    struct platform::ConfigGrid config;
+    auto arguments = platform::ArgumentsExperiment(program, platform::experiment_t::GRID);
+    arguments.parse();
+    auto grid_experiment = platform::GridExperiment(arguments, config);
+    platform::Timer timer;
+    timer.start();
+    struct platform::ConfigMPI mpi_config;
+    mpi_config.manager = 0; // which process is the manager
+    MPI_Init(nullptr, nullptr);
+    MPI_Comm_rank(MPI_COMM_WORLD, &mpi_config.rank);
+    MPI_Comm_size(MPI_COMM_WORLD, &mpi_config.n_procs);
+    if (mpi_config.n_procs < 2) {
+        throw std::runtime_error("Cannot use --experiment with less than 2 mpi processes, try mpirun -np 2 ...");
+    }
+    grid_experiment.go(mpi_config);
+    if (mpi_config.rank == mpi_config.manager) {
+        auto experiment = grid_experiment.getExperiment();
+        std::cout << "* Report of the computed hyperparameters" << std::endl;
+        auto duration = timer.getDuration();
+        experiment.setDuration(duration);
+        if (grid_experiment.haveToSaveResults()) {
+            experiment.saveResult();
+        }
+        experiment.report();
+        std::cout << "Process took " << duration << std::endl;
+    }
+    MPI_Finalize();
+}
 int main(int argc, char** argv)
 {
     //
@@ -238,15 +273,21 @@ int main(int argc, char** argv)
     assignModel(report_command);
     report_command.add_description("Report the computed hyperparameters of a model.");
 
-    // grid compute subparser
-    argparse::ArgumentParser compute_command("compute");
-    compute_command.add_description("Compute using mpi the hyperparameters of a model.");
-    assignModel(compute_command);
-    add_compute_args(compute_command);
+    // grid search subparser
+    argparse::ArgumentParser search_command("search");
+    search_command.add_description("Search using mpi the hyperparameters of a model.");
+    assignModel(search_command);
+    add_search_args(search_command);
 
+    // grid experiment subparser
+    argparse::ArgumentParser experiment_command("experiment");
+    experiment_command.add_description("Experiment like b_main using mpi.");
+    auto arguments = platform::ArgumentsExperiment(experiment_command, platform::experiment_t::GRID);
+    arguments.add_arguments();
     program.add_subparser(dump_command);
     program.add_subparser(report_command);
-    program.add_subparser(compute_command);
+    program.add_subparser(search_command);
+    program.add_subparser(experiment_command);
 
     // 
     // Process options
@@ -254,7 +295,7 @@ int main(int argc, char** argv)
     try {
         program.parse_args(argc, argv);
         bool found = false;
-        map<std::string, void(*)(argparse::ArgumentParser&)> commands = { {"dump", &dump}, {"report", &report}, {"compute", &compute} };
+        map<std::string, void(*)(argparse::ArgumentParser&)> commands = { {"dump", &dump}, {"report", &report}, {"search", &search}, { "experiment",&experiment } };
         for (const auto& command : commands) {
             if (program.is_subcommand_used(command.first)) {
                 std::invoke(command.second, program.at<argparse::ArgumentParser>(command.first));
@@ -263,7 +304,7 @@ int main(int argc, char** argv)
             }
         }
         if (!found) {
-            throw std::runtime_error("You must specify one of the following commands: dump, report, compute\n");
+            throw std::runtime_error("You must specify one of the following commands: dump, experiment, report, search \n");
         }
     }
     catch (const exception& err) {

src/commands/b_main.cpp

@@ -1,234 +1,36 @@
-#include <iostream>
 #include <argparse/argparse.hpp>
-#include <nlohmann/json.hpp>
 #include "main/Experiment.h"
-#include "common/Datasets.h"
-#include "common/DotEnv.h"
-#include "common/Paths.h"
-#include "main/Models.h"
-#include "main/modelRegister.h"
+#include "main/ArgumentsExperiment.h"
 #include "config_platform.h"
 
 
 using json = nlohmann::ordered_json;
 
 
-void manageArguments(argparse::ArgumentParser& program)
-{
-    auto env = platform::DotEnv();
-    auto datasets = platform::Datasets(false, platform::Paths::datasets());
-    auto& group = program.add_mutually_exclusive_group(true);
-    group.add_argument("-d", "--dataset")
-        .help("Dataset file name: " + datasets.toString())
-        .default_value("all")
-        .action([](const std::string& value) {
-        auto datasets = platform::Datasets(false, platform::Paths::datasets());
-        static std::vector<std::string> choices_datasets(datasets.getNames());
-        choices_datasets.push_back("all");
-        if (find(choices_datasets.begin(), choices_datasets.end(), value) != choices_datasets.end()) {
-            return value;
-        }
-        throw std::runtime_error("Dataset must be one of: " + datasets.toString());
-            }
-        );
-    group.add_argument("--datasets").nargs(1, 50).help("Datasets file names 1..50 separated by spaces").default_value(std::vector<std::string>());
-    group.add_argument("--datasets-file").default_value("").help("Datasets file name. Mutually exclusive with dataset. This file should contain a list of datasets to test.");
-    program.add_argument("--hyperparameters").default_value("{}").help("Hyperparameters passed to the model in Experiment");
-    program.add_argument("--hyper-file").default_value("").help("Hyperparameters file name." \
-        "Mutually exclusive with hyperparameters. This file should contain hyperparameters for each dataset in json format.");
-    program.add_argument("--hyper-best").default_value(false).help("Use best results of the model as source of hyperparameters").implicit_value(true);
-    program.add_argument("-m", "--model")
-        .help("Model to use: " + platform::Models::instance()->toString())
-        .action([](const std::string& value) {
-        static const std::vector<std::string> choices = platform::Models::instance()->getNames();
-        if (find(choices.begin(), choices.end(), value) != choices.end()) {
-            return value;
-        }
-        throw std::runtime_error("Model must be one of " + platform::Models::instance()->toString());
-            }
-        );
-    program.add_argument("--title").default_value("").help("Experiment title");
-    program.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
-    auto valid_choices = env.valid_tokens("discretize_algo");
-    auto& disc_arg = program.add_argument("--discretize-algo").help("Algorithm to use in discretization. Valid values: " + env.valid_values("discretize_algo")).default_value(env.get("discretize_algo"));
-    for (auto choice : valid_choices) {
-        disc_arg.choices(choice);
-    }
-    valid_choices = env.valid_tokens("smooth_strat");
-    auto& smooth_arg = program.add_argument("--smooth-strat").help("Smooth strategy used in Bayes Network node initialization. Valid values: " + env.valid_values("smooth_strat")).default_value(env.get("smooth_strat"));
-    for (auto choice : valid_choices) {
-        smooth_arg.choices(choice);
-    }
-    auto& score_arg = program.add_argument("-s", "--score").help("Score to use. Valid values: " + env.valid_values("score")).default_value(env.get("score"));
-    valid_choices = env.valid_tokens("score");
-    for (auto choice : valid_choices) {
-        score_arg.choices(choice);
-    }
-    program.add_argument("--generate-fold-files").help("generate fold information in datasets_experiment folder").default_value(false).implicit_value(true);
-    program.add_argument("--graph").help("generate graphviz dot files with the model").default_value(false).implicit_value(true);
-    program.add_argument("--no-train-score").help("Don't compute train score").default_value(false).implicit_value(true);
-    program.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
-    program.add_argument("--save").help("Save result (always save if no dataset is supplied)").default_value(false).implicit_value(true);
-    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
-    program.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
-        try {
-            auto k = stoi(value);
-            if (k < 2) {
-                throw std::runtime_error("Number of folds must be greater than 1");
-            }
-            return k;
-        }
-        catch (const runtime_error& err) {
-            throw std::runtime_error(err.what());
-        }
-        catch (...) {
-            throw std::runtime_error("Number of folds must be an integer");
-        }});
-        auto seed_values = env.getSeeds();
-        program.add_argument("--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
-}
-
 int main(int argc, char** argv)
 {
     argparse::ArgumentParser program("b_main", { platform_project_version.begin(), platform_project_version.end() });
-    manageArguments(program);
-    std::string file_name, model_name, title, hyperparameters_file, datasets_file, discretize_algo, smooth_strat, score;
-    json hyperparameters_json;
-    bool discretize_dataset, stratified, saveResults, quiet, no_train_score, generate_fold_files, graph, hyper_best;
-    std::vector<int> seeds;
-    std::vector<std::string> file_names;
-    std::vector<std::string> filesToTest;
-    int n_folds;
-    try {
-        program.parse_args(argc, argv);
-        file_name = program.get<std::string>("dataset");
-        file_names = program.get<std::vector<std::string>>("datasets");
-        datasets_file = program.get<std::string>("datasets-file");
-        model_name = program.get<std::string>("model");
-        discretize_dataset = program.get<bool>("discretize");
-        discretize_algo = program.get<std::string>("discretize-algo");
-        smooth_strat = program.get<std::string>("smooth-strat");
-        stratified = program.get<bool>("stratified");
-        quiet = program.get<bool>("quiet");
-        graph = program.get<bool>("graph");
-        n_folds = program.get<int>("folds");
-        score = program.get<std::string>("score");
-        seeds = program.get<std::vector<int>>("seeds");
-        auto hyperparameters = program.get<std::string>("hyperparameters");
-        hyperparameters_json = json::parse(hyperparameters);
-        hyperparameters_file = program.get<std::string>("hyper-file");
-        no_train_score = program.get<bool>("no-train-score");
-        hyper_best = program.get<bool>("hyper-best");
-        generate_fold_files = program.get<bool>("generate-fold-files");
-        if (hyper_best) {
-            // Build the best results file_name
-            hyperparameters_file = platform::Paths::results() + platform::Paths::bestResultsFile(score, model_name);
-            // ignore this parameter
-            hyperparameters = "{}";
-        } else {
-            if (hyperparameters_file != "" && hyperparameters != "{}") {
-                throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
-            }
-        }
-        title = program.get<std::string>("title");
-        if (title == "" && file_name == "all") {
-            throw runtime_error("title is mandatory if all datasets are to be tested");
-        }
-        saveResults = program.get<bool>("save");
-    }
-    catch (const exception& err) {
-        cerr << err.what() << std::endl;
-        cerr << program;
-        exit(1);
-    }
-    auto datasets = platform::Datasets(false, platform::Paths::datasets());
-    if (datasets_file != "") {
-        ifstream catalog(datasets_file);
-        if (catalog.is_open()) {
-            std::string line;
-            while (getline(catalog, line)) {
-                if (line.empty() || line[0] == '#') {
-                    continue;
-                }
-                if (!datasets.isDataset(line)) {
-                    cerr << "Dataset " << line << " not found" << std::endl;
-                    exit(1);
-                }
-                filesToTest.push_back(line);
-            }
-            catalog.close();
-            saveResults = true;
-            if (title == "") {
-                title = "Test " + to_string(filesToTest.size()) + " datasets (" + datasets_file + ") "\
-                    + model_name + " " + to_string(n_folds) + " folds";
-            }
-        } else {
-            throw std::invalid_argument("Unable to open catalog file. [" + datasets_file + "]");
-        }
-    } else {
-        if (file_names.size() > 0) {
-            for (auto file : file_names) {
-                if (!datasets.isDataset(file)) {
-                    cerr << "Dataset " << file << " not found" << std::endl;
-                    exit(1);
-                }
-            }
-            filesToTest = file_names;
-            saveResults = true;
-            if (title == "") {
-                title = "Test " + to_string(file_names.size()) + " datasets " + model_name + " " + to_string(n_folds) + " folds";
-            }
-        } else {
-            if (file_name != "all") {
-                if (!datasets.isDataset(file_name)) {
-                    cerr << "Dataset " << file_name << " not found" << std::endl;
-                    exit(1);
-                }
-                if (title == "") {
-                    title = "Test " + file_name + " " + model_name + " " + to_string(n_folds) + " folds";
-                }
-                filesToTest.push_back(file_name);
-            } else {
-                filesToTest = datasets.getNames();
-                saveResults = true;
-            }
-        }
-    }
-
-    platform::HyperParameters test_hyperparams;
-    if (hyperparameters_file != "") {
-        test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_file, hyper_best);
-    } else {
-        test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_json);
-    }
-
+    auto arguments = platform::ArgumentsExperiment(program, platform::experiment_t::NORMAL);
+    arguments.add_arguments();
+    arguments.parse_args(argc, argv);
     /*
      * Begin Processing
      */
-    auto env = platform::DotEnv();
-    auto experiment = platform::Experiment();
-    experiment.setTitle(title).setLanguage("c++").setLanguageVersion("gcc 14.1.1");
-    experiment.setDiscretizationAlgorithm(discretize_algo).setSmoothSrategy(smooth_strat);
-    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
-    experiment.setStratified(stratified).setNFolds(n_folds).setScoreName(score);
-    experiment.setHyperparameters(test_hyperparams);
-    for (auto seed : seeds) {
-        experiment.addRandomSeed(seed);
-    }
+     // Initialize the experiment class with the command line arguments
+    auto experiment = arguments.initializedExperiment();
     platform::Timer timer;
     timer.start();
-    experiment.go(filesToTest, quiet, no_train_score, generate_fold_files, graph);
+    experiment.go();
     experiment.setDuration(timer.getDuration());
-    if (!quiet) {
+    if (!arguments.isQuiet()) {
         // Classification report if only one dataset is tested
-        experiment.report(filesToTest.size() == 1);
+        experiment.report();
     }
-    if (saveResults) {
+    if (arguments.haveToSaveResults()) {
         experiment.saveResult();
     }
-    if (graph) {
+    if (arguments.doGraph()) {
         experiment.saveGraph();
     }
-    std::cout << "Done!" << std::endl;
     return 0;
 }

src/commands/b_manage.cpp

@@ -51,6 +51,66 @@ void handleResize(int sig)
     manager->updateSize(rows, cols);
 }
 
+void openFile(const std::string& fileName)
+{
+    // #ifdef __APPLE__
+    //     // macOS uses the "open" command
+    //     std::string command = "open";
+    // #elif defined(__linux__)
+    //     // Linux typically uses "xdg-open"
+    //     std::string command = "xdg-open";
+    // #else
+    //     // For other OSes, do nothing or handle differently
+    //     std::cerr << "Unsupported platform." << std::endl;
+    //     return;
+    // #endif
+    //     execlp(command.c_str(), command.c_str(), fileName.c_str(), NULL);
+#ifdef __APPLE__
+    const char* tool = "/usr/bin/open";
+#elif defined(__linux__)
+    const char* tool = "/usr/bin/xdg-open";
+#else
+    std::cerr << "Unsupported platform." << std::endl;
+    return;
+#endif
+
+    // We'll build an argv array for execve:
+    std::vector<char*> argv;
+    argv.push_back(const_cast<char*>(tool));               // argv[0]
+    argv.push_back(const_cast<char*>(fileName.c_str()));   // argv[1]
+    argv.push_back(nullptr);
+
+    // Make a new environment array, skipping BASH_FUNC_ variables
+    std::vector<std::string> filteredEnv;
+    for (char** env = environ; *env != nullptr; ++env) {
+        // *env is a string like "NAME=VALUE"
+        // We want to skip those starting with "BASH_FUNC_"
+        if (strncmp(*env, "BASH_FUNC_", 10) == 0) {
+            // skip it
+            continue;
+        }
+        filteredEnv.push_back(*env);
+    }
+
+    // Convert filteredEnv into a char* array
+    std::vector<char*> envp;
+    for (auto& var : filteredEnv) {
+        envp.push_back(const_cast<char*>(var.c_str()));
+    }
+    envp.push_back(nullptr);
+
+    // Now call execve with the cleaned environment
+    // NOTE: You may need a full path to the tool if it's not in PATH, or use which() logic
+    // For now, let's assume "open" or "xdg-open" is found in the default PATH:
+    execve(tool, argv.data(), envp.data());
+
+    // If we reach here, execve failed
+    perror("execve failed");
+    // This would terminate your current process if it's not in a child
+    // Usually you'd do something like:
+    _exit(EXIT_FAILURE);
+}
+
 int main(int argc, char** argv)
 {
     auto program = argparse::ArgumentParser("b_manage", { platform_project_version.begin(), platform_project_version.end() });
@@ -67,6 +127,11 @@ int main(int argc, char** argv)
     auto [rows, cols] = numRowsCols();
     manager = new platform::ManageScreen(rows, cols, model, score, platform, complete, partial, compare);
     manager->doMenu();
+    auto fileName = manager->getExcelFileName();
     delete manager;
+    if (!fileName.empty()) {
+        std::cout << "Opening " << fileName << std::endl;
+        openFile(fileName);
+    }
     return 0;
 }

src/commands/b_results.cpp

@@ -0,0 +1,102 @@
+#include <iostream>
+#include <filesystem>
+#include <fstream>
+#include <vector>
+#include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
+#include "common/Paths.h"
+#include "results/JsonValidator.h"
+#include "results/SchemaV1_0.h"
+#include "config_platform.h"
+
+using json = nlohmann::json;
+namespace fs = std::filesystem;
+void header(const std::string& message, int length, const std::string& symbol)
+{
+    std::cout << std::string(length + 11, symbol[0]) << std::endl;
+    std::cout << symbol << " " << std::setw(length + 7) << std::left << message << " " << symbol << std::endl;
+    std::cout << std::string(length + 11, symbol[0]) << std::endl;
+}
+int main(int argc, char* argv[])
+{
+    argparse::ArgumentParser program("b_results", { platform_project_version.begin(), platform_project_version.end() });
+    program.add_description("Check the results files and optionally fixes them.");
+    program.add_argument("--fix").help("Fix any errors in results").default_value(false).implicit_value(true);
+    program.add_argument("--file").help("check only this results file").default_value("");
+    std::string nameSuffix = "results_";
+    std::string schemaVersion = "1.0";
+    bool fix_it = false;
+    std::string selected_file;
+    try {
+        program.parse_args(argc, argv);
+        fix_it = program.get<bool>("fix");
+        selected_file = program.get<std::string>("file");
+    }
+    catch (const std::exception& err) {
+        std::cerr << err.what() << std::endl;
+        std::cerr << program;
+        exit(1);
+    }
+    //
+    // Determine the files to process
+    //
+    std::vector<std::string> result_files;
+    int max_length = 0;
+    if (selected_file != "") {
+        if (!selected_file.starts_with(platform::Paths::results())) {
+            selected_file = platform::Paths::results() + selected_file;
+        }
+        // Only check the selected file
+        result_files.push_back(selected_file);
+        max_length = selected_file.length();
+    } else {
+        // Load the result files and find the longest file name
+        for (const auto& entry : fs::directory_iterator(platform::Paths::results())) {
+            if (entry.is_regular_file() && entry.path().filename().string().starts_with(nameSuffix) && entry.path().filename().string().ends_with(".json")) {
+                std::string fileName = entry.path().string();
+                if (fileName.length() > max_length) {
+                    max_length = fileName.length();
+                }
+                result_files.push_back(fileName);
+            }
+        }
+    }
+    //
+    // Process the results files
+    //
+    if (result_files.empty()) {
+        std::cerr << "Error: No result files found." << std::endl;
+        return 1;
+    }
+    std::string header_message = "Processing " + std::to_string(result_files.size()) + " result files.";
+    header(header_message, max_length, "*");
+    platform::JsonValidator validator(platform::SchemaV1_0::schema);
+    int n_errors = 0;
+    std::vector<std::string> files_with_errors;
+    for (const auto& file_name : result_files) {
+        std::vector<std::string> errors = validator.validate_file(file_name);
+        if (!errors.empty()) {
+            n_errors++;
+            std::cout << std::setw(max_length) << std::left << file_name << ": " << errors.size() << " Errors:" << std::endl;
+            for (const auto& error : errors) {
+                std::cout << " - " << error << std::endl;
+            }
+            if (fix_it) {
+                validator.fix_it(file_name);
+                std::cout << " -> File fixed." << std::endl;
+            }
+            files_with_errors.push_back(file_name);
+        }
+    }
+    if (n_errors == 0) {
+        header("All files are valid.", max_length, "*");
+    } else {
+        std::string $verb = (fix_it) ? "had" : "have";
+        std::string msg = std::to_string(n_errors) + " files " + $verb + " errors.";
+        header(msg, max_length, "*");
+        for (const auto& file_name : files_with_errors) {
+            std::cout << "- " << file_name << std::endl;
+        }
+    }
+    return 0;
+}

src/common/Datasets.cpp

@@ -1,4 +1,5 @@
 #include <fstream>
+#include<algorithm>
 #include "Datasets.h"
 #include <nlohmann/json.hpp>
 
@@ -24,10 +25,20 @@ namespace platform {
             throw std::invalid_argument("Unable to open catalog file. [" + path + "all.txt" + "]");
         }
         std::string line;
+        std::vector<std::string> sorted_lines;
         while (getline(catalog, line)) {
             if (line.empty() || line[0] == '#') {
                 continue;
             }
+            sorted_lines.push_back(line);
+        }
+        sort(sorted_lines.begin(), sorted_lines.end(), [](const auto& lhs, const auto& rhs) {
+            const auto result = mismatch(lhs.cbegin(), lhs.cend(), rhs.cbegin(), rhs.cend(), [](const auto& lhs, const auto& rhs) {return tolower(lhs) == tolower(rhs);});
+
+            return result.second != rhs.cend() && (result.first == lhs.cend() || tolower(*result.first) < tolower(*result.second));
+            });
+
+        for (const auto& line : sorted_lines) {
             std::vector<std::string> tokens = split(line, ';');
             std::string name = tokens[0];
             std::string className;
@@ -70,6 +81,11 @@ namespace platform {
     {
         std::vector<std::string> result;
         transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
+        sort(result.begin(), result.end(), [](const auto& lhs, const auto& rhs) {
+            const auto result = mismatch(lhs.cbegin(), lhs.cend(), rhs.cbegin(), rhs.cend(), [](const auto& lhs, const auto& rhs) {return tolower(lhs) == tolower(rhs);});
+
+            return result.second != rhs.cend() && (result.first == lhs.cend() || tolower(*result.first) < tolower(*result.second));
+            });
         return result;
     }
     bool Datasets::isDataset(const std::string& name) const

src/experimental_clfs/CountingSemaphore.hpp

@@ -0,0 +1,53 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+    uint getCount() const
+    {
+        return count_;
+    }
+    uint getMaxCount() const
+    {
+        return max_count_;
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

src/experimental_clfs/ExpClf.cpp

@@ -0,0 +1,182 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "ExpClf.h"
+#include "TensorUtils.hpp"
+
+namespace platform {
+    ExpClf::ExpClf() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)
+    {
+        validHyperparameters = {};
+    }
+    //
+    // Parents
+    //
+    void ExpClf::add_active_parents(const std::vector<int>& active_parents)
+    {
+        for (const auto& parent : active_parents)
+            aode_.add_active_parent(parent);
+    }
+    void ExpClf::add_active_parent(int parent)
+    {
+        aode_.add_active_parent(parent);
+    }
+    void ExpClf::remove_last_parent()
+    {
+        aode_.remove_last_parent();
+    }
+    //
+    // Predict
+    //
+    std::vector<int> ExpClf::predict_spode(std::vector<std::vector<int>>& test_data, int parent)
+    {
+        int test_size = test_data[0].size();
+        int sample_size = test_data.size();
+        auto predictions = std::vector<int>(test_size);
+
+        int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+        std::vector<std::thread> threads;
+        auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<int>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<int> instance(sample_size);
+            for (int sample = begin; sample < begin + chunk; ++sample) {
+                for (int feature = 0; feature < sample_size; ++feature) {
+                    instance[feature] = samples[feature][sample];
+                }
+                predictions[sample] = aode_.predict_spode(instance, parent);
+            }
+            semaphore_.release();
+            };
+        for (int begin = 0; begin < test_size; begin += chunk_size) {
+            int chunk = std::min(chunk_size, test_size - begin);
+            semaphore_.acquire();
+            threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(predictions));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return predictions;
+    }
+    torch::Tensor ExpClf::predict(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        torch::Tensor y = torch::tensor(predict(X_));
+        return y;
+    }
+    torch::Tensor ExpClf::predict_proba(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto probabilities = predict_proba(X_);
+        auto n_samples = X.size(1);
+        int n_classes = probabilities[0].size();
+        auto y = torch::zeros({ n_samples, n_classes });
+        for (int i = 0; i < n_samples; i++) {
+            for (int j = 0; j < n_classes; j++) {
+                y[i][j] = probabilities[i][j];
+            }
+        }
+        return y;
+    }
+    float ExpClf::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto y_ = TensorUtils::to_vector<int>(y);
+        return score(X_, y_);
+    }
+    std::vector<std::vector<double>> ExpClf::predict_proba(const std::vector<std::vector<int>>& test_data)
+    {
+        int test_size = test_data[0].size();
+        int sample_size = test_data.size();
+        auto probabilities = std::vector<std::vector<double>>(test_size, std::vector<double>(aode_.statesClass()));
+
+        int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+        std::vector<std::thread> threads;
+        auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<std::vector<double>>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+
+            std::vector<int> instance(sample_size);
+            for (int sample = begin; sample < begin + chunk; ++sample) {
+                for (int feature = 0; feature < sample_size; ++feature) {
+                    instance[feature] = samples[feature][sample];
+                }
+                predictions[sample] = aode_.predict_proba(instance);
+            }
+            semaphore_.release();
+            };
+        for (int begin = 0; begin < test_size; begin += chunk_size) {
+            int chunk = std::min(chunk_size, test_size - begin);
+            semaphore_.acquire();
+            threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return probabilities;
+    }
+    std::vector<int> ExpClf::predict(std::vector<std::vector<int>>& test_data)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto probabilities = predict_proba(test_data);
+        std::vector<int> predictions(probabilities.size(), 0);
+
+        for (size_t i = 0; i < probabilities.size(); i++) {
+            predictions[i] = std::distance(probabilities[i].begin(), std::max_element(probabilities[i].begin(), probabilities[i].end()));
+        }
+
+        return predictions;
+    }
+    float ExpClf::score(std::vector<std::vector<int>>& test_data, std::vector<int>& labels)
+    {
+        Timer timer;
+        timer.start();
+        std::vector<int> predictions = predict(test_data);
+        int correct = 0;
+
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == labels[i]) {
+                correct++;
+            }
+        }
+        if (debug) {
+            std::cout << "* Time to predict: " << timer.getDurationString() << std::endl;
+        }
+        return static_cast<float>(correct) / predictions.size();
+    }
+
+    //
+    // statistics
+    //
+    int ExpClf::getNumberOfNodes() const
+    {
+        return aode_.getNumberOfNodes();
+    }
+    int ExpClf::getNumberOfEdges() const
+    {
+        return aode_.getNumberOfEdges();
+    }
+    int ExpClf::getNumberOfStates() const
+    {
+        return aode_.getNumberOfStates();
+    }
+    int ExpClf::getClassNumStates() const
+    {
+        return aode_.statesClass();
+    }
+
+
+}

src/experimental_clfs/ExpClf.h

@@ -0,0 +1,66 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef EXPCLF_H
+#define EXPCLF_H
+#include <vector>
+#include <string>
+#include <cmath>
+#include <algorithm>
+#include <limits>
+#include <bayesnet/ensembles/Boost.h>
+#include <bayesnet/network/Smoothing.h>
+#include "common/Timer.hpp"
+#include "CountingSemaphore.hpp"
+#include "Xaode.hpp"
+
+namespace platform {
+    class ExpClf : public bayesnet::Boost {
+    public:
+        ExpClf();
+        virtual ~ExpClf() = default;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<int> predict_spode(std::vector<std::vector<int>>& test_data, int parent);
+        std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>& X);
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        int getClassNumStates() const override;
+        std::vector<std::string> show() const override { return {}; }
+        std::vector<std::string> topological_order()  override { return {}; }
+        std::string dump_cpt() const override { return ""; }
+        void setDebug(bool debug) { this->debug = debug; }
+        bayesnet::status_t getStatus() const override { return status; }
+        std::vector<std::string> getNotes() const override { return notes; }
+        std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
+        void add_active_parents(const std::vector<int>& active_parents);
+        void add_active_parent(int parent);
+        void remove_last_parent();
+    protected:
+        bool debug = false;
+        Xaode aode_;
+        torch::Tensor weights_;
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+        inline void normalize_weights(int num_instances)
+        {
+            double sum = weights_.sum().item<double>();
+            if (sum == 0) {
+                weights_ = torch::full({ num_instances }, 1.0);
+            } else {
+                for (int i = 0; i < weights_.size(0); ++i) {
+                    weights_[i] = weights_[i].item<double>() * num_instances / sum;
+                }
+            }
+        }
+    private:
+        CountingSemaphore& semaphore_;
+    };
+}
+#endif // EXPCLF_H

src/experimental_clfs/ExpEnsemble.cpp

@@ -0,0 +1,158 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "ExpEnsemble.h"
+#include "TensorUtils.hpp"
+
+namespace platform {
+    ExpEnsemble::ExpEnsemble() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)
+    {
+        validHyperparameters = {};
+    }
+    //
+    // Parents
+    //
+    void ExpEnsemble::add_model(std::unique_ptr<XSpode> model)
+    {
+        models.push_back(std::move(model));
+        n_models++;
+    }
+    void ExpEnsemble::remove_last_model()
+    {
+        models.pop_back();
+        n_models--;
+    }
+    //
+    // Predict
+    //
+    torch::Tensor ExpEnsemble::predict(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        torch::Tensor y = torch::tensor(predict(X_));
+        return y;
+    }
+    torch::Tensor ExpEnsemble::predict_proba(torch::Tensor& X)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto probabilities = predict_proba(X_);
+        auto n_samples = X.size(1);
+        int n_classes = probabilities[0].size();
+        auto y = torch::zeros({ n_samples, n_classes });
+        for (int i = 0; i < n_samples; i++) {
+            for (int j = 0; j < n_classes; j++) {
+                y[i][j] = probabilities[i][j];
+            }
+        }
+        return y;
+    }
+    float ExpEnsemble::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        auto X_ = TensorUtils::to_matrix(X);
+        auto y_ = TensorUtils::to_vector<int>(y);
+        return score(X_, y_);
+    }
+    std::vector<std::vector<double>> ExpEnsemble::predict_proba(const std::vector<std::vector<int>>& test_data)
+    {
+        int test_size = test_data[0].size();
+        int sample_size = test_data.size();
+        auto probabilities = std::vector<std::vector<double>>(test_size, std::vector<double>(getClassNumStates()));
+        int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+        std::vector<std::thread> threads;
+        auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<std::vector<double>>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+
+            std::vector<int> instance(sample_size);
+            for (int sample = begin; sample < begin + chunk; ++sample) {
+                for (int feature = 0; feature < sample_size; ++feature) {
+                    instance[feature] = samples[feature][sample];
+                }
+                // predictions[sample] = aode_.predict_proba(instance);
+            }
+            semaphore_.release();
+            };
+        for (int begin = 0; begin < test_size; begin += chunk_size) {
+            int chunk = std::min(chunk_size, test_size - begin);
+            semaphore_.acquire();
+            threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+        }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        return probabilities;
+    }
+    std::vector<int> ExpEnsemble::predict(std::vector<std::vector<int>>& test_data)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto probabilities = predict_proba(test_data);
+        std::vector<int> predictions(probabilities.size(), 0);
+
+        for (size_t i = 0; i < probabilities.size(); i++) {
+            predictions[i] = std::distance(probabilities[i].begin(), std::max_element(probabilities[i].begin(), probabilities[i].end()));
+        }
+
+        return predictions;
+    }
+    float ExpEnsemble::score(std::vector<std::vector<int>>& test_data, std::vector<int>& labels)
+    {
+        Timer timer;
+        timer.start();
+        std::vector<int> predictions = predict(test_data);
+        int correct = 0;
+
+        for (size_t i = 0; i < predictions.size(); i++) {
+            if (predictions[i] == labels[i]) {
+                correct++;
+            }
+        }
+        if (debug) {
+            std::cout << "* Time to predict: " << timer.getDurationString() << std::endl;
+        }
+        return static_cast<float>(correct) / predictions.size();
+    }
+
+    //
+    // statistics
+    //
+    int ExpEnsemble::getNumberOfNodes() const
+    {
+        if (models_.empty()) {
+            return 0;
+        }
+        return n_models * (models_.at(0)->getNFeatures() + 1);
+    }
+    int ExpEnsemble::getNumberOfEdges() const
+    {
+        if (models_.empty()) {
+            return 0;
+        }
+        return n_models * (2 * models_.at(0)->getNFeatures() - 1);
+    }
+    int ExpEnsemble::getNumberOfStates() const
+    {
+        if (models_.empty()) {
+            return 0;
+        }
+        auto states = models_.at(0)->getStates();
+        int nFeatures = models_.at(0)->getNFeatures();
+        return std::accumulate(states.begin(), states.end(), 0) * nFeatures * n_models;
+    }
+    int ExpEnsemble::getClassNumStates() const
+    {
+        if (models_.empty()) {
+            return 0;
+        }
+        return models_.at(0)->statesClass();
+    }
+
+
+}

src/experimental_clfs/ExpEnsemble.h

@@ -0,0 +1,66 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef EXPENSEMBLE_H
+#define EXPENSEMBLE_H
+#include <vector>
+#include <string>
+#include <cmath>
+#include <algorithm>
+#include <limits>
+#include <bayesnet/ensembles/Boost.h>
+#include <bayesnet/network/Smoothing.h>
+#include "common/Timer.hpp"
+#include "CountingSemaphore.hpp"
+#include "XSpode.hpp"
+
+namespace platform {
+    class ExpEnsemble : public bayesnet::Boost {
+    public:
+        ExpEnsemble();
+        virtual ~ExpEnsemble() = default;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        std::vector<int> predict_spode(std::vector<std::vector<int>>& test_data, int parent);
+        std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>& X);
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        int getClassNumStates() const override;
+        std::vector<std::string> show() const override { return {}; }
+        std::vector<std::string> topological_order()  override { return {}; }
+        std::string dump_cpt() const override { return ""; }
+        void setDebug(bool debug) { this->debug = debug; }
+        bayesnet::status_t getStatus() const override { return status; }
+        std::vector<std::string> getNotes() const override { return notes; }
+        std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
+    protected:
+        void add_model(std::unique_ptr<XSpode> model);
+        void remove_last_model();
+        bool debug = false;
+        std::vector <std::unique_ptr<XSpode>> models_;
+        torch::Tensor weights_;
+        std::vector<double> significanceModels_;
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+        inline void normalize_weights(int num_instances)
+        {
+            double sum = weights_.sum().item<double>();
+            if (sum == 0) {
+                weights_ = torch::full({ num_instances }, 1.0);
+            } else {
+                for (int i = 0; i < weights_.size(0); ++i) {
+                    weights_[i] = weights_[i].item<double>() * num_instances / sum;
+                }
+            }
+        }
+    private:
+        CountingSemaphore& semaphore_;
+    };
+}
+#endif // EXPENSEMBLE_H

src/experimental_clfs/TensorUtils.hpp

@@ -0,0 +1,51 @@
+#ifndef TENSORUTILS_HPP
+#define TENSORUTILS_HPP
+#include <torch/torch.h>
+#include <vector>
+namespace platform {
+    class TensorUtils {
+    public:
+        static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
+        {
+            // Ensure tensor is contiguous in memory
+            auto X_contig = X.contiguous();
+
+            // Access tensor data pointer directly
+            auto data_ptr = X_contig.data_ptr<int>();
+
+            // IF you are using int64_t as the data type, use the following line
+            //auto data_ptr = X_contig.data_ptr<int64_t>();
+            //std::vector<std::vector<int64_t>> data(X.size(0), std::vector<int64_t>(X.size(1)));
+
+            // Prepare output container
+            std::vector<std::vector<int>> data(X.size(0), std::vector<int>(X.size(1)));
+
+            // Fill the 2D vector in a single loop using pointer arithmetic
+            int rows = X.size(0);
+            int cols = X.size(1);
+            for (int i = 0; i < rows; ++i) {
+                std::copy(data_ptr + i * cols, data_ptr + (i + 1) * cols, data[i].begin());
+            }
+            return data;
+        }
+        template <typename T>
+        static std::vector<T> to_vector(const torch::Tensor& y)
+        {
+            // Ensure the tensor is contiguous in memory
+            auto y_contig = y.contiguous();
+
+            // Access data pointer
+            auto data_ptr = y_contig.data_ptr<T>();
+
+            // Prepare output container
+            std::vector<T> data(y.size(0));
+
+            // Copy data efficiently
+            std::copy(data_ptr, data_ptr + y.size(0), data.begin());
+
+            return data;
+        }
+    };
+}
+
+#endif // TENSORUTILS_HPP

src/experimental_clfs/XA1DE.cpp

@@ -0,0 +1,20 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "XA1DE.h"
+#include "TensorUtils.hpp"
+
+namespace platform {
+    void XA1DE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
+    {
+        auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1));
+        auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." }));
+        int num_instances = X[0].size();
+        weights_ = torch::full({ num_instances }, 1.0);
+        //normalize_weights(num_instances);
+        aode_.fit(X, y, features, className, states, weights_, true, smoothing);
+    }
+}

src/experimental_clfs/XA1DE.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XA1DE_H
+#define XA1DE_H
+#include "Xaode.hpp"
+#include "ExpClf.h"
+#include <bayesnet/network/Smoothing.h>
+
+namespace platform {
+    class XA1DE : public ExpClf {
+    public:
+        XA1DE() = default;
+        virtual ~XA1DE() override = default;
+        std::string getVersion() override { return version; };
+    protected:
+        void buildModel(const torch::Tensor& weights) override {};
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        std::string version = "1.0.0";
+    };
+}
+#endif // XA1DE_H

src/experimental_clfs/XBAODE.cpp

@@ -0,0 +1,183 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <random> 
+#include <set>
+#include <functional>
+#include <limits.h>
+#include <tuple>
+#include "XBAODE.h"
+#include "XSpode.hpp"
+#include "TensorUtils.hpp"
+#include <loguru.hpp>
+
+namespace platform {
+    XBAODE::XBAODE()
+    {
+        validHyperparameters = { "alpha_block", "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
+            "predict_voting", "select_features" };
+    }
+    void XBAODE::add_model(std::unique_ptr<XSpode> model)
+    {
+        models.push_back(std::move(model));
+        n_models++;
+    }
+    void XBAODE::remove_last_model()
+    {
+        models.pop_back();
+        n_models--;
+    }
+    void XBAODE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
+    {
+        fitted = true;
+        X_train_ = TensorUtils::to_matrix(X_train);
+        y_train_ = TensorUtils::to_vector<int>(y_train);
+        X_test_ = TensorUtils::to_matrix(X_test);
+        y_test_ = TensorUtils::to_vector<int>(y_test);
+        maxTolerance = 3;
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("XBAODE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("XBAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
+        double alpha_t = 0;
+        weights_ = torch::full({ m }, 1.0 / static_cast<double>(m), torch::kFloat64); // m initialized in Classifier.cc
+        significanceModels.resize(n, 0.0); // n initialized in Classifier.cc
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        n_models = 0;
+        std::unique_ptr<XSpode> model;
+        if (selectFeatures) {
+            featuresUsed = featureSelection(weights_);
+            for (const auto& parent : featuresUsed) {
+                model = std::unique_ptr<XSpode>(new XSpode(parent));
+                model->fit(X_train_, y_train_, weights_, smoothing);
+                std::cout << model->getNFeatures() << std::endl;
+                add_model(std::move(model));
+            }
+            notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
+            auto ypred = ExpEnsemble::predict(X_train);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            // Update significance of the models
+            for (const auto& parent : featuresUsed) {
+                significanceModels_[parent] = alpha_t;
+            }
+            n_models = featuresUsed.size();
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == bayesnet::Orders.ASC;
+        std::mt19937 g{ 173 };
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
+            if (order_algorithm == bayesnet::Orders.RAND) {
+                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
+            }
+            // Remove used features
+            featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(), [&](auto x)
+                { return std::find(featuresUsed.begin(), featuresUsed.end(), x) != featuresUsed.end();}),
+                featureSelection.end()
+            );
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                model = std::unique_ptr<XSpode>(new XSpode(feature));
+                model->fit(X_train_, y_train_, weights_, smoothing);
+                std::vector<int> ypred;
+                if (alpha_block) {
+                    //
+                    // Compute the prediction with the current ensemble + model
+                    //
+                    // Add the model to the ensemble
+                    significanceModels[feature] = 1.0;
+                    add_model(std::move(model));
+                    // Compute the prediction
+                    ypred = ExpEnsemble::predict(X_train_);
+                    // Remove the model from the ensemble
+                    significanceModels[feature] = 0.0;
+                    model = std::move(models_.back());
+                    remove_last_model();
+                } else {
+                    ypred = model->predict(X_train_);
+                }
+                // Step 3.1: Compute the classifier amout of say
+                auto ypred_t = torch::tensor(ypred);
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                add_model(std::move(model));
+                significanceModels[feature] = alpha_t;
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
+            } // End of the pack
+            if (convergence && !finished) {
+                auto y_val_predict = ExpEnsemble::predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
+                    remove_last_model();
+                    significanceModels[featuresUsed[i]] = 0.0;
+                }
+                // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features used.", n_models, featuresUsed.size());
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (featuresUsed.size() != features.size()) {
+            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
+            status = bayesnet::WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+        return;
+    }
+}

src/experimental_clfs/XBAODE.h

@@ -0,0 +1,35 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XBAODE_H
+#define XBAODE_H
+#include <iostream>
+#include <vector>
+#include <cmath>
+#include <algorithm>
+#include <limits>
+#include "common/Timer.hpp"
+#include "ExpEnsemble.h"
+
+namespace platform {
+    class XBAODE : public Boost {
+
+        // Hay que hacer un vector de modelos entrenados y hacer un predict ensemble con todos ellos
+        // Probar XA1DE con smooth original y laplace y comprobar diferencias si se pasan pesos a 1 o a 1/m
+    public:
+        XBAODE();
+        std::string getVersion() override { return version; };
+    protected:
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        void add_model(std::unique_ptr<XSpode> model);
+        void remove_last_model();
+        std::vector<std::vector<int>> X_train_, X_test_;
+        std::vector<int> y_train_, y_test_;
+        std::string version = "0.9.7";
+    };
+}
+#endif // XBAODE_H

src/experimental_clfs/XSpode.hpp

@@ -0,0 +1,436 @@
+#ifndef XSPODE_H
+#define XSPODE_H
+
+#include <vector>
+#include <map>
+#include <stdexcept>
+#include <algorithm>
+#include <numeric>
+#include <string>
+#include <cmath>
+#include <limits>
+#include <sstream>
+#include <iostream>
+#include <torch/torch.h>
+#include <bayesnet/network/Smoothing.h>
+#include <bayesnet/classifiers/Classifier.h>
+#include "CountingSemaphore.hpp"
+
+
+namespace platform {
+
+    class XSpode : public bayesnet::Classifier {
+    public:
+        // --------------------------------------
+        // Constructor
+        //
+        // Supply which feature index is the single super-parent (“spIndex”).
+        // --------------------------------------
+        explicit XSpode(int spIndex)
+            : superParent_{ spIndex },
+            nFeatures_{ 0 },
+            statesClass_{ 0 },
+            fitted_{ false },
+            alpha_{ 1.0 },
+            initializer_{ 1.0 },
+            semaphore_{ CountingSemaphore::getInstance() } : bayesnet::Classifier(bayesnet::Network())
+        {
+        }
+
+        // --------------------------------------
+        // fit
+        // --------------------------------------
+        //
+        // Trains the SPODE given data:
+        //   X: X[f][n] is the f-th feature value for instance n
+        //   y: y[n] is the class value for instance n
+        //   states: a map or array that tells how many distinct states each feature and the class can take
+        //
+        // For example, states_.back() is the number of class states,
+        // and states_[f] is the number of distinct values for feature f.
+        //
+        // We only store conditional probabilities for:
+        //   p(x_sp| c)   (the super-parent feature)
+        //   p(x_child| c, x_sp)  for all child ≠ sp
+        //
+        // The “weights” can be a vector of per-instance weights; if not used, pass them as 1.0.
+        // --------------------------------------
+        void fit(const std::vector<std::vector<int>>& X,
+            const std::vector<int>& y,
+            const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
+        {
+            int numInstances = static_cast<int>(y.size());
+            nFeatures_ = static_cast<int>(X.size());
+
+            // Derive the number of states for each feature and for the class.
+            // (This is just one approach; adapt to match your environment.)
+            // Here, we assume the user also gave us the total #states per feature in e.g. statesMap.
+            // We'll simply reconstruct the integer states_ array. The last entry is statesClass_.
+            states_.resize(nFeatures_);
+            for (int f = 0; f < nFeatures_; f++) {
+                // Suppose you look up in “statesMap” by the feature name, or read directly from X.
+                // We'll assume states_[f] = max value in X[f] + 1.
+                auto maxIt = std::max_element(X[f].begin(), X[f].end());
+                states_[f] = (*maxIt) + 1;
+            }
+            // For the class: states_.back() = max(y)+1
+            statesClass_ = (*std::max_element(y.begin(), y.end())) + 1;
+
+            // Initialize counts
+            classCounts_.resize(statesClass_, 0.0);
+            // p(x_sp = spVal | c)
+            // We'll store these counts in spFeatureCounts_[spVal * statesClass_ + c].
+            spFeatureCounts_.resize(states_[superParent_] * statesClass_, 0.0);
+
+            // For each child ≠ sp, we store p(childVal| c, spVal) in a separate block of childCounts_.
+            // childCounts_ will be sized as sum_{child≠sp} (states_[child] * statesClass_ * states_[sp]).
+            // We also need an offset for each child to index into childCounts_.
+            childOffsets_.resize(nFeatures_, -1);
+            int totalSize = 0;
+            for (int f = 0; f < nFeatures_; f++) {
+                if (f == superParent_) continue; // skip sp
+                childOffsets_[f] = totalSize;
+                // block size for this child's counts: states_[f] * statesClass_ * states_[superParent_]
+                totalSize += (states_[f] * statesClass_ * states_[superParent_]);
+            }
+            childCounts_.resize(totalSize, 0.0);
+
+            // Accumulate raw counts
+            for (int n = 0; n < numInstances; n++) {
+                std::vector<int> instance(nFeatures_ + 1);
+                for (int f = 0; f < nFeatures_; f++) {
+                    instance[f] = X[f][n];
+                }
+                instance[nFeatures_] = y[n];
+                addSample(instance, weights[n].item<double>());
+            }
+
+            switch (smoothing) {
+                case bayesnet::Smoothing_t::ORIGINAL:
+                    alpha_ = 1.0 / numInstances;
+                    break;
+                case bayesnet::Smoothing_t::LAPLACE:
+                    alpha_ = 1.0;
+                    break;
+                default:
+                    alpha_ = 0.0; // No smoothing 
+            }
+            initializer_ = initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
+            // Convert raw counts to probabilities
+            computeProbabilities();
+            fitted_ = true;
+        }
+
+        // --------------------------------------
+        // addSample (only valid in COUNTS mode)
+        // --------------------------------------
+        //
+        // instance has size nFeatures_ + 1, with the class at the end.
+        // We add 1 to the appropriate counters for each (c, superParentVal, childVal).
+        //
+        void addSample(const std::vector<int>& instance, double weight)
+        {
+            if (weight <= 0.0) return;
+
+            int c = instance.back();
+            // (A) increment classCounts
+            classCounts_[c] += weight;
+
+            // (B) increment super-parent counts => p(x_sp | c)
+            int spVal = instance[superParent_];
+            spFeatureCounts_[spVal * statesClass_ + c] += weight;
+
+            // (C) increment child counts => p(childVal | c, x_sp)
+            for (int f = 0; f < nFeatures_; f++) {
+                if (f == superParent_) continue;
+                int childVal = instance[f];
+                int offset = childOffsets_[f];
+                // Compute index in childCounts_.
+                // Layout: [ offset + (spVal * states_[f] + childVal) * statesClass_ + c ]
+                int blockSize = states_[f] * statesClass_;
+                int idx = offset + spVal * blockSize + childVal * statesClass_ + c;
+                childCounts_[idx] += weight;
+            }
+        }
+
+        // --------------------------------------
+        // computeProbabilities
+        // --------------------------------------
+        //
+        // Once all samples are added in COUNTS mode, call this to:
+        //    p(c)
+        //    p(x_sp = spVal | c)
+        //    p(x_child = v | c, x_sp = s_sp)
+        //
+        // We store them in the corresponding *Probs_ arrays for inference.
+        // --------------------------------------
+        void computeProbabilities()
+        {
+            double totalCount = std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
+
+            // p(c) => classPriors_
+            classPriors_.resize(statesClass_, 0.0);
+            if (totalCount <= 0.0) {
+                // fallback => uniform
+                double unif = 1.0 / static_cast<double>(statesClass_);
+                for (int c = 0; c < statesClass_; c++) {
+                    classPriors_[c] = unif;
+                }
+            } else {
+                for (int c = 0; c < statesClass_; c++) {
+                    classPriors_[c] = (classCounts_[c] + alpha_)
+                        / (totalCount + alpha_ * statesClass_);
+                }
+            }
+
+            // p(x_sp | c)
+            spFeatureProbs_.resize(spFeatureCounts_.size());
+            // denominator for spVal * statesClass_ + c is just classCounts_[c] + alpha_ * (#states of sp)
+            int spCard = states_[superParent_];
+            for (int spVal = 0; spVal < spCard; spVal++) {
+                for (int c = 0; c < statesClass_; c++) {
+                    double denom = classCounts_[c] + alpha_ * spCard;
+                    double num = spFeatureCounts_[spVal * statesClass_ + c] + alpha_;
+                    spFeatureProbs_[spVal * statesClass_ + c] = (denom <= 0.0 ? 0.0 : num / denom);
+                }
+            }
+
+            // p(x_child | c, x_sp)
+            childProbs_.resize(childCounts_.size());
+            for (int f = 0; f < nFeatures_; f++) {
+                if (f == superParent_) continue;
+                int offset = childOffsets_[f];
+                int childCard = states_[f];
+
+                // For each spVal, c, childVal in childCounts_:
+                for (int spVal = 0; spVal < spCard; spVal++) {
+                    for (int childVal = 0; childVal < childCard; childVal++) {
+                        for (int c = 0; c < statesClass_; c++) {
+                            int idx = offset + spVal * (childCard * statesClass_)
+                                + childVal * statesClass_
+                                + c;
+
+                            double num = childCounts_[idx] + alpha_;
+                            // denominator = spFeatureCounts_[spVal * statesClass_ + c] + alpha_ * (#states of child)
+                            double denom = spFeatureCounts_[spVal * statesClass_ + c]
+                                + alpha_ * childCard;
+                            childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
+                        }
+                    }
+                }
+            }
+
+        }
+
+        // --------------------------------------
+        // predict_proba
+        // --------------------------------------
+        //
+        // For a single instance x of dimension nFeatures_:
+        //  P(c | x) ∝ p(c) × p(x_sp | c) × ∏(child ≠ sp) p(x_child | c, x_sp).
+        //
+        // Then we normalize the result.
+        // --------------------------------------
+        std::vector<double> predict_proba(const std::vector<int>& instance) const
+        {
+            std::vector<double> probs(statesClass_, 0.0);
+
+            // Multiply p(c) × p(x_sp | c)
+            int spVal = instance[superParent_];
+            for (int c = 0; c < statesClass_; c++) {
+                double pc = classPriors_[c];
+                double pSpC = spFeatureProbs_[spVal * statesClass_ + c];
+                probs[c] = pc * pSpC * initializer_;
+            }
+
+            // Multiply by each child’s probability p(x_child | c, x_sp)
+            for (int feature = 0; feature < nFeatures_; feature++) {
+                if (feature == superParent_) continue;  // skip sp
+                int sf = instance[feature];
+                int offset = childOffsets_[feature];
+                int childCard = states_[feature]; // not used directly, but for clarity
+                // Index into childProbs_ = offset + spVal*(childCard*statesClass_) + childVal*statesClass_ + c
+                int base = offset + spVal * (childCard * statesClass_) + sf * statesClass_;
+                for (int c = 0; c < statesClass_; c++) {
+                    probs[c] *= childProbs_[base + c];
+                }
+            }
+
+            // Normalize
+            normalize(probs);
+            return probs;
+        }
+        std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>& test_data)
+        {
+            int test_size = test_data[0].size();
+            int sample_size = test_data.size();
+            auto probabilities = std::vector<std::vector<double>>(test_size, std::vector<double>(statesClass_));
+
+            int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+            std::vector<std::thread> threads;
+            auto worker = [&](const std::vector<std::vector<int>>& samples, int begin, int chunk, int sample_size, std::vector<std::vector<double>>& predictions) {
+                std::string threadName = "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+                pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+                pthread_setname_np(threadName.c_str());
+#endif
+
+                std::vector<int> instance(sample_size);
+                for (int sample = begin; sample < begin + chunk; ++sample) {
+                    for (int feature = 0; feature < sample_size; ++feature) {
+                        instance[feature] = samples[feature][sample];
+                    }
+                    predictions[sample] = predict_proba(instance);
+                }
+                semaphore_.release();
+                };
+            for (int begin = 0; begin < test_size; begin += chunk_size) {
+                int chunk = std::min(chunk_size, test_size - begin);
+                semaphore_.acquire();
+                threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+            }
+            for (auto& thread : threads) {
+                thread.join();
+            }
+            return probabilities;
+        }
+
+        // --------------------------------------
+        // predict
+        // --------------------------------------
+        //
+        // Return the class argmax( P(c|x) ).
+        // --------------------------------------
+        int predict(const std::vector<int>& instance) const
+        {
+            auto p = predict_proba(instance);
+            return static_cast<int>(std::distance(p.begin(),
+                std::max_element(p.begin(), p.end())));
+        }
+        std::vector<int> predict(std::vector<std::vector<int>>& test_data)
+        {
+            if (!fitted_) {
+                throw std::logic_error(CLASSIFIER_NOT_FITTED);
+            }
+            auto probabilities = predict_proba(test_data);
+            std::vector<int> predictions(probabilities.size(), 0);
+
+            for (size_t i = 0; i < probabilities.size(); i++) {
+                predictions[i] = std::distance(probabilities[i].begin(), std::max_element(probabilities[i].begin(), probabilities[i].end()));
+            }
+
+            return predictions;
+        }
+
+        // --------------------------------------
+        // Utility: normalize
+        // --------------------------------------
+        void normalize(std::vector<double>& v) const
+        {
+            double sum = 0.0;
+            for (auto val : v) { sum += val; }
+            if (sum <= 0.0) {
+                return;
+            }
+            for (auto& val : v) {
+                val /= sum;
+            }
+        }
+
+        // --------------------------------------
+        // debug printing, if desired
+        // --------------------------------------
+        std::string to_string() const
+        {
+            std::ostringstream oss;
+            oss << "---- SPODE Model ----\n"
+                << "nFeatures_  = " << nFeatures_ << "\n"
+                << "superParent_ = " << superParent_ << "\n"
+                << "statesClass_ = " << statesClass_ << "\n"
+                << "\n";
+
+            oss << "States: [";
+            for (int s : states_) oss << s << " ";
+            oss << "]\n";
+
+            oss << "classCounts_: [";
+            for (double c : classCounts_) oss << c << " ";
+            oss << "]\n";
+
+            oss << "classPriors_: [";
+            for (double c : classPriors_) oss << c << " ";
+            oss << "]\n";
+
+            oss << "spFeatureCounts_: size = " << spFeatureCounts_.size() << "\n[";
+            for (double c : spFeatureCounts_) oss << c << " ";
+            oss << "]\n";
+
+            oss << "spFeatureProbs_: size = " << spFeatureProbs_.size() << "\n[";
+            for (double c : spFeatureProbs_) oss << c << " ";
+            oss << "]\n";
+
+            oss << "childCounts_: size = " << childCounts_.size() << "\n[";
+            for (double cc : childCounts_) oss << cc << " ";
+            oss << "]\n";
+
+            oss << "childProbs_: size = " << childProbs_.size() << "\n[";
+            for (double cp : childProbs_) oss << cp << " ";
+            oss << "]\n";
+
+            oss << "childOffsets_: [";
+            for (int co : childOffsets_) oss << co << " ";
+            oss << "]\n";
+
+            oss << "---------------------\n";
+            return oss.str();
+        }
+        int statesClass() const { return statesClass_; }
+        int getNFeatures() const { return nFeatures_; }
+        int getNumberOfStates() const
+        {
+            return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+        }
+        int getNumberOfEdges() const
+        {
+            return nFeatures_ * (2 * nFeatures_ - 1);
+        }
+        std::vector<int>& getStates() { return states_; }
+
+    private:
+        // --------------------------------------
+        // MEMBERS
+        // --------------------------------------
+
+        int superParent_;                  // which feature is the single super-parent
+        int nFeatures_;
+        int statesClass_;
+        bool fitted_ = false;
+        std::vector<int> states_;          // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
+
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+
+        // Class counts
+        std::vector<double> classCounts_;  // [c], accumulative
+        std::vector<double> classPriors_;  // [c], after normalization
+
+        // For p(x_sp = spVal | c)
+        std::vector<double> spFeatureCounts_; // [spVal * statesClass_ + c]
+        std::vector<double> spFeatureProbs_;  // same shape, after normalization
+
+        // For p(x_child = childVal | x_sp = spVal, c)
+        // childCounts_ is big enough to hold all child features except sp:
+        //   For each child f, we store childOffsets_[f] as the start index, then
+        //   childVal, spVal, c => the data.
+        std::vector<double> childCounts_;
+        std::vector<double> childProbs_;
+        std::vector<int>    childOffsets_;
+
+        double alpha_ = 1.0;
+        double initializer_; // for numerical stability
+        CountingSemaphore& semaphore_;
+    };
+
+} // namespace platform
+
+#endif // XSPODE_H

src/experimental_clfs/Xaode.hpp

@@ -0,0 +1,478 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+// Based on the Geoff. I. Webb A1DE java algorithm
+// https://weka.sourceforge.io/packageMetaData/AnDE/Latest.html
+
+#ifndef XAODE_H
+#define XAODE_H
+#include <vector>
+#include <map>
+#include <stdexcept>
+#include <algorithm>
+#include <numeric>
+#include <string>
+#include <cmath>
+#include <limits>
+#include <sstream>
+#include <torch/torch.h>
+#include <bayesnet/network/Smoothing.h>
+
+
+namespace platform {
+    class Xaode {
+    public:
+        // -------------------------------------------------------
+        // The Xaode can be EMPTY (just created), in COUNTS mode (accumulating raw counts)
+        // or PROBS mode (storing conditional probabilities).
+        enum class MatrixState {
+            EMPTY,
+            COUNTS,
+            PROBS
+        };
+        std::vector<double> significance_models_;
+        Xaode() : nFeatures_{ 0 }, statesClass_{ 0 }, matrixState_{ MatrixState::EMPTY } {}
+        // -------------------------------------------------------
+        // fit
+        // -------------------------------------------------------
+        //
+        // Classifiers interface
+        // all parameter decide if the model is initialized with all the parents active or none of them
+        //
+        // states.size() = nFeatures + 1,
+        //   where states.back() = number of class states.
+        //
+        // We'll store:
+        //  1) p(x_i=si | c) in classFeatureProbs_
+        //  2) p(x_j=sj | c, x_i=si) in data_, with i<j => i is "superparent," j is "child."
+        //
+        // Internally, in COUNTS mode, data_ accumulates raw counts, then
+        // computeProbabilities(...) normalizes them into conditionals.
+        void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bool all_parents, const bayesnet::Smoothing_t smoothing)
+        {
+            int num_instances = X[0].size();
+            nFeatures_ = X.size();
+
+            significance_models_.resize(nFeatures_, (all_parents ? 1.0 : 0.0));
+            for (int i = 0; i < nFeatures_; i++) {
+                if (all_parents) active_parents.push_back(i);
+                states_.push_back(*max_element(X[i].begin(), X[i].end()) + 1);
+            }
+            states_.push_back(*max_element(y.begin(), y.end()) + 1);
+            //
+            statesClass_ = states_.back();
+            classCounts_.resize(statesClass_, 0.0);
+            classPriors_.resize(statesClass_, 0.0);
+            //
+            // Initialize data structures
+            //
+            active_parents.resize(nFeatures_);
+            int totalStates = std::accumulate(states_.begin(), states_.end(), 0) - statesClass_;
+
+            // For p(x_i=si | c), we store them in a 1D array classFeatureProbs_ after we compute.
+            // We'll need the offsets for each feature i in featureClassOffset_.
+            featureClassOffset_.resize(nFeatures_);
+            // We'll store p(x_child=sj | c, x_sp=si) for each pair (i<j).
+            // So data_(i, si, j, sj, c) indexes into a big 1D array with an offset.
+            // For p(x_i=si | c), we store them in a 1D array classFeatureProbs_ after we compute.
+            // We'll need the offsets for each feature i in featureClassOffset_.
+            featureClassOffset_.resize(nFeatures_);
+            pairOffset_.resize(totalStates);
+            int feature_offset = 0;
+            int runningOffset = 0;
+            int feature = 0, index = 0;
+            for (int i = 0; i < nFeatures_; ++i) {
+                featureClassOffset_[i] = feature_offset;
+                feature_offset += states_[i];
+                for (int j = 0; j < states_[i]; ++j) {
+                    pairOffset_[feature++] = index;
+                    index += runningOffset;
+                }
+                runningOffset += states_[i];
+            }
+            int totalSize = index * statesClass_;
+            data_.resize(totalSize);
+            dataOpp_.resize(totalSize);
+
+            classFeatureCounts_.resize(feature_offset * statesClass_);
+            classFeatureProbs_.resize(feature_offset * statesClass_);
+
+            matrixState_ = MatrixState::COUNTS;
+            //
+            // Add samples
+            //
+            std::vector<int> instance(nFeatures_ + 1);
+            for (int n_instance = 0; n_instance < num_instances; n_instance++) {
+                for (int feature = 0; feature < nFeatures_; feature++) {
+                    instance[feature] = X[feature][n_instance];
+                }
+                instance[nFeatures_] = y[n_instance];
+                addSample(instance, weights[n_instance].item<double>());
+            }
+            switch (smoothing) {
+                case bayesnet::Smoothing_t::ORIGINAL:
+                    alpha_ = 1.0 / num_instances;
+                    break;
+                case bayesnet::Smoothing_t::LAPLACE:
+                    alpha_ = 1.0;
+                    break;
+                default:
+                    alpha_ = 0.0; // No smoothing 
+            }
+            initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
+            computeProbabilities();
+        }
+        std::string to_string() const
+        {
+            std::ostringstream ostream;
+            ostream << "-------- Xaode.status --------" << std::endl
+                << "- nFeatures = " << nFeatures_ << std::endl
+                << "- statesClass = " << statesClass_ << std::endl
+                << "- matrixState = " << (matrixState_ == MatrixState::COUNTS ? "COUNTS" : "PROBS") << std::endl;
+            ostream << "- states: size: " << states_.size() << std::endl;
+            for (int s : states_) ostream << s << " "; ostream << std::endl;
+            ostream << "- classCounts: size: " << classCounts_.size() << std::endl;
+            for (double cc : classCounts_) ostream << cc << " "; ostream << std::endl;
+            ostream << "- classPriors: size: " << classPriors_.size() << std::endl;
+            for (double cp : classPriors_) ostream << cp << " "; ostream << std::endl;
+            ostream << "- classFeatureCounts: size: " << classFeatureCounts_.size() << std::endl;
+            for (double cfc : classFeatureCounts_) ostream << cfc << " "; ostream << std::endl;
+            ostream << "- classFeatureProbs: size: " << classFeatureProbs_.size() << std::endl;
+            for (double cfp : classFeatureProbs_) ostream << cfp << " "; ostream << std::endl;
+            ostream << "- featureClassOffset: size: " << featureClassOffset_.size() << std::endl;
+            for (int f : featureClassOffset_) ostream << f << " "; ostream << std::endl;
+            ostream << "- pairOffset_: size: " << pairOffset_.size() << std::endl;
+            for (int p : pairOffset_) ostream << p << " "; ostream << std::endl;
+            ostream << "- data: size: " << data_.size() << std::endl;
+            for (double d : data_) ostream << d << " "; ostream << std::endl;
+            ostream << "- dataOpp: size: " << dataOpp_.size() << std::endl;
+            for (double d : dataOpp_) ostream << d << " "; ostream << std::endl;
+            ostream << "--------------------------------" << std::endl;
+            std::string output = ostream.str();
+            return output;
+        }
+        // -------------------------------------------------------
+        // addSample (only in COUNTS mode)
+        // -------------------------------------------------------
+        // 
+        // instance should have the class at the end.
+        // 
+        void addSample(const std::vector<int>& instance, double weight)
+        {
+            //
+            // (A) increment classCounts_
+            // (B) increment feature–class counts => for p(x_i|c)
+            // (C) increment pair (superparent= i, child= j) counts => data_ 
+            //
+            int c = instance.back();
+            if (weight <= 0.0) {
+                return;
+            }
+            // (A) increment classCounts_
+            classCounts_[c] += weight;
+
+            // (B,C)
+            // We'll store raw counts now and turn them into p(child| c, superparent) later.
+            int idx, fcIndex, sp, sc, i_offset;
+            for (int parent = 0; parent < nFeatures_; ++parent) {
+                sp = instance[parent];
+                // (B) increment feature–class counts => for p(x_i|c)
+                fcIndex = (featureClassOffset_[parent] + sp) * statesClass_ + c;
+                classFeatureCounts_[fcIndex] += weight;
+                // (C) increment pair (superparent= i, child= j) counts => data_
+                i_offset = pairOffset_[featureClassOffset_[parent] + sp];
+                for (int child = 0; child < parent; ++child) {
+                    sc = instance[child];
+                    idx = (i_offset + featureClassOffset_[child] + sc) * statesClass_ + c;
+                    data_[idx] += weight;
+                }
+            }
+        }
+        // -------------------------------------------------------
+        // computeProbabilities
+        // -------------------------------------------------------
+        //
+        // Once all samples are added in COUNTS mode, call this to:
+        //  1) compute p(c) => classPriors_
+        //  2) compute p(x_i=si | c) => classFeatureProbs_
+        //  3) compute p(x_j=sj | c, x_i=si) => data_ (for i<j) dataOpp_ (for i>j)
+        //
+        void computeProbabilities()
+        {
+            if (matrixState_ != MatrixState::COUNTS) {
+                throw std::logic_error("computeProbabilities: must be in COUNTS mode.");
+            }
+            double totalCount = std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
+            // (1) p(c)
+            if (totalCount <= 0.0) {
+                // fallback => uniform
+                double unif = 1.0 / statesClass_;
+                for (int c = 0; c < statesClass_; ++c) {
+                    classPriors_[c] = unif;
+                }
+            } else {
+                for (int c = 0; c < statesClass_; ++c) {
+                    classPriors_[c] = (classCounts_[c] + alpha_) / (totalCount + alpha_ * statesClass_);
+                }
+            }
+            // (2) p(x_i=si | c) => classFeatureProbs_
+            int idx, sf;
+            double denom;
+            for (int feature = 0; feature < nFeatures_; ++feature) {
+                sf = states_[feature];
+                for (int c = 0; c < statesClass_; ++c) {
+                    denom = classCounts_[c] + alpha_ * sf;
+                    for (int sf_value = 0; sf_value < sf; ++sf_value) {
+                        idx = (featureClassOffset_[feature] + sf_value) * statesClass_ + c;
+                        classFeatureProbs_[idx] = (classFeatureCounts_[idx] + alpha_) / denom;
+                    }
+                }
+            }
+            // getCountFromTable(int classVal, int pIndex, int childIndex)
+            // (3) p(x_c=sc | c, x_p=sp) => data_(parent,sp,child,sc,c)
+            // (3) p(x_p=sp | c, x_c=sc) => dataOpp_(child,sc,parent,sp,c)
+            //                    C(x_c, x_p, c) + alpha_
+            // P(x_p | x_c, c) = -----------------------------------
+            //                           C(x_c, c) + alpha_
+            double pcc_count, pc_count, cc_count;
+            double conditionalProb, oppositeCondProb;
+            int part1, part2, p1, part2_class, p1_class;
+            for (int parent = 1; parent < nFeatures_; ++parent) {
+                for (int sp = 0; sp < states_[parent]; ++sp) {
+                    p1 = featureClassOffset_[parent] + sp;
+                    part1 = pairOffset_[p1];
+                    p1_class = p1 * statesClass_;
+                    for (int child = 0; child < parent; ++child) {
+                        for (int sc = 0; sc < states_[child]; ++sc) {
+                            part2 = featureClassOffset_[child] + sc;
+                            part2_class = part2 * statesClass_;
+                            for (int c = 0; c < statesClass_; c++) {
+                                idx = (part1 + part2) * statesClass_ + c;
+                                // Parent, Child, Class Count
+                                pcc_count = data_[idx];
+                                // Parent, Class count
+                                pc_count = classFeatureCounts_[p1_class + c];
+                                // Child, Class count
+                                cc_count = classFeatureCounts_[part2_class + c];
+                                // p(x_c=sc | c, x_p=sp)
+                                conditionalProb = (pcc_count + alpha_) / (pc_count + alpha_ * states_[child]);
+                                data_[idx] = conditionalProb;
+                                // p(x_p=sp | c, x_c=sc)
+                                oppositeCondProb = (pcc_count + alpha_) / (cc_count + alpha_ * states_[parent]);
+                                dataOpp_[idx] = oppositeCondProb;
+                            }
+                        }
+                    }
+                }
+            }
+            matrixState_ = MatrixState::PROBS;
+        }
+        // -------------------------------------------------------
+        // predict_proba_spode
+        // -------------------------------------------------------
+        //
+        // Single-superparent approach:
+        // P(c | x) ∝ p(c) * p(x_sp| c) * ∏_{i≠sp} p(x_i | c, x_sp)
+        //
+        // 'instance' should have size == nFeatures_ (no class).
+        // sp in [0..nFeatures_).
+        // We multiply p(c) * p(x_sp| c) * p(x_i| c, x_sp).
+        // Then normalize the distribution.
+        //
+        std::vector<double> predict_proba_spode(const std::vector<int>& instance, int parent)
+        {
+            // accumulates posterior probabilities for each class
+            auto probs = std::vector<double>(statesClass_);
+            auto spodeProbs = std::vector<double>(statesClass_, 0.0);
+            if (std::find(active_parents.begin(), active_parents.end(), parent) == active_parents.end()) {
+                return spodeProbs;
+            }
+            // Initialize the probabilities with the feature|class probabilities x class priors
+            int localOffset;
+            int sp = instance[parent];
+            localOffset = (featureClassOffset_[parent] + sp) * statesClass_;
+            for (int c = 0; c < statesClass_; ++c) {
+                spodeProbs[c] = classFeatureProbs_[localOffset + c] * classPriors_[c] * initializer_;
+            }
+            int idx, base, sc, parent_offset;
+            for (int child = 0; child < nFeatures_; ++child) {
+                if (child == parent) {
+                    continue;
+                }
+                sc = instance[child];
+                if (child > parent) {
+                    parent_offset = pairOffset_[featureClassOffset_[child] + sc];
+                    base = (parent_offset + featureClassOffset_[parent] + sp) * statesClass_;
+                } else {
+                    parent_offset = pairOffset_[featureClassOffset_[parent] + sp];
+                    base = (parent_offset + featureClassOffset_[child] + sc) * statesClass_;
+                }
+                for (int c = 0; c < statesClass_; ++c) {
+                    /*
+                    * The probability P(xc|xp,c) is stored in dataOpp_, and
+                    * the probability P(xp|xc,c) is stored in data_
+                    */
+                    idx = base + c;
+                    double factor = child > parent ? dataOpp_[idx] : data_[idx];
+                    // double factor = data_[idx];
+                    spodeProbs[c] *= factor;
+                }
+            }
+            // Normalize the probabilities
+            normalize(spodeProbs);
+            return spodeProbs;
+        }
+        int predict_spode(const std::vector<int>& instance, int parent)
+        {
+            auto probs = predict_proba_spode(instance, parent);
+            return (int)std::distance(probs.begin(), std::max_element(probs.begin(), probs.end()));
+        }
+        // -------------------------------------------------------
+        // predict_proba
+        // -------------------------------------------------------
+        //
+        // P(c | x) ∝ p(c) * ∏_{i} p(x_i | c) * ∏_{i<j} p(x_j | c, x_i) * p(x_i | c, x_j)
+        //
+        // 'instance' should have size == nFeatures_ (no class).
+        // We multiply p(c) * p(x_i| c) * p(x_j| c, x_i) for all i, j.
+        // Then normalize the distribution.
+        //
+        std::vector<double> predict_proba(const std::vector<int>& instance)
+        {
+            // accumulates posterior probabilities for each class
+            auto probs = std::vector<double>(statesClass_);
+            auto spodeProbs = std::vector<std::vector<double>>(nFeatures_, std::vector<double>(statesClass_));
+            // Initialize the probabilities with the feature|class probabilities
+            int localOffset;
+            for (int feature = 0; feature < nFeatures_; ++feature) {
+                // if feature is not in the active_parents, skip it
+                if (std::find(active_parents.begin(), active_parents.end(), feature) == active_parents.end()) {
+                    continue;
+                }
+                localOffset = (featureClassOffset_[feature] + instance[feature]) * statesClass_;
+                for (int c = 0; c < statesClass_; ++c) {
+                    spodeProbs[feature][c] = classFeatureProbs_[localOffset + c] * classPriors_[c] * initializer_;
+                }
+            }
+            int idx, base, sp, sc, parent_offset;
+            for (int parent = 1; parent < nFeatures_; ++parent) {
+                // if parent is not in the active_parents, skip it
+                if (std::find(active_parents.begin(), active_parents.end(), parent) == active_parents.end()) {
+                    continue;
+                }
+                sp = instance[parent];
+                parent_offset = pairOffset_[featureClassOffset_[parent] + sp];
+                for (int child = 0; child < parent; ++child) {
+                    sc = instance[child];
+                    if (child > parent) {
+                        parent_offset = pairOffset_[featureClassOffset_[child] + sc];
+                        base = (parent_offset + featureClassOffset_[parent] + sp) * statesClass_;
+                    } else {
+                        parent_offset = pairOffset_[featureClassOffset_[parent] + sp];
+                        base = (parent_offset + featureClassOffset_[child] + sc) * statesClass_;
+                    }
+                    for (int c = 0; c < statesClass_; ++c) {
+                        /*
+                         * The probability P(xc|xp,c) is stored in dataOpp_, and
+                         * the probability P(xp|xc,c) is stored in data_
+                         */
+                        idx = base + c;
+                        double factor_child = child > parent ? data_[idx] : dataOpp_[idx];
+                        double factor_parent = child > parent ? dataOpp_[idx] : data_[idx];
+                        spodeProbs[child][c] *= factor_child;
+                        spodeProbs[parent][c] *= factor_parent;
+                    }
+                }
+            }
+            /* add all the probabilities for each class */
+            for (int c = 0; c < statesClass_; ++c) {
+                for (int i = 0; i < nFeatures_; ++i) {
+                    probs[c] += spodeProbs[i][c] * significance_models_[i];
+                }
+            }
+            // Normalize the probabilities
+            normalize(probs);
+            return probs;
+        }
+        void normalize(std::vector<double>& probs) const
+        {
+            double sum = std::accumulate(probs.begin(), probs.end(), 0.0);
+            if (std::isnan(sum)) {
+                throw std::runtime_error("Can't normalize array. Sum is NaN.");
+            }
+            if (sum == 0) {
+                return;
+            }
+            for (int i = 0; i < (int)probs.size(); i++) {
+                probs[i] /= sum;
+            }
+        }
+        // Returns current mode: INIT, COUNTS or PROBS
+        MatrixState state() const
+        {
+            return matrixState_;
+        }
+        int statesClass() const
+        {
+            return statesClass_;
+        }
+        int nFeatures() const
+        {
+            return nFeatures_;
+        }
+        int getNumberOfStates() const
+        {
+            return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+        }
+        int getNumberOfEdges() const
+        {
+            return nFeatures_ * (2 * nFeatures_ - 1);
+        }
+        int getNumberOfNodes() const
+        {
+            return (nFeatures_ + 1) * nFeatures_;
+        }
+        void add_active_parent(int active_parent)
+        {
+            active_parents.push_back(active_parent);
+        }
+        void remove_last_parent()
+        {
+            active_parents.pop_back();
+        }
+
+    private:
+        // -----------
+        // MEMBER DATA
+        // -----------
+        std::vector<int> states_;            // [states_feat0, ..., states_feat(n-1), statesClass_]
+        int nFeatures_;
+        int statesClass_;
+
+        // data_ means p(child=sj | c, superparent= si) after normalization.
+        // But in COUNTS mode, it accumulates raw counts.
+        std::vector<int> pairOffset_;
+        // data_ stores p(child=sj | c, superparent=si) for each pair (i<j).
+        std::vector<double> data_;
+        // dataOpp_ stores p(superparent=si | c, child=sj) for each pair (i<j).
+        std::vector<double> dataOpp_;
+
+        // classCounts_[c]
+        std::vector<double> classCounts_;
+        std::vector<double> classPriors_;       // => p(c)
+
+        // For p(x_i=si| c), we store counts in classFeatureCounts_ => offset by featureClassOffset_[i]
+        std::vector<int> featureClassOffset_;
+        std::vector<double> classFeatureCounts_;
+        std::vector<double> classFeatureProbs_;  // => p(x_i=si | c) after normalization
+
+        MatrixState matrixState_;
+
+        double alpha_ = 1.0; // Laplace smoothing
+        double initializer_ = 1.0;
+        std::vector<int> active_parents;
+    };
+}
+#endif // XAODE_H

src/grid/GridBase.cpp

@@ -0,0 +1,314 @@
+#include <random>
+#include <cstddef>
+#include "common/DotEnv.h"
+#include "common/Paths.h"
+#include "common/Colors.h"
+#include "GridBase.h"
+
+
+namespace platform {
+
+    GridBase::GridBase(struct ConfigGrid& config)
+    {
+        this->config = config;
+        auto env = platform::DotEnv();
+        this->config.platform = env.get("platform");
+
+    }
+    void GridBase::validate_config()
+    {
+        if (config.smooth_strategy == "ORIGINAL")
+            smooth_type = bayesnet::Smoothing_t::ORIGINAL;
+        else if (config.smooth_strategy == "LAPLACE")
+            smooth_type = bayesnet::Smoothing_t::LAPLACE;
+        else if (config.smooth_strategy == "CESTNIK")
+            smooth_type = bayesnet::Smoothing_t::CESTNIK;
+        else {
+            std::cerr << "GridBase: Unknown smoothing strategy: " << config.smooth_strategy << std::endl;
+            exit(1);
+        }
+    }
+    std::string GridBase::get_color_rank(int rank)
+    {
+        auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN(), Colors::YELLOW(), Colors::BLACK() };
+        std::string id = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
+        auto idx = rank % id.size();
+        return *(colors.begin() + rank % colors.size()) + id[idx];
+    }
+    void GridBase::shuffle_and_progress_bar(json& tasks)
+    {
+        // Shuffle the array so heavy datasets are eas  ier spread across the workers
+        std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle
+        std::shuffle(tasks.begin(), tasks.end(), g);
+        std::cout << "* Number of tasks: " << tasks.size() << std::endl;
+        std::cout << separator << std::flush;
+        for (int i = 0; i < tasks.size(); ++i) {
+            if ((i + 1) % 10 == 0)
+                std::cout << separator;
+            else
+                std::cout << (i + 1) % 10;
+        }
+        std::cout << separator << std::endl << separator << std::flush;
+    }
+    json GridBase::build_tasks(Datasets& datasets)
+    {
+        /*
+        * Each task is a json object with the following structure:
+        * {
+        *   "dataset": "dataset_name",
+        *   "idx_dataset": idx_dataset, // used to identify the dataset in the results
+        *    // this index is relative to the list of used datasets in the actual run not to the whole datasets list
+        *   "seed": # of seed to use,
+        *   "fold": # of fold to process
+        * }
+        * This way a task consists in process all combinations of hyperparameters for a dataset, seed and fold
+        */
+        auto tasks = json::array();
+        auto all_datasets = datasets.getNames();
+        auto datasets_names = filterDatasets(datasets);
+        for (int idx_dataset = 0; idx_dataset < datasets_names.size(); ++idx_dataset) {
+            auto dataset = datasets_names[idx_dataset];
+            for (const auto& seed : config.seeds) {
+                for (int n_fold = 0; n_fold < config.n_folds; n_fold++) {
+                    json task = {
+                        { "dataset", dataset },
+                        { "idx_dataset", idx_dataset},
+                        { "seed", seed },
+                        { "fold", n_fold},
+                    };
+                    tasks.push_back(task);
+                }
+            }
+        }
+        shuffle_and_progress_bar(tasks);
+        return tasks;
+    }
+    void GridBase::summary(json& all_results, json& tasks, struct ConfigMPI& config_mpi)
+    {
+        // Report the tasks done by each worker, showing dataset number, seed, fold and time spent
+        // The format I want to show is:
+        // worker, dataset, seed, fold, time
+        // with headers
+        std::cout << Colors::RESET() << "* Summary of tasks done by each worker" << std::endl;
+        json worker_tasks = json::array();
+        for (int i = 0; i < config_mpi.n_procs; ++i) {
+            worker_tasks.push_back(json::array());
+        }
+        int max_dataset = 7;
+        for (const auto& [key, results] : all_results.items()) {
+            auto dataset = key;
+            if (dataset.size() > max_dataset)
+                max_dataset = dataset.size();
+            for (const auto& result : results) {
+                int n_task = result["task"].get<int>();
+                json task = tasks[n_task];
+                auto seed = task["seed"].get<int>();
+                auto fold = task["fold"].get<int>();
+                auto time = result["time"].get<double>();
+                auto worker = result["process"].get<int>();
+                json line = {
+                    { "dataset", dataset },
+                    { "seed", seed },
+                    { "fold", fold },
+                    { "time", time }
+                };
+                worker_tasks[worker].push_back(line);
+            }
+        }
+        std::cout << Colors::MAGENTA() << " W  " << setw(max_dataset) << std::left << "Dataset";
+        std::cout << " Seed Fold Time" << std::endl;
+        std::cout << "=== " << std::string(max_dataset, '=') << " ==== ==== " << std::string(15, '=') << std::endl;
+        for (int worker = 0; worker < config_mpi.n_procs; ++worker) {
+            auto color = (worker % 2) ? Colors::CYAN() : Colors::BLUE();
+            std::cout << color << std::right << setw(3) << worker << " ";
+            if (worker == config_mpi.manager) {
+                std::cout << "Manager" << std::endl;
+                continue;
+            }
+            if (worker_tasks[worker].empty()) {
+                std::cout << "No tasks" << std::endl;
+                continue;
+            }
+            bool first = true;
+            double total = 0.0;
+            int num_tasks = 0;
+            for (const auto& task : worker_tasks[worker]) {
+                num_tasks++;
+                if (!first)
+                    std::cout << std::string(4, ' ');
+                else
+                    first = false;
+                std::cout << std::left << setw(max_dataset) << task["dataset"].get<std::string>();
+                std::cout << " " << setw(4) << std::right << task["seed"].get<int>();
+                std::cout << " " << setw(4) << task["fold"].get<int>();
+                std::cout << " " << setw(15) << std::setprecision(7) << std::fixed << task["time"].get<double>() << std::endl;
+                total += task["time"].get<double>();
+            }
+            if (num_tasks > 1) {
+                std::cout << Colors::MAGENTA() << "    ";
+                std::cout << setw(max_dataset) << "Total (" << setw(2) << std::right << num_tasks << ")" << std::string(7, '.');
+                std::cout << " " << setw(15) << std::setprecision(7) << std::fixed << total << std::endl;
+            }
+        }
+    }
+    void GridBase::go(struct ConfigMPI& config_mpi)
+    {
+        /*
+        * Each task is a json object with the data needed by the process
+        *
+        * The overall process consists in these steps:
+           * 0. Validate config, create the MPI result type & tasks
+           * 0.1 Create the MPI result type
+           * 0.2 Manager creates the tasks
+           * 1. Manager will broadcast the tasks to all the processes
+           * 1.1 Broadcast the number of tasks
+           * 1.2 Broadcast the length of the following string
+           * 1.2 Broadcast the tasks as a char* string
+           * 2a. Producer delivers the tasks to the consumers
+           * 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
+           * 2a.2 Producer will send the end message to all the consumers
+           * 2b. Consumers process the tasks and send the results to the producer
+           * 2b.1 Consumers announce to the producer that they are ready to receive a task
+           * 2b.2 Consumers receive the task from the producer and process it
+           * 2b.3 Consumers send the result to the producer
+           * 3. Manager compile results for each dataset
+           * 3.1 Loop thru all the results obtained from each outer fold (task) and select the best
+           * 3.2 Save the results
+           * 3.3 Summary of jobs done
+        */
+        //
+        // 0.1 Create the MPI result type
+        //
+        validate_config();
+        Task_Result result;
+        int tasks_size;
+        MPI_Datatype MPI_Result;
+        MPI_Datatype type[11] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_DOUBLE, MPI_INT, MPI_INT };
+        int blocklen[11] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
+        MPI_Aint disp[11];
+        disp[0] = offsetof(Task_Result, idx_dataset);
+        disp[1] = offsetof(Task_Result, idx_combination);
+        disp[2] = offsetof(Task_Result, n_fold);
+        disp[3] = offsetof(Task_Result, score);
+        disp[4] = offsetof(Task_Result, time);
+        disp[5] = offsetof(Task_Result, time_train);
+        disp[6] = offsetof(Task_Result, nodes);
+        disp[7] = offsetof(Task_Result, leaves);
+        disp[8] = offsetof(Task_Result, depth);
+        disp[9] = offsetof(Task_Result, process);
+        disp[10] = offsetof(Task_Result, task);
+        MPI_Type_create_struct(11, blocklen, disp, type, &MPI_Result);
+        MPI_Type_commit(&MPI_Result);
+        //
+        // 0.2 Manager creates the tasks
+        //
+        char* msg;
+        json tasks;
+        auto env = platform::DotEnv();
+        auto datasets = Datasets(config.discretize, Paths::datasets(), env.get("discretize_algo"));
+        if (config_mpi.rank == config_mpi.manager) {
+            timer.start();
+            tasks = build_tasks(datasets);
+            auto tasks_str = tasks.dump();
+            tasks_size = tasks_str.size();
+            msg = new char[tasks_size + 1];
+            strcpy(msg, tasks_str.c_str());
+        }
+        //
+        // 1. Manager will broadcast the tasks to all the processes
+        //
+        MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD);
+        if (config_mpi.rank != config_mpi.manager) {
+            msg = new char[tasks_size + 1];
+        }
+        MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
+        tasks = json::parse(msg);
+        delete[] msg;
+
+
+        if (config_mpi.rank == config_mpi.manager) {
+            //
+            // 2a. Producer delivers the tasks to the consumers
+            //
+            auto datasets_names = filterDatasets(datasets);
+            json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result);
+            std::cout << separator << std::endl;
+            //
+            // 3. Manager compile results for each dataset
+            //
+            auto results = initializeResults();
+            compile_results(results, all_results, config.model);
+            //
+            // 3.2 Save the results
+            //
+            save(results);
+            //
+            // 3.3 Summary of jobs done
+            //
+            if (!config.quiet)
+                summary(all_results, tasks, config_mpi);
+        } else {
+            //
+            // 2b. Consumers process the tasks and send the results to the producer
+            //
+            consumer(datasets, tasks, config, config_mpi, MPI_Result);
+        }
+    }
+    json GridBase::producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
+    {
+        Task_Result result;
+        json results;
+        int num_tasks = tasks.size();
+        //
+        // 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
+        //
+        for (int i = 0; i < num_tasks; ++i) {
+            MPI_Status status;
+            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
+            if (status.MPI_TAG == TAG_RESULT) {
+                //Store result
+                store_result(names, result, results);
+
+            }
+            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
+        }
+        //
+        // 2a.2 Producer will send the end message to all the consumers
+        //
+        for (int i = 0; i < config_mpi.n_procs - 1; ++i) {
+            MPI_Status status;
+            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
+            if (status.MPI_TAG == TAG_RESULT) {
+                //Store result
+                store_result(names, result, results);
+            }
+            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
+        }
+        return results;
+    }
+    void GridBase::consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
+    {
+        Task_Result result;
+        //
+        // 2b.1 Consumers announce to the producer that they are ready to receive a task
+        //
+        MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_QUERY, MPI_COMM_WORLD);
+        int task;
+        while (true) {
+            MPI_Status status;
+            //
+            // 2b.2 Consumers receive the task from the producer and process it
+            //
+            MPI_Recv(&task, 1, MPI_INT, config_mpi.manager, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
+            if (status.MPI_TAG == TAG_END) {
+                break;
+            }
+            consumer_go(config, config_mpi, tasks, task, datasets, &result);
+            //
+            // 2b.3 Consumers send the result to the producer
+            //
+            MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD);
+        }
+    }
+
+}

src/grid/GridBase.h

@@ -0,0 +1,39 @@
+#ifndef GRIDBASE_H
+#define GRIDBASE_H
+#include <string>
+#include <mpi.h>
+#include <nlohmann/json.hpp>
+#include "common/Datasets.h"
+#include "common/Timer.hpp"
+#include "main/HyperParameters.h"
+#include "GridConfig.h"
+
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class GridBase {
+    public:
+        explicit GridBase(struct ConfigGrid& config);
+        ~GridBase() = default;
+        void go(struct ConfigMPI& config_mpi);
+        void validate_config();
+    protected:
+        json build_tasks(Datasets& datasets);
+        virtual void save(json& results) = 0;
+        virtual std::vector<std::string> filterDatasets(Datasets& datasets) const = 0;
+        virtual json initializeResults() = 0;
+        virtual void compile_results(json& results, json& all_results, std::string& model) = 0;
+        virtual json store_result(std::vector<std::string>& names, Task_Result& result, json& results) = 0;
+        virtual void consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result) = 0;
+        void shuffle_and_progress_bar(json& tasks);
+        json producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
+        void consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result);
+        std::string get_color_rank(int rank);
+        void summary(json& all_results, json& tasks, struct ConfigMPI& config_mpi);
+        struct ConfigGrid config;
+        Timer timer; // used to measure the time of the whole process
+        const std::string separator = "|";
+        bayesnet::Smoothing_t smooth_type{ bayesnet::Smoothing_t::NONE };
+    };
+} /* namespace platform */
+#endif

src/grid/GridConfig.h

@@ -0,0 +1,55 @@
+#ifndef GRIDCONFIG_H
+#define GRIDCONFIG_H
+#include <string>
+#include <map>
+#include <mpi.h>
+#include <nlohmann/json.hpp>
+#include "common/Datasets.h"
+#include "common/Timer.hpp"
+#include "main/HyperParameters.h"
+#include "GridData.h"
+#include "GridConfig.h"
+#include "bayesnet/network/Network.h"
+
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    struct ConfigGrid {
+        std::string model;
+        std::string score;
+        std::string continue_from;
+        std::string platform;
+        std::string smooth_strategy;
+        bool quiet;
+        bool only; // used with continue_from to only compute that dataset
+        bool discretize;
+        bool stratified;
+        int nested;
+        int n_folds;
+        json excluded;
+        std::vector<int> seeds;
+    };
+    struct ConfigMPI {
+        int rank;
+        int n_procs;
+        int manager;
+    };
+    typedef struct {
+        uint idx_dataset;
+        uint idx_combination;
+        int n_fold;
+        double score; // Experiment: Score test, no score train in this case
+        double time; // Experiment: Time test
+        double time_train;
+        double nodes; // Experiment specific
+        double leaves; // Experiment specific
+        double depth; // Experiment specific
+        int process;
+        int task;
+    } Task_Result;
+    const int TAG_QUERY = 1;
+    const int TAG_RESULT = 2;
+    const int TAG_TASK = 3;
+    const int TAG_END = 4;
+} /* namespace platform */
+#endif

src/grid/GridExperiment.cpp

@@ -0,0 +1,196 @@
+#include <iostream>
+#include <cstddef>
+#include <torch/torch.h>
+#include <folding.hpp>
+#include "main/Models.h"
+#include "common/Paths.h"
+#include "common/Utils.h"
+#include "GridExperiment.h"
+
+namespace platform {
+    // GridExperiment::GridExperiment(argparse::ArgumentParser& program, struct ConfigGrid& config) : arguments(program), GridBase(config)
+    GridExperiment::GridExperiment(ArgumentsExperiment& program, struct ConfigGrid& config) : arguments(program), GridBase(config)
+    {
+        experiment = arguments.initializedExperiment();
+        filesToTest = arguments.getFilesToTest();
+        saveResults = arguments.haveToSaveResults();
+        this->config.model = experiment.getModel();
+        this->config.score = experiment.getScore();
+        this->config.discretize = experiment.isDiscretized();
+        this->config.stratified = experiment.isStratified();
+        this->config.smooth_strategy = experiment.getSmoothStrategy();
+        this->config.n_folds = experiment.getNFolds();
+        this->config.seeds = experiment.getRandomSeeds();
+        this->config.quiet = experiment.isQuiet();
+    }
+    json GridExperiment::getResults()
+    {
+        return computed_results;
+    }
+    std::vector<std::string> GridExperiment::filterDatasets(Datasets& datasets) const
+    {
+        return filesToTest;
+    }
+    json GridExperiment::initializeResults()
+    {
+        json results;
+        return results;
+    }
+    void GridExperiment::save(json& results)
+    {
+    }
+    void GridExperiment::compile_results(json& results, json& all_results, std::string& model)
+    {
+        auto datasets = Datasets(false, Paths::datasets());
+        nlohmann::json temp = all_results; // To restore the order of the data by dataset name
+        all_results = temp;
+        for (const auto& result_item : all_results.items()) {
+            // each result has the results of all the outer folds as each one were a different task
+            auto dataset_name = result_item.key();
+            auto data = result_item.value();
+            auto result = json::object();
+            int data_size = data.size();
+            auto score = torch::zeros({ data_size }, torch::kFloat64);
+            auto score_train = torch::zeros({ data_size }, torch::kFloat64);
+            auto time_test = torch::zeros({ data_size }, torch::kFloat64);
+            auto time_train = torch::zeros({ data_size }, torch::kFloat64);
+            auto nodes = torch::zeros({ data_size }, torch::kFloat64);
+            auto leaves = torch::zeros({ data_size }, torch::kFloat64);
+            auto depth = torch::zeros({ data_size }, torch::kFloat64);
+            auto& dataset = datasets.getDataset(dataset_name);
+            dataset.load();
+            //
+            // Prepare Result
+            //
+            auto partial_result = PartialResult();
+            partial_result.setSamples(dataset.getNSamples()).setFeatures(dataset.getNFeatures()).setClasses(dataset.getNClasses());
+            partial_result.setHyperparameters(experiment.getHyperParameters().get(dataset_name));
+            for (int fold = 0; fold < data_size; ++fold) {
+                partial_result.addScoreTest(data[fold]["score"]);
+                partial_result.addScoreTrain(0.0);
+                partial_result.addTimeTest(data[fold]["time"]);
+                partial_result.addTimeTrain(data[fold]["time_train"]);
+                score[fold] = data[fold]["score"].get<double>();
+                time_test[fold] = data[fold]["time"].get<double>();
+                time_train[fold] = data[fold]["time_train"].get<double>();
+                nodes[fold] = data[fold]["nodes"].get<double>();
+                leaves[fold] = data[fold]["leaves"].get<double>();
+                depth[fold] = data[fold]["depth"].get<double>();
+            }
+            partial_result.setGraph(std::vector<std::string>());
+            partial_result.setScoreTest(torch::mean(score).item<double>()).setScoreTrain(0.0);
+            partial_result.setScoreTestStd(torch::std(score).item<double>()).setScoreTrainStd(0.0);
+            partial_result.setTrainTime(torch::mean(time_train).item<double>()).setTestTime(torch::mean(time_test).item<double>());
+            partial_result.setTrainTimeStd(torch::std(time_train).item<double>()).setTestTimeStd(torch::std(time_test).item<double>());
+            partial_result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(leaves).item<double>()).setDepth(torch::mean(depth).item<double>());
+            partial_result.setDataset(dataset_name).setNotes(std::vector<std::string>());
+            partial_result.setConfusionMatrices(json::array());
+            experiment.addResult(partial_result);
+        }
+        auto clf = Models::instance()->create(experiment.getModel());
+        experiment.setModelVersion(clf->getVersion());
+        computed_results = results;
+    }
+    json GridExperiment::store_result(std::vector<std::string>& names, Task_Result& result, json& results)
+    {
+        json json_result = {
+            { "score", result.score },
+            { "combination", result.idx_combination },
+            { "fold", result.n_fold },
+            { "time", result.time },
+            { "time_train", result.time_train },
+            { "dataset", result.idx_dataset },
+            { "nodes", result.nodes },
+            { "leaves", result.leaves },
+            { "depth", result.depth },
+            { "process", result.process },
+            { "task", result.task }
+        };
+        auto name = names[result.idx_dataset];
+        if (!results.contains(name)) {
+            results[name] = json::array();
+        }
+        results[name].push_back(json_result);
+        return results;
+    }
+    void GridExperiment::consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
+    {
+        //
+        // initialize
+        //
+        Timer train_timer, test_timer;
+        json task = tasks[n_task];
+        auto model = config.model;
+        auto dataset_name = task["dataset"].get<std::string>();
+        auto idx_dataset = task["idx_dataset"].get<int>();
+        auto seed = task["seed"].get<int>();
+        auto n_fold = task["fold"].get<int>();
+        bool stratified = config.stratified;
+        bayesnet::Smoothing_t smooth;
+        if (config.smooth_strategy == "ORIGINAL")
+            smooth = bayesnet::Smoothing_t::ORIGINAL;
+        else if (config.smooth_strategy == "LAPLACE")
+            smooth = bayesnet::Smoothing_t::LAPLACE;
+        else if (config.smooth_strategy == "CESTNIK")
+            smooth = bayesnet::Smoothing_t::CESTNIK;
+        //
+        // Generate the hyperparameters combinations
+        //
+        auto& dataset = datasets.getDataset(dataset_name);
+        dataset.load();
+        auto [X, y] = dataset.getTensors();
+        auto features = dataset.getFeatures();
+        auto className = dataset.getClassName();
+        //
+        // Start working on task
+        //
+        folding::Fold* fold;
+        if (stratified)
+            fold = new folding::StratifiedKFold(config.n_folds, y, seed);
+        else
+            fold = new folding::KFold(config.n_folds, y.size(0), seed);
+        train_timer.start();
+        auto [train, test] = fold->getFold(n_fold);
+        auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
+        auto states = dataset.getStates(); // Get the states of the features Once they are discretized
+
+        //
+        // Build Classifier with selected hyperparameters
+        //
+        auto clf = Models::instance()->create(config.model);
+        auto valid = clf->getValidHyperparameters();
+        auto hyperparameters = experiment.getHyperParameters();
+        hyperparameters.check(valid, dataset_name);
+        clf->setHyperparameters(hyperparameters.get(dataset_name));
+        //
+        // Train model
+        //
+        clf->fit(X_train, y_train, features, className, states, smooth);
+        auto train_time = train_timer.getDuration();
+        //
+        // Test model
+        //
+        test_timer.start();
+        double score = clf->score(X_test, y_test);
+        delete fold;
+        auto test_time = test_timer.getDuration();
+        //
+        // Return the result
+        //
+        result->idx_dataset = task["idx_dataset"].get<int>();
+        result->idx_combination = 0;
+        result->score = score;
+        result->n_fold = n_fold;
+        result->time = test_time;
+        result->time_train = train_time;
+        result->nodes = clf->getNumberOfNodes();
+        result->leaves = clf->getNumberOfEdges();
+        result->depth = clf->getNumberOfStates();
+        result->process = config_mpi.rank;
+        result->task = n_task;
+        //
+        // Update progress bar
+        //
+        std::cout << get_color_rank(config_mpi.rank) << std::flush;
+    }
+} /* namespace platform */

src/grid/GridExperiment.h

@@ -0,0 +1,38 @@
+#ifndef GRIDEXPERIMENT_H
+#define GRIDEXPERIMENT_H
+#include <string>
+#include <mpi.h>
+#include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
+#include "common/Datasets.h"
+#include "main/Experiment.h"
+#include "main/HyperParameters.h"
+#include "main/ArgumentsExperiment.h"
+#include "GridBase.h"
+
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class GridExperiment : public GridBase {
+    public:
+        explicit GridExperiment(ArgumentsExperiment& program, struct ConfigGrid& config);
+        ~GridExperiment() = default;
+        json getResults();
+        Experiment& getExperiment() { return experiment; }
+        size_t numFiles() const { return filesToTest.size(); }
+        bool haveToSaveResults() const { return saveResults; }
+    private:
+        ArgumentsExperiment& arguments;
+        Experiment experiment;
+        json computed_results;
+        bool saveResults = false;
+        std::vector<std::string> filesToTest;
+        void save(json& results);
+        json initializeResults();
+        std::vector<std::string> filterDatasets(Datasets& datasets) const;
+        void compile_results(json& results, json& all_results, std::string& model);
+        json store_result(std::vector<std::string>& names, Task_Result& result, json& results);
+        void consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result);
+    };
+} /* namespace platform */
+#endif

src/grid/GridSearch.cpp

@@ -1,21 +1,14 @@
 #include <iostream>
-#include <cstddef>
 #include <torch/torch.h>
 #include <folding.hpp>
 #include "main/Models.h"
 #include "common/Paths.h"
-#include "common/Colors.h"
 #include "common/Utils.h"
+#include "common/Colors.h"
 #include "GridSearch.h"
 
 namespace platform {
-
-    std::string get_color_rank(int rank)
-    {
-        auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
-        return *(colors.begin() + rank % colors.size());
-    }
-    GridSearch::GridSearch(struct ConfigGrid& config) : config(config)
+    GridSearch::GridSearch(struct ConfigGrid& config) : GridBase(config)
     {
     }
     json GridSearch::loadResults()
@@ -59,333 +52,13 @@ namespace platform {
         }
         return datasets_names;
     }
-    json GridSearch::build_tasks_mpi(int rank)
-    {
-        auto tasks = json::array();
-        auto grid = GridData(Paths::grid_input(config.model));
-        auto datasets = Datasets(false, Paths::datasets());
-        auto all_datasets = datasets.getNames();
-        auto datasets_names = filterDatasets(datasets);
-        for (int idx_dataset = 0; idx_dataset < datasets_names.size(); ++idx_dataset) {
-            auto dataset = datasets_names[idx_dataset];
-            for (const auto& seed : config.seeds) {
-                auto combinations = grid.getGrid(dataset);
-                for (int n_fold = 0; n_fold < config.n_folds; n_fold++) {
-                    json task = {
-                        { "dataset", dataset },
-                        { "idx_dataset", idx_dataset},
-                        { "seed", seed },
-                        { "fold", n_fold},
-                    };
-                    tasks.push_back(task);
-                }
-            }
-        }
-        // Shuffle the array so heavy datasets are spread across the workers
-        std::mt19937 g{ 271 }; // Use fixed seed to obtain the same shuffle
-        std::shuffle(tasks.begin(), tasks.end(), g);
-        std::cout << get_color_rank(rank) << "* Number of tasks: " << tasks.size() << std::endl;
-        std::cout << separator;
-        for (int i = 0; i < tasks.size(); ++i) {
-            std::cout << (i + 1) % 10;
-        }
-        std::cout << separator << std::endl << separator << std::flush;
-        return tasks;
-    }
-    void process_task_mpi_consumer(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
-    {
-        // initialize
-        Timer timer;
-        timer.start();
-        json task = tasks[n_task];
-        auto model = config.model;
-        auto grid = GridData(Paths::grid_input(model));
-        auto dataset_name = task["dataset"].get<std::string>();
-        auto idx_dataset = task["idx_dataset"].get<int>();
-        auto seed = task["seed"].get<int>();
-        auto n_fold = task["fold"].get<int>();
-        bool stratified = config.stratified;
-        // Generate the hyperparamters combinations
-        auto& dataset = datasets.getDataset(dataset_name);
-        auto combinations = grid.getGrid(dataset_name);
-        dataset.load();
-        auto [X, y] = dataset.getTensors();
-        auto features = dataset.getFeatures();
-        auto className = dataset.getClassName();
-        //
-        // Start working on task
-        //
-        folding::Fold* fold;
-        if (stratified)
-            fold = new folding::StratifiedKFold(config.n_folds, y, seed);
-        else
-            fold = new folding::KFold(config.n_folds, y.size(0), seed);
-        auto [train, test] = fold->getFold(n_fold);
-        auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
-        auto states = dataset.getStates(); // Get the states of the features Once they are discretized
-        double best_fold_score = 0.0;
-        int best_idx_combination = -1;
-        bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE;
-        json best_fold_hyper;
-        for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
-            auto hyperparam_line = combinations[idx_combination];
-            auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
-            folding::Fold* nested_fold;
-            if (config.stratified)
-                nested_fold = new folding::StratifiedKFold(config.nested, y_train, seed);
-            else
-                nested_fold = new folding::KFold(config.nested, y_train.size(0), seed);
-            double score = 0.0;
-            for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
-                // Nested level fold
-                auto [train_nested, test_nested] = nested_fold->getFold(n_nested_fold);
-                auto train_nested_t = torch::tensor(train_nested);
-                auto test_nested_t = torch::tensor(test_nested);
-                auto X_nested_train = X_train.index({ "...", train_nested_t });
-                auto y_nested_train = y_train.index({ train_nested_t });
-                auto X_nested_test = X_train.index({ "...", test_nested_t });
-                auto y_nested_test = y_train.index({ test_nested_t });
-                // Build Classifier with selected hyperparameters
-                auto clf = Models::instance()->create(config.model);
-                auto valid = clf->getValidHyperparameters();
-                hyperparameters.check(valid, dataset_name);
-                clf->setHyperparameters(hyperparameters.get(dataset_name));
-                // Train model
-                clf->fit(X_nested_train, y_nested_train, features, className, states, smoothing);
-                // Test model
-                score += clf->score(X_nested_test, y_nested_test);
-            }
-            delete nested_fold;
-            score /= config.nested;
-            if (score > best_fold_score) {
-                best_fold_score = score;
-                best_idx_combination = idx_combination;
-                best_fold_hyper = hyperparam_line;
-            }
-        }
-        delete fold;
-        // Build Classifier with the best hyperparameters to obtain the best score
-        auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
-        auto clf = Models::instance()->create(config.model);
-        auto valid = clf->getValidHyperparameters();
-        hyperparameters.check(valid, dataset_name);
-        clf->setHyperparameters(best_fold_hyper);
-        clf->fit(X_train, y_train, features, className, states, smoothing);
-        best_fold_score = clf->score(X_test, y_test);
-        // Return the result
-        result->idx_dataset = task["idx_dataset"].get<int>();
-        result->idx_combination = best_idx_combination;
-        result->score = best_fold_score;
-        result->n_fold = n_fold;
-        result->time = timer.getDuration();
-        // Update progress bar
-        std::cout << get_color_rank(config_mpi.rank) << "*" << std::flush;
-    }
-    json store_result(std::vector<std::string>& names, Task_Result& result, json& results)
-    {
-        json json_result = {
-            { "score", result.score },
-            { "combination", result.idx_combination },
-            { "fold", result.n_fold },
-            { "time", result.time },
-            { "dataset", result.idx_dataset }
-        };
-        auto name = names[result.idx_dataset];
-        if (!results.contains(name)) {
-            results[name] = json::array();
-        }
-        results[name].push_back(json_result);
-        return results;
-    }
-    json producer(std::vector<std::string>& names, json& tasks, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
-    {
-        Task_Result result;
-        json results;
-        int num_tasks = tasks.size();
-
-        //
-        // 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
-        //
-        for (int i = 0; i < num_tasks; ++i) {
-            MPI_Status status;
-            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_RESULT) {
-                //Store result
-                store_result(names, result, results);
-            }
-            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_TASK, MPI_COMM_WORLD);
-        }
-        //
-        // 2a.2 Producer will send the end message to all the consumers
-        //
-        for (int i = 0; i < config_mpi.n_procs - 1; ++i) {
-            MPI_Status status;
-            MPI_Recv(&result, 1, MPI_Result, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_RESULT) {
-                //Store result
-                store_result(names, result, results);
-            }
-            MPI_Send(&i, 1, MPI_INT, status.MPI_SOURCE, TAG_END, MPI_COMM_WORLD);
-        }
-        return results;
-    }
-    void select_best_results_folds(json& results, json& all_results, std::string& model)
-    {
-        Timer timer;
-        auto grid = GridData(Paths::grid_input(model));
-        //
-        // Select the best result of the computed outer folds
-        //
-        for (const auto& result : all_results.items()) {
-            // each result has the results of all the outer folds as each one were a different task
-            double best_score = 0.0;
-            json best;
-            for (const auto& result_fold : result.value()) {
-                double score = result_fold["score"].get<double>();
-                if (score > best_score) {
-                    best_score = score;
-                    best = result_fold;
-                }
-            }
-            auto dataset = result.key();
-            auto combinations = grid.getGrid(dataset);
-            json json_best = {
-                    { "score", best_score },
-                    { "hyperparameters", combinations[best["combination"].get<int>()] },
-                    { "date", get_date() + " " + get_time() },
-                    { "grid", grid.getInputGrid(dataset) },
-                    { "duration", timer.translate2String(best["time"].get<double>()) }
-            };
-            results[dataset] = json_best;
-        }
-    }
-    void consumer(Datasets& datasets, json& tasks, struct ConfigGrid& config, struct ConfigMPI& config_mpi, MPI_Datatype& MPI_Result)
-    {
-        Task_Result result;
-        //
-        // 2b.1 Consumers announce to the producer that they are ready to receive a task
-        //
-        MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_QUERY, MPI_COMM_WORLD);
-        int task;
-        while (true) {
-            MPI_Status status;
-            //
-            // 2b.2 Consumers receive the task from the producer and process it
-            //
-            MPI_Recv(&task, 1, MPI_INT, config_mpi.manager, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
-            if (status.MPI_TAG == TAG_END) {
-                break;
-            }
-            process_task_mpi_consumer(config, config_mpi, tasks, task, datasets, &result);
-            //
-            // 2b.3 Consumers send the result to the producer
-            //
-            MPI_Send(&result, 1, MPI_Result, config_mpi.manager, TAG_RESULT, MPI_COMM_WORLD);
-        }
-    }
-    void GridSearch::go(struct ConfigMPI& config_mpi)
-    {
-        /*
-        * Each task is a json object with the following structure:
-        * {
-        *   "dataset": "dataset_name",
-        *   "idx_dataset": idx_dataset, // used to identify the dataset in the results
-        *    // this index is relative to the used datasets in the actual run not to the whole datasets
-        *   "seed": # of seed to use,
-        *   "Fold": # of fold to process
-        * }
-        *
-        * The overall process consists in these steps:
-           * 0. Create the MPI result type & tasks
-           * 0.1 Create the MPI result type
-           * 0.2 Manager creates the tasks
-           * 1. Manager will broadcast the tasks to all the processes
-           * 1.1 Broadcast the number of tasks
-           * 1.2 Broadcast the length of the following string
-           * 1.2 Broadcast the tasks as a char* string
-           * 2a. Producer delivers the tasks to the consumers
-           * 2a.1 Producer will loop to send all the tasks to the consumers and receive the results
-           * 2a.2 Producer will send the end message to all the consumers
-           * 2b. Consumers process the tasks and send the results to the producer
-           * 2b.1 Consumers announce to the producer that they are ready to receive a task
-           * 2b.2 Consumers receive the task from the producer and process it
-           * 2b.3 Consumers send the result to the producer
-           * 3. Manager select the bests sccores for each dataset
-           * 3.1 Loop thru all the results obtained from each outer fold (task) and select the best
-           * 3.2 Save the results
-        */
-        //
-        // 0.1 Create the MPI result type
-        //
-        Task_Result result;
-        int tasks_size;
-        MPI_Datatype MPI_Result;
-        MPI_Datatype type[5] = { MPI_UNSIGNED, MPI_UNSIGNED, MPI_INT, MPI_DOUBLE, MPI_DOUBLE };
-        int blocklen[5] = { 1, 1, 1, 1, 1 };
-        MPI_Aint disp[5];
-        disp[0] = offsetof(Task_Result, idx_dataset);
-        disp[1] = offsetof(Task_Result, idx_combination);
-        disp[2] = offsetof(Task_Result, n_fold);
-        disp[3] = offsetof(Task_Result, score);
-        disp[4] = offsetof(Task_Result, time);
-        MPI_Type_create_struct(5, blocklen, disp, type, &MPI_Result);
-        MPI_Type_commit(&MPI_Result);
-        //
-        // 0.2 Manager creates the tasks
-        //
-        char* msg;
-        json tasks;
-        if (config_mpi.rank == config_mpi.manager) {
-            timer.start();
-            tasks = build_tasks_mpi(config_mpi.rank);
-            auto tasks_str = tasks.dump();
-            tasks_size = tasks_str.size();
-            msg = new char[tasks_size + 1];
-            strcpy(msg, tasks_str.c_str());
-        }
-        //
-        // 1. Manager will broadcast the tasks to all the processes
-        //
-        MPI_Bcast(&tasks_size, 1, MPI_INT, config_mpi.manager, MPI_COMM_WORLD);
-        if (config_mpi.rank != config_mpi.manager) {
-            msg = new char[tasks_size + 1];
-        }
-        MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
-        tasks = json::parse(msg);
-        delete[] msg;
-        auto env = platform::DotEnv();
-        auto datasets = Datasets(config.discretize, Paths::datasets(), env.get("discretize_algo"));
-
-        if (config_mpi.rank == config_mpi.manager) {
-            //
-            // 2a. Producer delivers the tasks to the consumers
-            //
-            auto datasets_names = filterDatasets(datasets);
-            json all_results = producer(datasets_names, tasks, config_mpi, MPI_Result);
-            std::cout << get_color_rank(config_mpi.rank) << separator << std::endl;
-            //
-            // 3. Manager select the bests sccores for each dataset
-            //
-            auto results = initializeResults();
-            select_best_results_folds(results, all_results, config.model);
-            //
-            // 3.2 Save the results
-            //
-            save(results);
-        } else {
-            //
-            // 2b. Consumers process the tasks and send the results to the producer
-            //
-            consumer(datasets, tasks, config, config_mpi, MPI_Result);
-        }
-    }
     json GridSearch::initializeResults()
     {
         // Load previous results if continue is set
         json results;
         if (config.continue_from != NO_CONTINUE()) {
             if (!config.quiet)
-                std::cout << "* Loading previous results" << std::endl;
+                std::cout << Colors::RESET() << "* Loading previous results" << std::endl;
             try {
                 std::ifstream file(Paths::grid_output(config.model));
                 if (file.is_open()) {
@@ -420,4 +93,167 @@ namespace platform {
         };
         file << output.dump(4);
     }
-} /* namespace platform */
+    void GridSearch::compile_results(json& results, json& all_results, std::string& model)
+    {
+        Timer timer;
+        auto grid = GridData(Paths::grid_input(model));
+        //
+        // Select the best result of the computed outer folds
+        //
+        for (const auto& result : all_results.items()) {
+            // each result has the results of all the outer folds as each one were a different task
+            double best_score = 0.0;
+            json best;
+            for (const auto& result_fold : result.value()) {
+                double score = result_fold["score"].get<double>();
+                if (score > best_score) {
+                    best_score = score;
+                    best = result_fold;
+                }
+            }
+            auto dataset = result.key();
+            auto combinations = grid.getGrid(dataset);
+            json json_best = {
+                    { "score", best_score },
+                    { "hyperparameters", combinations[best["combination"].get<int>()] },
+                    { "date", get_date() + " " + get_time() },
+                    { "grid", grid.getInputGrid(dataset) },
+                    { "duration", timer.translate2String(best["time"].get<double>()) }
+            };
+            results[dataset] = json_best;
+        }
+    }
+    json GridSearch::store_result(std::vector<std::string>& names, Task_Result& result, json& results)
+    {
+        json json_result = {
+            { "score", result.score },
+            { "combination", result.idx_combination },
+            { "fold", result.n_fold },
+            { "time", result.time },
+            { "dataset", result.idx_dataset },
+            { "process", result.process },
+            { "task", result.task }
+        };
+        auto name = names[result.idx_dataset];
+        if (!results.contains(name)) {
+            results[name] = json::array();
+        }
+        results[name].push_back(json_result);
+        return results;
+    }
+    void GridSearch::consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result)
+    {
+        //
+        // initialize
+        //
+        Timer timer;
+        timer.start();
+        json task = tasks[n_task];
+        auto model = config.model;
+        auto grid = GridData(Paths::grid_input(model));
+        auto dataset_name = task["dataset"].get<std::string>();
+        auto idx_dataset = task["idx_dataset"].get<int>();
+        auto seed = task["seed"].get<int>();
+        auto n_fold = task["fold"].get<int>();
+        bool stratified = config.stratified;
+        bayesnet::Smoothing_t smooth;
+        if (config.smooth_strategy == "ORIGINAL")
+            smooth = bayesnet::Smoothing_t::ORIGINAL;
+        else if (config.smooth_strategy == "LAPLACE")
+            smooth = bayesnet::Smoothing_t::LAPLACE;
+        else if (config.smooth_strategy == "CESTNIK")
+            smooth = bayesnet::Smoothing_t::CESTNIK;
+        //
+        // Generate the hyperparameters combinations
+        //
+        auto& dataset = datasets.getDataset(dataset_name);
+        auto combinations = grid.getGrid(dataset_name);
+        dataset.load();
+        auto [X, y] = dataset.getTensors();
+        auto features = dataset.getFeatures();
+        auto className = dataset.getClassName();
+        //
+        // Start working on task
+        //
+        folding::Fold* fold;
+        if (stratified)
+            fold = new folding::StratifiedKFold(config.n_folds, y, seed);
+        else
+            fold = new folding::KFold(config.n_folds, y.size(0), seed);
+        auto [train, test] = fold->getFold(n_fold);
+        auto [X_train, X_test, y_train, y_test] = dataset.getTrainTestTensors(train, test);
+        auto states = dataset.getStates(); // Get the states of the features Once they are discretized
+        float best_fold_score = 0.0;
+        int best_idx_combination = -1;
+        json best_fold_hyper;
+        for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
+            auto hyperparam_line = combinations[idx_combination];
+            auto hyperparameters = platform::HyperParameters(datasets.getNames(), hyperparam_line);
+            folding::Fold* nested_fold;
+            if (config.stratified)
+                nested_fold = new folding::StratifiedKFold(config.nested, y_train, seed);
+            else
+                nested_fold = new folding::KFold(config.nested, y_train.size(0), seed);
+            double score = 0.0;
+            for (int n_nested_fold = 0; n_nested_fold < config.nested; n_nested_fold++) {
+                //
+                // Nested level fold
+                //
+                auto [train_nested, test_nested] = nested_fold->getFold(n_nested_fold);
+                auto train_nested_t = torch::tensor(train_nested);
+                auto test_nested_t = torch::tensor(test_nested);
+                auto X_nested_train = X_train.index({ "...", train_nested_t });
+                auto y_nested_train = y_train.index({ train_nested_t });
+                auto X_nested_test = X_train.index({ "...", test_nested_t });
+                auto y_nested_test = y_train.index({ test_nested_t });
+                //
+                // Build Classifier with selected hyperparameters
+                //
+                auto clf = Models::instance()->create(config.model);
+                auto valid = clf->getValidHyperparameters();
+                hyperparameters.check(valid, dataset_name);
+                clf->setHyperparameters(hyperparameters.get(dataset_name));
+                //
+                // Train model
+                //
+                clf->fit(X_nested_train, y_nested_train, features, className, states, smooth);
+                //
+                // Test model
+                //
+                score += clf->score(X_nested_test, y_nested_test);
+            }
+            delete nested_fold;
+            score /= config.nested;
+            if (score > best_fold_score) {
+                best_fold_score = score;
+                best_idx_combination = idx_combination;
+                best_fold_hyper = hyperparam_line;
+            }
+        }
+        delete fold;
+        //
+        // Build Classifier with the best hyperparameters to obtain the best score
+        //
+        auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
+        auto clf = Models::instance()->create(config.model);
+        auto valid = clf->getValidHyperparameters();
+        hyperparameters.check(valid, dataset_name);
+        clf->setHyperparameters(best_fold_hyper);
+        clf->fit(X_train, y_train, features, className, states, smooth);
+        best_fold_score = clf->score(X_test, y_test);
+        //
+        // Return the result
+        //
+        result->idx_dataset = task["idx_dataset"].get<int>();
+        result->idx_combination = best_idx_combination;
+        result->score = best_fold_score;
+        result->n_fold = n_fold;
+        result->time = timer.getDuration();
+        result->process = config_mpi.rank;
+        result->task = n_task;
+        //
+        // Update progress bar
+        //
+        std::cout << get_color_rank(config_mpi.rank) << std::flush;
+    }
+} /* namespace platform */

src/grid/GridSearch.h

@@ -4,47 +4,20 @@
 #include <map>
 #include <mpi.h>
 #include <nlohmann/json.hpp>
+#include <folding.hpp>
 #include "common/Datasets.h"
-#include "common/Timer.h"
+#include "common/Timer.hpp"
 #include "main/HyperParameters.h"
 #include "GridData.h"
+#include "GridBase.h"
+#include "bayesnet/network/Network.h"
+
 
 namespace platform {
     using json = nlohmann::ordered_json;
-    struct ConfigGrid {
-        std::string model;
-        std::string score;
-        std::string continue_from;
-        std::string platform;
-        bool quiet;
-        bool only; // used with continue_from to only compute that dataset
-        bool discretize;
-        bool stratified;
-        int nested;
-        int n_folds;
-        json excluded;
-        std::vector<int> seeds;
-    };
-    struct ConfigMPI {
-        int rank;
-        int n_procs;
-        int manager;
-    };
-    typedef struct {
-        uint idx_dataset;
-        uint idx_combination;
-        int n_fold;
-        double score;
-        double time;
-    } Task_Result;
-    const int TAG_QUERY = 1;
-    const int TAG_RESULT = 2;
-    const int TAG_TASK = 3;
-    const int TAG_END = 4;
-    class GridSearch {
+    class GridSearch : public GridBase {
     public:
         explicit GridSearch(struct ConfigGrid& config);
-        void go(struct ConfigMPI& config_mpi);
         ~GridSearch() = default;
         json loadResults();
         static inline std::string NO_CONTINUE() { return "NO_CONTINUE"; }
@@ -52,10 +25,9 @@ namespace platform {
         void save(json& results);
         json initializeResults();
         std::vector<std::string> filterDatasets(Datasets& datasets) const;
-        struct ConfigGrid config;
-        json build_tasks_mpi(int rank);
-        Timer timer; // used to measure the time of the whole process
-        const std::string separator = "|";
+        void compile_results(json& results, json& all_results, std::string& model);
+        json store_result(std::vector<std::string>& names, Task_Result& result, json& results);
+        void consumer_go(struct ConfigGrid& config, struct ConfigMPI& config_mpi, json& tasks, int n_task, Datasets& datasets, Task_Result* result);
     };
 } /* namespace platform */
 #endif

src/main/ArgumentsExperiment.cpp

@@ -0,0 +1,230 @@
+#include "common/Datasets.h"
+#include "common/DotEnv.h"
+#include "common/Paths.h"
+#include "main/Models.h"
+#include "main/modelRegister.h"
+#include "ArgumentsExperiment.h"
+namespace platform {
+    ArgumentsExperiment::ArgumentsExperiment(argparse::ArgumentParser& program, experiment_t type) : arguments{ program }, type{ type }
+    {
+    }
+    void ArgumentsExperiment::add_arguments()
+    {
+        auto env = platform::DotEnv();
+        auto datasets = platform::Datasets(false, platform::Paths::datasets());
+        auto& group = arguments.add_mutually_exclusive_group(true);
+        group.add_argument("-d", "--dataset")
+            .help("Dataset file name: " + datasets.toString())
+            .default_value("all")
+            .action([](const std::string& value) {
+            auto datasets = platform::Datasets(false, platform::Paths::datasets());
+            static std::vector<std::string> choices_datasets(datasets.getNames());
+            choices_datasets.push_back("all");
+            if (find(choices_datasets.begin(), choices_datasets.end(), value) != choices_datasets.end()) {
+                return value;
+            }
+            throw std::runtime_error("Dataset must be one of: " + datasets.toString());
+                }
+            );
+        group.add_argument("--datasets").nargs(1, 50).help("Datasets file names 1..50 separated by spaces").default_value(std::vector<std::string>());
+        group.add_argument("--datasets-file").default_value("").help("Datasets file name. Mutually exclusive with dataset. This file should contain a list of datasets to test.");
+        arguments.add_argument("--hyperparameters").default_value("{}").help("Hyperparameters passed to the model in Experiment");
+        arguments.add_argument("--hyper-file").default_value("").help("Hyperparameters file name." \
+            "Mutually exclusive with hyperparameters. This file should contain hyperparameters for each dataset in json format.");
+        arguments.add_argument("--hyper-best").default_value(false).help("Use best results of the model as source of hyperparameters").implicit_value(true);
+        arguments.add_argument("-m", "--model")
+            .help("Model to use: " + platform::Models::instance()->toString())
+            .action([](const std::string& value) {
+            static const std::vector<std::string> choices = platform::Models::instance()->getNames();
+            if (find(choices.begin(), choices.end(), value) != choices.end()) {
+                return value;
+            }
+            throw std::runtime_error("Model must be one of " + platform::Models::instance()->toString());
+                }
+            );
+        arguments.add_argument("--title").default_value("").help("Experiment title");
+        arguments.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
+        auto valid_choices = env.valid_tokens("discretize_algo");
+        auto& disc_arg = arguments.add_argument("--discretize-algo").help("Algorithm to use in discretization. Valid values: " + env.valid_values("discretize_algo")).default_value(env.get("discretize_algo"));
+        for (auto choice : valid_choices) {
+            disc_arg.choices(choice);
+        }
+        valid_choices = env.valid_tokens("smooth_strat");
+        auto& smooth_arg = arguments.add_argument("--smooth-strat").help("Smooth strategy used in Bayes Network node initialization. Valid values: " + env.valid_values("smooth_strat")).default_value(env.get("smooth_strat"));
+        for (auto choice : valid_choices) {
+            smooth_arg.choices(choice);
+        }
+        auto& score_arg = arguments.add_argument("-s", "--score").help("Score to use. Valid values: " + env.valid_values("score")).default_value(env.get("score"));
+        valid_choices = env.valid_tokens("score");
+        for (auto choice : valid_choices) {
+            score_arg.choices(choice);
+        }
+        arguments.add_argument("--no-train-score").help("Don't compute train score").default_value(false).implicit_value(true);
+        arguments.add_argument("--quiet").help("Don't display detailed progress").default_value(false).implicit_value(true);
+        arguments.add_argument("--save").help("Save result (always save even if a dataset is supplied)").default_value(false).implicit_value(true);
+        arguments.add_argument("--stratified").help("If Stratified KFold is to be done").default_value((bool)stoi(env.get("stratified"))).implicit_value(true);
+        arguments.add_argument("-f", "--folds").help("Number of folds").default_value(stoi(env.get("n_folds"))).scan<'i', int>().action([](const std::string& value) {
+            try {
+                auto k = stoi(value);
+                if (k < 2) {
+                    throw std::runtime_error("Number of folds must be greater than 1");
+                }
+                return k;
+            }
+            catch (const runtime_error& err) {
+                throw std::runtime_error(err.what());
+            }
+            catch (...) {
+                throw std::runtime_error("Number of folds must be an integer");
+            }});
+            auto seed_values = env.getSeeds();
+            arguments.add_argument("--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
+            if (type == experiment_t::NORMAL) {
+                arguments.add_argument("--generate-fold-files").help("generate fold information in datasets_experiment folder").default_value(false).implicit_value(true);
+                arguments.add_argument("--graph").help("generate graphviz dot files with the model").default_value(false).implicit_value(true);
+            }
+    }
+    void ArgumentsExperiment::parse_args(int argc, char** argv)
+    {
+        try {
+            arguments.parse_args(argc, argv);
+        }
+        catch (const exception& err) {
+            cerr << err.what() << std::endl;
+            cerr << arguments;
+            exit(1);
+        }
+        parse();
+    }
+
+    void ArgumentsExperiment::parse()
+    {
+        try {
+            file_name = arguments.get<std::string>("dataset");
+            file_names = arguments.get<std::vector<std::string>>("datasets");
+            datasets_file = arguments.get<std::string>("datasets-file");
+            model_name = arguments.get<std::string>("model");
+            discretize_dataset = arguments.get<bool>("discretize");
+            discretize_algo = arguments.get<std::string>("discretize-algo");
+            smooth_strat = arguments.get<std::string>("smooth-strat");
+            stratified = arguments.get<bool>("stratified");
+            quiet = arguments.get<bool>("quiet");
+            n_folds = arguments.get<int>("folds");
+            score = arguments.get<std::string>("score");
+            seeds = arguments.get<std::vector<int>>("seeds");
+            auto hyperparameters = arguments.get<std::string>("hyperparameters");
+            hyperparameters_json = json::parse(hyperparameters);
+            hyperparameters_file = arguments.get<std::string>("hyper-file");
+            no_train_score = arguments.get<bool>("no-train-score");
+            hyper_best = arguments.get<bool>("hyper-best");
+            if (hyper_best) {
+                // Build the best results file_name
+                hyperparameters_file = platform::Paths::results() + platform::Paths::bestResultsFile(score, model_name);
+                // ignore this parameter
+                hyperparameters = "{}";
+            } else {
+                if (hyperparameters_file != "" && hyperparameters != "{}") {
+                    throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
+                }
+            }
+            title = arguments.get<std::string>("title");
+            if (title == "" && file_name == "all") {
+                throw runtime_error("title is mandatory if all datasets are to be tested");
+            }
+            saveResults = arguments.get<bool>("save");
+            if (type == experiment_t::NORMAL) {
+                graph = arguments.get<bool>("graph");
+                generate_fold_files = arguments.get<bool>("generate-fold-files");
+            } else {
+                graph = false;
+                generate_fold_files = false;
+            }
+        }
+        catch (const exception& err) {
+            cerr << err.what() << std::endl;
+            cerr << arguments;
+            exit(1);
+        }
+        auto datasets = platform::Datasets(false, platform::Paths::datasets());
+        if (datasets_file != "") {
+            ifstream catalog(datasets_file);
+            if (catalog.is_open()) {
+                std::string line;
+                while (getline(catalog, line)) {
+                    if (line.empty() || line[0] == '#') {
+                        continue;
+                    }
+                    if (!datasets.isDataset(line)) {
+                        cerr << "Dataset " << line << " not found" << std::endl;
+                        exit(1);
+                    }
+                    filesToTest.push_back(line);
+                }
+                catalog.close();
+                saveResults = true;
+                if (title == "") {
+                    title = "Test " + to_string(filesToTest.size()) + " datasets (" + datasets_file + ") "\
+                        + model_name + " " + to_string(n_folds) + " folds";
+                }
+            } else {
+                throw std::invalid_argument("Unable to open catalog file. [" + datasets_file + "]");
+            }
+        } else {
+            if (file_names.size() > 0) {
+                for (auto file : file_names) {
+                    if (!datasets.isDataset(file)) {
+                        cerr << "Dataset " << file << " not found" << std::endl;
+                        exit(1);
+                    }
+                }
+                filesToTest = file_names;
+                sort(filesToTest.begin(), filesToTest.end(), [](const auto& lhs, const auto& rhs) {
+                    const auto result = mismatch(lhs.cbegin(), lhs.cend(), rhs.cbegin(), rhs.cend(), [](const auto& lhs, const auto& rhs) {return tolower(lhs) == tolower(rhs);});
+
+                    return result.second != rhs.cend() && (result.first == lhs.cend() || tolower(*result.first) < tolower(*result.second));
+                    });
+                saveResults = true;
+                if (title == "") {
+                    title = "Test " + to_string(file_names.size()) + " datasets " + model_name + " " + to_string(n_folds) + " folds";
+                }
+            } else {
+                if (file_name != "all") {
+                    if (!datasets.isDataset(file_name)) {
+                        cerr << "Dataset " << file_name << " not found" << std::endl;
+                        exit(1);
+                    }
+                    if (title == "") {
+                        title = "Test " + file_name + " " + model_name + " " + to_string(n_folds) + " folds";
+                    }
+                    filesToTest.push_back(file_name);
+                } else {
+                    filesToTest = datasets.getNames();
+                    saveResults = true;
+                }
+            }
+        }
+        if (hyperparameters_file != "") {
+            test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_file, hyper_best);
+        } else {
+            test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_json);
+        }
+    }
+    Experiment& ArgumentsExperiment::initializedExperiment()
+    {
+        auto env = platform::DotEnv();
+        experiment.setTitle(title).setLanguage("c++").setLanguageVersion("gcc 14.1.1");
+        experiment.setDiscretizationAlgorithm(discretize_algo).setSmoothSrategy(smooth_strat);
+        experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
+        experiment.setStratified(stratified).setNFolds(n_folds).setScoreName(score);
+        experiment.setHyperparameters(test_hyperparams);
+        for (auto seed : seeds) {
+            experiment.addRandomSeed(seed);
+        }
+        experiment.setFilesToTest(filesToTest);
+        experiment.setQuiet(quiet);
+        experiment.setNoTrainScore(no_train_score);
+        experiment.setGenerateFoldFiles(generate_fold_files);
+        experiment.setGraph(graph);
+        return experiment;
+    }
+}

src/main/ArgumentsExperiment.h

@@ -0,0 +1,39 @@
+#ifndef ARGUMENTSEXPERIMENT_H
+#define ARGUMENTSEXPERIMENT_H
+#include <string>
+#include <iostream>
+#include <vector>
+#include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
+#include "Experiment.h"
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    enum class experiment_t { NORMAL, GRID };
+    class ArgumentsExperiment {
+    public:
+        ArgumentsExperiment(argparse::ArgumentParser& program, experiment_t type);
+        ~ArgumentsExperiment() = default;
+        std::vector<std::string> getFilesToTest() const { return filesToTest; }
+        void add_arguments();
+        void parse_args(int argc, char** argv);
+        void parse();
+        Experiment& initializedExperiment();
+        bool isQuiet() const { return quiet; }
+        bool haveToSaveResults() const { return saveResults; }
+        bool doGraph() const { return graph; }
+    private:
+        Experiment experiment;
+        experiment_t type;
+        argparse::ArgumentParser& arguments;
+        std::string file_name, model_name, title, hyperparameters_file, datasets_file, discretize_algo, smooth_strat, score;
+        json hyperparameters_json;
+        bool discretize_dataset, stratified, saveResults, quiet, no_train_score, generate_fold_files, graph, hyper_best;
+        std::vector<int> seeds;
+        std::vector<std::string> file_names;
+        std::vector<std::string> filesToTest;
+        platform::HyperParameters test_hyperparams;
+        int n_folds;
+    };
+}
+#endif

src/main/Experiment.cpp

@@ -9,14 +9,16 @@ namespace platform {
 
     void Experiment::saveResult()
     {
+        result.setSchemaVersion("1.0");
+        result.check();
         result.save();
         std::cout << "Result saved in " << Paths::results() << result.getFilename() << std::endl;
     }
-    void Experiment::report(bool classification_report)
+    void Experiment::report()
     {
         ReportConsole report(result.getJson());
         report.show();
-        if (classification_report) {
+        if (filesToTest.size() == 1) {
             std::cout << report.showClassificationReport(Colors::BLUE());
         }
     }
@@ -41,9 +43,25 @@ namespace platform {
             }
         }
     }
-    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files, bool graph)
+    Experiment& Experiment::setSmoothSrategy(const std::string& smooth_strategy)
     {
-        for (auto fileName : filesToProcess) {
+        this->smooth_strategy = smooth_strategy;
+        this->result.setSmoothStrategy(smooth_strategy);
+        if (smooth_strategy == "ORIGINAL")
+            smooth_type = bayesnet::Smoothing_t::ORIGINAL;
+        else if (smooth_strategy == "LAPLACE")
+            smooth_type = bayesnet::Smoothing_t::LAPLACE;
+        else if (smooth_strategy == "CESTNIK")
+            smooth_type = bayesnet::Smoothing_t::CESTNIK;
+        else {
+            std::cerr << "Experiment: Unknown smoothing strategy: " << smooth_strategy << std::endl;
+            exit(1);
+        }
+        return *this;
+    }
+    void Experiment::go()
+    {
+        for (auto fileName : filesToTest) {
             if (fileName.size() > max_name)
                 max_name = fileName.size();
         }
@@ -58,14 +76,16 @@ namespace platform {
             std::cout << " ( " << Colors::GREEN() << "b" << Colors::RESET() << " )  Scoring train dataset" << std::endl;
             std::cout << " ( " << Colors::GREEN() << "c" << Colors::RESET() << " )  Scoring test dataset" << std::endl << std::endl;
             std::cout << Colors::YELLOW() << "Note: fold number in this color means fitting had issues such as not using all features in BoostAODE classifier" << std::endl << std::endl;
-            std::cout << Colors::GREEN() << left << "  #  " << setw(max_name) << "Dataset" << " #Samp #Feat Seed Status" << string(3 * nfolds - 2, ' ') << " Time" << std::endl;
-            std::cout << " --- " << string(max_name, '-') << " ----- ----- ---- " << string(4 + 3 * nfolds, '-') << " ----------" << Colors::RESET() << std::endl;
+            int nc = 4 + 3 * nfolds + (nfolds >= 10 ? nfolds - 10 + 1 : 0);
+            std::cout << Colors::GREEN() << left << "  #  " << setw(max_name) << "Dataset" << " #Samp #Feat Seed Status" << string(nc - 6, ' ') << setw(11) << " Time" << " Score" << std::endl;
+            std::cout << " --- " << string(max_name, '-') << " ----- ----- ---- " << string(nc, '-') << " ----------" << " ---------";
+            std::cout << Colors::RESET() << std::endl;
         }
         int num = 0;
-        for (auto fileName : filesToProcess) {
+        for (auto fileName : filesToTest) {
             if (!quiet)
                 std::cout << " " << setw(3) << right << num++ << " " << setw(max_name) << left << fileName << right << flush;
-            cross_validation(fileName, quiet, no_train_score, generate_fold_files, graph);
+            cross_validation(fileName);
             if (!quiet)
                 std::cout << std::endl;
         }
@@ -95,7 +115,8 @@ namespace platform {
     }
     void showProgress(int fold, const std::string& color, const std::string& phase)
     {
-        std::string prefix = phase == "-" ? "" : "\b\b\b\b";
+        int nc = fold >= 10 ? 5 : 4;
+        std::string prefix = phase == "-" ? "" : std::string(nc, '\b');
         std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
 
     }
@@ -137,7 +158,7 @@ namespace platform {
         file << output.dump(4);
         file.close();
     }
-    void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score, bool generate_fold_files, bool graph)
+    void Experiment::cross_validation(const std::string& fileName)
     {
         //
         // Load dataset and prepare data
@@ -277,10 +298,13 @@ namespace platform {
             }
             if (!quiet) {
                 seed_timer.stop();
-                std::cout << "end. [" << seed_timer.getDurationString() << "]" << std::endl;
+                std::cout << "end. " << std::setw(10) << std::right << seed_timer.getDurationString();
             }
             delete fold;
         }
+        // Show Results
+        if (!quiet)
+            std::cout << " " << setw(9) << right << std::fixed << std::setprecision(7) << torch::mean(score_test).item<double>();
         //
         // Store result totals in Result
         //

src/main/Experiment.h

@@ -25,21 +25,7 @@ namespace platform {
         {
             this->discretization_algo = discretization_algo; this->result.setDiscretizationAlgorithm(discretization_algo); return *this;
         }
-        Experiment& setSmoothSrategy(const std::string& smooth_strategy)
-        {
-            this->smooth_strategy = smooth_strategy; this->result.setSmoothStrategy(smooth_strategy);
-            if (smooth_strategy == "ORIGINAL")
-                smooth_type = bayesnet::Smoothing_t::ORIGINAL;
-            else if (smooth_strategy == "LAPLACE")
-                smooth_type = bayesnet::Smoothing_t::LAPLACE;
-            else if (smooth_strategy == "CESTNIK")
-                smooth_type = bayesnet::Smoothing_t::CESTNIK;
-            else {
-                std::cerr << "Experiment: Unknown smoothing strategy: " << smooth_strategy << std::endl;
-                exit(1);
-            }
-            return *this;
-        }
+        Experiment& setSmoothSrategy(const std::string& smooth_strategy);
         Experiment& setLanguageVersion(const std::string& language_version) { this->result.setLanguageVersion(language_version); return *this; }
         Experiment& setDiscretized(bool discretized) { this->discretized = discretized; result.setDiscretized(discretized); return *this; }
         Experiment& setStratified(bool stratified) { this->stratified = stratified; result.setStratified(stratified); return *this; }
@@ -48,18 +34,33 @@ namespace platform {
         Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); result.addSeed(randomSeed); return *this; }
         Experiment& setDuration(float duration) { this->result.setDuration(duration); return *this; }
         Experiment& setHyperparameters(const HyperParameters& hyperparameters_) { this->hyperparameters = hyperparameters_; return *this; }
-        void cross_validation(const std::string& fileName, bool quiet, bool no_train_score, bool generate_fold_files, bool graph);
-        void go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files, bool graph);
+        HyperParameters& getHyperParameters() { return hyperparameters; }
+        std::string getModel() const { return result.getModel(); }
+        std::string getScore() const { return result.getScoreName(); }
+        bool isDiscretized() const { return discretized; }
+        bool isStratified() const { return stratified; }
+        bool isQuiet() const { return quiet; }
+        std::string getSmoothStrategy() const { return smooth_strategy; }
+        int getNFolds() const { return nfolds; }
+        std::vector<int> getRandomSeeds() const { return randomSeeds; }
+        void cross_validation(const std::string& fileName);
+        void go();
         void saveResult();
         void show();
         void saveGraph();
-        void report(bool classification_report = false);
+        void report();
+        void setFilesToTest(const std::vector<std::string>& filesToTest) { this->filesToTest = filesToTest; }
+        void setQuiet(bool quiet) { this->quiet = quiet; }
+        void setNoTrainScore(bool no_train_score) { this->no_train_score = no_train_score; }
+        void setGenerateFoldFiles(bool generate_fold_files) { this->generate_fold_files = generate_fold_files; }
+        void setGraph(bool graph) { this->graph = graph; }
     private:
         score_t parse_score() const;
         Result result;
-        bool discretized{ false }, stratified{ false };
+        bool discretized{ false }, stratified{ false }, generate_fold_files{ false }, graph{ false }, quiet{ false }, no_train_score{ false };
         std::vector<PartialResult> results;
         std::vector<int> randomSeeds;
+        std::vector<std::string> filesToTest;
         std::string discretization_algo;
         std::string smooth_strategy;
         bayesnet::Smoothing_t smooth_type{ bayesnet::Smoothing_t::NONE };

src/main/Models.h

@@ -5,11 +5,15 @@
 #include <bayesnet/ensembles/AODE.h>
 #include <bayesnet/ensembles/A2DE.h>
 #include <bayesnet/ensembles/AODELd.h>
+#include <bayesnet/ensembles/XBAODE.h>
+#include <bayesnet/ensembles/XBA2DE.h>
 #include <bayesnet/ensembles/BoostAODE.h>
 #include <bayesnet/ensembles/BoostA2DE.h>
 #include <bayesnet/classifiers/TAN.h>
 #include <bayesnet/classifiers/KDB.h>
 #include <bayesnet/classifiers/SPODE.h>
+#include <bayesnet/classifiers/XSPODE.h>
+#include <bayesnet/classifiers/XSP2DE.h>
 #include <bayesnet/classifiers/SPnDE.h>
 #include <bayesnet/classifiers/TANLd.h>
 #include <bayesnet/classifiers/KDBLd.h>
@@ -20,6 +24,8 @@
 #include <pyclassifiers/SVC.h>
 #include <pyclassifiers/XGBoost.h>
 #include <pyclassifiers/RandomForest.h>
+#include "../experimental_clfs/XA1DE.h"
+
 namespace platform {
     class Models {
     public:
@@ -42,4 +48,4 @@ namespace platform {
         Registrar(const std::string& className, function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
     };
 }
-#endif
+#endif

src/main/modelRegister.h

@@ -1,39 +1,49 @@
 #ifndef MODELREGISTER_H
 #define MODELREGISTER_H
-
-static platform::Registrar registrarT("TAN",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
-static platform::Registrar registrarTLD("TANLd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
-static platform::Registrar registrarS("SPODE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
-static platform::Registrar registrarSn("SPnDE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPnDE({ 0, 1 });});
-static platform::Registrar registrarSLD("SPODELd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
-static platform::Registrar registrarK("KDB",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
-static platform::Registrar registrarKLD("KDBLd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
-static platform::Registrar registrarA("AODE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
-static platform::Registrar registrarA2("A2DE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::A2DE();});
-static platform::Registrar registrarALD("AODELd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
-static platform::Registrar registrarBA("BoostAODE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
-static platform::Registrar registrarBA2("BoostA2DE",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostA2DE();});
-static platform::Registrar registrarSt("STree",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::STree();});
-static platform::Registrar registrarOdte("Odte",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::ODTE();});
-static platform::Registrar registrarSvc("SVC",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::SVC();});
-static platform::Registrar registrarRaF("RandomForest",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
-static platform::Registrar registrarXGB("XGBoost",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
-
-#endif
+namespace platform {
+    static Registrar registrarT("TAN",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
+    static Registrar registrarTLD("TANLd",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
+    static Registrar registrarS("SPODE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
+    static Registrar registrarSn("SPnDE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPnDE({ 0, 1 });});
+    static Registrar registrarSLD("SPODELd",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
+    static Registrar registrarK("KDB",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
+    static Registrar registrarKLD("KDBLd",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
+    static Registrar registrarA("AODE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
+    static Registrar registrarA2("A2DE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::A2DE();});
+    static Registrar registrarALD("AODELd",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
+    static Registrar registrarBA("BoostAODE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
+    static Registrar registrarBA2("BoostA2DE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostA2DE();});
+    static Registrar registrarSt("STree",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::STree();});
+    static Registrar registrarOdte("Odte",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::ODTE();});
+    static Registrar registrarSvc("SVC",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::SVC();});
+    static Registrar registrarRaF("RandomForest",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
+    static Registrar registrarXGB("XGBoost",
+        [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
+    static Registrar registrarXSPODE("XSPODE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSpode(0);});
+    static Registrar registrarXSP2DE("XSP2DE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XSp2de(0, 1);});
+    static Registrar registrarXBAODE("XBAODE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XBAODE();});
+    static Registrar registrarXBA2DE("XBA2DE",
+        [](void) -> bayesnet::BaseClassifier* { return new bayesnet::XBA2DE();});
+    static Registrar registrarXA1DE("XA1DE",
+            [](void) -> bayesnet::BaseClassifier* { return new XA1DE();});
+}
+#endif

src/manage/ManageScreen.cpp

@@ -82,10 +82,12 @@ namespace platform {
             workbook_close(workbook);
         }
         if (didExcel) {
-            std::cout << Colors::MAGENTA() << "Excel file created: " << Paths::excel() + Paths::excelResults() << std::endl;
+            excelFileName = Paths::excel() + Paths::excelResults();
+            std::cout << Colors::MAGENTA() << "Excel file created: " << excelFileName << std::endl;
         }
         std::cout << Colors::RESET() << "Done!" << std::endl;
     }
+
     std::string ManageScreen::getVersions()
     {
         std::string kfold_version = folding::KFold(5, 100).version();
@@ -257,8 +259,9 @@ namespace platform {
         auto [index_from, index_to] = paginator[static_cast<int>(output_type)].getOffset();
         for (int i = index_from; i <= index_to; i++) {
             auto color = (i % 2) ? Colors::BLUE() : Colors::CYAN();
-            std::cout << color << std::setw(3) << std::fixed << std::right << i << " ";
-            std::cout << results.at(i).to_string(maxModel, maxTitle) << std::endl;
+            auto color_status = results.at(i).check().size() == 0 ? color : Colors::RED();
+            std::cout << color_status << std::setw(3) << std::fixed << std::right << i << " ";
+            std::cout << color << results.at(i).to_string(maxModel, maxTitle) << std::endl;
         }
         //
         // Status Area
@@ -311,6 +314,34 @@ namespace platform {
             return "Reporting " + results.at(index).getFilename();
         }
     }
+    void ManageScreen::changeModel(const int index)
+    {
+        std::cout << "Old model: " << results.at(index).getModel() << std::endl;
+        std::cout << "New model: ";
+        std::string newModel;
+        getline(std::cin, newModel);
+        if (newModel.empty()) {
+            list("Model not changed", Colors::YELLOW());
+            return;
+        }
+        if (newModel == results.at(index).getModel()) {
+            list("Model already set to " + newModel, Colors::RED());
+            return;
+        }
+        // Remove the old result file
+        std::string oldFile = Paths::results() + results.at(index).getFilename();
+        std::filesystem::remove(oldFile);
+        // Actually change the model
+        results.at(index).setModel(newModel);
+        results.at(index).save();
+        int newModelSize = static_cast<int>(newModel.size());
+        if (newModelSize > maxModel) {
+            maxModel = newModelSize;
+            header_lengths[2] = maxModel;
+            updateSize(rows, cols);
+        }
+        list("Model changed to " + newModel, Colors::GREEN());
+    }
     std::pair<std::string, std::string> ManageScreen::sortList()
     {
         std::vector<std::tuple<std::string, char, bool>>  sortOptions = {
@@ -371,6 +402,7 @@ namespace platform {
             {"list", 'l', false},
             {"Delete", 'D', true},
             {"datasets", 'd', false},
+            {"change model", 'm', true},
             {"hide", 'h', true},
             {"sort", 's', false},
             {"report", 'r', true},
@@ -457,20 +489,19 @@ namespace platform {
                     index_A = index;
                     list("A set to " + std::to_string(index), Colors::GREEN());
                     break;
-                case 'B': // set_b or back to list
-                    if (output_type == OutputType::EXPERIMENTS) {
-                        if (index == index_A) {
-                            list("A and B cannot be the same!", Colors::RED());
-                            break;
-                        }
-                        index_B = index;
-                        list("B set to " + std::to_string(index), Colors::GREEN());
-                    } else {
-                        // back to show the report
-                        output_type = OutputType::RESULT;
-                        paginator[static_cast<int>(OutputType::DETAIL)].setPage(1);
-                        list(STATUS_OK, STATUS_COLOR);
+                case 'B': // set_b
+                    if (index == index_A) {
+                        list("A and B cannot be the same!", Colors::RED());
+                        break;
                     }
+                    index_B = index;
+                    list("B set to " + std::to_string(index), Colors::GREEN());
+                    break;
+                case 'b': // back to list
+                    // back to show the report
+                    output_type = OutputType::RESULT;
+                    paginator[static_cast<int>(OutputType::DETAIL)].setPage(1);
+                    list(STATUS_OK, STATUS_COLOR);
                     break;
                 case 'c':
                     if (index_A == -1 || index_B == -1) {
@@ -497,6 +528,9 @@ namespace platform {
                     paginator[static_cast<int>(OutputType::EXPERIMENTS)].setTotal(results.size());
                     list(filename + " deleted!", Colors::RED());
                     break;
+                case 'm':
+                    changeModel(index);
+                    break;
                 case 'h':
                     {
                         std::string status_message;
@@ -543,7 +577,6 @@ namespace platform {
                     break;
                 case 't':
                     {
-                        std::string status_message;
                         std::cout << "Title: " << results.at(index).getTitle() << std::endl;
                         std::cout << "New title: ";
                         std::string newTitle;
@@ -551,8 +584,7 @@ namespace platform {
                         if (!newTitle.empty()) {
                             results.at(index).setTitle(newTitle);
                             results.at(index).save();
-                            status_message = "Title changed to " + newTitle;
-                            list(status_message, Colors::GREEN());
+                            list("Title changed to " + newTitle, Colors::GREEN());
                             break;
                         }
                         list("No title change!", Colors::YELLOW());

src/manage/ManageScreen.h

@@ -19,6 +19,7 @@ namespace platform {
         ~ManageScreen() = default;
         void doMenu();
         void updateSize(int rows, int cols);
+        std::string getExcelFileName() const { return excelFileName; }
     private:
         void list(const std::string& status, const std::string& color);
         void list_experiments(const std::string& status, const std::string& color);
@@ -27,6 +28,7 @@ namespace platform {
         void list_datasets(const std::string& status, const std::string& color);
         bool confirmAction(const std::string& intent, const std::string& fileName) const;
         std::string report(const int index, const bool excelReport);
+        void changeModel(const int index);
         std::string report_compared();
         std::pair<std::string, std::string> sortList();
         std::string getVersions();
@@ -57,6 +59,7 @@ namespace platform {
         std::vector<Paginator> paginator;
         ResultsManager results;
         lxw_workbook* workbook;
+        std::string excelFileName;
     };
 }
 #endif

src/reports/DatasetsConsole.cpp

@@ -28,7 +28,7 @@ namespace platform {
         auto datasets_names = datasets.getNames();
         int maxName = std::max(size_t(7), (*max_element(datasets_names.begin(), datasets_names.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size());
         std::vector<std::string> header_labels = { " #", "Dataset", "Sampl.", "Feat.", "#Num.", "Cls", "Balance" };
-        std::vector<int> header_lengths = { 3, maxName, 6, 5, 5, 3, DatasetsConsole::BALANCE_LENGTH };
+        std::vector<int> header_lengths = { 3, maxName, 6, 6, 6, 3, DatasetsConsole::BALANCE_LENGTH };
         sheader << Colors::GREEN();
         for (int i = 0; i < header_labels.size(); i++) {
             sheader << setw(header_lengths[i]) << left << header_labels[i] << " ";
@@ -51,14 +51,14 @@ namespace platform {
             auto& dataset = datasets.getDataset(dataset_name);
             dataset.load();
             auto nSamples = dataset.getNSamples();
-            line << setw(6) << right << nSamples << " ";
+            line << setw(header_lengths[2]) << right << nSamples << " ";
             auto nFeatures = dataset.getFeatures().size();
-            line << setw(5) << right << nFeatures << " ";
+            line << setw(header_lengths[3]) << right << nFeatures << " ";
             auto numericFeatures = dataset.getNumericFeatures();
             auto num = std::count(numericFeatures.begin(), numericFeatures.end(), true);
-            line << setw(5) << right << num << " ";
+            line << setw(header_lengths[4]) << right << num << " ";
             auto nClasses = dataset.getNClasses();
-            line << setw(3) << right << nClasses << " ";
+            line << setw(header_lengths[5]) << right << nClasses << " ";
             std::string sep = "";
             oss.str("");
             for (auto number : dataset.getClassesCounts()) {

src/reports/ReportConsole.cpp

@@ -2,7 +2,7 @@
 #include <locale>
 #include "best/BestScore.h"
 #include "common/CLocale.h"
-#include "common/Timer.h"
+#include "common/Timer.hpp"
 #include "ReportConsole.h"
 #include "main/Scores.h"
 
@@ -49,7 +49,8 @@ namespace platform {
         oss << "Execution took  " << timer.translate2String(data["duration"].get<float>())
             << " on " << data["platform"].get<std::string>() << " Language: " << data["language"].get<std::string>();
         sheader << headerLine(oss.str());
-        sheader << headerLine("Score is " + data["score_name"].get<std::string>());
+        std::string schema_version = data.find("schema_version") != data.end() ? data["schema_version"].get<std::string>() : "-";
+        sheader << headerLine("Score is " + data["score_name"].get<std::string>() + " Schema version: " + schema_version);
         sheader << std::string(MAXL, '*') << std::endl;
         sheader << std::endl;
     }
@@ -83,7 +84,7 @@ namespace platform {
         }
         std::vector<std::string> header_labels = { " #", "Dataset", "Sampl.", "Feat.", "Cls", nodes_label, leaves_label, depth_label, "Score", "Time", "Hyperparameters" };
         sheader << Colors::GREEN();
-        std::vector<int> header_lengths = { 3, maxDataset, 6, 5, 3, 9, 9, 9, 15, 20, maxHyper };
+        std::vector<int> header_lengths = { 3, maxDataset, 6, 6, 3, 13, 13, 13, 15, 20, maxHyper };
         for (int i = 0; i < header_labels.size(); i++) {
             sheader << std::setw(header_lengths[i]) << std::left << header_labels[i] << " ";
         }
@@ -107,11 +108,11 @@ namespace platform {
             line << std::setw(3) << std::right << index++ << " ";
             line << std::setw(maxDataset) << std::left << r["dataset"].get<std::string>() << " ";
             line << std::setw(6) << std::right << r["samples"].get<int>() << " ";
-            line << std::setw(5) << std::right << r["features"].get<int>() << " ";
+            line << std::setw(6) << std::right << r["features"].get<int>() << " ";
             line << std::setw(3) << std::right << r["classes"].get<int>() << " ";
-            line << std::setw(9) << std::setprecision(2) << std::fixed << r["nodes"].get<float>() << " ";
-            line << std::setw(9) << std::setprecision(2) << std::fixed << r["leaves"].get<float>() << " ";
-            line << std::setw(9) << std::setprecision(2) << std::fixed << r["depth"].get<float>() << " ";
+            line << std::setw(13) << std::setprecision(2) << std::fixed << r["nodes"].get<float>() << " ";
+            line << std::setw(13) << std::setprecision(2) << std::fixed << r["leaves"].get<float>() << " ";
+            line << std::setw(13) << std::setprecision(2) << std::fixed << r["depth"].get<float>() << " ";
             line << std::setw(8) << std::right << std::setprecision(6) << std::fixed << r["score"].get<double>() << "±" << std::setw(6) << std::setprecision(4) << std::fixed << r["score_std"].get<double>();
             const std::string status = compareResult(r["dataset"].get<std::string>(), r["score"].get<double>());
             line << status;
@@ -223,7 +224,7 @@ namespace platform {
     std::string ReportConsole::buildClassificationReport(json& result, std::string color)
     {
         std::stringstream oss;
-        if (result.find("confusion_matrices") == result.end())
+        if (result.find("confusion_matrices") == result.end() || result["confusion_matrices"].size() == 0)
             return "";
         bool second_header = false;
         int lines_header = 0;

src/results/JsonValidator.h

@@ -0,0 +1,137 @@
+#ifndef JSONVALIDATOR_H
+#define JSONVALIDATOR_H
+#include <fstream>
+#include <vector>
+#include <regex>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class JsonValidator {
+    public:
+        JsonValidator(const json& schema) : schema(schema) {}
+
+        std::vector<std::string> validate_file(const std::string& fileName)
+        {
+            auto data = load_json_file(fileName);
+            return validate(data);
+        }
+        std::vector<std::string> validate(const json& data)
+        {
+            std::vector<std::string> errors;
+            // Validate the top-level object
+            validateObject("", schema, data, errors);
+            return errors;
+        }
+        json load_json_file(const std::string& fileName)
+        {
+            std::ifstream file(fileName);
+            if (!file.is_open()) {
+                throw std::runtime_error("Error: Unable to open file " + fileName);
+            }
+            json data;
+            file >> data;
+            file.close();
+            return data;
+        }
+        void fix_it(const std::string& fileName)
+        {
+            // Load JSON file
+            auto data = load_json_file(fileName);
+            // Fix fields
+            for (const auto& [key, value] : schema["properties"].items()) {
+                if (!data.contains(key)) {
+                    // Set default value if specified in the schema
+                    if (value.contains("default")) {
+                        data[key] = value["default"];
+                    } else if (value["type"] == "array") {
+                        data[key] = json::array();
+                    } else if (value["type"] == "object") {
+                        data[key] = json::object();
+                    } else {
+                        data[key] = nullptr;
+                    }
+                }
+                // Fix const fields to match the schema value
+                if (value.contains("const")) {
+                    data[key] = value["const"];
+                }
+            }
+            // Save fixed JSON
+            std::ofstream outFile(fileName);
+            if (!outFile.is_open()) {
+                std::cerr << "Error: Unable to open file for writing." << std::endl;
+                return;
+            }
+            outFile << data.dump(4);
+            outFile.close();
+        }
+
+    private:
+        json schema;
+
+        void validateObject(const std::string& path, const json& schema, const json& data, std::vector<std::string>& errors)
+        {
+            if (schema.contains("required")) {
+                for (const auto& requiredField : schema["required"]) {
+                    if (!data.contains(requiredField)) {
+                        std::string fullPath = path.empty() ? requiredField.get<std::string>() : path + "." + requiredField.get<std::string>();
+                        errors.push_back("Missing required field: " + fullPath);
+                    }
+                }
+            }
+            if (schema.contains("properties")) {
+                for (const auto& [key, value] : schema["properties"].items()) {
+                    if (data.contains(key)) {
+                        std::string fullPath = path.empty() ? key : path + "." + key;
+                        validateField(fullPath, value, data[key], errors);  // Pass data[key] for nested validation
+                    } else if (value.contains("required")) {
+                        errors.push_back("Missing required field: " + (path.empty() ? key : path + "." + key));
+                    }
+                }
+            }
+        }
+
+        void validateField(const std::string& field, const json& value, const json& data, std::vector<std::string>& errors)
+        {
+            if (value.contains("type")) {
+                const std::string& type = value["type"];
+                if (type == "array") {
+                    if (!data.is_array()) {
+                        errors.push_back("Field '" + field + "' should be an array.");
+                        return;
+                    }
+
+                    if (value.contains("items")) {
+                        for (size_t i = 0; i < data.size(); ++i) {
+                            validateObject(field + "[" + std::to_string(i) + "]", value["items"], data[i], errors);
+                        }
+                    }
+                } else if (type == "object") {
+                    if (!data.is_object()) {
+                        errors.push_back("Field '" + field + "' should be an object.");
+                        return;
+                    }
+
+                    validateObject(field, value, data, errors);
+                } else if (type == "string" && !data.is_string()) {
+                    errors.push_back("Field '" + field + "' should be a string.");
+                } else if (type == "number" && !data.is_number()) {
+                    errors.push_back("Field '" + field + "' should be a number.");
+                } else if (type == "integer" && !data.is_number_integer()) {
+                    errors.push_back("Field '" + field + "' should be an integer.");
+                } else if (type == "boolean" && !data.is_boolean()) {
+                    errors.push_back("Field '" + field + "' should be a boolean.");
+                }
+            }
+            if (value.contains("const")) {
+                const auto& expectedValue = value["const"];
+                if (data != expectedValue) {
+                    errors.push_back("Field '" + field + "' has an invalid value. Expected: " +
+                        expectedValue.dump() + ", Found: " + data.dump());
+                }
+            }
+        }
+    };
+}
+#endif

src/results/Result.cpp

@@ -8,6 +8,8 @@
 #include "common/Paths.h"
 #include "common/Symbols.h"
 #include "Result.h"
+#include "JsonValidator.h"
+#include "SchemaV1_0.h"
 
 namespace platform {
     std::string get_actual_date()
@@ -62,7 +64,11 @@ namespace platform {
     {
         return data;
     }
-
+    std::vector<std::string> Result::check()
+    {
+        platform::JsonValidator validator(platform::SchemaV1_0::schema);
+        return validator.validate(data);
+    }
     void Result::save()
     {
         std::ofstream file(Paths::results() + getFilename());

src/results/Result.h

@@ -4,7 +4,7 @@
 #include <vector>
 #include <string>
 #include <nlohmann/json.hpp>
-#include "common/Timer.h"
+#include "common/Timer.hpp"
 #include "main/HyperParameters.h"
 #include "main/PartialResult.h"
 
@@ -16,6 +16,7 @@ namespace platform {
         Result();
         Result& load(const std::string& path, const std::string& filename);
         void save();
+        std::vector<std::string> check();
         // Getters
         json getJson();
         std::string to_string(int maxModel, int maxTitle) const;
@@ -28,7 +29,7 @@ namespace platform {
         std::string getModel() const { return data["model"].get<std::string>(); };
         std::string getPlatform() const { return data["platform"].get<std::string>(); };
         std::string getScoreName() const { return data["score_name"].get<std::string>(); };
-
+        void setSchemaVersion(const std::string& version) { data["schema_version"] = version; };
         bool isComplete() const { return complete; };
         json getData() const { return data; }
         // Setters

src/results/SchemaV1_0.h

@@ -0,0 +1,103 @@
+#ifndef SCHEMAV1_0_H
+#define SCHEMAV1_0_H
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class SchemaV1_0 {
+    public:
+        // Define JSON schema
+        const static json schema;
+
+    };
+    const json SchemaV1_0::schema = {
+        {"$schema", "http://json-schema.org/draft-07/schema#"},
+        {"type", "object"},
+        {"properties", {
+            {"schema_version", {
+                {"type", "string"},
+                {"pattern", "^\\d+\\.\\d+$"},
+                {"default", "1.0"},
+                {"const", "1.0"}  // Fixed schema version for this schema
+            }},
+            {"date", {{"type", "string"}, {"format", "date"}}},
+            {"time", {{"type", "string"}, {"pattern", "^\\d{2}:\\d{2}:\\d{2}$"}}},
+            {"title", {{"type", "string"}}},
+            {"language", {{"type", "string"}}},
+            {"language_version", {{"type", "string"}}},
+            {"discretized", {{"type", "boolean"}, {"default", false}}},
+            {"model", {{"type", "string"}}},
+            {"platform", {{"type", "string"}}},
+            {"stratified", {{"type", "boolean"}, {"default", false}}},
+            {"folds", {{"type", "integer"}, {"default", 0}}},
+            {"score_name", {{"type", "string"}}},
+            {"version", {{"type", "string"}}},
+            {"duration", {{"type", "number"}, {"default", 0}}},
+            {"results", {
+                {"type", "array"},
+                {"items", {
+                    {"type", "object"},
+                    {"properties", {
+                        {"scores_train", {{"type", "array"}, {"items", {{"type", "number"}}}}},
+                        {"scores_test", {{"type", "array"}, {"items", {{"type", "number"}}}}},
+                        {"times_train", {{"type", "array"}, {"items", {{"type", "number"}}}}},
+                        {"times_test", {{"type", "array"}, {"items", {{"type", "number"}}}}},
+                        {"notes", {{"type", "array"}, {"items", {{"type", "string"}}}}},
+                        {"train_time", {{"type", "number"}, {"default", 0}}},
+                        {"train_time_std", {{"type", "number"}, {"default", 0}}},
+                        {"test_time", {{"type", "number"}, {"default", 0}}},
+                        {"test_time_std", {{"type", "number"}, {"default", 0}}},
+                        {"samples", {{"type", "integer"}, {"default", 0}}},
+                        {"features", {{"type", "integer"}, {"default", 0}}},
+                        {"classes", {{"type", "integer"}, {"default", 0}}},
+                        {"hyperparameters", {
+                            {"type", "object"},
+                            {"additionalProperties", {
+                                {"oneOf", {
+                                    {{"type", "number"}},  // Field can be a number
+                                    {{"type", "string"}}   // Field can also be a string
+                                }}
+                            }}
+                        }},
+                        {"score", {{"type", "number"}, {"default", 0}}},
+                        {"score_train", {{"type", "number"}, {"default", 0}}},
+                        {"score_std", {{"type", "number"}, {"default", 0}}},
+                        {"score_train_std", {{"type", "number"}, {"default", 0}}},
+                        {"time", {{"type", "number"}, {"default", 0}}},
+                        {"time_std", {{"type", "number"}, {"default", 0}}},
+                        {"nodes", {{"type", "number"}, {"default", 0}}},
+                        {"leaves", {{"type", "number"}, {"default", 0}}},
+                        {"depth", {{"type", "number"}, {"default", 0}}},
+                        {"dataset", {{"type", "string"}}},
+                        {"confusion_matrices", {
+                            {"type", "array"},
+                            {"items", {
+                                {"type", "object"},
+                                {"patternProperties", {
+                                    {".*", {
+                                        {"type", "array"},
+                                        {"items", {{"type", "integer"}}}
+                                    }}
+                                }},
+                                {"additionalProperties", false}
+                            }}
+                        }}
+                    }},
+                    {"required", {
+                        "scores_train", "scores_test", "times_train", "times_test",
+                        "train_time", "train_time_std", "test_time", "test_time_std",
+                        "samples", "features", "classes", "hyperparameters", "score", "score_train",
+                        "score_std", "score_train_std", "time", "time_std", "nodes", "leaves",
+                        "depth", "dataset"
+                    }}
+                }}
+            }}
+        }},
+        {"required", {
+            "schema_version", "date", "time", "title", "language", "language_version",
+            "discretized", "model", "platform", "stratified", "folds", "score_name",
+            "version", "duration", "results"
+        }}
+    };
+}
+#endif