Add sort results by title

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(Platform
-  VERSION 1.1.0
+  VERSION 1.1.1
   DESCRIPTION "Platform to run Experiments with classifiers."
   HOMEPAGE_URL "https://github.com/rmontanana/platform"
   LANGUAGES CXX
@@ -13,6 +13,7 @@ set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED            ON)
 set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
+set(CMAKE_POSITION_INDEPENDENT_CODE        ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
@@ -66,7 +67,7 @@ find_package(arff-files CONFIG REQUIRED)
 find_package(fimdlp CONFIG REQUIRED)
 find_package(folding CONFIG REQUIRED)
 find_package(bayesnet CONFIG REQUIRED)
-find_package(pyclassifiers CONFIG REQUIRED)
+# find_package(pyclassifiers CONFIG REQUIRED)
 find_package(libxlsxwriter CONFIG REQUIRED)
 find_package(Boost REQUIRED COMPONENTS python)
 

Makefile

@@ -111,9 +111,8 @@ release: ## Build a Release version of the project with Conan
 opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running Platform tests...";
-	@$(MAKE) clean
 	@$(MAKE) debug
-	@$(call "Compile_target", "Debug", "$(f_debug)", $(test_targets))
+	@$(call compile_target, "Debug", "$(f_debug)", $(test_targets))
 	@for t in $(test_targets); do \
 		if [ -f $(f_debug)/tests/$$t ]; then \
 			cd $(f_debug)/tests ; \

conanfile.py

@@ -15,13 +15,13 @@ class PlatformConan(ConanFile):
     def requirements(self):
         # Core dependencies from vcpkg.json
         self.requires("argparse/3.2")
-        self.requires("libtorch/2.7.0")
+        self.requires("libtorch/2.7.1")
         self.requires("nlohmann_json/3.11.3")
-        self.requires("folding/1.1.1")
-        self.requires("fimdlp/2.1.0")
-        self.requires("arff-files/1.2.0")
-        self.requires("bayesnet/1.2.0")
-        self.requires("pyclassifiers/1.0.3")
+        self.requires("folding/1.1.2")
+        self.requires("fimdlp/2.1.1")
+        self.requires("arff-files/1.2.1")
+        self.requires("bayesnet/1.2.1")
+        # self.requires("pyclassifiers/1.0.3")
         self.requires("libxlsxwriter/1.2.2")
         
     def build_requirements(self):

src/CMakeLists.txt

@@ -18,7 +18,7 @@ add_executable(
     experimental_clfs/DecisionTree.cpp
     experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_best Boost::boost pyclassifiers::pyclassifiers bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy libxlsxwriter::libxlsxwriter)
+target_link_libraries(b_best Boost::boost  bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy libxlsxwriter::libxlsxwriter)
 
 # b_grid
 set(grid_sources GridSearch.cpp GridData.cpp GridExperiment.cpp GridBase.cpp )
@@ -33,7 +33,7 @@ add_executable(b_grid commands/b_grid.cpp ${grid_sources}
     experimental_clfs/DecisionTree.cpp
     experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} pyclassifiers::pyclassifiers bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)
+target_link_libraries(b_grid ${MPI_CXX_LIBRARIES}  bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)
 
 # b_list
 add_executable(b_list commands/b_list.cpp
@@ -46,7 +46,7 @@ add_executable(b_list commands/b_list.cpp
     experimental_clfs/DecisionTree.cpp
     experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_list pyclassifiers::pyclassifiers bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy libxlsxwriter::libxlsxwriter)
+target_link_libraries(b_list  bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy libxlsxwriter::libxlsxwriter)
 
 # b_main
 set(main_sources Experiment.cpp Models.cpp HyperParameters.cpp Scores.cpp ArgumentsExperiment.cpp)
@@ -61,7 +61,7 @@ add_executable(b_main commands/b_main.cpp ${main_sources}
     experimental_clfs/DecisionTree.cpp
     experimental_clfs/AdaBoost.cpp
 )
-target_link_libraries(b_main PRIVATE nlohmann_json::nlohmann_json pyclassifiers::pyclassifiers bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)
+target_link_libraries(b_main PRIVATE nlohmann_json::nlohmann_json  bayesnet::bayesnet argparse::argparse fimdlp::fimdlp ${Python3_LIBRARIES} torch::torch Boost::python Boost::numpy)
 
 # b_manage
 set(manage_sources ManageScreen.cpp OptionsMenu.cpp ResultsManager.cpp)

src/best/BestResults.cpp

@@ -41,8 +41,7 @@ namespace platform {
         }
         json bests;
         for (const auto& file : files) {
-            auto result = Result();
-            result.load(path, file);
+            auto result = Result(path, file);
             auto data = result.getJson();
             for (auto const& item : data.at("results")) {
                 bool update = true;

src/common/DiscretizationRegister.h

@@ -1,8 +1,15 @@
 #ifndef DISCRETIZATIONREGISTER_H
 #define DISCRETIZATIONREGISTER_H
 #include <common/Discretization.h>
+#include <limits>
 static platform::RegistrarDiscretization registrarM("mdlp",
     [](void) -> mdlp::Discretizer* { return new mdlp::CPPFImdlp();});
+static platform::RegistrarDiscretization registrarM3("mdlp3",
+    [](void) -> mdlp::Discretizer* { return new mdlp::CPPFImdlp(3, numeric_limits<int>::max(), 3);});
+static platform::RegistrarDiscretization registrarM4("mdlp4",
+    [](void) -> mdlp::Discretizer* { return new mdlp::CPPFImdlp(3, numeric_limits<int>::max(), 4);});
+static platform::RegistrarDiscretization registrarM5("mdlp5",
+    [](void) -> mdlp::Discretizer* { return new mdlp::CPPFImdlp(3, numeric_limits<int>::max(), 5);});
 static platform::RegistrarDiscretization registrarBU3("bin3u",
     [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(3, mdlp::strategy_t::UNIFORM);});
 static platform::RegistrarDiscretization registrarBQ3("bin3q",

src/common/DotEnv.h

@@ -21,7 +21,7 @@ namespace platform {
             {
                 {"depth", {"any"}},
                 {"discretize", {"0", "1"}},
-                {"discretize_algo", {"mdlp", "bin3u", "bin3q", "bin4u", "bin4q", "bin5q", "bin5u", "bin6q", "bin6u", "bin7q", "bin7u", "bin8q", "bin8u", "bin9q", "bin9u", "bin10q", "bin10u"}},
+                {"discretize_algo", {"mdlp", "mdlp3", "mdlp4", "mdlp5", "bin3u", "bin3q", "bin4u", "bin4q", "bin5q", "bin5u", "bin6q", "bin6u", "bin7q", "bin7u", "bin8q", "bin8u", "bin9q", "bin9u", "bin10q", "bin10u"}},
                 {"experiment", {"discretiz", "odte", "covid", "Test"}},
                 {"fit_features", {"0", "1"}},
                 {"framework", {"bulma", "bootstrap"}},

src/common/TensorUtils.h

@@ -1,18 +0,0 @@
-#ifndef TENSOR_UTILS_H
-#define TENSOR_UTILS_H
-
-#include <torch/torch.h>
-#include <vector>
-
-namespace platform {
-    template <typename T>
-    std::vector<T> tensorToVector(const torch::Tensor& tensor)
-    {
-        torch::Tensor contig_tensor = tensor.contiguous();
-        auto num_elements = contig_tensor.numel();
-        const T* tensor_data = contig_tensor.data_ptr<T>();
-        std::vector<T> result(tensor_data, tensor_data + num_elements);
-        return result;
-    }
-}
-#endif

src/common/TensorUtils.hpp

@@ -5,6 +5,15 @@
 namespace platform {
     class TensorUtils {
     public:
+        template <typename T>
+        static std::vector<T> tensorToVector(const torch::Tensor& tensor)
+        {
+            torch::Tensor contig_tensor = tensor.contiguous();
+            auto num_elements = contig_tensor.numel();
+            const T* tensor_data = contig_tensor.data_ptr<T>();
+            std::vector<T> result(tensor_data, tensor_data + num_elements);
+            return result;
+        }
         static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
         {
             // Ensure tensor is contiguous in memory
@@ -53,7 +62,7 @@ namespace platform {
             torch::Tensor tensor = torch::empty({ static_cast<long>(rows), static_cast<long>(cols) }, torch::kInt64);
             for (size_t i = 0; i < rows; ++i) {
                 for (size_t j = 0; j < cols; ++j) {
-                    tensor.index_put_({ static_cast<long>(i), static_cast<long>(j) }, data[i][j]);
+                    tensor.index_put_({static_cast<int64_t>(i), static_cast<int64_t>(j)}, torch::scalar_tensor(data[i][j]));
                 }
             }
             return tensor;

src/experimental_clfs/AdaBoost.cpp

@@ -11,7 +11,7 @@
 #include <numeric>
 #include <sstream>
 #include <iomanip>
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 // Conditional debug macro for performance-critical sections
 #define DEBUG_LOG(condition, ...) \

src/experimental_clfs/AdaBoost.h

@@ -38,7 +38,7 @@ namespace bayesnet {
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
         void setDebug(bool debug) { this->debug = debug; }
 
     protected:

src/experimental_clfs/DecisionTree.cpp

@@ -10,7 +10,7 @@
 #include <sstream>
 #include <iomanip>
 #include <limits>
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace bayesnet {
 

src/experimental_clfs/DecisionTree.h

@@ -40,7 +40,7 @@ namespace bayesnet {
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X);
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
 
         // Make predictions for a single sample
         int predictSample(const torch::Tensor& x) const;

src/experimental_clfs/ExpClf.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "ExpClf.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     ExpClf::ExpClf() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)

src/experimental_clfs/ExpEnsemble.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "ExpEnsemble.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     ExpEnsemble::ExpEnsemble() : semaphore_{ CountingSemaphore::getInstance() }, Boost(false)

src/experimental_clfs/XA1DE.cpp

@@ -5,7 +5,7 @@
 // ***************************************************************
 
 #include "XA1DE.h"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 
 namespace platform {
     void XA1DE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)

src/experimental_clfs/XBAODE.cpp

@@ -10,7 +10,7 @@
 #include <tuple>
 #include "XBAODE.h"
 #include "XSpode.hpp"
-#include "TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 #include <loguru.hpp>
 
 namespace platform {

src/main/Models.h

@@ -19,12 +19,12 @@
 #include <bayesnet/classifiers/KDBLd.h>
 #include <bayesnet/classifiers/SPODELd.h>
 #include <bayesnet/classifiers/SPODELd.h>
-#include <pyclassifiers/STree.h>
-#include <pyclassifiers/ODTE.h>
-#include <pyclassifiers/SVC.h>
-#include <pyclassifiers/XGBoost.h>
-#include <pyclassifiers/AdaBoostPy.h>
-#include <pyclassifiers/RandomForest.h>
+// #include <pyclassifiers/STree.h>
+// #include <pyclassifiers/ODTE.h>
+// #include <pyclassifiers/SVC.h>
+// #include <pyclassifiers/XGBoost.h>
+// #include <pyclassifiers/AdaBoostPy.h>
+// #include <pyclassifiers/RandomForest.h>
 #include "../experimental_clfs/XA1DE.h"
 #include "../experimental_clfs/AdaBoost.h"
 #include "../experimental_clfs/DecisionTree.h"

src/main/RocAuc.cpp

@@ -3,7 +3,7 @@
 #include <numeric>
 #include <utility>
 #include "RocAuc.h"
-#include "common/TensorUtils.h" // tensorToVector
+#include "common/TensorUtils.hpp" // tensorToVector
 namespace platform {
 
     double RocAuc::compute(const torch::Tensor& y_proba, const torch::Tensor& labels)

src/main/Scores.cpp

@@ -1,6 +1,6 @@
 #include <sstream>
 #include "Scores.h"
-#include "common/TensorUtils.h" // tensorToVector
+#include "common/TensorUtils.hpp" // tensorToVector
 #include "common/Colors.h"
 namespace platform {
     Scores::Scores(torch::Tensor& y_test, torch::Tensor& y_proba, int num_classes, std::vector<std::string> labels) : num_classes(num_classes), labels(labels), y_test(y_test), y_proba(y_proba)
@@ -50,7 +50,7 @@ namespace platform {
         auto nClasses = num_classes;
         if (num_classes == 2)
             nClasses = 1;
-        auto y_testv = tensorToVector<int>(y_test);
+        auto y_testv = TensorUtils::tensorToVector<int>(y_test);
         std::vector<double> aucScores(nClasses, 0.0);
         std::vector<std::pair<double, int>> scoresAndLabels;
         for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {

src/main/modelRegister.h

@@ -25,18 +25,18 @@ namespace platform {
         [](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 registrarAdaPy("AdaBoostPy",
-        [](void) -> bayesnet::BaseClassifier* { return new pywrap::AdaBoostPy();});
+    // 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 registrarAdaPy("AdaBoostPy",
+    //     [](void) -> bayesnet::BaseClassifier* { return new pywrap::AdaBoostPy();});
     static Registrar registrarAda("AdaBoost",
         [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AdaBoost();});
     static Registrar registrarDT("DecisionTree",

src/manage/ManageScreen.cpp

@@ -28,7 +28,7 @@ namespace platform {
         maxTitle = results.maxTitleSize();
         header_lengths = { 3, 10, maxModel, 11, 10, 12, 2, 3, 7, maxTitle };
         header_labels = { " #", "Date", "Model", "Score Name", "Score", "Platform", "SD", "C/P", "Time", "Title" };
-        sort_fields = { "Date", "Model", "Score", "Time" };
+        sort_fields = { "Date", "Model", "Score", "Time", "Title" };
         updateSize(rows, cols);
         // Initializes the paginator for each output type (experiments, datasets, result)
         for (int i = 0; i < static_cast<int>(OutputType::Count); i++) {
@@ -346,9 +346,10 @@ namespace platform {
     {
         std::vector<std::tuple<std::string, char, bool>>  sortOptions = {
             {"date", 'd', false},
+            {"model", 'm', false},
             {"score", 's', false},
             {"time", 't', false},
-            {"model", 'm', false},
+            {"title", 'i', false},
             {"ascending+", '+', false},
             {"descending-", '-', false}
         };
@@ -379,6 +380,9 @@ namespace platform {
             case 'm':
                 sort_field = SortField::MODEL;
                 break;
+            case 'i':
+                sort_field = SortField::TITLE;
+                break;
             case '+':
                 sort_type = SortType::ASC;
                 break;

src/manage/ResultsManager.cpp

@@ -13,8 +13,7 @@ namespace platform {
         for (const auto& file : directory_iterator(path)) {
             auto filename = file.path().filename().string();
             if (filename.find(".json") != std::string::npos && filename.find("results_") == 0) {
-                auto result = Result();
-                result.load(path, filename);
+                auto result = Result(path, filename);
                 bool addResult = true;
                 if (platform != "any" && result.getPlatform() != platform
                     || model != "any" && result.getModel() != model
@@ -89,6 +88,16 @@ namespace platform {
             return a.getDuration() > b.getDuration();
             });
     }
+    void ResultsManager::sortTitle(SortType type)
+    {
+        if (empty())
+            return;
+        sort(files.begin(), files.end(), [type](const Result& a, const Result& b) {
+            if (type == SortType::ASC)
+                return a.getTitle() < b.getTitle();
+            return a.getTitle() > b.getTitle();
+            });
+    }
     void ResultsManager::sortScore(SortType type)
     {
         if (empty())
@@ -120,6 +129,9 @@ namespace platform {
             case SortField::DURATION:
                 sortDuration(type);
                 break;
+            case SortField::TITLE:
+                sortTitle(type);
+                break;
         }
     }
     bool ResultsManager::empty() const

src/manage/ResultsManager.h

@@ -15,6 +15,7 @@ namespace platform {
         MODEL = 1,
         SCORE = 2,
         DURATION = 3,
+        TITLE = 4,
     };
     class ResultsManager {
     public:
@@ -24,6 +25,7 @@ namespace platform {
         void sortDate(SortType type);
         void sortScore(SortType type);
         void sortModel(SortType type);
+        void sortTitle(SortType type);
         void sortDuration(SortType type);
         int maxModelSize() const { return maxModel; };
         int maxTitleSize() const { return maxTitle; };

src/reports/ExcelFile.cpp

@@ -54,10 +54,8 @@ namespace platform {
     }
     void ExcelFile::setProperties(std::string title)
     {
-        char line[title.size() + 1];
-        strcpy(line, title.c_str());
         lxw_doc_properties properties = {
-            .title = line,
+            .title = title.c_str(),
             .subject = (char*)"Machine learning results",
             .author = (char*)"Ricardo Montañana Gómez",
             .manager = (char*)"Dr. J. A. Gámez, Dr. J. M. Puerta",

src/results/Result.cpp

@@ -1,6 +1,8 @@
 #include <filesystem>
 #include <fstream>
 #include <sstream>
+#include <random>
+#include <cstdlib>
 #include "best/BestScore.h"
 #include "common/Colors.h"
 #include "common/DotEnv.h"
@@ -34,14 +36,25 @@ namespace platform {
     }
     Result::Result()
     {
+        path = Paths::results();
+        fileName = "none";
         data["date"] = get_actual_date();
         data["time"] = get_actual_time();
         data["results"] = json::array();
         data["seeds"] = json::array();
+        complete = false;
     }
-
-    Result& Result::load(const std::string& path, const std::string& fileName)
+    std::string Result::getFilename() const
     {
+        if (fileName == "none") {
+            throw std::runtime_error("Filename is not set. Use save() method to generate a filename.");
+        }
+        return fileName;
+    }
+    Result::Result(const std::string& path, const std::string& fileName)
+    {
+        this->path = path;
+        this->fileName = fileName;
         std::ifstream resultData(path + "/" + fileName);
         if (resultData.is_open()) {
             data = json::parse(resultData);
@@ -58,7 +71,6 @@ namespace platform {
             score /= best.second;
         }
         complete = data["results"].size() > 1;
-        return *this;
     }
     json Result::getJson()
     {
@@ -71,11 +83,15 @@ namespace platform {
     }
     void Result::save(const std::string& path)
     {
-        std::ofstream file(path + getFilename());
+        do {
+            fileName = generateFileName();
+        }
+        while (std::filesystem::exists(path + fileName));
+        std::ofstream file(path + fileName);
         file << data;
         file.close();
     }
-    std::string Result::getFilename() const
+    std::string Result::generateFileName()
     {
         std::ostringstream oss;
         std::string stratified;
@@ -85,13 +101,31 @@ namespace platform {
         catch (nlohmann::json_abi_v3_11_3::detail::type_error) {
             stratified = data["stratified"].get<int>() == 1 ? "1" : "0";
         }
+        auto generateRandomString = [](int length) -> std::string {
+            const char alphanum[] =
+                "0123456789"
+                "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+                "abcdefghijklmnopqrstuvwxyz";
+
+            // Use thread-local static generator to avoid interfering with global random state
+            thread_local static std::random_device rd;
+            thread_local static std::mt19937 gen(rd());
+            std::uniform_int_distribution<> dis(0, sizeof(alphanum) - 2);
+
+            std::string result;
+            for (int i = 0; i < length; ++i) {
+                result += alphanum[dis(gen)];
+            }
+            return result;
+            };
         oss << "results_"
             << data.at("score_name").get<std::string>() << "_"
             << data.at("model").get<std::string>() << "_"
             << data.at("platform").get<std::string>() << "_"
             << data["date"].get<std::string>() << "_"
             << data["time"].get<std::string>() << "_"
-            << stratified << ".json";
+            << stratified << "_"
+            << generateRandomString(5) << ".json";
         return oss.str();
     }
     std::string Result::to_string(int maxModel, int maxTitle) const

src/results/Result.h

@@ -5,6 +5,7 @@
 #include <string>
 #include <nlohmann/json.hpp>
 #include "common/Timer.hpp"
+#include "common/Paths.h"
 #include "main/HyperParameters.h"
 #include "main/PartialResult.h"
 
@@ -14,7 +15,7 @@ namespace platform {
     class Result {
     public:
         Result();
-        Result& load(const std::string& path, const std::string& filename);
+        Result(const std::string& path, const std::string& filename);
         void save(const std::string& path);
         std::vector<std::string> check();
         // Getters
@@ -49,6 +50,9 @@ namespace platform {
         void setNFolds(int nfolds) { data["folds"] = nfolds; };
         void setPlatform(const std::string& platform_name) { data["platform"] = platform_name; };
     private:
+        std::string generateFileName();
+        std::string path;
+        std::string fileName;
         json data;
         bool complete;
         double score = 0.0;

src/results/ResultsDataset.cpp

@@ -13,8 +13,7 @@ namespace platform {
         for (const auto& file : directory_iterator(path)) {
             auto filename = file.path().filename().string();
             if (filename.find(".json") != std::string::npos && filename.find("results_") == 0) {
-                auto result = Result();
-                result.load(path, filename);
+                auto result = Result(path, filename);
                 if (model != "any" && result.getModel() != model)
                     continue;
                 auto data = result.getData()["results"];

tests/CMakeLists.txt

@@ -13,6 +13,6 @@ if(ENABLE_TESTING)
     )
     add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
     target_link_libraries(${TEST_PLATFORM} PUBLIC 
-      torch::torch fimdlp:fimdlp Catch2::Catch2WithMain bayesnet::bayesnet pyclassifiers::pyclassifiers)
+      torch::torch fimdlp::fimdlp Catch2::Catch2WithMain bayesnet::bayesnet)
     add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})
 endif(ENABLE_TESTING)

tests/TestAdaBoost.cpp

@@ -13,7 +13,7 @@
 #include <stdexcept>
 #include "experimental_clfs/AdaBoost.h"
 #include "experimental_clfs/DecisionTree.h"
-#include "experimental_clfs/TensorUtils.hpp"
+#include "common/TensorUtils.hpp"
 #include "TestUtils.h"
 
 using namespace bayesnet;

tests/TestPlatform.cpp

@@ -15,25 +15,25 @@
 TEST_CASE("Test Platform version", "[Platform]")
 {
     std::string version = { platform_project_version.begin(), platform_project_version.end() };
-    REQUIRE(version == "1.1.0");
+    REQUIRE(version == "1.1.1");
 }
 TEST_CASE("Test Folding library version", "[Folding]")
 {
     std::string version = folding::KFold(5, 100).version();
-    REQUIRE(version == "1.1.1");
+    REQUIRE(version == "1.1.2");
 }
 TEST_CASE("Test BayesNet version", "[BayesNet]")
 {
     std::string version = bayesnet::TAN().getVersion();
-    REQUIRE(version == "1.1.2");
+    REQUIRE(version == "1.2.1");
 }
 TEST_CASE("Test mdlp version", "[mdlp]")
 {
     std::string version = mdlp::CPPFImdlp::version();
-    REQUIRE(version == "2.0.1");
+    REQUIRE(version == "2.1.1");
 }
 TEST_CASE("Test Arff version", "[Arff]")
 {
     std::string version = ArffFiles().version();
-    REQUIRE(version == "1.1.0");
+    REQUIRE(version == "1.2.1");
 }

tests/TestUtils.h

@@ -5,7 +5,7 @@
 #include <vector>
 #include <map>
 #include <tuple>
-#include <ArffFiles/ArffFiles.hpp>
+#include <ArffFiles.hpp>
 #include <fimdlp/CPPFImdlp.h>
 
 bool file_exists(const std::string& name);