Rename config.h to config_platform.h

Add bin5..bin10 q & u discretizers algos
Fix trouble in computing states
Update mdlp to 2.0.0

.gitmodules

@@ -10,10 +10,12 @@
 [submodule "lib/libxlsxwriter"]
 	path = lib/libxlsxwriter
 	url = https://github.com/jmcnamara/libxlsxwriter.git
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-        update = merge
 [submodule "lib/folding"]
 	path = lib/folding
 	url = https://github.com/rmontanana/folding
+[submodule "lib/Files"]
+	path = lib/Files
+	url = https://github.com/rmontanana/ArffFiles
+[submodule "lib/mdlp"]
+	path = lib/mdlp
+	url = https://github.com/rmontanana/mdlp

.vscode/c_cpp_properties.json

@@ -11,7 +11,18 @@
             ],
             "cStandard": "c17",
             "cppStandard": "c++17",
-            "compileCommands": "${workspaceFolder}/cmake-build-release/compile_commands.json"
+            "compileCommands": "${workspaceFolder}/cmake-build-release/compile_commands.json",
+            "configurationProvider": "ms-vscode.cmake-tools"
+        },
+        {
+            "name": "Linux",
+            "includePath": [
+                "${workspaceFolder}/**"
+            ],
+            "defines": [],
+            "cStandard": "c17",
+            "cppStandard": "c++17",
+            "configurationProvider": "ms-vscode.cmake-tools"
         }
     ],
     "version": 4

.vscode/launch.json

@@ -62,9 +62,9 @@
                 "--stratified",
                 "--discretize",
                 "-d",
-                "iris",
+                "glass",
                 "--hyperparameters",
-                "{\"repeatSparent\": true, \"maxModels\": 12}"
+                "{\"block_update\": true}"
             ],
             "cwd": "/home/rmontanana/Code/discretizbench",
         },
@@ -99,7 +99,9 @@
             "request": "launch",
             "program": "${workspaceFolder}/build_debug/src/b_list",
             "args": [
-                "--excel"
+                "results",
+                "-d",
+                "mfeat-morphological"
             ],
             //"cwd": "/Users/rmontanana/Code/discretizbench",
             "cwd": "${workspaceFolder}/../discretizbench",
@@ -108,12 +110,13 @@
             "name": "test",
             "type": "lldb",
             "request": "launch",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests",
+            "program": "${workspaceFolder}/build_debug/tests/unit_tests_platform",
             "args": [
-                "-c=\"Metrics Test\"",
+                "[Scores]",
+                // "-c=\"Metrics Test\"",
                 // "-s",
             ],
-            "cwd": "${workspaceFolder}/build/tests",
+            "cwd": "${workspaceFolder}/build_debug/tests",
         },
         {
             "name": "Build & debug active file",

CMakeLists.txt

@@ -1,16 +1,12 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(Platform
-  VERSION 1.0.4
+  VERSION 1.1.0
   DESCRIPTION "Platform to run Experiments with classifiers."
   HOMEPAGE_URL "https://github.com/rmontanana/platform"
   LANGUAGES CXX
 )
 
-if (CODE_COVERAGE AND NOT ENABLE_TESTING)
-  MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
-endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
-
 find_package(Torch REQUIRED)
 
 if (POLICY CMP0135)
@@ -25,6 +21,8 @@ set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          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} -Ofast")
+set(CMAKE_CXX_FLAGS_DEBUG " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
 
 # Options
 # -------
@@ -48,7 +46,7 @@ if(Boost_FOUND)
 endif()
 
 # Python
-find_package(Python3 3.11...3.11.9 COMPONENTS Interpreter Development REQUIRED)
+find_package(Python3 3.11 COMPONENTS Interpreter Development REQUIRED)
 message("Python3_LIBRARIES=${Python3_LIBRARIES}")
 
 # CMakes modules
@@ -60,7 +58,6 @@ if (CODE_COVERAGE)
     enable_testing()
     include(CodeCoverage)
     MESSAGE("Code coverage enabled")
-    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
     SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -76,15 +73,24 @@ add_git_submodule("lib/mdlp")
 find_library(XLSXWRITER_LIB NAMES libxlsxwriter.dylib libxlsxwriter.so PATHS ${Platform_SOURCE_DIR}/lib/libxlsxwriter/lib)
 message("XLSXWRITER_LIB=${XLSXWRITER_LIB}")
 
-find_library(PyClassifiers NAMES libPyClassifiers PyClassifiers libPyClassifiers.a)
-find_library(BayesNet NAMES libBayesNet BayesNet libBayesNet.a)
+find_library(PyClassifiers NAMES libPyClassifiers PyClassifiers libPyClassifiers.a PATHS ${Platform_SOURCE_DIR}/../lib/lib REQUIRED)
+find_path(PyClassifiers_INCLUDE_DIRS REQUIRED NAMES pyclassifiers PATHS ${Platform_SOURCE_DIR}/../lib/include)
+find_library(BayesNet NAMES libBayesNet BayesNet libBayesNet.a PATHS ${Platform_SOURCE_DIR}/../lib/lib REQUIRED)
+find_path(Bayesnet_INCLUDE_DIRS REQUIRED NAMES bayesnet PATHS ${Platform_SOURCE_DIR}/../lib/include)
+
+message(STATUS "PyClassifiers=${PyClassifiers}")
+message(STATUS "PyClassifiers_INCLUDE_DIRS=${PyClassifiers_INCLUDE_DIRS}")
+message(STATUS "BayesNet=${BayesNet}")
+message(STATUS "Bayesnet_INCLUDE_DIRS=${Bayesnet_INCLUDE_DIRS}")
 
 # Subdirectories
 # --------------
-add_subdirectory(lib/Files)
+## Configure test data path
+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

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 rmontanana
+Copyright (c) 2024 Ricardo Montañana Gómez
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 

Makefile

@@ -6,7 +6,6 @@ f_release = build_release
 f_debug = build_debug
 app_targets = b_best b_list b_main b_manage b_grid
 test_targets = unit_tests_platform
-n_procs = -j 16
 
 define ClearTests
 	@for t in $(test_targets); do \
@@ -41,7 +40,7 @@ setup: ## Install dependencies for tests and coverage
 dest ?= ${HOME}/bin
 install: ## Copy binary files to bin folder
 	@echo "Destination folder: $(dest)"
-	make buildr
+	@make buildr
 	@echo "*******************************************"
 	@echo ">>> Copying files to $(dest)"
 	@echo "*******************************************"
@@ -56,10 +55,10 @@ 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 $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) PlatformSample --parallel
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug Platform tests...";
@@ -87,7 +86,7 @@ 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
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel
 	@for t in $(test_targets); do \
 		if [ -f $(f_debug)/tests/$$t ]; then \
 			cd $(f_debug)/tests ; \
@@ -96,6 +95,14 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 	done
 	@echo ">>> Done";
 
+fname = iris
+example: ## Build sample
+	@echo ">>> Building Sample...";
+	@cmake --build build_debug -t sample
+	build_debug/sample/PlatformSample --model BoostAODE --dataset $(fname) --discretize --stratified
+	@echo ">>> Done";
+
+
 coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Building tests with coverage..."
 	@$(MAKE) test
@@ -105,7 +112,7 @@ coverage: ## Run tests and generate coverage report (build/index.html)
 
 help: ## Show help message
 	@IFS=$$'\n' ; \
-	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
+	help_lines=(`grep -Fh "##" $(MAKEFILE_LIST) | grep -Fv fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
 	printf "%s\n\n" "Usage: make [task]"; \
 	printf "%-20s %s\n" "task" "help" ; \
 	printf "%-20s %s\n" "------" "----" ; \

README.md

@@ -1,10 +1,8 @@
-# Platform
+# <img src="logo.png" alt="logo" width="50"/> Platform
 
-Platform to run Bayesian Networks and Machine Learning Classifiers experiments.
-
-# Platform
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+![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)
 
 Platform to run Bayesian Networks and Machine Learning Classifiers experiments.
 
@@ -22,11 +20,18 @@ In Linux sometimes the library libstdc++ is mistaken from the miniconda installa
 libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by b_xxxx)
 ```
 
-The solution is to erase the libstdc++ library from the miniconda installation:
+The solution is to erase the libstdc++ library from the miniconda installation and no further compilation is needed.
 
 ### MPI
 
-In Linux just install openmpi & openmpi-devel packages. Only if cmake can't find openmpi installation (like in Oracle Linux) set the following variable:
+In Linux just install openmpi & openmpi-devel packages.
+
+```bash
+source /etc/profile.d/modules.sh
+module load mpi/openmpi-x86_64
+```
+
+If cmake can't find openmpi installation (like in Oracle Linux) set the following variable:
 
 ```bash
 export MPI_HOME="/usr/lib64/openmpi"
@@ -99,8 +104,6 @@ List all the datasets and its properties. The datasets are located in the _datas
 
 where <real_features> can be either the word _all_ or a list of numbers separated by commas, i.e. [0,3,6,7]
 
-![b_list](img/blist.gif)
-
 ### b_grid
 
 Run a grid search over the parameters of the classifiers. The parameters are defined in the file _grid.txt_ located in the grid folder of the experiments. The file has to be created with the following format:
@@ -140,14 +143,10 @@ Run the main experiment. There are several hyperparameters that can set in comma
 - -\-title <title_text>: Title of the experiment (optional if only one dataset is specificied).
 - -\-quiet: Don't display detailed progress and result of the experiment.
 
-![b_main](img/bmain.gif)
-
 ### b_manage
 
 Manage the results of the experiments.
 
-![b_manage](img/bmanage.gif)
-
 ### b_best
 
 Get and optionally compare the best results of the experiments. The results can be stored in an MS Excel file.

config/CMakeLists.txt

@@ -1,4 +1,4 @@
 configure_file(
   "config.h.in"
-  "${CMAKE_BINARY_DIR}/configured_files/include/config.h" ESCAPE_QUOTES
+  "${CMAKE_BINARY_DIR}/configured_files/include/config_platform.h" ESCAPE_QUOTES
 )

config/config.h.in

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef PLATFORM_H
+#define PLATFORM_H
 #include <string>
 #include <string_view>
 
@@ -8,3 +8,4 @@ static constexpr std::string_view platform_project_version = "@PROJECT_VERSION@"
 static constexpr std::string_view platform_project_description = "@PROJECT_DESCRIPTION@";
 static constexpr std::string_view platform_git_sha = "@GIT_SHA@";
 static constexpr std::string_view platform_data_path = "@Platform_SOURCE_DIR@/tests/data/";
+#endif

gitmodules

@@ -1,8 +1,3 @@
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-	main = main
-	update = merge
 [submodule "lib/catch2"]
 	path = lib/catch2
         main  = v2.x

lib/Files/ArffFiles.cc

@@ -1,168 +0,0 @@
-#include "ArffFiles.h"
-#include <fstream>
-#include <sstream>
-#include <map>
-#include <iostream>
-
-ArffFiles::ArffFiles() = default;
-
-std::vector<std::string> ArffFiles::getLines() const
-{
-    return lines;
-}
-
-unsigned long int ArffFiles::getSize() const
-{
-    return lines.size();
-}
-
-std::vector<std::pair<std::string, std::string>> ArffFiles::getAttributes() const
-{
-    return attributes;
-}
-
-std::string ArffFiles::getClassName() const
-{
-    return className;
-}
-
-std::string ArffFiles::getClassType() const
-{
-    return classType;
-}
-
-std::vector<std::vector<float>>& ArffFiles::getX()
-{
-    return X;
-}
-
-std::vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-
-void ArffFiles::loadCommon(std::string fileName)
-{
-    std::ifstream file(fileName);
-    if (!file.is_open()) {
-        throw std::invalid_argument("Unable to open file");
-    }
-    std::string line;
-    std::string keyword;
-    std::string attribute;
-    std::string type;
-    std::string type_w;
-    while (getline(file, line)) {
-        if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
-            continue;
-        }
-        if (line.find("@attribute") != std::string::npos || line.find("@ATTRIBUTE") != std::string::npos) {
-            std::stringstream ss(line);
-            ss >> keyword >> attribute;
-            type = "";
-            while (ss >> type_w)
-                type += type_w + " ";
-            attributes.emplace_back(trim(attribute), trim(type));
-            continue;
-        }
-        if (line[0] == '@') {
-            continue;
-        }
-        lines.push_back(line);
-    }
-    file.close();
-    if (attributes.empty())
-        throw std::invalid_argument("No attributes found");
-}
-
-void ArffFiles::load(const std::string& fileName, bool classLast)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    if (classLast) {
-        className = std::get<0>(attributes.back());
-        classType = std::get<1>(attributes.back());
-        attributes.pop_back();
-        labelIndex = static_cast<int>(attributes.size());
-    } else {
-        className = std::get<0>(attributes.front());
-        classType = std::get<1>(attributes.front());
-        attributes.erase(attributes.begin());
-        labelIndex = 0;
-    }
-    generateDataset(labelIndex);
-}
-void ArffFiles::load(const std::string& fileName, const std::string& name)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    bool found = false;
-    for (int i = 0; i < attributes.size(); ++i) {
-        if (attributes[i].first == name) {
-            className = std::get<0>(attributes[i]);
-            classType = std::get<1>(attributes[i]);
-            attributes.erase(attributes.begin() + i);
-            labelIndex = i;
-            found = true;
-            break;
-        }
-    }
-    if (!found) {
-        throw std::invalid_argument("Class name not found");
-    }
-    generateDataset(labelIndex);
-}
-
-void ArffFiles::generateDataset(int labelIndex)
-{
-    X = std::vector<std::vector<float>>(attributes.size(), std::vector<float>(lines.size()));
-    auto yy = std::vector<std::string>(lines.size(), "");
-    auto removeLines = std::vector<int>(); // Lines with missing values
-    for (size_t i = 0; i < lines.size(); i++) {
-        std::stringstream ss(lines[i]);
-        std::string value;
-        int pos = 0;
-        int xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                if (value == "?") {
-                    X[xIndex++][i] = -1;
-                    removeLines.push_back(i);
-                } else
-                    X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    for (auto i : removeLines) {
-        yy.erase(yy.begin() + i);
-        for (auto& x : X) {
-            x.erase(x.begin() + i);
-        }
-    }
-    y = factorize(yy);
-}
-
-std::string ArffFiles::trim(const std::string& source)
-{
-    std::string s(source);
-    s.erase(0, s.find_first_not_of(" '\n\r\t"));
-    s.erase(s.find_last_not_of(" '\n\r\t") + 1);
-    return s;
-}
-
-std::vector<int> ArffFiles::factorize(const std::vector<std::string>& labels_t)
-{
-    std::vector<int> yy;
-    yy.reserve(labels_t.size());
-    std::map<std::string, int> labelMap;
-    int i = 0;
-    for (const std::string& label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}

lib/Files/ArffFiles.h

@@ -1,32 +0,0 @@
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-
-#include <string>
-#include <vector>
-
-class ArffFiles {
-private:
-    std::vector<std::string> lines;
-    std::vector<std::pair<std::string, std::string>> attributes;
-    std::string className;
-    std::string classType;
-    std::vector<std::vector<float>> X;
-    std::vector<int> y;
-    void generateDataset(int);
-    void loadCommon(std::string);
-public:
-    ArffFiles();
-    void load(const std::string&, bool = true);
-    void load(const std::string&, const std::string&);
-    std::vector<std::string> getLines() const;
-    unsigned long int getSize() const;
-    std::string getClassName() const;
-    std::string getClassType() const;
-    static std::string trim(const std::string&);
-    std::vector<std::vector<float>>& getX();
-    std::vector<int>& getY();
-    std::vector<std::pair<std::string, std::string>> getAttributes() const;
-    static std::vector<int> factorize(const std::vector<std::string>& labels_t);
-};
-
-#endif

lib/Files/CMakeLists.txt

@@ -1 +0,0 @@
-add_library(ArffFiles ArffFiles.cc)

sample/CMakeLists.txt

@@ -3,12 +3,13 @@ include_directories(
     ${Platform_SOURCE_DIR}/src/main
     ${Python3_INCLUDE_DIRS}
     ${Platform_SOURCE_DIR}/lib/Files
-    ${Platform_SOURCE_DIR}/lib/mdlp
+    ${Platform_SOURCE_DIR}/lib/mdlp/src
     ${Platform_SOURCE_DIR}/lib/argparse/include
     ${Platform_SOURCE_DIR}/lib/folding
     ${Platform_SOURCE_DIR}/lib/json/include
     ${CMAKE_BINARY_DIR}/configured_files/include
-    /usr/local/include
+    ${PyClassifiers_INCLUDE_DIRS}
+    ${Bayesnet_INCLUDE_DIRS}
 )
 add_executable(PlatformSample sample.cpp ${Platform_SOURCE_DIR}/src/main/Models.cpp) 
-target_link_libraries(PlatformSample "${PyClassifiers}" "${BayesNet}" ArffFiles mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
+target_link_libraries(PlatformSample "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)

sample/sample.cpp

@@ -5,13 +5,13 @@
 #include <torch/torch.h>
 #include <argparse/argparse.hpp>
 #include <nlohmann/json.hpp>
-#include <ArffFiles.h>
-#include <CPPFImdlp.h>
+#include <ArffFiles.hpp>
+#include <fimdlp/CPPFImdlp.h>
 #include <folding.hpp>
 #include <bayesnet/utils/BayesMetrics.h>
 #include "Models.h"
 #include "modelRegister.h"
-#include "config.h"
+#include "config_platform.h"
 
 const std::string PATH = { platform_data_path.begin(), platform_data_path.end() };
 
@@ -79,11 +79,11 @@ int main(int argc, char** argv)
         }
         throw runtime_error("file must be one of {diabetes, ecoli, glass, iris, kdd_JapaneseVowels, letter, liver-disorders, mfeat-factors}");
             }
-    );
+        );
     program.add_argument("-p", "--path")
         .help(" folder where the data files are located, default")
         .default_value(std::string{ PATH }
-    );
+        );
     program.add_argument("-m", "--model")
         .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
@@ -93,7 +93,7 @@ int main(int argc, char** argv)
         }
         throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
             }
-    );
+        );
     program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
     program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
     program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
@@ -112,129 +112,130 @@ int main(int argc, char** argv)
         catch (...) {
             throw runtime_error("Number of folds must be an integer");
         }});
-    program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
-    bool class_last, stratified, tensors, dump_cpt;
-    std::string model_name, file_name, path, complete_file_name;
-    int nFolds, seed;
-    try {
-        program.parse_args(argc, argv);
-        file_name = program.get<std::string>("dataset");
-        path = program.get<std::string>("path");
-        model_name = program.get<std::string>("model");
-        complete_file_name = path + file_name + ".arff";
-        stratified = program.get<bool>("stratified");
-        tensors = program.get<bool>("tensors");
-        nFolds = program.get<int>("folds");
-        seed = program.get<int>("seed");
-        dump_cpt = program.get<bool>("dumpcpt");
-        class_last = datasets[file_name];
-        if (!file_exists(complete_file_name)) {
-            throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
+        program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
+        bool class_last, stratified, tensors, dump_cpt;
+        std::string model_name, file_name, path, complete_file_name;
+        int nFolds, seed;
+        try {
+            program.parse_args(argc, argv);
+            file_name = program.get<std::string>("dataset");
+            path = program.get<std::string>("path");
+            model_name = program.get<std::string>("model");
+            complete_file_name = path + file_name + ".arff";
+            stratified = program.get<bool>("stratified");
+            tensors = program.get<bool>("tensors");
+            nFolds = program.get<int>("folds");
+            seed = program.get<int>("seed");
+            dump_cpt = program.get<bool>("dumpcpt");
+            class_last = datasets[file_name];
+            if (!file_exists(complete_file_name)) {
+                throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
+            }
+        }
+        catch (const exception& err) {
+            cerr << err.what() << std::endl;
+            cerr << program;
+            exit(1);
         }
-    }
-    catch (const exception& err) {
-        cerr << err.what() << std::endl;
-        cerr << program;
-        exit(1);
-    }
 
-    /*
-    * Begin Processing
-    */
-    auto handler = ArffFiles();
-    handler.load(complete_file_name, class_last);
-    // Get Dataset X, y
-    std::vector<mdlp::samples_t>& X = handler.getX();
-    mdlp::labels_t& y = handler.getY();
-    // Get className & Features
-    auto className = handler.getClassName();
-    std::vector<std::string> features;
-    auto attributes = handler.getAttributes();
-    transform(attributes.begin(), attributes.end(), back_inserter(features),
-        [](const pair<std::string, std::string>& item) { return item.first; });
-    // Discretize Dataset
-    auto [Xd, maxes] = discretize(X, y, features);
-    maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    map<std::string, std::vector<int>> states;
-    for (auto feature : features) {
-        states[feature] = std::vector<int>(maxes[feature]);
-    }
-    states[className] = std::vector<int>(maxes[className]);
-    auto clf = platform::Models::instance()->create(model_name);
-    clf->fit(Xd, y, features, className, states);
-    if (dump_cpt) {
-        std::cout << "--- CPT Tables ---" << std::endl;
-        clf->dump_cpt();
-    }
-    auto lines = clf->show();
-    for (auto line : lines) {
-        std::cout << line << std::endl;
-    }
-    std::cout << "--- Topological Order ---" << std::endl;
-    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);
-            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);
-            std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
-            nodes += clf->getNumberOfNodes();
-            score_train = clf->score(Xtrain, ytrain);
-            score_test = clf->score(Xtest, ytest);
+        /*
+        * Begin Processing
+        */
+        auto handler = ArffFiles();
+        handler.load(complete_file_name, class_last);
+        // Get Dataset X, y
+        std::vector<mdlp::samples_t>& X = handler.getX();
+        mdlp::labels_t& y = handler.getY();
+        // Get className & Features
+        auto className = handler.getClassName();
+        std::vector<std::string> features;
+        auto attributes = handler.getAttributes();
+        transform(attributes.begin(), attributes.end(), back_inserter(features),
+            [](const pair<std::string, std::string>& item) { return item.first; });
+        // Discretize Dataset
+        auto [Xd, maxes] = discretize(X, y, features);
+        maxes[className] = *max_element(y.begin(), y.end()) + 1;
+        map<std::string, std::vector<int>> states;
+        for (auto feature : features) {
+            states[feature] = std::vector<int>(maxes[feature]);
         }
+        states[className] = std::vector<int>(maxes[className]);
+        auto clf = platform::Models::instance()->create(model_name);
+        bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::ORIGINAL;
+        clf->fit(Xd, y, features, className, states, smoothing);
         if (dump_cpt) {
             std::cout << "--- CPT Tables ---" << std::endl;
             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;
+        auto lines = clf->show();
+        for (auto line : lines) {
+            std::cout << line << std::endl;
+        }
+        std::cout << "--- Topological Order ---" << std::endl;
+        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);
+            }
+            if (dump_cpt) {
+                std::cout << "--- CPT Tables ---" << std::endl;
+                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

@@ -2,7 +2,7 @@ include_directories(
     ## Libs
     ${Platform_SOURCE_DIR}/lib/Files
     ${Platform_SOURCE_DIR}/lib/folding
-    ${Platform_SOURCE_DIR}/lib/mdlp
+    ${Platform_SOURCE_DIR}/lib/mdlp/src
     ${Platform_SOURCE_DIR}/lib/argparse/include
     ${Platform_SOURCE_DIR}/lib/json/include
     ${Platform_SOURCE_DIR}/lib/libxlsxwriter/include
@@ -10,43 +10,60 @@ include_directories(
     ${MPI_CXX_INCLUDE_DIRS}
     ${TORCH_INCLUDE_DIRS}
     ${CMAKE_BINARY_DIR}/configured_files/include
-    /usr/local/include
+    ${PyClassifiers_INCLUDE_DIRS}
+    ${Bayesnet_INCLUDE_DIRS}
     ## Platform
     ${Platform_SOURCE_DIR}/src
+    ${Platform_SOURCE_DIR}/results
 )
 
 # b_best
-set(best_sources b_best.cpp BestResults.cpp  Statistics.cpp BestResultsExcel.cpp)
-list(TRANSFORM best_sources PREPEND best/)
 add_executable(
-    b_best ${best_sources} main/Result.cpp 
-    reports/ReportExcel.cpp reports/ReportBase.cpp reports/ExcelFile.cpp common/Datasets.cpp common/Dataset.cpp main/Models.cpp)
-target_link_libraries(b_best Boost::boost "${PyClassifiers}" "${BayesNet}" ArffFiles mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
+    b_best commands/b_best.cpp best/Statistics.cpp
+    best/BestResultsExcel.cpp best/BestResultsTex.cpp best/BestResultsMd.cpp best/BestResults.cpp
+    common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp 
+    main/Models.cpp main/Scores.cpp
+    reports/ReportExcel.cpp reports/ReportBase.cpp reports/ExcelFile.cpp
+    results/Result.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 b_grid.cpp GridSearch.cpp GridData.cpp)
+set(grid_sources GridSearch.cpp GridData.cpp)
 list(TRANSFORM grid_sources PREPEND grid/)
-add_executable(b_grid ${grid_sources} main/HyperParameters.cpp main/Models.cpp common/Datasets.cpp common/Dataset.cpp)
-target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" "${BayesNet}" ArffFiles mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
+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 
+)
+target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
 
 # b_list
-set(list_sources b_list.cpp DatasetsExcel.cpp)
-list(TRANSFORM list_sources PREPEND list/)
-add_executable(b_list ${list_sources} common/Datasets.cpp common/Dataset.cpp reports/ReportExcel.cpp reports/ExcelFile.cpp reports/ReportBase.cpp)
-target_link_libraries(b_list "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
+add_executable(b_list commands/b_list.cpp
+    common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
+    main/Models.cpp main/Scores.cpp
+    reports/ReportExcel.cpp reports/ExcelFile.cpp reports/ReportBase.cpp reports/DatasetsExcel.cpp reports/DatasetsConsole.cpp reports/ReportsPaged.cpp
+    results/Result.cpp results/ResultsDatasetExcel.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
+)
+target_link_libraries(b_list "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
 # b_main
-set(main_sources b_main.cpp Experiment.cpp Models.cpp HyperParameters.cpp)
+set(main_sources Experiment.cpp Models.cpp HyperParameters.cpp Scores.cpp)
 list(TRANSFORM main_sources PREPEND main/)
-add_executable(b_main ${main_sources} common/Datasets.cpp common/Dataset.cpp reports/ReportConsole.cpp reports/ReportBase.cpp main/Result.cpp)
-target_link_libraries(b_main "${PyClassifiers}" "${BayesNet}" ArffFiles mdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
+add_executable(b_main commands/b_main.cpp ${main_sources} 
+    common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
+    reports/ReportConsole.cpp reports/ReportBase.cpp 
+    results/Result.cpp
+)
+target_link_libraries(b_main "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
 
 # b_manage
-set(manage_sources b_manage.cpp ManageResults.cpp CommandParser.cpp ResultsManager.cpp)
+set(manage_sources ManageScreen.cpp OptionsMenu.cpp ResultsManager.cpp)
 list(TRANSFORM manage_sources PREPEND manage/)
 add_executable(
-    b_manage ${manage_sources} main/Result.cpp 
-    reports/ReportConsole.cpp reports/ReportExcel.cpp reports/ReportExcelCompared.cpp reports/ReportBase.cpp reports/ExcelFile.cpp 
-    common/Datasets.cpp common/Dataset.cpp
+    b_manage commands/b_manage.cpp ${manage_sources} 
+    common/Datasets.cpp common/Dataset.cpp common/Discretization.cpp
+    reports/ReportConsole.cpp reports/ReportExcel.cpp reports/ReportExcelCompared.cpp reports/ReportBase.cpp reports/ExcelFile.cpp reports/DatasetsConsole.cpp reports/ReportsPaged.cpp
+    results/Result.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
+    main/Scores.cpp
 )
-target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
+target_link_libraries(b_manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" fimdlp "${BayesNet}")

src/best/BestResults.cpp

@@ -6,9 +6,13 @@
 #include <algorithm>
 #include "common/Colors.h"
 #include "common/CLocale.h"
-#include "main/Result.h"
+#include "common/Paths.h"
+#include "common/Utils.h" // compute_std
+#include "results/Result.h"
 #include "BestResultsExcel.h"
-#include "Statistics.h"
+#include "BestResultsTex.h"
+#include "BestResultsMd.h"
+#include "best/Statistics.h"
 #include "BestResults.h"
 
 
@@ -51,20 +55,20 @@ namespace platform {
                     }
                 }
                 if (update) {
-                    bests[datasetName] = { item.at("score").get<double>(), item.at("hyperparameters"), file };
+                    bests[datasetName] = { item.at("score").get<double>(), item.at("hyperparameters"), file, item.at("score_std").get<double>() };
                 }
             }
         }
-        std::string bestFileName = path + bestResultFile();
+        if (bests.empty()) {
+            std::cerr << Colors::MAGENTA() << "No results found for model " << model << " and score " << score << Colors::RESET() << std::endl;
+            exit(1);
+        }
+        std::string bestFileName = path + Paths::bestResultsFile(score, model);
         std::ofstream file(bestFileName);
         file << bests;
         file.close();
         return bestFileName;
     }
-    std::string BestResults::bestResultFile()
-    {
-        return "best_results_" + score + "_" + model + ".json";
-    }
     std::pair<std::string, std::string> getModelScore(std::string name)
     {
         // results_accuracy_BoostAODE_MacBookpro16_2023-09-06_12:27:00_1.json
@@ -146,7 +150,7 @@ namespace platform {
     }
     void BestResults::listFile()
     {
-        std::string bestFileName = path + bestResultFile();
+        std::string bestFileName = path + Paths::bestResultsFile(score, model);
         if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
             fclose(fileTest);
         } else {
@@ -170,10 +174,9 @@ namespace platform {
         std::cout << Colors::GREEN() << " #  " << std::setw(maxDatasetName + 1) << std::left << "Dataset" << "Score       " << std::setw(maxFileName) << "File" << " Hyperparameters" << std::endl;
         std::cout << "=== " << std::string(maxDatasetName, '=') << " =========== " << std::string(maxFileName, '=') << " " << std::string(maxHyper, '=') << std::endl;
         auto i = 0;
-        bool odd = true;
         double total = 0;
         for (auto const& item : data.items()) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            auto color = (i % 2) ? Colors::BLUE() : Colors::CYAN();
             double value = item.value().at(0).get<double>();
             std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
             std::cout << std::setw(maxDatasetName) << std::left << item.key() << " ";
@@ -182,7 +185,6 @@ namespace platform {
             std::cout << item.value().at(1) << " ";
             std::cout << std::endl;
             total += value;
-            odd = !odd;
         }
         std::cout << Colors::GREEN() << "=== " << std::string(maxDatasetName, '=') << " ===========" << std::endl;
         std::cout << Colors::GREEN() << "    Total" << std::string(maxDatasetName - 5, '.') << " " << std::setw(11) << std::setprecision(8) << std::fixed << total << std::endl;
@@ -194,7 +196,7 @@ namespace platform {
         auto maxDate = std::filesystem::file_time_type::max();
         for (const auto& model : models) {
             this->model = model;
-            std::string bestFileName = path + bestResultFile();
+            std::string bestFileName = path + Paths::bestResultsFile(score, model);
             if (FILE* fileTest = fopen(bestFileName.c_str(), "r")) {
                 fclose(fileTest);
             } else {
@@ -211,13 +213,20 @@ namespace platform {
         table["dateTable"] = ftime_to_string(maxDate);
         return table;
     }
-    void BestResults::printTableResults(std::vector<std::string> models, json table)
+
+    void BestResults::printTableResults(std::vector<std::string> models, json table, bool tex)
     {
         std::stringstream oss;
         oss << Colors::GREEN() << "Best results for " << score << " as of " << table.at("dateTable").get<std::string>() << std::endl;
         std::cout << oss.str();
         std::cout << std::string(oss.str().size() - 8, '-') << std::endl;
         std::cout << Colors::GREEN() << " #  " << std::setw(maxDatasetName + 1) << std::left << std::string("Dataset");
+        auto bestResultsTex = BestResultsTex();
+        auto bestResultsMd = BestResultsMd();
+        if (tex) {
+            bestResultsTex.results_header(models, table.at("dateTable").get<std::string>());
+            bestResultsMd.results_header(models, table.at("dateTable").get<std::string>());
+        }
         for (const auto& model : models) {
             std::cout << std::setw(maxModelName) << std::left << model << " ";
         }
@@ -228,15 +237,15 @@ namespace platform {
         }
         std::cout << std::endl;
         auto i = 0;
-        bool odd = true;
-        std::map<std::string, double> totals;
+        std::map<std::string, std::vector<double>> totals;
         int nDatasets = table.begin().value().size();
-        for (const auto& model : models) {
-            totals[model] = 0.0;
-        }
         auto datasets = getDatasets(table.begin().value());
+        if (tex) {
+            bestResultsTex.results_body(datasets, table);
+            bestResultsMd.results_body(datasets, table);
+        }
         for (auto const& dataset_ : datasets) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            auto color = (i % 2) ? Colors::BLUE() : Colors::CYAN();
             std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
             std::cout << std::setw(maxDatasetName) << std::left << dataset_ << " ";
             double maxValue = 0;
@@ -269,31 +278,37 @@ namespace platform {
                 if (value == -1) {
                     std::cout << Colors::YELLOW() << std::setw(maxModelName) << std::right << "N/A" << " ";
                 } else {
-                    totals[model] += value;
+                    totals[model].push_back(value);
                     std::cout << efectiveColor << std::setw(maxModelName) << std::setprecision(maxModelName - 2) << std::fixed << value << " ";
                 }
             }
             std::cout << std::endl;
-            odd = !odd;
         }
         std::cout << Colors::GREEN() << "=== " << std::string(maxDatasetName, '=') << " ";
         for (const auto& model : models) {
             std::cout << std::string(maxModelName, '=') << " ";
         }
         std::cout << std::endl;
-        std::cout << Colors::GREEN() << "    Totals" << std::string(maxDatasetName - 6, '.') << " ";
+        std::cout << Colors::GREEN() << "    Average" << std::string(maxDatasetName - 7, '.') << " ";
         double max_value = 0.0;
+        std::string best_model = "";
         for (const auto& total : totals) {
-            if (total.second > max_value) {
-                max_value = total.second;
+            auto actual = std::reduce(total.second.begin(), total.second.end());
+            if (actual > max_value) {
+                max_value = actual;
+                best_model = total.first;
             }
         }
+        if (tex) {
+            bestResultsTex.results_footer(totals, best_model);
+            bestResultsMd.results_footer(totals, best_model);
+        }
         for (const auto& model : models) {
-            std::string efectiveColor = Colors::GREEN();
-            if (totals[model] == max_value) {
-                efectiveColor = Colors::RED();
-            }
-            std::cout << efectiveColor << std::right << std::setw(maxModelName) << std::setprecision(maxModelName - 4) << std::fixed << totals[model] << " ";
+            std::string efectiveColor = model == best_model ? Colors::RED() : Colors::GREEN();
+            double value = std::reduce(totals[model].begin(), totals[model].end()) / nDatasets;
+            double std_value = compute_std(totals[model], value);
+            std::cout << efectiveColor << std::right << std::setw(maxModelName) << std::setprecision(maxModelName - 4) << std::fixed << value << " ";
+
         }
         std::cout << std::endl;
     }
@@ -306,26 +321,34 @@ namespace platform {
             json table = buildTableResults(models);
             std::vector<std::string> datasets = getDatasets(table.begin().value());
             BestResultsExcel excel_report(score, datasets);
-            excel_report.reportSingle(model, path + bestResultFile());
-            messageExcelFile(excel_report.getFileName());
+            excel_report.reportSingle(model, path + Paths::bestResultsFile(score, model));
+            messageOutputFile("Excel", excel_report.getFileName());
         }
     }
-    void BestResults::reportAll(bool excel)
+    void BestResults::reportAll(bool excel, bool tex)
     {
         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);
+        printTableResults(models, table, tex);
         // Compute the Friedman test
         std::map<std::string, std::map<std::string, float>> ranksModels;
         if (friedman) {
             Statistics stats(models, datasets, table, significance);
             auto result = stats.friedmanTest();
-            stats.postHocHolmTest(result);
+            stats.postHocHolmTest(result, tex);
             ranksModels = stats.getRanks();
         }
+        if (tex) {
+            messageOutputFile("TeX", Paths::tex() + Paths::tex_output());
+            messageOutputFile("MarkDown", Paths::tex() + Paths::md_output());
+            if (friedman) {
+                messageOutputFile("TeX", Paths::tex() + Paths::tex_post_hoc());
+                messageOutputFile("MarkDown", Paths::tex() + Paths::md_post_hoc());
+            }
+        }
         if (excel) {
             BestResultsExcel excel(score, datasets);
             excel.reportAll(models, table, ranksModels, friedman, significance);
@@ -346,13 +369,14 @@ namespace platform {
                     }
                 }
                 model = models.at(idx);
-                excel.reportSingle(model, path + bestResultFile());
+                excel.reportSingle(model, path + Paths::bestResultsFile(score, model));
             }
-            messageExcelFile(excel.getFileName());
+            messageOutputFile("Excel", excel.getFileName());
         }
     }
-    void BestResults::messageExcelFile(const std::string& fileName)
+    void BestResults::messageOutputFile(const std::string& title, const std::string& fileName)
     {
-        std::cout << Colors::YELLOW() << "** Excel file generated: " << fileName << Colors::RESET() << std::endl;
+        std::cout << Colors::YELLOW() << "** " << std::setw(5) << std::left << title
+            << " file generated: " << fileName << Colors::RESET() << std::endl;
     }
 }

src/best/BestResults.h

@@ -1,9 +1,10 @@
-#pragma once
-
+#ifndef BESTRESULTS_H
+#define BESTRESULTS_H
 #include <string>
 #include <nlohmann/json.hpp>
-using json = nlohmann::json;
 namespace platform {
+    using json = nlohmann::ordered_json;
+
     class BestResults {
     public:
         explicit BestResults(const std::string& path, const std::string& score, const std::string& model, const std::string& dataset, bool friedman, double significance = 0.05)
@@ -12,16 +13,15 @@ namespace platform {
         }
         std::string build();
         void reportSingle(bool excel);
-        void reportAll(bool excel);
+        void reportAll(bool excel, bool tex);
         void buildAll();
     private:
         std::vector<std::string> getModels();
         std::vector<std::string> getDatasets(json table);
         std::vector<std::string> loadResultFiles();
-        void messageExcelFile(const std::string& fileName);
+        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);
-        std::string bestResultFile();
+        void printTableResults(std::vector<std::string> models, json table, bool tex);
         json loadFile(const std::string& fileName);
         void listFile();
         std::string path;
@@ -34,3 +34,4 @@ namespace platform {
         int maxDatasetName = 0;
     };
 }
+#endif

src/best/BestResultsExcel.cpp

@@ -3,7 +3,7 @@
 #include <nlohmann/json.hpp>
 #include "common/Paths.h"
 #include "reports/ReportExcel.h"
-#include "Statistics.h"
+#include "best/Statistics.h"
 #include "BestResultsExcel.h"
 
 namespace platform {
@@ -32,7 +32,7 @@ namespace platform {
     }
     BestResultsExcel::BestResultsExcel(const std::string& score, const std::vector<std::string>& datasets) : score(score), datasets(datasets)
     {
-        file_name = "BestResults.xlsx";
+        file_name = Paths::bestResultsExcel(score);
         workbook = workbook_new(getFileName().c_str());
         setProperties("Best Results");
         int maxDatasetName = (*max_element(datasets.begin(), datasets.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
@@ -64,19 +64,21 @@ namespace platform {
         json data = loadResultData(fileName);
 
         std::string title = "Best results for " + model;
-        worksheet_merge_range(worksheet, 0, 0, 0, 4, title.c_str(), styles["headerFirst"]);
+        worksheet_merge_range(worksheet, 0, 0, 0, 5, title.c_str(), styles["headerFirst"]);
         // Body header
         row = 3;
         int col = 1;
-        writeString(row, 0, "Nº", "bodyHeader");
+        writeString(row, 0, "#", "bodyHeader");
         writeString(row, 1, "Dataset", "bodyHeader");
         writeString(row, 2, "Score", "bodyHeader");
         writeString(row, 3, "File", "bodyHeader");
         writeString(row, 4, "Hyperparameters", "bodyHeader");
+        writeString(row, 5, "F", "bodyHeader");
         auto i = 0;
         std::string hyperparameters;
         int hypSize = 22;
         std::map<std::string, std::string> files; // map of files imported and their tabs
+        int numLines = data.size();
         for (auto const& item : data.items()) {
             row++;
             writeInt(row, 0, i++, "ints");
@@ -104,6 +106,8 @@ namespace platform {
                 hypSize = hyperparameters.size();
             }
             writeString(row, 4, hyperparameters, "text");
+            std::string countHyperparameters = "=COUNTIF(e5:e" + std::to_string(numLines + 4) + ", e" + std::to_string(row + 1) + ")";
+            worksheet_write_formula(worksheet, row, 5, countHyperparameters.c_str(), efectiveStyle("ints"));
         }
         row++;
         // Set Totals
@@ -180,7 +184,7 @@ namespace platform {
         // Body header
         row = 3;
         int col = 1;
-        writeString(row, 0, "Nº", "bodyHeader");
+        writeString(row, 0, "#", "bodyHeader");
         writeString(row, 1, "Dataset", "bodyHeader");
         for (const auto& model : models) {
             writeString(row, ++col, model.c_str(), "bodyHeader");

src/best/BestResultsExcel.h

@@ -1,14 +1,13 @@
-#pragma once
-
+#ifndef BESTRESULTSEXCEL_H
+#define BESTRESULTSEXCEL_H
 #include <vector>
 #include <map>
 #include <nlohmann/json.hpp>
 #include "reports/ExcelFile.h"
 
-using json = nlohmann::json;
 
 namespace platform {
-
+    using json = nlohmann::ordered_json;
     class BestResultsExcel : public ExcelFile {
     public:
         BestResultsExcel(const std::string& score, const std::vector<std::string>& datasets);
@@ -34,3 +33,4 @@ namespace platform {
         int datasetNameSize = 25; // Min size of the column
     };
 }
+#endif

src/best/BestResultsMd.cpp

@@ -0,0 +1,103 @@
+#include <iostream>
+#include "BestResultsMd.h"
+#include "common/Utils.h" // compute_std
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    void BestResultsMd::openMdFile(const std::string& name)
+    {
+        handler.open(name);
+        if (!handler.is_open()) {
+            std::cerr << "Error opening file " << name << std::endl;
+            exit(1);
+        }
+    }
+    void BestResultsMd::results_header(const std::vector<std::string>& models, const std::string& date)
+    {
+        this->models = models;
+        auto file_name = Paths::tex() + Paths::md_output();
+        openMdFile(file_name);
+        handler << "<!-- This file has been generated by the platform program" << std::endl;
+        handler << "  Date: " << date.c_str() << std::endl;
+        handler << "" << std::endl;
+        handler << "  Table of results" << std::endl;
+        handler << "-->" << std::endl;
+        handler << "| # | Dataset |";
+        for (const auto& model : models) {
+            handler << " " << model.c_str() << " |";
+        }
+        handler << std::endl;
+        handler << "|--: | :--- |";
+        for (const auto& model : models) {
+            handler << " :---: |";
+        }
+        handler << std::endl;
+    }
+    void BestResultsMd::results_body(const std::vector<std::string>& datasets, json& table)
+    {
+        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 {
+                    value = table[model].at(dataset).at(0).get<double>();
+                }
+                catch (nlohmann::json_abi_v3_11_3::detail::out_of_range err) {
+                    value = -1.0;
+                }
+                if (value > max_value) {
+                    max_value = value;
+                }
+            }
+            handler << "| " << ++i << " | " << dataset.c_str() << " | ";
+            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>();
+                const char* bold = value == max_value ? "**" : "";
+                handler << bold << std::setprecision(4) << std::fixed << value << "±" << std::setprecision(3) << std_value << bold << " | ";
+            }
+            handler << std::endl;
+        }
+    }
+    void BestResultsMd::results_footer(const std::map<std::string, std::vector<double>>& totals, const std::string& best_model)
+    {
+        handler << "| | **Average Score** | ";
+        int nDatasets = totals.begin()->second.size();
+        for (const auto& model : models) {
+            double value = std::reduce(totals.at(model).begin(), totals.at(model).end()) / nDatasets;
+            double std_value = compute_std(totals.at(model), value);
+            const char* bold = model == best_model ? "**" : "";
+            handler << bold << std::setprecision(4) << std::fixed << value << "±" << std::setprecision(3) << std::fixed << std_value << bold << " | ";
+        }
+
+        handler.close();
+    }
+    void BestResultsMd::holm_test(struct HolmResult& holmResult, const std::string& date)
+    {
+        auto file_name = Paths::tex() + Paths::md_post_hoc();
+        openMdFile(file_name);
+        handler << "<!-- This file has been generated by the platform program" << std::endl;
+        handler << "  Date: " << date.c_str() << std::endl;
+        handler << std::endl;
+        handler << "  Post-hoc handler test" << std::endl;
+        handler << "-->" << std::endl;
+        handler << "Post-hoc Holm test: H<sub>0</sub>: There is no significant differences between the control model and the other models." << std::endl << std::endl;
+        handler << "| classifier | pvalue | rank | win | tie | loss | H<sub>0</sub> |" << std::endl;
+        handler << "| :-- | --: | --: | --:| --: | --: | :--: |" << std::endl;
+        for (auto const& line : holmResult.holmLines) {
+            auto textStatus = !line.reject ? "**" : " ";
+            if (line.model == holmResult.model) {
+                handler << "| " << line.model << " | - | " << std::fixed << std::setprecision(2) << line.rank << " | - | - | - |" << std::endl;
+            } else {
+                handler << "| " << line.model << " | " << textStatus << std::scientific << std::setprecision(4) << line.pvalue << textStatus << " |";
+                handler << std::fixed << std::setprecision(2) << line.rank << " | " << line.wtl.win << " | " << line.wtl.tie << " | " << line.wtl.loss << " |";
+                handler << (line.reject ? "rejected" : "**accepted**") << " |" << std::endl;
+            }
+        }
+        handler << std::endl;
+        handler.close();
+    }
+}

src/best/BestResultsMd.h

@@ -0,0 +1,24 @@
+#ifndef BEST_RESULTS_MD_H
+#define BEST_RESULTS_MD_H
+#include <map>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include "common/Paths.h"
+#include "Statistics.h"
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class BestResultsMd {
+    public:
+        BestResultsMd() = default;
+        ~BestResultsMd() = 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_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:
+        void openMdFile(const std::string& name);
+        std::ofstream handler;
+        std::vector<std::string> models;
+    };
+}
+#endif

src/best/BestResultsTex.cpp

@@ -0,0 +1,117 @@
+#include <iostream>
+#include "BestResultsTex.h"
+#include "common/Utils.h" // compute_std
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    void BestResultsTex::openTexFile(const std::string& name)
+    {
+        handler.open(name);
+        if (!handler.is_open()) {
+            std::cerr << "Error opening file " << name << std::endl;
+            exit(1);
+        }
+    }
+    void BestResultsTex::results_header(const std::vector<std::string>& models, const std::string& date)
+    {
+        this->models = models;
+        auto file_name = Paths::tex() + Paths::tex_output();
+        openTexFile(file_name);
+        handler << "%% This file has been generated by the platform program" << std::endl;
+        handler << "%% Date: " << date.c_str() << std::endl;
+        handler << "%%" << std::endl;
+        handler << "%% Table of results" << std::endl;
+        handler << "%%" << std::endl;
+        handler << "\\begin{table}[htbp] " << std::endl;
+        handler << "\\centering " << std::endl;
+        handler << "\\tiny " << std::endl;
+        handler << "\\renewcommand{\\arraystretch }{1.2} " << std::endl;
+        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;
+        handler << "\\hline " << std::endl;
+        handler << "" << std::endl;
+        for (const auto& model : models) {
+            handler << "& " << model.c_str();
+        }
+        handler << "\\\\" << std::endl;
+        handler << "\\hline" << std::endl;
+    }
+    void BestResultsTex::results_body(const std::vector<std::string>& datasets, json& table)
+    {
+        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 {
+                    value = table[model].at(dataset).at(0).get<double>();
+                }
+                catch (nlohmann::json_abi_v3_11_3::detail::out_of_range err) {
+                    value = -1.0;
+                }
+                if (value > max_value) {
+                    max_value = value;
+                }
+            }
+            handler << ++i << " ";
+            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>();
+                const char* bold = value == max_value ? "\\bfseries" : "";
+                handler << "& " << bold << std::setprecision(4) << std::fixed << value << "$\\pm$" << std::setprecision(3) << std_value;
+            }
+            handler << "\\\\" << std::endl;
+        }
+    }
+    void BestResultsTex::results_footer(const std::map<std::string, std::vector<double>>& totals, const std::string& best_model)
+    {
+        handler << "\\hline" << std::endl;
+        handler << "Average ";
+        int nDatasets = totals.begin()->second.size();
+        for (const auto& model : models) {
+            double value = std::reduce(totals.at(model).begin(), totals.at(model).end()) / nDatasets;
+            double std_value = compute_std(totals.at(model), value);
+            const char* bold = model == best_model ? "\\bfseries" : "";
+            handler << "& " << bold << std::setprecision(4) << std::fixed << value << "$\\pm$" << std::setprecision(3) << std::fixed << std_value;
+        }
+        handler << "\\\\" << std::endl;
+        handler << "\\hline " << std::endl;
+        handler << "\\end{tabular}" << std::endl;
+        handler << "\\end{table}" << std::endl;
+        handler.close();
+    }
+    void BestResultsTex::holm_test(struct HolmResult& holmResult, const std::string& date)
+    {
+        auto file_name = Paths::tex() + Paths::tex_post_hoc();
+        openTexFile(file_name);
+        handler << "%% This file has been generated by the platform program" << std::endl;
+        handler << "%% Date: " << date.c_str() << std::endl;
+        handler << "%%" << std::endl;
+        handler << "%% Post-hoc handler test" << std::endl;
+        handler << "%%" << std::endl;
+        handler << "\\begin{table}[htbp]" << std::endl;
+        handler << "\\centering" << std::endl;
+        handler << "\\caption{Results of the post-hoc test for the mean accuracy of the algorithms.}\\label{tab:tests}" << std::endl;
+        handler << "\\begin{tabular}{lrrrrr}" << std::endl;
+        handler << "\\hline" << std::endl;
+        handler << "classifier & pvalue & rank & win & tie & loss\\\\" << std::endl;
+        handler << "\\hline" << std::endl;
+        for (auto const& line : holmResult.holmLines) {
+            auto textStatus = !line.reject ? "\\bf " : " ";
+            if (line.model == holmResult.model) {
+                handler << line.model << " & - & " << std::fixed << std::setprecision(2) << line.rank << " & - & - & - \\\\" << std::endl;
+            } else {
+                handler << line.model << " & " << textStatus << std::scientific << std::setprecision(4) << line.pvalue << " & ";
+                handler << std::fixed << std::setprecision(2) << line.rank << " & " << line.wtl.win << " & " << line.wtl.tie << " & " << line.wtl.loss << "\\\\" << std::endl;
+            }
+        }
+        handler << "\\hline " << std::endl;
+        handler << "\\end{tabular}" << std::endl;
+        handler << "\\end{table}" << std::endl;
+        handler.close();
+    }
+}

src/best/BestResultsTex.h

@@ -0,0 +1,24 @@
+#ifndef BEST_RESULTS_TEX_H
+#define BEST_RESULTS_TEX_H
+#include <map>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include "common/Paths.h"
+#include "Statistics.h"
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class BestResultsTex {
+    public:
+        BestResultsTex() = default;
+        ~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_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:
+        void openTexFile(const std::string& name);
+        std::ofstream handler;
+        std::vector<std::string> models;
+    };
+}
+#endif

src/best/BestScore.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef BESTSCORE_H
+#define BESTSCORE_H
 #include <string>
 #include <map>
 #include <utility>
@@ -24,3 +24,4 @@ namespace platform {
         }
     };
 }
+#endif

src/best/Statistics.cpp

@@ -4,6 +4,8 @@
 #include "common/Colors.h"
 #include "common/Symbols.h"
 #include "common/CLocale.h"
+#include "BestResultsTex.h"
+#include "BestResultsMd.h"
 #include "Statistics.h"
 
 
@@ -113,7 +115,7 @@ namespace platform {
         }
     }
 
-    void Statistics::postHocHolmTest(bool friedmanResult)
+    void Statistics::postHocHolmTest(bool friedmanResult, bool tex)
     {
         if (!fitted) {
             fit();
@@ -130,7 +132,7 @@ namespace platform {
                 stats[i] = 0.0;
                 continue;
             }
-            double z = abs(ranks.at(models[controlIdx]) - ranks.at(models[i])) / diff;
+            double z = std::abs(ranks.at(models[controlIdx]) - ranks.at(models[i])) / diff;
             double p_value = (long double)2 * (1 - cdf(dist, z));
             stats[i] = p_value;
         }
@@ -195,6 +197,12 @@ namespace platform {
         if (output) {
             std::cout << oss.str();
         }
+        if (tex) {
+            BestResultsTex bestResultsTex;
+            BestResultsMd bestResultsMd;
+            bestResultsTex.holm_test(holmResult, get_date() + " " + get_time());
+            bestResultsMd.holm_test(holmResult, get_date() + " " + get_time());
+        }
     }
     bool Statistics::friedmanTest()
     {

src/best/Statistics.h

@@ -1,13 +1,13 @@
-#pragma once
-
+#ifndef STATISTICS_H
+#define STATISTICS_H
 #include <iostream>
 #include <vector>
 #include <map>
 #include <nlohmann/json.hpp>
 
-using json = nlohmann::json;
-
 namespace platform {
+    using json = nlohmann::ordered_json;
+
     struct WTL {
         int win;
         int tie;
@@ -34,7 +34,7 @@ namespace platform {
     public:
         Statistics(const std::vector<std::string>& models, const std::vector<std::string>& datasets, const json& data, double significance = 0.05, bool output = true);
         bool friedmanTest();
-        void postHocHolmTest(bool friedmanResult);
+        void postHocHolmTest(bool friedmanResult, bool tex=false);
         FriedmanResult& getFriedmanResult();
         HolmResult& getHolmResult();
         std::map<std::string, std::map<std::string, float>>& getRanks();
@@ -60,3 +60,4 @@ namespace platform {
         std::map<std::string, std::map<std::string, float>> ranksModels;
     };
 }
+#endif

src/commands/b_best.cpp

@@ -4,27 +4,19 @@
 #include "main/modelRegister.h"
 #include "common/Paths.h"
 #include "common/Colors.h"
-#include "BestResults.h"
-#include "config.h"
+#include "best/BestResults.h"
+#include "config_platform.h"
 
 void manageArguments(argparse::ArgumentParser& program)
 {
     program.add_argument("-m", "--model")
-        .help("Model to use: " + platform::Models::instance()->toString() + " or any")
-        .action([](const std::string& value) {
-        std::vector<std::string> valid(platform::Models::instance()->getNames());
-        valid.push_back("any");
-        static const std::vector<std::string> choices = valid;
-        if (find(choices.begin(), choices.end(), value) != choices.end()) {
-            return value;
-        }
-        throw std::runtime_error("Model must be one of " + platform::Models::instance()->toString() + " or any");
-            }
-    );
+        .help("Model to use or any")
+        .default_value("any");
     program.add_argument("-d", "--dataset").default_value("any").help("Filter results of the selected model) (any for all datasets)");
     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("--level").help("significance level").default_value(0.05).scan<'g', double>().action([](const std::string& value) {
         try {
             auto k = std::stod(value);
@@ -46,7 +38,7 @@ 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;
+    bool build, report, friedman, excel, tex;
     double level;
     try {
         program.parse_args(argc, argv);
@@ -55,6 +47,7 @@ int main(int argc, char** argv)
         score = program.get<std::string>("score");
         friedman = program.get<bool>("friedman");
         excel = program.get<bool>("excel");
+        tex = program.get<bool>("tex");
         level = program.get<double>("level");
         if (model == "" || score == "") {
             throw std::runtime_error("Model and score name must be supplied");
@@ -74,7 +67,7 @@ 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);
+        results.reportAll(excel, tex);
     } else {
         std::string fileName = results.build();
         std::cout << Colors::GREEN() << fileName << " created!" << Colors::RESET() << std::endl;

src/commands/b_grid.cpp

@@ -10,10 +10,10 @@
 #include "common/Timer.h"
 #include "common/Colors.h"
 #include "common/DotEnv.h"
-#include "GridSearch.h"
-#include "config.h"
+#include "grid/GridSearch.h"
+#include "config_platform.h"
 
-using json = nlohmann::json;
+using json = nlohmann::ordered_json;
 const int MAXL = 133;
 
 void assignModel(argparse::ArgumentParser& parser)
@@ -29,7 +29,7 @@ void assignModel(argparse::ArgumentParser& parser)
         }
         throw std::runtime_error("Model must be one of " + models->toString());
             }
-    );
+        );
 }
 void add_compute_args(argparse::ArgumentParser& program)
 {
@@ -54,23 +54,23 @@ void add_compute_args(argparse::ArgumentParser& program)
         catch (...) {
             throw std::runtime_error("Number of nested folds must be an integer");
         }});
-    program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
-    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");
+        program.add_argument("--score").help("Score used in gridsearch").default_value("accuracy");
+        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;
             }
-            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("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
+            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("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
 }
 std::string headerLine(const std::string& text, int utf = 0)
 {
@@ -93,21 +93,27 @@ void list_dump(std::string& model)
         if (item.first.size() > max_dataset) {
             max_dataset = item.first.size();
         }
-        if (item.second.dump().size() > max_hyper) {
-            max_hyper = item.second.dump().size();
+        for (auto const& [key, value] : item.second.items()) {
+            if (value.dump().size() > max_hyper) {
+                max_hyper = value.dump().size();
+            }
         }
     }
     std::cout << Colors::GREEN() << left << " #  " << left << setw(max_dataset) << "Dataset" << " #Com. "
         << setw(max_hyper) << "Hyperparameters" << std::endl;
     std::cout << "=== " << string(max_dataset, '=') << " ===== " << string(max_hyper, '=') << std::endl;
-    bool odd = true;
+    int i = 0;
     for (auto const& item : combinations) {
-        auto color = odd ? Colors::CYAN() : Colors::BLUE();
+        auto color = (i++ % 2) ? Colors::CYAN() : Colors::BLUE();
         std::cout << color;
         auto num_combinations = data.getNumCombinations(item.first);
         std::cout << setw(3) << fixed << right << ++index << left << " " << setw(max_dataset) << item.first
-            << " " << setw(5) << right << num_combinations << " " << setw(max_hyper) << left << item.second.dump() << std::endl;
-        odd = !odd;
+            << " " << setw(5) << right << num_combinations << " ";
+        std::string prefix = "";
+        for (auto const& [key, value] : item.second.items()) {
+            std::cout << prefix << setw(max_hyper) << std::left << value.dump() << std::endl;
+            prefix = string(11 + max_dataset, ' ');
+        }
     }
     std::cout << Colors::RESET() << std::endl;
 }
@@ -141,17 +147,15 @@ void list_results(json& results, std::string& model)
         << "Duration " << setw(8) << "Score" << " " << "Hyperparameters" << std::endl;
     std::cout << "=== " << string(spaces, '=') << " " << string(19, '=') << " " << string(8, '=') << " "
         << string(8, '=') << " " << string(hyperparameters_spaces, '=') << std::endl;
-    bool odd = true;
     int index = 0;
     for (const auto& item : results["results"].items()) {
-        auto color = odd ? Colors::CYAN() : Colors::BLUE();
+        auto color = (index % 2) ? Colors::CYAN() : Colors::BLUE();
         auto value = item.value();
         std::cout << color;
         std::cout << std::setw(3) << std::right << index++ << " ";
         std::cout << left << setw(spaces) << item.key() << " " << value["date"].get<string>()
             << " " << setw(8) << right << value["duration"].get<string>() << " " << setw(8) << setprecision(6)
             << fixed << right << value["score"].get<double>() << " " << value["hyperparameters"].dump() << std::endl;
-        odd = !odd;
     }
     std::cout << Colors::RESET() << std::endl;
 }

src/commands/b_list.cpp

@@ -0,0 +1,110 @@
+#include <iostream>
+#include <locale>
+#include <map>
+#include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
+#include "main/Models.h"
+#include "main/modelRegister.h"
+#include "common/Paths.h"
+#include "common/Colors.h"
+#include "common/Datasets.h"
+#include "reports/DatasetsExcel.h"
+#include "reports/DatasetsConsole.h"
+#include "results/ResultsDatasetConsole.h"
+#include "results/ResultsDataset.h"
+#include "results/ResultsDatasetExcel.h"
+#include "config_platform.h"
+
+
+void list_datasets(argparse::ArgumentParser& program)
+{
+    auto excel = program.get<bool>("excel");
+    auto report = platform::DatasetsConsole();
+    report.report();
+    std::cout << report.getOutput();
+    if (excel) {
+        auto data = report.getData();
+        auto report = platform::DatasetsExcel();
+        report.report(data);
+        std::cout << std::endl << Colors::GREEN() << "Output saved in " << report.getFileName() << std::endl;
+    }
+}
+
+void list_results(argparse::ArgumentParser& program)
+{
+    auto dataset = program.get<string>("dataset");
+    auto score = program.get<string>("score");
+    auto model = program.get<string>("model");
+    auto excel = program.get<bool>("excel");
+    auto report = platform::ResultsDatasetsConsole();
+    if (!report.report(dataset, score, model))
+        return;
+    std::cout << report.getOutput();
+    if (excel) {
+        auto data = report.getData();
+        auto report = platform::ResultsDatasetExcel();
+        report.report(data);
+        std::cout << std::endl << Colors::GREEN() << "Output saved in " << report.getFileName() << std::endl;
+    }
+}
+
+int main(int argc, char** argv)
+{
+    argparse::ArgumentParser program("b_list", { platform_project_version.begin(), platform_project_version.end() });
+    //
+    // datasets subparser
+    //
+    argparse::ArgumentParser datasets_command("datasets");
+    datasets_command.add_description("List datasets available in the platform.");
+    datasets_command.add_argument("--excel").help("Output in Excel format").default_value(false).implicit_value(true);
+    //
+    // results subparser
+    //
+    argparse::ArgumentParser results_command("results");
+    results_command.add_description("List the results of a given dataset.");
+    auto datasets = platform::Datasets(false, platform::Paths::datasets());
+    results_command.add_argument("-d", "--dataset")
+        .help("Dataset to use " + datasets.toString())
+        .required()
+        .action([](const std::string& value) {
+        auto datasets = platform::Datasets(false, platform::Paths::datasets());
+        static const std::vector<std::string> choices = datasets.getNames();
+        if (find(choices.begin(), choices.end(), value) != choices.end()) {
+            return value;
+        }
+        throw std::runtime_error("Dataset must be one of " + datasets.toString());
+            }
+        );
+    results_command.add_argument("-m", "--model")
+        .help("Model to use or any")
+        .default_value("any");
+    results_command.add_argument("--excel").help("Output in Excel format").default_value(false).implicit_value(true);
+    results_command.add_argument("-s", "--score").default_value("accuracy").help("Filter results of the score name supplied");
+
+    // Add subparsers
+    program.add_subparser(datasets_command);
+    program.add_subparser(results_command);
+    // Parse command line and execute
+    try {
+        program.parse_args(argc, argv);
+        bool found = false;
+        map<std::string, void(*)(argparse::ArgumentParser&)> commands = { {"datasets", &list_datasets}, {"results", &list_results} };
+        for (const auto& command : commands) {
+            if (program.is_subcommand_used(command.first)) {
+                std::invoke(command.second, program.at<argparse::ArgumentParser>(command.first));
+                found = true;
+                break;
+            }
+        }
+        if (!found) {
+            throw std::runtime_error("You must specify one of the following commands: {datasets, results}\n");
+        }
+    }
+    catch (const exception& err) {
+        cerr << err.what() << std::endl;
+        cerr << program;
+        exit(1);
+    }
+    std::cout << Colors::RESET() << std::endl;
+    return 0;
+}

src/commands/b_main.cpp

@@ -0,0 +1,234 @@
+#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 "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);
+    }
+
+    /*
+     * 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);
+    }
+    platform::Timer timer;
+    timer.start();
+    experiment.go(filesToTest, quiet, no_train_score, generate_fold_files, graph);
+    experiment.setDuration(timer.getDuration());
+    if (!quiet) {
+        // Classification report if only one dataset is tested
+        experiment.report(filesToTest.size() == 1);
+    }
+    if (saveResults) {
+        experiment.saveResult();
+    }
+    if (graph) {
+        experiment.saveGraph();
+    }
+    std::cout << "Done!" << std::endl;
+    return 0;
+}

src/commands/b_manage.cpp

@@ -1,23 +1,25 @@
 #include <iostream>
+#include <sys/ioctl.h>
+#include <utility>
+#include <unistd.h>
 #include <argparse/argparse.hpp>
-#include "ManageResults.h"
-#include "config.h"
+#include "manage/ManageScreen.h"
+#include <signal.h>
+#include "config_platform.h"
 
+platform::ManageScreen* manager = nullptr;
 
 void manageArguments(argparse::ArgumentParser& program, int argc, char** argv)
 {
-    program.add_argument("-n", "--number").default_value(0).help("Number of results to show (0 = all)").scan<'i', int>();
     program.add_argument("-m", "--model").default_value("any").help("Filter results of the selected model)");
     program.add_argument("-s", "--score").default_value("any").help("Filter results of the score name supplied");
+    program.add_argument("--platform").default_value("any").help("Filter results of the selected platform");
     program.add_argument("--complete").help("Show only results with all datasets").default_value(false).implicit_value(true);
     program.add_argument("--partial").help("Show only partial results").default_value(false).implicit_value(true);
     program.add_argument("--compare").help("Compare with best results").default_value(false).implicit_value(true);
     try {
         program.parse_args(argc, argv);
-        auto number = program.get<int>("number");
-        if (number < 0) {
-            throw std::runtime_error("Number of results must be greater than or equal to 0");
-        }
+        auto platform = program.get<std::string>("platform");
         auto model = program.get<std::string>("model");
         auto score = program.get<std::string>("score");
         auto complete = program.get<bool>("complete");
@@ -31,19 +33,40 @@ void manageArguments(argparse::ArgumentParser& program, int argc, char** argv)
     }
 }
 
+std::pair<int, int> numRowsCols()
+{
+#ifdef TIOCGSIZE
+    struct ttysize ts;
+    ioctl(STDIN_FILENO, TIOCGSIZE, &ts);
+    return { ts.ts_lines, ts.ts_cols };
+#elif defined(TIOCGWINSZ)
+    struct winsize ts;
+    ioctl(STDIN_FILENO, TIOCGWINSZ, &ts);
+    return { ts.ws_row, ts.ws_col };
+#endif /* TIOCGSIZE */
+}
+void handleResize(int sig)
+{
+    auto [rows, cols] = numRowsCols();
+    manager->updateSize(rows, cols);
+}
+
 int main(int argc, char** argv)
 {
     auto program = argparse::ArgumentParser("b_manage", { platform_project_version.begin(), platform_project_version.end() });
     manageArguments(program, argc, argv);
-    int number = program.get<int>("number");
     std::string model = program.get<std::string>("model");
     std::string score = program.get<std::string>("score");
-    auto complete = program.get<bool>("complete");
-    auto partial = program.get<bool>("partial");
-    auto compare = program.get<bool>("compare");
+    std::string platform = program.get<std::string>("platform");
+    bool complete = program.get<bool>("complete");
+    bool partial = program.get<bool>("partial");
+    bool compare = program.get<bool>("compare");
     if (complete)
         partial = false;
-    auto manager = platform::ManageResults(number, model, score, complete, partial, compare);
-    manager.doMenu();
+    signal(SIGWINCH, handleResize);
+    auto [rows, cols] = numRowsCols();
+    manager = new platform::ManageScreen(rows, cols, model, score, platform, complete, partial, compare);
+    manager->doMenu();
+    delete manager;
     return 0;
 }

src/common/CLocale.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef CLOCALE_H
+#define CLOCALE_H
 #include <locale>
 #include <iostream>
 #include <string>
@@ -19,3 +19,4 @@ namespace platform {
         }
     };
 }
+#endif

src/common/Colors.h

@@ -1,15 +1,30 @@
-#pragma once
-
+#ifndef COLORS_H
+#define COLORS_H
 #include <string>
 class Colors {
 public:
-    static std::string MAGENTA() { return "\033[1;35m"; }
+    static std::string BLACK() { return "\033[1;30m"; }
+    static std::string IBLACK() { return "\033[0;90m"; }
     static std::string BLUE() { return "\033[1;34m"; }
-    static std::string CYAN() { return "\033[1;36m"; }
-    static std::string GREEN() { return "\033[1;32m"; }
-    static std::string YELLOW() { return "\033[1;33m"; }
-    static std::string RED() { return "\033[1;31m"; }
-    static std::string WHITE() { return "\033[1;37m"; }
     static std::string IBLUE() { return "\033[0;94m"; }
+    static std::string CYAN() { return "\033[1;36m"; }
+    static std::string ICYAN() { return "\033[0;96m"; }
+    static std::string GREEN() { return "\033[1;32m"; }
+    static std::string IGREEN() { return "\033[0;92m"; }
+    static std::string MAGENTA() { return "\033[1;35m"; }
+    static std::string IMAGENTA() { return "\033[0;95m"; }
+    static std::string RED() { return "\033[1;31m"; }
+    static std::string IRED() { return "\033[0;91m"; }
+    static std::string YELLOW() { return "\033[1;33m"; }
+    static std::string IYELLOW() { return "\033[0;93m"; }
+    static std::string WHITE() { return "\033[1;37m"; }
+    static std::string IWHITE() { return "\033[0;97m"; }
     static std::string RESET() { return "\033[0m"; }
+    static std::string BOLD() { return "\033[1m"; }
+    static std::string UNDERLINE() { return "\033[4m"; }
+    static std::string BLINK() { return "\033[5m"; }
+    static std::string REVERSE() { return "\033[7m"; }
+    static std::string CONCEALED() { return "\033[8m"; }
+    static std::string CLRSCR() { return "\033[2J\033[1;1H"; }
 };
+#endif

src/common/Dataset.cpp

@@ -1,24 +1,26 @@
-#include <ArffFiles.h>
+#include <ArffFiles.hpp>
 #include <fstream>
 #include "Dataset.h"
 namespace platform {
-    Dataset::Dataset(const Dataset& dataset) : path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples), n_features(dataset.n_features), features(dataset.features), states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y), Xv(dataset.Xv), Xd(dataset.Xd), yv(dataset.yv), fileType(dataset.fileType)
+    const std::string message_dataset_not_loaded = "Dataset not loaded.";
+    Dataset::Dataset(const Dataset& dataset) :
+        path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples),
+        n_features(dataset.n_features), numericFeatures(dataset.numericFeatures), features(dataset.features),
+        states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y),
+        X_train(dataset.X_train), X_test(dataset.X_test), Xv(dataset.Xv), yv(dataset.yv),
+        fileType(dataset.fileType)
     {
     }
     std::string Dataset::getName() const
     {
         return name;
     }
-    std::string Dataset::getClassName() const
-    {
-        return className;
-    }
     std::vector<std::string> Dataset::getFeatures() const
     {
         if (loaded) {
             return features;
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     int Dataset::getNFeatures() const
@@ -26,7 +28,7 @@ namespace platform {
         if (loaded) {
             return n_features;
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     int Dataset::getNSamples() const
@@ -34,7 +36,40 @@ namespace platform {
         if (loaded) {
             return n_samples;
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
+        }
+    }
+    std::string Dataset::getClassName() const
+    {
+        return className;
+    }
+    int Dataset::getNClasses() const
+    {
+        if (loaded) {
+            return *std::max_element(yv.begin(), yv.end()) + 1;
+        } else {
+            throw std::invalid_argument(message_dataset_not_loaded);
+        }
+    }
+    std::vector<std::string> Dataset::getLabels() const
+    {
+        // Return the labels factorization result
+        if (loaded) {
+            return labels;
+        } else {
+            throw std::invalid_argument(message_dataset_not_loaded);
+        }
+    }
+    std::vector<int> Dataset::getClassesCounts() const
+    {
+        if (loaded) {
+            std::vector<int> counts(*std::max_element(yv.begin(), yv.end()) + 1);
+            for (auto y : yv) {
+                counts[y]++;
+            }
+            return counts;
+        } else {
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     std::map<std::string, std::vector<int>> Dataset::getStates() const
@@ -42,7 +77,7 @@ namespace platform {
         if (loaded) {
             return states;
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     pair<std::vector<std::vector<float>>&, std::vector<int>&> Dataset::getVectors()
@@ -50,60 +85,56 @@ namespace platform {
         if (loaded) {
             return { Xv, yv };
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<std::vector<std::vector<int>>&, std::vector<int>&> Dataset::getVectorsDiscretized()
-    {
-        if (loaded) {
-            return { Xd, yv };
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     pair<torch::Tensor&, torch::Tensor&> Dataset::getTensors()
     {
         if (loaded) {
-            buildTensors();
             return { X, y };
         } else {
-            throw std::invalid_argument("Dataset not loaded.");
+            throw std::invalid_argument(message_dataset_not_loaded);
         }
     }
     void Dataset::load_csv()
     {
         ifstream file(path + "/" + name + ".csv");
-        if (file.is_open()) {
-            std::string line;
-            getline(file, line);
-            std::vector<std::string> tokens = split(line, ',');
-            features = std::vector<std::string>(tokens.begin(), tokens.end() - 1);
-            if (className == "-1") {
-                className = tokens.back();
-            }
-            for (auto i = 0; i < features.size(); ++i) {
-                Xv.push_back(std::vector<float>());
-            }
-            while (getline(file, line)) {
-                tokens = split(line, ',');
-                for (auto i = 0; i < features.size(); ++i) {
-                    Xv[i].push_back(stof(tokens[i]));
-                }
-                yv.push_back(stoi(tokens.back()));
-            }
-            file.close();
-        } else {
+        if (!file.is_open()) {
             throw std::invalid_argument("Unable to open dataset file.");
         }
+        labels.clear();
+        std::string line;
+        getline(file, line);
+        std::vector<std::string> tokens = split(line, ',');
+        features = std::vector<std::string>(tokens.begin(), tokens.end() - 1);
+        if (className == "-1") {
+            className = tokens.back();
+        }
+        for (auto i = 0; i < features.size(); ++i) {
+            Xv.push_back(std::vector<float>());
+        }
+        while (getline(file, line)) {
+            tokens = split(line, ',');
+            for (auto i = 0; i < features.size(); ++i) {
+                Xv[i].push_back(stof(tokens[i]));
+            }
+            auto label = trim(tokens.back());
+            if (find(labels.begin(), labels.end(), label) == labels.end()) {
+                labels.push_back(label);
+            }
+            yv.push_back(stoi(label));
+        }
+        file.close();
     }
     void Dataset::computeStates()
     {
         for (int i = 0; i < features.size(); ++i) {
-            states[features[i]] = std::vector<int>(*max_element(Xd[i].begin(), Xd[i].end()) + 1);
-            auto item = states.at(features[i]);
-            iota(begin(item), end(item), 0);
+            auto [max_value, idx] = torch::max(X_train.index({ i, "..." }), 0);
+            states[features[i]] = std::vector<int>(max_value.item<int>() + 1);
+            iota(begin(states.at(features[i])), end(states.at(features[i])), 0);
         }
-        states[className] = std::vector<int>(*max_element(yv.begin(), yv.end()) + 1);
+        auto [max_value, idx] = torch::max(y_train, 0);
+        states[className] = std::vector<int>(max_value.item<int>() + 1);
         iota(begin(states.at(className)), end(states.at(className)), 0);
     }
     void Dataset::load_arff()
@@ -117,6 +148,7 @@ namespace platform {
         className = arff.getClassName();
         auto attributes = arff.getAttributes();
         transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& attribute) { return attribute.first; });
+        labels = arff.getLabels();
     }
     std::vector<std::string> tokenize(std::string line)
     {
@@ -139,31 +171,35 @@ namespace platform {
     void Dataset::load_rdata()
     {
         ifstream file(path + "/" + name + "_R.dat");
-        if (file.is_open()) {
-            std::string line;
-            getline(file, line);
-            line = ArffFiles::trim(line);
-            std::vector<std::string> tokens = tokenize(line);
-            transform(tokens.begin(), tokens.end() - 1, back_inserter(features), [](const auto& attribute) { return ArffFiles::trim(attribute); });
-            if (className == "-1") {
-                className = ArffFiles::trim(tokens.back());
-            }
-            for (auto i = 0; i < features.size(); ++i) {
-                Xv.push_back(std::vector<float>());
-            }
-            while (getline(file, line)) {
-                tokens = tokenize(line);
-                // We have to skip the first token, which is the instance number.
-                for (auto i = 1; i < features.size() + 1; ++i) {
-                    const float value = stof(tokens[i]);
-                    Xv[i - 1].push_back(value);
-                }
-                yv.push_back(stoi(tokens.back()));
-            }
-            file.close();
-        } else {
+        if (!file.is_open()) {
             throw std::invalid_argument("Unable to open dataset file.");
         }
+        std::string line;
+        labels.clear();
+        getline(file, line);
+        line = ArffFiles::trim(line);
+        std::vector<std::string> tokens = tokenize(line);
+        transform(tokens.begin(), tokens.end() - 1, back_inserter(features), [](const auto& attribute) { return ArffFiles::trim(attribute); });
+        if (className == "-1") {
+            className = ArffFiles::trim(tokens.back());
+        }
+        for (auto i = 0; i < features.size(); ++i) {
+            Xv.push_back(std::vector<float>());
+        }
+        while (getline(file, line)) {
+            tokens = tokenize(line);
+            // We have to skip the first token, which is the instance number.
+            for (auto i = 1; i < features.size() + 1; ++i) {
+                const float value = stof(tokens[i]);
+                Xv[i - 1].push_back(value);
+            }
+            auto label = trim(tokens.back());
+            if (find(labels.begin(), labels.end(), label) == labels.end()) {
+                labels.push_back(label);
+            }
+            yv.push_back(stoi(label));
+        }
+        file.close();
     }
     void Dataset::load()
     {
@@ -177,39 +213,66 @@ namespace platform {
         } else if (fileType == RDATA) {
             load_rdata();
         }
-        if (discretize) {
-            Xd = discretizeDataset(Xv, yv);
-            computeStates();
-        }
         n_samples = Xv[0].size();
         n_features = Xv.size();
-        loaded = true;
-    }
-    void Dataset::buildTensors()
-    {
-        if (discretize) {
-            X = torch::zeros({ static_cast<int>(n_features), static_cast<int>(n_samples) }, torch::kInt32);
+        if (numericFeaturesIdx.size() == 0) {
+            numericFeatures = std::vector<bool>(n_features, false);
         } else {
-            X = torch::zeros({ static_cast<int>(n_features), static_cast<int>(n_samples) }, torch::kFloat32);
-        }
-        for (int i = 0; i < features.size(); ++i) {
-            if (discretize) {
-                X.index_put_({ i,  "..." }, torch::tensor(Xd[i], torch::kInt32));
+            if (numericFeaturesIdx.at(0) == -1) {
+                numericFeatures = std::vector<bool>(n_features, true);
             } else {
-                X.index_put_({ i,  "..." }, torch::tensor(Xv[i], torch::kFloat32));
+                numericFeatures = std::vector<bool>(n_features, false);
+                for (auto i : numericFeaturesIdx) {
+                    numericFeatures[i] = true;
+                }
             }
         }
-        y = torch::tensor(yv, torch::kInt32);
-    }
-    std::vector<mdlp::labels_t> Dataset::discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
-    {
-        std::vector<mdlp::labels_t> Xd;
-        auto fimdlp = mdlp::CPPFImdlp();
-        for (int i = 0; i < X.size(); i++) {
-            fimdlp.fit(X[i], y);
-            mdlp::labels_t& xd = fimdlp.transform(X[i]);
-            Xd.push_back(xd);
+        // Build Tensors
+        X = torch::zeros({ n_features, n_samples }, torch::kFloat32);
+        for (int i = 0; i < features.size(); ++i) {
+            X.index_put_({ i,  "..." }, torch::tensor(Xv[i], torch::kFloat32));
         }
-        return Xd;
+        y = torch::tensor(yv, torch::kInt32);
+        loaded = true;
+    }
+    std::tuple<torch::Tensor&, torch::Tensor&, torch::Tensor&, torch::Tensor&> Dataset::getTrainTestTensors(std::vector<int>& train, std::vector<int>& test)
+    {
+        if (!loaded) {
+            throw std::invalid_argument(message_dataset_not_loaded);
+        }
+        auto train_t = torch::tensor(train);
+        int samples_train = train.size();
+        int samples_test = test.size();
+        auto test_t = torch::tensor(test);
+        X_train = X.index({ "...", train_t });
+        y_train = y.index({ train_t });
+        X_test = X.index({ "...", test_t });
+        y_test = y.index({ test_t });
+        if (discretize) {
+            auto discretizer = Discretization::instance()->create(discretizer_algorithm);
+            auto X_train_d = torch::zeros({ n_features, samples_train }, torch::kInt32);
+            auto X_test_d = torch::zeros({ n_features, samples_test }, torch::kInt32);
+            for (auto feature = 0; feature < n_features; ++feature) {
+                if (numericFeatures[feature]) {
+                    auto feature_train = X_train.index({ feature, "..." });
+                    auto feature_test = X_test.index({ feature, "..." });
+                    auto feature_train_disc = discretizer->fit_transform_t(feature_train, y_train);
+                    auto feature_test_disc = discretizer->transform_t(feature_test);
+                    X_train_d.index_put_({ feature, "..." }, feature_train_disc);
+                    X_test_d.index_put_({ feature, "..." }, feature_test_disc);
+                } else {
+                    X_train_d.index_put_({ feature, "..." }, X_train.index({ feature, "..." }).to(torch::kInt32));
+                    X_test_d.index_put_({ feature, "..." }, X_test.index({ feature, "..." }).to(torch::kInt32));
+                }
+            }
+            X_train = X_train_d;
+            X_test = X_test_d;
+            assert(X_train.dtype() == torch::kInt32);
+            assert(X_test.dtype() == torch::kInt32);
+            computeStates();
+        }
+        assert(y_train.dtype() == torch::kInt32);
+        assert(y_test.dtype() == torch::kInt32);
+        return { X_train, X_test, y_train, y_test };
     }
 }

src/common/Dataset.h

@@ -1,77 +1,60 @@
-#pragma once
-
+#ifndef DATASET_H
+#define DATASET_H
 #include <torch/torch.h>
 #include <map>
 #include <vector>
 #include <string>
-#include <CPPFImdlp.h>
+#include <tuple>
+#include <common/DiscretizationRegister.h>
 #include "Utils.h"
+#include "SourceData.h"
 namespace platform {
-    enum fileType_t { CSV, ARFF, RDATA };
-    class SourceData {
-    public:
-        SourceData(std::string source)
-        {
-            if (source == "Surcov") {
-                path = "datasets/";
-                fileType = CSV;
-            } else if (source == "Arff") {
-                path = "datasets/";
-                fileType = ARFF;
-            } else if (source == "Tanveer") {
-                path = "data/";
-                fileType = RDATA;
-            } else {
-                throw std::invalid_argument("Unknown source.");
-            }
-        }
-        std::string getPath()
-        {
-            return path;
-        }
-        fileType_t getFileType()
-        {
-            return fileType;
-        }
-    private:
-        std::string path;
-        fileType_t fileType;
-    };
     class Dataset {
+    public:
+        Dataset(const std::string& path, const std::string& name, const std::string& className, bool discretize, fileType_t fileType, std::vector<int> numericFeaturesIdx, std::string discretizer_algo = "none") :
+            path(path), name(name), className(className), discretize(discretize),
+            loaded(false), fileType(fileType), numericFeaturesIdx(numericFeaturesIdx), discretizer_algorithm(discretizer_algo)
+        {
+        };
+        explicit Dataset(const Dataset&);
+        std::string getName() const;
+        std::string getClassName() const;
+        int getNClasses() const;
+        std::vector<std::string> getLabels() const; // return the labels factorization result
+        std::vector<int> getClassesCounts() const;
+        std::vector<string> getFeatures() const;
+        std::map<std::string, std::vector<int>> getStates() const;
+        std::pair<vector<std::vector<float>>&, std::vector<int>&> getVectors();
+        std::pair<torch::Tensor&, torch::Tensor&> getTensors();
+        std::tuple<torch::Tensor&, torch::Tensor&, torch::Tensor&, torch::Tensor&> getTrainTestTensors(std::vector<int>& train, std::vector<int>& test);
+        int getNFeatures() const;
+        int getNSamples() const;
+        std::vector<bool>& getNumericFeatures() { return numericFeatures; }
+        void load();
+        const bool inline isLoaded() const { return loaded; };
     private:
         std::string path;
         std::string name;
         fileType_t fileType;
         std::string className;
         int n_samples{ 0 }, n_features{ 0 };
+        std::vector<int> numericFeaturesIdx;
+        std::string discretizer_algorithm;
+        std::vector<bool> numericFeatures; // true if feature is numeric
         std::vector<std::string> features;
+        std::vector<std::string> labels;
         std::map<std::string, std::vector<int>> states;
         bool loaded;
         bool discretize;
         torch::Tensor X, y;
+        torch::Tensor X_train, X_test, y_train, y_test;
         std::vector<std::vector<float>> Xv;
-        std::vector<std::vector<int>> Xd;
         std::vector<int> yv;
-        void buildTensors();
         void load_csv();
         void load_arff();
         void load_rdata();
         void computeStates();
         std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-    public:
-        Dataset(const std::string& path, const std::string& name, const std::string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
-        explicit Dataset(const Dataset&);
-        std::string getName() const;
-        std::string getClassName() const;
-        std::vector<string> getFeatures() const;
-        std::map<std::string, std::vector<int>> getStates() const;
-        std::pair<vector<std::vector<float>>&, std::vector<int>&> getVectors();
-        std::pair<vector<std::vector<int>>&, std::vector<int>&> getVectorsDiscretized();
-        std::pair<torch::Tensor&, torch::Tensor&> getTensors();
-        int getNFeatures() const;
-        int getNSamples() const;
-        void load();
-        const bool inline isLoaded() const { return loaded; };
     };
 };
-
+#endif

src/common/Datasets.cpp

@@ -1,32 +1,70 @@
 #include <fstream>
 #include "Datasets.h"
+#include <nlohmann/json.hpp>
+
 namespace platform {
+    using json = nlohmann::ordered_json;
+    const std::string message_dataset_not_loaded = "dataset not loaded.";
+    Datasets::Datasets(bool discretize, std::string sfileType, std::string discretizer_algorithm) :
+        discretize(discretize), sfileType(sfileType), discretizer_algorithm(discretizer_algorithm)
+    {
+        if ((discretizer_algorithm == "none" || discretizer_algorithm == "") && discretize) {
+            throw std::runtime_error("Can't discretize without discretization algorithm");
+        }
+        load();
+    }
     void Datasets::load()
     {
         auto sd = SourceData(sfileType);
         fileType = sd.getFileType();
         path = sd.getPath();
         ifstream catalog(path + "all.txt");
-        if (catalog.is_open()) {
-            std::string line;
-            while (getline(catalog, line)) {
-                if (line.empty() || line[0] == '#') {
-                    continue;
-                }
-                std::vector<std::string> tokens = split(line, ',');
-                std::string name = tokens[0];
-                std::string className;
-                if (tokens.size() == 1) {
-                    className = "-1";
-                } else {
-                    className = tokens[1];
-                }
-                datasets[name] = make_unique<Dataset>(path, name, className, discretize, fileType);
-            }
-            catalog.close();
-        } else {
+        std::vector<int> numericFeaturesIdx;
+        if (!catalog.is_open()) {
             throw std::invalid_argument("Unable to open catalog file. [" + path + "all.txt" + "]");
         }
+        std::string line;
+        while (getline(catalog, line)) {
+            if (line.empty() || line[0] == '#') {
+                continue;
+            }
+            std::vector<std::string> tokens = split(line, ';');
+            std::string name = tokens[0];
+            std::string className;
+            numericFeaturesIdx.clear();
+            int size = tokens.size();
+            switch (size) {
+                case 1:
+                    className = "-1";
+                    numericFeaturesIdx.push_back(-1);
+                    break;
+                case 2:
+                    className = tokens[1];
+                    numericFeaturesIdx.push_back(-1);
+                    break;
+                case 3:
+                    {
+                        className = tokens[1];
+                        auto numericFeatures = tokens[2];
+                        if (numericFeatures == "all") {
+                            numericFeaturesIdx.push_back(-1);
+                        } else {
+                            if (numericFeatures != "none") {
+                                auto features = json::parse(numericFeatures);
+                                for (auto& f : features) {
+                                    numericFeaturesIdx.push_back(f);
+                                }
+                            }
+                        }
+                    }
+                    break;
+                default:
+                    throw std::invalid_argument("Invalid catalog file format.");
+
+            }
+            datasets[name] = make_unique<Dataset>(path, name, className, discretize, fileType, numericFeaturesIdx, discretizer_algorithm);
+        }
+        catalog.close();
     }
     std::vector<std::string> Datasets::getNames()
     {
@@ -34,94 +72,6 @@ namespace platform {
         transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
         return result;
     }
-    std::vector<std::string> Datasets::getFeatures(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getFeatures();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    map<std::string, std::vector<int>> Datasets::getStates(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getStates();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    void Datasets::loadDataset(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return;
-        } else {
-            datasets.at(name)->load();
-        }
-    }
-    std::string Datasets::getClassName(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getClassName();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Datasets::getNSamples(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            return datasets.at(name)->getNSamples();
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    int Datasets::getNClasses(const std::string& name)
-    {
-        if (datasets.at(name)->isLoaded()) {
-            auto className = datasets.at(name)->getClassName();
-            if (discretize) {
-                auto states = getStates(name);
-                return states.at(className).size();
-            }
-            auto [Xv, yv] = getVectors(name);
-            return *std::max_element(yv.begin(), yv.end()) + 1;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    std::vector<int> Datasets::getClassesCounts(const std::string& name) const
-    {
-        if (datasets.at(name)->isLoaded()) {
-            auto [Xv, yv] = datasets.at(name)->getVectors();
-            std::vector<int> counts(*std::max_element(yv.begin(), yv.end()) + 1);
-            for (auto y : yv) {
-                counts[y]++;
-            }
-            return counts;
-        } else {
-            throw std::invalid_argument("Dataset not loaded.");
-        }
-    }
-    pair<std::vector<std::vector<float>>&, std::vector<int>&> Datasets::getVectors(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getVectors();
-    }
-    pair<std::vector<std::vector<int>>&, std::vector<int>&> Datasets::getVectorsDiscretized(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getVectorsDiscretized();
-    }
-    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(const std::string& name)
-    {
-        if (!datasets[name]->isLoaded()) {
-            datasets[name]->load();
-        }
-        return datasets[name]->getTensors();
-    }
     bool Datasets::isDataset(const std::string& name) const
     {
         return datasets.find(name) != datasets.end();

src/common/Datasets.h

@@ -1,29 +1,22 @@
-#pragma once
-
+#ifndef DATASETS_H
+#define DATASETS_H
 #include "Dataset.h"
 namespace platform {
     class Datasets {
+    public:
+        explicit Datasets(bool discretize, std::string sfileType, std::string discretizer_algorithm = "none");
+        std::vector<std::string> getNames();
+        bool isDataset(const std::string& name) const;
+        Dataset& getDataset(const std::string& name) const { return *datasets.at(name); }
+        std::string toString() const;
     private:
         std::string path;
         fileType_t fileType;
         std::string sfileType;
+        std::string discretizer_algorithm;
         std::map<std::string, std::unique_ptr<Dataset>> datasets;
         bool discretize;
         void load(); // Loads the list of datasets
-    public:
-        explicit Datasets(bool discretize, std::string sfileType) : discretize(discretize), sfileType(sfileType) { load(); };
-        std::vector<string> getNames();
-        std::vector<string> getFeatures(const std::string& name) const;
-        int getNSamples(const std::string& name) const;
-        std::string getClassName(const std::string& name) const;
-        int getNClasses(const std::string& name);
-        std::vector<int> getClassesCounts(const std::string& name) const;
-        std::map<std::string, std::vector<int>> getStates(const std::string& name) const;
-        std::pair<std::vector<std::vector<float>>&, std::vector<int>&> getVectors(const std::string& name);
-        std::pair<std::vector<std::vector<int>>&, std::vector<int>&> getVectorsDiscretized(const std::string& name);
-        std::pair<torch::Tensor&, torch::Tensor&> getTensors(const std::string& name);
-        bool isDataset(const std::string& name) const;
-        void loadDataset(const std::string& name) const;
-        std::string toString() const;
     };
 };
+#endif

src/common/Discretization.cpp

@@ -0,0 +1,55 @@
+#include "Discretization.h"
+
+namespace platform {
+    // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
+    Discretization* Discretization::factory = nullptr;
+    Discretization* Discretization::instance()
+    {
+        //manages singleton
+        if (factory == nullptr)
+            factory = new Discretization();
+        return factory;
+    }
+    void Discretization::registerFactoryFunction(const std::string& name,
+        function<mdlp::Discretizer* (void)> classFactoryFunction)
+    {
+        // register the class factory function
+        functionRegistry[name] = classFactoryFunction;
+    }
+    std::shared_ptr<mdlp::Discretizer> Discretization::create(const std::string& name)
+    {
+        mdlp::Discretizer* instance = nullptr;
+
+        // find name in the registry and call factory method.
+        auto it = functionRegistry.find(name);
+        if (it != functionRegistry.end())
+            instance = it->second();
+        // wrap instance in a shared ptr and return
+        if (instance != nullptr)
+            return std::unique_ptr<mdlp::Discretizer>(instance);
+        else
+            throw std::runtime_error("Discretizer not found: " + name);
+    }
+    std::vector<std::string> Discretization::getNames()
+    {
+        std::vector<std::string> names;
+        transform(functionRegistry.begin(), functionRegistry.end(), back_inserter(names),
+            [](const pair<std::string, function<mdlp::Discretizer* (void)>>& pair) { return pair.first; });
+        return names;
+    }
+    std::string Discretization::toString()
+    {
+        std::string result = "";
+        std::string sep = "";
+        for (const auto& pair : functionRegistry) {
+            result += sep + pair.first;
+            sep = ", ";
+        }
+        return "{" + result + "}";
+    }
+    RegistrarDiscretization::RegistrarDiscretization(const std::string& name, function<mdlp::Discretizer* (void)> classFactoryFunction)
+    {
+        // register the class factory function 
+        Discretization::instance()->registerFactoryFunction(name, classFactoryFunction);
+    }
+}

src/common/Discretization.h

@@ -0,0 +1,33 @@
+#ifndef DISCRETIZATION_H
+#define DISCRETIZATION_H
+#include <map>
+#include <memory>
+#include <string>
+#include <functional>
+#include <vector>
+#include <fimdlp/Discretizer.h>
+#include <fimdlp/BinDisc.h>
+#include <fimdlp/CPPFImdlp.h>
+namespace platform {
+    class Discretization {
+    public:
+        Discretization(Discretization&) = delete;
+        void operator=(const Discretization&) = delete;
+        // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
+        static Discretization* instance();
+        std::shared_ptr<mdlp::Discretizer> create(const std::string& name);
+        void registerFactoryFunction(const std::string& name,
+            function<mdlp::Discretizer* (void)> classFactoryFunction);
+        std::vector<string> getNames();
+        std::string toString();
+    private:
+        map<std::string, function<mdlp::Discretizer* (void)>> functionRegistry;
+        static Discretization* factory; //singleton
+        Discretization() {};
+    };
+    class RegistrarDiscretization {
+    public:
+        RegistrarDiscretization(const std::string& className, function<mdlp::Discretizer* (void)> classFactoryFunction);
+    };
+}
+#endif

src/common/DiscretizationRegister.h

@@ -0,0 +1,38 @@
+#ifndef DISCRETIZATIONREGISTER_H
+#define DISCRETIZATIONREGISTER_H
+#include <common/Discretization.h>
+static platform::RegistrarDiscretization registrarM("mdlp",
+    [](void) -> mdlp::Discretizer* { return new mdlp::CPPFImdlp();});
+static platform::RegistrarDiscretization registrarBU3("bin3u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(3, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ3("bin3q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(3, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU4("bin4u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(4, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ4("bin4q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(4, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU5("bin5u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(5, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ5("bin5q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(5, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU6("bin6u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(6, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ6("bin6q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(6, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU7("bin7u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(7, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ7("bin7q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(7, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU8("bin8u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(8, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ8("bin8q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(8, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU9("bin9u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(9, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ9("bin9q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(9, mdlp::strategy_t::QUANTILE);});
+static platform::RegistrarDiscretization registrarBU10("bin10u",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(10, mdlp::strategy_t::UNIFORM);});
+static platform::RegistrarDiscretization registrarBQ10("bin10q",
+    [](void) -> mdlp::Discretizer* { return new mdlp::BinDisc(10, mdlp::strategy_t::QUANTILE);});
+#endif

src/common/DotEnv.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef DOTENV_H
+#define DOTENV_H
 #include <string>
 #include <map>
 #include <fstream>
@@ -13,9 +13,55 @@ namespace platform {
     class DotEnv {
     private:
         std::map<std::string, std::string> env;
+        std::map<std::string, std::vector<std::string>> valid;
     public:
-        DotEnv()
+        DotEnv(bool create = false)
         {
+            valid =
+            {
+                {"depth", {"any"}},
+                {"discretize", {"0", "1"}},
+                {"discretize_algo", {"mdlp", "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"}},
+                {"ignore_nan", {"0", "1"}},
+                {"leaves", {"any"}},
+                {"margin", {"0.1", "0.2", "0.3"}},
+                {"model", {"any"}},
+                {"n_folds", {"5", "10"}},
+                {"nodes", {"any"}},
+                {"platform", {"any"}},
+                {"stratified", {"0", "1"}},
+                {"score", {"accuracy", "roc-auc-ovr"}},
+                {"seeds", {"any"}},
+                {"smooth_strat", {"ORIGINAL", "LAPLACE", "CESTNIK"}},
+                {"source_data", {"Arff", "Tanveer", "Surcov", "Test"}},
+            };
+            if (create) {
+                // For testing purposes
+                std::ofstream file(".env");
+                file << "experiment=Test" << std::endl;
+                file << "source_data=Test" << std::endl;
+                file << "margin=0.1" << std::endl;
+                file << "score=accuracy" << std::endl;
+                file << "platform=um790Linux" << std::endl;
+                file << "n_folds=5" << std::endl;
+                file << "discretize_algo=mdlp" << std::endl;
+                file << "smooth_strat=ORIGINAL" << std::endl;
+                file << "stratified=0" << std::endl;
+                file << "model=TAN" << std::endl;
+                file << "seeds=[271]" << std::endl;
+                file << "discretize=0" << std::endl;
+                file << "ignore_nan=0" << std::endl;
+                file << "nodes=Nodes" << std::endl;
+                file << "leaves=Edges" << std::endl;
+                file << "depth=States" << std::endl;
+                file << "fit_features=0" << std::endl;
+                file << "framework=bulma" << std::endl;
+                file << "margin=0.1" << std::endl;
+                file.close();
+            }
             std::ifstream file(".env");
             if (!file.is_open()) {
                 std::cerr << "File .env not found" << std::endl;
@@ -30,12 +76,62 @@ namespace platform {
                 std::istringstream iss(line);
                 std::string key, value;
                 if (std::getline(iss, key, '=') && std::getline(iss, value)) {
+                    key = trim(key);
+                    value = trim(value);
+                    parse(key, value);
                     env[key] = value;
                 }
             }
+            parseEnv();
+        }
+        void parse(const std::string& key, const std::string& value)
+        {
+            if (valid.find(key) == valid.end()) {
+                std::cerr << "Invalid key in .env: " << key << std::endl;
+                exit(1);
+            }
+            if (valid[key].front() == "any") {
+                return;
+            }
+            if (std::find(valid[key].begin(), valid[key].end(), value) == valid[key].end()) {
+                std::cerr << "Invalid value in .env: " << key << " = " << value << std::endl;
+                exit(1);
+            }
+        }
+        std::vector<std::string> valid_tokens(const std::string& key)
+        {
+            if (valid.find(key) == valid.end()) {
+                return {};
+            }
+            return valid.at(key);
+        }
+        std::string valid_values(const std::string& key)
+        {
+            std::string valid_values = "{", sep = "";
+            if (valid.find(key) == valid.end()) {
+                return "{}";
+            }
+            for (const auto& value : valid.at(key)) {
+                valid_values += sep + value;
+                sep = ", ";
+            }
+            return valid_values + "}";
+        }
+        void parseEnv()
+        {
+            for (auto& [key, values] : valid) {
+                if (env.find(key) == env.end()) {
+                    std::cerr << "Key not found in .env: " << key << ", valid values: " << valid_values(key) << std::endl;
+                    exit(1);
+                }
+            }
         }
         std::string get(const std::string& key)
         {
+            if (env.find(key) == env.end()) {
+                std::cerr << "Key not found in .env: " << key << std::endl;
+                exit(1);
+            }
             return env.at(key);
         }
         std::vector<int> getSeeds()
@@ -52,3 +148,4 @@ namespace platform {
         }
     };
 }
+#endif

src/common/Paths.h

@@ -1,20 +1,35 @@
-#pragma once
-
+#ifndef PATHS_H
+#define PATHS_H
 #include <string>
 #include <filesystem>
 #include "DotEnv.h"
 namespace platform {
     class Paths {
     public:
-        static std::string results() { return "results/"; }
-        static std::string hiddenResults() { return "hidden_results/"; }
-        static std::string excel() { return "excel/"; }
-        static std::string grid() { return "grid/"; }
+        static std::string createIfNotExists(const std::string& folder)
+        {
+            if (!std::filesystem::exists(folder)) {
+                std::filesystem::create_directory(folder);
+            }
+            return folder;
+        }
+        static std::string results() { return createIfNotExists("results/"); }
+        static std::string hiddenResults() { return createIfNotExists("hidden_results/"); }
+        static std::string excel() { return createIfNotExists("excel/"); }
+        static std::string grid() { return createIfNotExists("grid/"); }
+        static std::string graphs() { return createIfNotExists("graphs/"); }
+        static std::string tex() { return createIfNotExists("tex/"); }
         static std::string datasets()
         {
             auto env = platform::DotEnv();
             return env.get("source_data");
         }
+        static std::string experiment_file(const std::string& fileName, bool discretize, bool stratified, int seed, int nfold)
+        {
+            std::string disc = discretize ? "_disc_" : "_ndisc_";
+            std::string strat = stratified ? "strat_" : "nstrat_";
+            return "datasets_experiment/" + fileName + disc + strat + std::to_string(seed) + "_" + std::to_string(nfold) + ".json";
+        }
         static void createPath(const std::string& path)
         {
             // Create directory if it does not exist
@@ -25,6 +40,14 @@ namespace platform {
                 throw std::runtime_error("Could not create directory " + path);
             }
         }
+        static std::string bestResultsFile(const std::string& score, const std::string& model)
+        {
+            return "best_results_" + score + "_" + model + ".json";
+        }
+        static std::string bestResultsExcel(const std::string& score)
+        {
+            return "BestResults_" + score + ".xlsx";
+        }
         static std::string excelResults() { return "some_results.xlsx"; }
         static std::string grid_input(const std::string& model)
         {
@@ -34,5 +57,22 @@ namespace platform {
         {
             return grid() + "grid_" + model + "_output.json";
         }
+        static std::string tex_output()
+        {
+            return "results.tex";
+        }
+        static std::string md_output()
+        {
+            return "results.md";
+        }
+        static std::string tex_post_hoc()
+        {
+            return "post_hoc.tex";
+        }
+        static std::string md_post_hoc()
+        {
+            return "post_hoc.md";
+        }
     };
 }
+#endif

src/common/SourceData.h.in

@@ -0,0 +1,38 @@
+#ifndef SOURCEDATA_H
+#define SOURCEDATA_H
+namespace platform {
+    enum fileType_t { CSV, ARFF, RDATA };
+    class SourceData {
+    public:
+        SourceData(std::string source)
+        {
+            if (source == "Surcov") {
+                path = "datasets/";
+                fileType = CSV;
+            } else if (source == "Arff") {
+                path = "datasets/";
+                fileType = ARFF;
+            } else if (source == "Tanveer") {
+                path = "data/";
+                fileType = RDATA;
+            } else if (source == "Test") {
+                path = "@TEST_DATA_PATH@/";
+                fileType = ARFF;
+            } else {
+                throw std::invalid_argument("Unknown source.");
+            }
+        }
+        std::string getPath()
+        {
+            return path;
+        }
+        fileType_t getFileType()
+        {
+            return fileType;
+        }
+    private:
+        std::string path;
+        fileType_t fileType;
+    };
+}
+#endif

src/common/Symbols.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef SYMBOLS_H
+#define SYMBOLS_H
 #include <string>
 namespace platform {
     class Symbols {
@@ -9,9 +9,13 @@ namespace platform {
         inline static const std::string black_star{ "\u2605" };
         inline static const std::string cross{ "\u2717" };
         inline static const std::string upward_arrow{ "\u27B6" };
-        inline static const std::string down_arrow{ "\u27B4" };
+        inline static const std::string downward_arrow{ "\u27B4" };
+        inline static const std::string up_arrow{ "\u2B06" };
+        inline static const std::string down_arrow{ "\u2B07" };
+        inline static const std::string ellipsis{ "\u2026" };
         inline static const std::string equal_best{ check_mark };
         inline static const std::string better_best{ black_star };
         inline static const std::string notebook{ "\U0001F5C8" };
     };
 }
+#endif

src/common/Timer.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef TIMER_H
+#define TIMER_H
 #include <chrono>
 #include <string>
 #include <sstream>
@@ -40,3 +40,4 @@ namespace platform {
         }
     };
 } /* namespace platform */
+#endif

src/common/Utils.h

@@ -1,18 +1,18 @@
-#pragma once
-
+#ifndef UTILS_H
+#define UTILS_H
 #include <sstream>
 #include <string>
 #include <vector>
+#include <algorithm>
+#include <torch/torch.h>
 namespace platform {
-    //static std::vector<std::string> split(const std::string& text, char delimiter);
-    static std::vector<std::string> split(const std::string& text, char delimiter)
+    template <typename T>
+    std::vector<T> tensorToVector(const torch::Tensor& tensor)
     {
-        std::vector<std::string> result;
-        std::stringstream ss(text);
-        std::string token;
-        while (std::getline(ss, token, delimiter)) {
-            result.push_back(token);
-        }
+        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::string trim(const std::string& str)
@@ -26,4 +26,45 @@ namespace platform {
             }).base(), result.end());
         return result;
     }
+    static std::vector<std::string> split(const std::string& text, char delimiter)
+    {
+        std::vector<std::string> result;
+        std::stringstream ss(text);
+        std::string token;
+        while (std::getline(ss, token, delimiter)) {
+            result.push_back(trim(token));
+        }
+        return result;
+    }
+    inline double compute_std(std::vector<double> values, double mean)
+    {
+        // Compute standard devation of the values
+        double sum = 0.0;
+        for (const auto& value : values) {
+            sum += std::pow(value - mean, 2);
+        }
+        double variance = sum / values.size();
+        return std::sqrt(variance);
+    }
+    inline std::string get_date()
+    {
+        time_t rawtime;
+        tm* timeinfo;
+        time(&rawtime);
+        timeinfo = std::localtime(&rawtime);
+        std::ostringstream oss;
+        oss << std::put_time(timeinfo, "%Y-%m-%d");
+        return oss.str();
+    }
+    inline std::string get_time()
+    {
+        time_t rawtime;
+        tm* timeinfo;
+        time(&rawtime);
+        timeinfo = std::localtime(&rawtime);
+        std::ostringstream oss;
+        oss << std::put_time(timeinfo, "%H:%M:%S");
+        return oss.str();
+    }
 }
+#endif

src/grid/GridData.h

@@ -1,12 +1,12 @@
-#pragma once
-
+#ifndef GRIDDATA_H
+#define GRIDDATA_H
 #include <string>
 #include <vector>
 #include <map>
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     const std::string ALL_DATASETS = "all";
     class GridData {
     public:
@@ -23,3 +23,4 @@ namespace platform {
         std::map<std::string, json> grid;
     };
 } /* namespace platform */
+#endif

src/grid/GridSearch.cpp

@@ -5,29 +5,11 @@
 #include "main/Models.h"
 #include "common/Paths.h"
 #include "common/Colors.h"
+#include "common/Utils.h"
 #include "GridSearch.h"
 
 namespace platform {
-    std::string get_date()
-    {
-        time_t rawtime;
-        tm* timeinfo;
-        time(&rawtime);
-        timeinfo = std::localtime(&rawtime);
-        std::ostringstream oss;
-        oss << std::put_time(timeinfo, "%Y-%m-%d");
-        return oss.str();
-    }
-    std::string get_time()
-    {
-        time_t rawtime;
-        tm* timeinfo;
-        time(&rawtime);
-        timeinfo = std::localtime(&rawtime);
-        std::ostringstream oss;
-        oss << std::put_time(timeinfo, "%H:%M:%S");
-        return oss.str();
-    }
+
     std::string get_color_rank(int rank)
     {
         auto colors = { Colors::WHITE(), Colors::RED(), Colors::GREEN(),  Colors::BLUE(), Colors::MAGENTA(), Colors::CYAN() };
@@ -103,11 +85,11 @@ namespace platform {
         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 << "|";
+        std::cout << separator;
         for (int i = 0; i < tasks.size(); ++i) {
             std::cout << (i + 1) % 10;
         }
-        std::cout << "|" << std::endl << "|" << std::flush;
+        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)
@@ -118,17 +100,18 @@ namespace platform {
         json task = tasks[n_task];
         auto model = config.model;
         auto grid = GridData(Paths::grid_input(model));
-        auto dataset = task["dataset"].get<std::string>();
+        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 combinations = grid.getGrid(dataset);
-        auto [X, y] = datasets.getTensors(dataset);
-        auto states = datasets.getStates(dataset);
-        auto features = datasets.getFeatures(dataset);
-        auto className = datasets.getClassName(dataset);
+        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
         //
@@ -138,14 +121,11 @@ namespace platform {
         else
             fold = new folding::KFold(config.n_folds, y.size(0), seed);
         auto [train, test] = fold->getFold(n_fold);
-        auto train_t = torch::tensor(train);
-        auto test_t = torch::tensor(test);
-        auto X_train = X.index({ "...", train_t });
-        auto y_train = y.index({ train_t });
-        auto X_test = X.index({ "...", test_t });
-        auto y_test = y.index({ test_t });
+        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];
@@ -168,10 +148,10 @@ namespace platform {
                 // Build Classifier with selected hyperparameters
                 auto clf = Models::instance()->create(config.model);
                 auto valid = clf->getValidHyperparameters();
-                hyperparameters.check(valid, dataset);
-                clf->setHyperparameters(hyperparameters.get(dataset));
+                hyperparameters.check(valid, dataset_name);
+                clf->setHyperparameters(hyperparameters.get(dataset_name));
                 // Train model
-                clf->fit(X_nested_train, y_nested_train, features, className, states);
+                clf->fit(X_nested_train, y_nested_train, features, className, states, smoothing);
                 // Test model
                 score += clf->score(X_nested_test, y_nested_test);
             }
@@ -188,9 +168,9 @@ namespace platform {
         auto hyperparameters = platform::HyperParameters(datasets.getNames(), best_fold_hyper);
         auto clf = Models::instance()->create(config.model);
         auto valid = clf->getValidHyperparameters();
-        hyperparameters.check(valid, dataset);
+        hyperparameters.check(valid, dataset_name);
         clf->setHyperparameters(best_fold_hyper);
-        clf->fit(X_train, y_train, features, className, states);
+        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>();
@@ -373,14 +353,16 @@ namespace platform {
         MPI_Bcast(msg, tasks_size + 1, MPI_CHAR, config_mpi.manager, MPI_COMM_WORLD);
         tasks = json::parse(msg);
         delete[] msg;
-        auto datasets = Datasets(config.discretize, Paths::datasets());
+        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) << "|" << std::endl;
+            std::cout << get_color_rank(config_mpi.rank) << separator << std::endl;
             //
             // 3. Manager select the bests sccores for each dataset
             //

src/grid/GridSearch.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef GRIDSEARCH_H
+#define GRIDSEARCH_H
 #include <string>
 #include <map>
 #include <mpi.h>
@@ -10,7 +10,7 @@
 #include "GridData.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     struct ConfigGrid {
         std::string model;
         std::string score;
@@ -55,5 +55,7 @@ namespace platform {
         struct ConfigGrid config;
         json build_tasks_mpi(int rank);
         Timer timer; // used to measure the time of the whole process
+        const std::string separator = "|";
     };
 } /* namespace platform */
+#endif

src/list/b_list.cpp

@@ -1,140 +0,0 @@
-#include <iostream>
-#include <locale>
-#include <map>
-#include <argparse/argparse.hpp>
-#include <nlohmann/json.hpp>
-#include "common/Paths.h"
-#include "common/Colors.h"
-#include "common/Datasets.h"
-#include "DatasetsExcel.h"
-#include "config.h"
-
-const int BALANCE_LENGTH = 75;
-
-struct separated : numpunct<char> {
-    char do_decimal_point() const { return ','; }
-    char do_thousands_sep() const { return '.'; }
-    std::string do_grouping() const { return "\03"; }
-};
-
-std::string outputBalance(const std::string& balance)
-{
-    auto temp = std::string(balance);
-    while (temp.size() > BALANCE_LENGTH - 1) {
-        auto part = temp.substr(0, BALANCE_LENGTH);
-        std::cout << part << std::endl;
-        std::cout << setw(52) << " ";
-        temp = temp.substr(BALANCE_LENGTH);
-    }
-    return temp;
-}
-
-void list_datasets(argparse::ArgumentParser& program)
-{
-    auto datasets = platform::Datasets(false, platform::Paths::datasets());
-    auto excel = program.get<bool>("--excel");
-    locale mylocale(std::cout.getloc(), new separated);
-    locale::global(mylocale);
-    std::cout.imbue(mylocale);
-    std::cout << Colors::GREEN() << " #  Dataset                        Sampl. Feat. Cls Balance" << std::endl;
-    std::string balanceBars = std::string(BALANCE_LENGTH, '=');
-    std::cout << "=== ============================== ====== ===== === " << balanceBars << std::endl;
-    int num = 0;
-    json data;
-    for (const auto& dataset : datasets.getNames()) {
-        auto color = num % 2 ? Colors::CYAN() : Colors::BLUE();
-        std::cout << color << setw(3) << right << num++ << " ";
-        std::cout << setw(30) << left << dataset << " ";
-        datasets.loadDataset(dataset);
-        auto nSamples = datasets.getNSamples(dataset);
-        std::cout << setw(6) << right << nSamples << " ";
-        std::cout << setw(5) << right << datasets.getFeatures(dataset).size() << " ";
-        std::cout << setw(3) << right << datasets.getNClasses(dataset) << " ";
-        std::stringstream oss;
-        std::string sep = "";
-        for (auto number : datasets.getClassesCounts(dataset)) {
-            oss << sep << std::setprecision(2) << fixed << (float)number / nSamples * 100.0 << "% (" << number << ")";
-            sep = " / ";
-        }
-        auto balance = outputBalance(oss.str());
-        std::cout << balance << std::endl;
-        // Store data for Excel report
-        data[dataset] = json::object();
-        data[dataset]["samples"] = nSamples;
-        data[dataset]["features"] = datasets.getFeatures(dataset).size();
-        data[dataset]["classes"] = datasets.getNClasses(dataset);
-        data[dataset]["balance"] = oss.str();
-    }
-    if (excel) {
-        auto report = platform::DatasetsExcel();
-        report.report(data);
-        std::cout << std::endl << Colors::GREEN() << "Output saved in " << report.getFileName() << std::endl;
-    }
-}
-
-void list_results(argparse::ArgumentParser& program)
-{
-    std::cout << "Results" << std::endl;
-    auto dataset = program.get<string>("--dataset");
-    auto score = program.get<string>("--score");
-
-}
-
-int main(int argc, char** argv)
-{
-    argparse::ArgumentParser program("b_list", { platform_project_version.begin(), platform_project_version.end() });
-    //
-    // datasets subparser
-    //
-    argparse::ArgumentParser datasets_command("datasets");
-    datasets_command.add_description("List datasets available in the platform.");
-    datasets_command.add_argument("--excel")
-        .help("Output in Excel format")
-        .default_value(false)
-        .implicit_value(true);
-    //
-    // results subparser
-    //
-    argparse::ArgumentParser results_command("results");
-    results_command.add_description("List the results of a given dataset.");
-    auto datasets = platform::Datasets(false, platform::Paths::datasets());
-    results_command.add_argument("-d", "--dataset")
-        .help("Dataset to use " + datasets.toString())
-        .required()
-        .action([](const std::string& value) {
-        auto datasets = platform::Datasets(false, platform::Paths::datasets());
-        static const std::vector<std::string> choices = datasets.getNames();
-        if (find(choices.begin(), choices.end(), value) != choices.end()) {
-            return value;
-        }
-        throw std::runtime_error("Dataset must be one of " + datasets.toString());
-            }
-    );
-    program.add_argument("-s", "--score").default_value("accuracy").help("Filter results of the score name supplied");
-    // Add subparsers
-    program.add_subparser(datasets_command);
-    program.add_subparser(results_command);
-    // Parse command line and execute
-    try {
-        program.parse_args(argc, argv);
-        bool found = false;
-        map<std::string, void(*)(argparse::ArgumentParser&)> commands = { {"datasets", &list_datasets}, {"results", &list_results} };
-        for (const auto& command : commands) {
-            if (program.is_subcommand_used(command.first)) {
-                std::invoke(command.second, program.at<argparse::ArgumentParser>(command.first));
-                found = true;
-                break;
-            }
-        }
-        if (!found) {
-            throw std::runtime_error("You must specify one of the following commands: datasets, results\n");
-        }
-    }
-    catch (const exception& err) {
-        cerr << err.what() << std::endl;
-        cerr << program;
-        exit(1);
-    }
-    std::cout << Colors::RESET() << std::endl;
-    return 0;
-}

src/main/Experiment.cpp

@@ -2,30 +2,55 @@
 #include "reports/ReportConsole.h"
 #include "common/Paths.h"
 #include "Models.h"
+#include "Scores.h"
 #include "Experiment.h"
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     void Experiment::saveResult()
     {
         result.save();
+        std::cout << "Result saved in " << Paths::results() << result.getFilename() << std::endl;
     }
-    void Experiment::report()
+    void Experiment::report(bool classification_report)
     {
         ReportConsole report(result.getJson());
         report.show();
+        if (classification_report) {
+            std::cout << report.showClassificationReport(Colors::BLUE());
+        }
     }
     void Experiment::show()
     {
         std::cout << result.getJson().dump(4) << std::endl;
     }
-    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score)
+    void Experiment::saveGraph()
+    {
+        std::cout << "Saving graphs..." << std::endl;
+        auto data = result.getJson();
+        for (const auto& item : data["results"]) {
+            auto graphs = item["graph"];
+            int i = 0;
+            for (const auto& graph : graphs) {
+                i++;
+                auto fileName = Paths::graphs() + result.getFilename() + "_graph_" + item["dataset"].get<std::string>() + "_" + std::to_string(i) + ".dot";
+                auto file = std::ofstream(fileName);
+                file << graph.get<std::string>();
+                file.close();
+                std::cout << "Graph saved in " << fileName << std::endl;
+            }
+        }
+    }
+    void Experiment::go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score, bool generate_fold_files, bool graph)
     {
         for (auto fileName : filesToProcess) {
             if (fileName.size() > max_name)
                 max_name = fileName.size();
         }
         std::cout << Colors::MAGENTA() << "*** Starting experiment: " << result.getTitle() << " ***" << Colors::RESET() << std::endl << std::endl;
+        auto clf = Models::instance()->create(result.getModel());
+        auto version = clf->getVersion();
+        std::cout << Colors::BLUE() << " Using " << result.getModel() << " ver. " << version << std::endl << std::endl;
         if (!quiet) {
             std::cout << Colors::GREEN() << " Status Meaning" << std::endl;
             std::cout << " ------ --------------------------------" << Colors::RESET() << std::endl;
@@ -33,14 +58,14 @@ 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" << std::endl;
-            std::cout << " --- " << string(max_name, '-') << " ----- ----- ---- " << string(4 + 3 * nfolds, '-') << Colors::RESET() << 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 num = 0;
         for (auto fileName : filesToProcess) {
             if (!quiet)
                 std::cout << " " << setw(3) << right << num++ << " " << setw(max_name) << left << fileName << right << flush;
-            cross_validation(fileName, quiet, no_train_score);
+            cross_validation(fileName, quiet, no_train_score, generate_fold_files, graph);
             if (!quiet)
                 std::cout << std::endl;
         }
@@ -60,44 +85,106 @@ namespace platform {
                 return Colors::RESET();
         }
     }
-
+    score_t Experiment::parse_score() const
+    {
+        if (result.getScoreName() == "accuracy")
+            return score_t::ACCURACY;
+        if (result.getScoreName() == "roc-auc-ovr")
+            return score_t::ROC_AUC_OVR;
+        throw std::runtime_error("Unknown score: " + result.getScoreName());
+    }
     void showProgress(int fold, const std::string& color, const std::string& phase)
     {
-        std::string prefix = phase == "a" ? "" : "\b\b\b\b";
+        std::string prefix = phase == "-" ? "" : "\b\b\b\b";
         std::cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
 
     }
-    void Experiment::cross_validation(const std::string& fileName, bool quiet, bool no_train_score)
+    void generate_files(const std::string& fileName, bool discretize, bool stratified, int seed, int nfold, torch::Tensor X_train, torch::Tensor y_train, torch::Tensor X_test, torch::Tensor y_test, std::vector<int>& train, std::vector<int>& test)
     {
-        auto datasets = Datasets(discretized, Paths::datasets());
-        // Get dataset
-        auto [X, y] = datasets.getTensors(fileName);
-        auto states = datasets.getStates(fileName);
-        auto features = datasets.getFeatures(fileName);
-        auto samples = datasets.getNSamples(fileName);
-        auto className = datasets.getClassName(fileName);
-        if (!quiet) {
-            std::cout << " " << setw(5) << samples << " " << setw(5) << features.size() << flush;
+        std::string file_name = Paths::experiment_file(fileName, discretize, stratified, seed, nfold);
+        auto file = std::ofstream(file_name);
+        json output;
+        output["seed"] = seed;
+        output["nfold"] = nfold;
+        output["X_train"] = json::array();
+        auto n = X_train.size(1);
+        for (int i = 0; i < X_train.size(0); i++) {
+            if (X_train.dtype() == torch::kFloat32) {
+                auto xvf_ptr = X_train.index({ i }).data_ptr<float>();
+                auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
+                output["X_train"].push_back(feature);
+            } else {
+                auto feature = std::vector<int>(X_train.index({ i }).data_ptr<int>(), X_train.index({ i }).data_ptr<int>() + n);
+                output["X_train"].push_back(feature);
+            }
         }
+        output["y_train"] = std::vector<int>(y_train.data_ptr<int>(), y_train.data_ptr<int>() + n);
+        output["X_test"] = json::array();
+        n = X_test.size(1);
+        for (int i = 0; i < X_test.size(0); i++) {
+            if (X_train.dtype() == torch::kFloat32) {
+                auto xvf_ptr = X_test.index({ i }).data_ptr<float>();
+                auto feature = std::vector<float>(xvf_ptr, xvf_ptr + n);
+                output["X_test"].push_back(feature);
+            } else {
+                auto feature = std::vector<int>(X_test.index({ i }).data_ptr<int>(), X_test.index({ i }).data_ptr<int>() + n);
+                output["X_test"].push_back(feature);
+            }
+        }
+        output["y_test"] = std::vector<int>(y_test.data_ptr<int>(), y_test.data_ptr<int>() + n);
+        output["train"] = train;
+        output["test"] = test;
+        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)
+    {
+        //
+        // Load dataset and prepare data
+        //
+        auto datasets = Datasets(discretized, Paths::datasets(), discretization_algo);
+        auto& dataset = datasets.getDataset(fileName);
+        dataset.load();
+        auto [X, y] = dataset.getTensors(); // Only need y for folding
+        auto features = dataset.getFeatures();
+        auto n_features = dataset.getNFeatures();
+        auto n_samples = dataset.getNSamples();
+        auto className = dataset.getClassName();
+        auto labels = dataset.getLabels();
+        int num_classes = dataset.getNClasses();
+        if (!quiet) {
+            std::cout << " " << setw(5) << n_samples << " " << setw(5) << n_features << flush;
+        }
+        //
         // Prepare Result
+        //
         auto partial_result = PartialResult();
-        auto [values, counts] = at::_unique(y);
-        partial_result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
+        partial_result.setSamples(n_samples).setFeatures(n_features).setClasses(num_classes);
         partial_result.setHyperparameters(hyperparameters.get(fileName));
+        //
         // Initialize results std::vectors
+        //
         int nResults = nfolds * static_cast<int>(randomSeeds.size());
-        auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
-        auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
+        auto score_test = torch::zeros({ nResults }, torch::kFloat64);
+        auto score_train = torch::zeros({ nResults }, torch::kFloat64);
         auto train_time = torch::zeros({ nResults }, torch::kFloat64);
         auto test_time = torch::zeros({ nResults }, torch::kFloat64);
         auto nodes = torch::zeros({ nResults }, torch::kFloat64);
         auto edges = torch::zeros({ nResults }, torch::kFloat64);
         auto num_states = torch::zeros({ nResults }, torch::kFloat64);
+        json confusion_matrices = json::array();
+        json confusion_matrices_train = json::array();
         std::vector<std::string> notes;
-        Timer train_timer, test_timer;
+        std::vector<std::string> graphs;
+        Timer train_timer, test_timer, seed_timer;
         int item = 0;
         bool first_seed = true;
+        //
+        // Loop over random seeds
+        //
+        auto score = parse_score();
         for (auto seed : randomSeeds) {
+            seed_timer.start();
             if (!quiet) {
                 string prefix = " ";
                 if (!first_seed) {
@@ -110,26 +197,33 @@ namespace platform {
             if (stratified)
                 fold = new folding::StratifiedKFold(nfolds, y, seed);
             else
-                fold = new folding::KFold(nfolds, y.size(0), seed);
+                fold = new folding::KFold(nfolds, n_samples, seed);
+            //
+            // Loop over folds
+            //
             for (int nfold = 0; nfold < nfolds; nfold++) {
                 auto clf = Models::instance()->create(result.getModel());
+                if (!quiet)
+                    showProgress(nfold + 1, getColor(clf->getStatus()), "-");
                 setModelVersion(clf->getVersion());
                 auto valid = clf->getValidHyperparameters();
                 hyperparameters.check(valid, fileName);
                 clf->setHyperparameters(hyperparameters.get(fileName));
+                //
                 // Split train - test dataset
+                //
                 train_timer.start();
                 auto [train, test] = fold->getFold(nfold);
-                auto train_t = torch::tensor(train);
-                auto test_t = torch::tensor(test);
-                auto X_train = X.index({ "...", train_t });
-                auto y_train = y.index({ train_t });
-                auto X_test = X.index({ "...", test_t });
-                auto y_test = y.index({ test_t });
+                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
+                if (generate_fold_files)
+                    generate_files(fileName, discretized, stratified, seed, nfold, X_train, y_train, X_test, y_test, train, test);
                 if (!quiet)
                     showProgress(nfold + 1, getColor(clf->getStatus()), "a");
+                //
                 // Train model
-                clf->fit(X_train, y_train, features, className, states);
+                //
+                clf->fit(X_train, y_train, features, className, states, smooth_type);
                 if (!quiet)
                     showProgress(nfold + 1, getColor(clf->getStatus()), "b");
                 auto clf_notes = clf->getNotes();
@@ -139,37 +233,67 @@ namespace platform {
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();
                 train_time[item] = train_timer.getDuration();
-                double accuracy_train_value = 0.0;
+                double score_train_value = 0.0;
+                //
                 // Score train
-                if (!no_train_score)
-                    accuracy_train_value = clf->score(X_train, y_train);
+                //
+                if (!no_train_score) {
+                    auto y_proba_train = clf->predict_proba(X_train);
+                    Scores scores(y_train, y_proba_train, num_classes, labels);
+                    score_train_value = score == score_t::ACCURACY ? scores.accuracy() : scores.auc();
+                    confusion_matrices_train.push_back(scores.get_confusion_matrix_json(true));
+                }
+                //
                 // Test model
+                //
                 if (!quiet)
                     showProgress(nfold + 1, getColor(clf->getStatus()), "c");
                 test_timer.start();
-                auto accuracy_test_value = clf->score(X_test, y_test);
+                // auto y_predict = clf->predict(X_test);
+                auto y_proba_test = clf->predict_proba(X_test);
+                Scores scores(y_test, y_proba_test, num_classes, labels);
+                auto score_test_value = score == score_t::ACCURACY ? scores.accuracy() : scores.auc();
                 test_time[item] = test_timer.getDuration();
-                accuracy_train[item] = accuracy_train_value;
-                accuracy_test[item] = accuracy_test_value;
+                score_train[item] = score_train_value;
+                score_test[item] = score_test_value;
+                confusion_matrices.push_back(scores.get_confusion_matrix_json(true));
                 if (!quiet)
                     std::cout << "\b\b\b, " << flush;
+                //
                 // Store results and times in std::vector
-                partial_result.addScoreTrain(accuracy_train_value);
-                partial_result.addScoreTest(accuracy_test_value);
+                //
+                partial_result.addScoreTrain(score_train_value);
+                partial_result.addScoreTest(score_test_value);
                 partial_result.addTimeTrain(train_time[item].item<double>());
                 partial_result.addTimeTest(test_time[item].item<double>());
                 item++;
+                if (graph) {
+                    std::string result = "";
+                    for (const auto& line : clf->graph()) {
+                        result += line + "\n";
+                    }
+                    graphs.push_back(result);
+                }
+            }
+            if (!quiet) {
+                seed_timer.stop();
+                std::cout << "end. [" << seed_timer.getDurationString() << "]" << std::endl;
             }
-            if (!quiet)
-                std::cout << "end. " << flush;
             delete fold;
         }
-        partial_result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
-        partial_result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
+        //
+        // Store result totals in Result
+        //
+        partial_result.setGraph(graphs);
+        partial_result.setScoreTest(torch::mean(score_test).item<double>()).setScoreTrain(torch::mean(score_train).item<double>());
+        partial_result.setScoreTestStd(torch::std(score_test).item<double>()).setScoreTrainStd(torch::std(score_train).item<double>());
         partial_result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
         partial_result.setTestTimeStd(torch::std(test_time).item<double>()).setTrainTimeStd(torch::std(train_time).item<double>());
         partial_result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
         partial_result.setDataset(fileName).setNotes(notes);
+        partial_result.setConfusionMatrices(confusion_matrices);
+        if (!no_train_score)
+            partial_result.setConfusionMatricesTrain(confusion_matrices_train);
         addResult(partial_result);
     }
 }

src/main/Experiment.h

@@ -1,16 +1,17 @@
-#pragma once
-
+#ifndef EXPERIMENT_H
+#define EXPERIMENT_H
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
 #include <string>
 #include <folding.hpp>
 #include "bayesnet/BaseClassifier.h"
 #include "HyperParameters.h"
-#include "Result.h"
+#include "results/Result.h"
+#include "bayesnet/network/Network.h"
 
 namespace platform {
-    using json = nlohmann::json;
-
+    using json = nlohmann::ordered_json;
+    enum class score_t { NONE, ACCURACY, ROC_AUC_OVR };
     class Experiment {
     public:
         Experiment() = default;
@@ -20,6 +21,25 @@ namespace platform {
         Experiment& setModelVersion(const std::string& model_version) { this->result.setModelVersion(model_version); return *this; }
         Experiment& setModel(const std::string& model) { this->result.setModel(model); return *this; }
         Experiment& setLanguage(const std::string& language) { this->result.setLanguage(language); return *this; }
+        Experiment& setDiscretizationAlgorithm(const std::string& discretization_algo)
+        {
+            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& 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; }
@@ -28,18 +48,24 @@ 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);
-        void go(std::vector<std::string> filesToProcess, bool quiet, bool no_train_score);
+        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);
         void saveResult();
         void show();
-        void report();
+        void saveGraph();
+        void report(bool classification_report = false);
     private:
+        score_t parse_score() const;
         Result result;
         bool discretized{ false }, stratified{ false };
         std::vector<PartialResult> results;
         std::vector<int> randomSeeds;
+        std::string discretization_algo;
+        std::string smooth_strategy;
+        bayesnet::Smoothing_t smooth_type{ bayesnet::Smoothing_t::NONE };
         HyperParameters hyperparameters;
         int nfolds{ 0 };
         int max_name{ 7 }; // max length of dataset name for formatting (default 7)
     };
-}
+}
+#endif

src/main/HyperParameters.cpp

@@ -10,16 +10,9 @@ namespace platform {
         for (const auto& item : datasets) {
             hyperparameters[item] = hyperparameters_;
         }
+        normalize_nested(datasets);
     }
-    // https://www.techiedelight.com/implode-a-vector-of-strings-into-a-comma-separated-string-in-cpp/
-    std::string join(std::vector<std::string> const& strings, std::string delim)
-    {
-        std::stringstream ss;
-        std::copy(strings.begin(), strings.end(),
-            std::ostream_iterator<std::string>(ss, delim.c_str()));
-        return ss.str();
-    }
-    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file)
+    HyperParameters::HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file, bool best)
     {
         // Check if file exists
         std::ifstream file(hyperparameters_file);
@@ -28,7 +21,14 @@ namespace platform {
         }
         // Check if file is a json
         json file_hyperparameters = json::parse(file);
-        auto input_hyperparameters = file_hyperparameters["results"];
+        json input_hyperparameters;
+        if (best) {
+            for (const auto& [key, value] : file_hyperparameters.items()) {
+                input_hyperparameters[key]["hyperparameters"] = value[1];
+            }
+        } else {
+            input_hyperparameters = file_hyperparameters["results"];
+        }
         // Check if hyperparameters are valid
         for (const auto& dataset : datasets) {
             if (!input_hyperparameters.contains(dataset)) {
@@ -38,6 +38,24 @@ namespace platform {
             }
             hyperparameters[dataset] = input_hyperparameters[dataset]["hyperparameters"].get<json>();
         }
+        normalize_nested(datasets);
+    }
+    void HyperParameters::normalize_nested(const std::vector<std::string>& datasets)
+    {
+        // for (const auto& dataset : datasets) {
+        //     if (hyperparameters[dataset].contains("be_hyperparams")) {
+        //         // Odte has base estimator hyperparameters set this way
+        //         hyperparameters[dataset]["be_hyperparams"] = hyperparameters[dataset]["be_hyperparams"].dump();
+        //     }
+        // }
+    }
+    // https://www.techiedelight.com/implode-a-vector-of-strings-into-a-comma-separated-string-in-cpp/
+    std::string join(std::vector<std::string> const& strings, std::string delim)
+    {
+        std::stringstream ss;
+        std::copy(strings.begin(), strings.end(),
+            std::ostream_iterator<std::string>(ss, delim.c_str()));
+        return ss.str();
     }
     void HyperParameters::check(const std::vector<std::string>& valid, const std::string& fileName)
     {

src/main/HyperParameters.h

@@ -1,22 +1,27 @@
-#pragma once
-
+#ifndef HYPERPARAMETERS_H
+#define HYPERPARAMETERS_H
 #include <string>
 #include <map>
 #include <vector>
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class HyperParameters {
     public:
         HyperParameters() = default;
+        // Constructor to use command line hyperparameters
         explicit HyperParameters(const std::vector<std::string>& datasets, const json& hyperparameters_);
-        explicit HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file);
+        // Constructor to use hyperparameters file generated by grid or by best results
+        explicit HyperParameters(const std::vector<std::string>& datasets, const std::string& hyperparameters_file, bool best = false);
         ~HyperParameters() = default;
         bool notEmpty(const std::string& key) const { return !hyperparameters.at(key).empty(); }
         void check(const std::vector<std::string>& valid, const std::string& fileName);
         json get(const std::string& fileName);
     private:
+        void normalize_nested(const std::vector<std::string>& datasets);
         std::map<std::string, json> hyperparameters;
+        bool best = false; // Used to separate grid/best hyperparameters as the format of those files are different
     };
 } /* namespace platform */
+#endif

src/main/Models.h

@@ -1,16 +1,20 @@
-#pragma once
-
+#ifndef MODELS_H
+#define MODELS_H
 #include <map>
 #include <bayesnet/BaseClassifier.h>
 #include <bayesnet/ensembles/AODE.h>
+#include <bayesnet/ensembles/A2DE.h>
 #include <bayesnet/ensembles/AODELd.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/SPnDE.h>
 #include <bayesnet/classifiers/TANLd.h>
 #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>
@@ -18,10 +22,6 @@
 #include <pyclassifiers/RandomForest.h>
 namespace platform {
     class Models {
-    private:
-        map<std::string, function<bayesnet::BaseClassifier* (void)>> functionRegistry;
-        static Models* factory; //singleton
-        Models() {};
     public:
         Models(Models&) = delete;
         void operator=(const Models&) = delete;
@@ -32,10 +32,14 @@ namespace platform {
             function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
         std::vector<string> getNames();
         std::string toString();
-
+    private:
+        map<std::string, function<bayesnet::BaseClassifier* (void)>> functionRegistry;
+        static Models* factory; //singleton
+        Models() {};
     };
     class Registrar {
     public:
         Registrar(const std::string& className, function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
     };
 }
+#endif

src/main/PartialResult.h

@@ -1,10 +1,10 @@
-#pragma once
-
+#ifndef PARTIAL_RESULT_H
+#define PARTIAL_RESULT_H
 #include <string>
 #include <nlohmann/json.hpp>
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
     class PartialResult {
 
     public:
@@ -15,6 +15,7 @@ namespace platform {
             data["times_train"] = json::array();
             data["times_test"] = json::array();
             data["notes"] = json::array();
+            data["graph"] = json::array();
             data["train_time"] = 0.0;
             data["train_time_std"] = 0.0;
             data["test_time"] = 0.0;
@@ -27,6 +28,14 @@ namespace platform {
             data["notes"].insert(data["notes"].end(), notes_.begin(), notes_.end());
             return *this;
         }
+        PartialResult& setGraph(const std::vector<std::string>& graph)
+        {
+            json graph_ = graph;
+            data["graph"].insert(data["graph"].end(), graph_.begin(), graph_.end());
+            return *this;
+        }
+        PartialResult& setConfusionMatrices(const json& confusion_matrices) { data["confusion_matrices"] = confusion_matrices; return *this; }
+        PartialResult& setConfusionMatricesTrain(const json& confusion_matrices) { data["confusion_matrices_train"] = confusion_matrices; return *this; }
         PartialResult& setHyperparameters(const json& hyperparameters) { data["hyperparameters"] = hyperparameters; return *this; }
         PartialResult& setSamples(int samples) { data["samples"] = samples; return *this; }
         PartialResult& setFeatures(int features) { data["features"] = features; return *this; }
@@ -71,3 +80,4 @@ namespace platform {
         json data;
     };
 }
+#endif

src/main/RocAuc.cpp

@@ -0,0 +1,67 @@
+#include <sstream>
+#include <algorithm>
+#include <numeric>
+#include <utility>
+#include "RocAuc.h"
+namespace platform {
+    
+    double RocAuc::compute(const torch::Tensor& y_proba, const torch::Tensor& labels)
+    {
+        size_t nClasses = y_proba.size(1);
+        // In binary classification problem there's no need to calculate the average of the AUCs
+        if (nClasses == 2)
+            nClasses = 1;
+        size_t nSamples = y_proba.size(0);
+        y_test = tensorToVector(labels);
+        std::vector<double> aucScores(nClasses, 0.0);
+        for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {
+            scoresAndLabels.clear();
+            for (size_t i = 0; i < nSamples; ++i) {
+                scoresAndLabels.emplace_back(y_proba[i][classIdx].item<float>(), y_test[i] == classIdx ? 1 : 0);
+            }
+            aucScores[classIdx] = compute_common(nSamples, classIdx);
+        }
+        return std::accumulate(aucScores.begin(), aucScores.end(), 0.0) / nClasses;
+    }
+    double RocAuc::compute(const std::vector<std::vector<double>>& y_proba, const std::vector<int>& labels)
+    {
+        y_test = labels;
+        size_t nClasses = y_proba[0].size();
+        // In binary classification problem there's no need to calculate the average of the AUCs
+        if (nClasses == 2)
+            nClasses = 1;
+        size_t nSamples = y_proba.size();
+        std::vector<double> aucScores(nClasses, 0.0);
+        for (size_t classIdx = 0; classIdx < nClasses; ++classIdx) {
+            scoresAndLabels.clear();
+            for (size_t i = 0; i < nSamples; ++i) {
+                scoresAndLabels.emplace_back(y_proba[i][classIdx], labels[i] == classIdx ? 1 : 0);
+            }
+            aucScores[classIdx] = compute_common(nSamples, classIdx);
+        }
+        return std::accumulate(aucScores.begin(), aucScores.end(), 0.0) / nClasses;
+    }
+    double RocAuc::compute_common(size_t nSamples, size_t classIdx)
+    {
+        std::sort(scoresAndLabels.begin(), scoresAndLabels.end(), std::greater<>());
+        std::vector<double> tpr, fpr;
+        double tp = 0, fp = 0;
+        double totalPos = std::count(y_test.begin(), y_test.end(), classIdx);
+        double totalNeg = nSamples - totalPos;
+
+        for (const auto& [score, label] : scoresAndLabels) {
+            if (label == 1) {
+                tp += 1;
+            } else {
+                fp += 1;
+            }
+            tpr.push_back(tp / totalPos);
+            fpr.push_back(fp / totalNeg);
+        }
+        double auc = 0.0;
+        for (size_t i = 1; i < tpr.size(); ++i) {
+            auc += 0.5 * (fpr[i] - fpr[i - 1]) * (tpr[i] + tpr[i - 1]);
+        }
+        return auc;
+    }
+}

src/main/RocAuc.h

@@ -0,0 +1,21 @@
+#ifndef ROCAUC_H
+#define ROCAUC_H
+#include <torch/torch.h>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class RocAuc {
+    public:
+        RocAuc() = default;
+        double compute(const std::vector<std::vector<double>>& y_proba, const std::vector<int>& y_test);
+        double compute(const torch::Tensor& y_proba, const torch::Tensor& y_test);
+    private:
+        double compute_common(size_t nSamples, size_t classIdx);
+        std::vector<std::pair<double, int>> scoresAndLabels;
+        std::vector<int> y_test;
+    };
+}
+#endif

src/main/Scores.cpp

@@ -0,0 +1,270 @@
+#include <sstream>
+#include "Scores.h"
+#include "common/Utils.h" // 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)
+    {
+        if (labels.size() == 0) {
+            init_default_labels();
+        }
+        total = y_test.size(0);
+        auto y_pred = y_proba.argmax(1);
+        accuracy_value = (y_pred == y_test).sum().item<float>() / total;
+        init_confusion_matrix();
+        for (int i = 0; i < total; i++) {
+            int actual = y_test[i].item<int>();
+            int predicted = y_pred[i].item<int>();
+            confusion_matrix[actual][predicted] += 1;
+        }
+    }
+    Scores::Scores(const json& confusion_matrix_)
+    {
+        json values;
+        total = 0;
+        num_classes = confusion_matrix_.size();
+        init_confusion_matrix();
+        int i = 0;
+        for (const auto& item : confusion_matrix_.items()) {
+            values = item.value();
+            json key = item.key();
+            if (key.is_number_integer()) {
+                labels.push_back("Class " + std::to_string(key.get<int>()));
+            } else {
+                labels.push_back(key.get<std::string>());
+            }
+            for (int j = 0; j < num_classes; ++j) {
+                int value_int = values[j].get<int>();
+                confusion_matrix[i][j] = value_int;
+                total += value_int;
+            }
+            i++;
+        }
+        compute_accuracy_value();
+    }
+    float Scores::auc()
+    {
+        size_t nSamples = y_test.numel();
+        if (nSamples == 0) return 0;
+        // In binary classification problem there's no need to calculate the average of the AUCs
+        auto nClasses = num_classes;
+        if (num_classes == 2)
+            nClasses = 1;
+        auto y_testv = 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) {
+            if (classIdx >= y_proba.size(1)) {
+                std::cerr << "AUC warning - class index out of range" << std::endl;
+                return 0;
+            }
+            scoresAndLabels.clear();
+            for (size_t i = 0; i < nSamples; ++i) {
+                scoresAndLabels.emplace_back(y_proba[i][classIdx].item<float>(), y_testv[i] == classIdx ? 1 : 0);
+            }
+            std::sort(scoresAndLabels.begin(), scoresAndLabels.end(), std::greater<>());
+            std::vector<double> tpr, fpr;
+            double tp = 0, fp = 0;
+            double totalPos = std::count(y_testv.begin(), y_testv.end(), classIdx);
+            double totalNeg = nSamples - totalPos;
+            for (const auto& [score, label] : scoresAndLabels) {
+                if (label == 1) {
+                    tp += 1;
+                } else {
+                    fp += 1;
+                }
+                tpr.push_back(tp / totalPos);
+                fpr.push_back(fp / totalNeg);
+            }
+            double auc = 0.0;
+            for (size_t i = 1; i < tpr.size(); ++i) {
+                auc += 0.5 * (fpr[i] - fpr[i - 1]) * (tpr[i] + tpr[i - 1]);
+            }
+            aucScores[classIdx] = auc;
+        }
+        return std::accumulate(aucScores.begin(), aucScores.end(), 0.0) / nClasses;
+    }
+    Scores Scores::create_aggregate(const json& data, const std::string key)
+    {
+        auto scores = Scores(data[key][0]);
+        for (int i = 1; i < data[key].size(); i++) {
+            auto score = Scores(data[key][i]);
+            scores.aggregate(score);
+        }
+        return scores;
+    }
+    void Scores::compute_accuracy_value()
+    {
+        accuracy_value = 0;
+        for (int i = 0; i < num_classes; i++) {
+            accuracy_value += confusion_matrix[i][i].item<int>();
+        }
+        accuracy_value /= total;
+        accuracy_value = std::min(accuracy_value, 1.0f);
+    }
+    void Scores::init_confusion_matrix()
+    {
+        confusion_matrix = torch::zeros({ num_classes, num_classes }, torch::kInt32);
+    }
+    void Scores::init_default_labels()
+    {
+        for (int i = 0; i < num_classes; i++) {
+            labels.push_back("Class " + std::to_string(i));
+        }
+    }
+    void Scores::aggregate(const Scores& a)
+    {
+        if (a.num_classes != num_classes)
+            throw std::invalid_argument("The number of classes must be the same");
+        confusion_matrix += a.confusion_matrix;
+        total += a.total;
+        compute_accuracy_value();
+    }
+    float Scores::accuracy()
+    {
+        return accuracy_value;
+    }
+    float Scores::f1_score(int num_class)
+    {
+        // Compute f1_score in a one vs rest fashion
+        auto precision_value = precision(num_class);
+        auto recall_value = recall(num_class);
+        if (precision_value + recall_value == 0) return 0; // Avoid division by zero (0/0 = 0)
+        return 2 * precision_value * recall_value / (precision_value + recall_value);
+    }
+    float Scores::f1_weighted()
+    {
+        float f1_weighted = 0;
+        for (int i = 0; i < num_classes; i++) {
+            f1_weighted += confusion_matrix[i].sum().item<int>() * f1_score(i);
+        }
+        return f1_weighted / total;
+    }
+    float Scores::f1_macro()
+    {
+        float f1_macro = 0;
+        for (int i = 0; i < num_classes; i++) {
+            f1_macro += f1_score(i);
+        }
+        return f1_macro / num_classes;
+    }
+    float Scores::precision(int num_class)
+    {
+        int tp = confusion_matrix[num_class][num_class].item<int>();
+        int fp = confusion_matrix.index({ "...", num_class }).sum().item<int>() - tp;
+        int fn = confusion_matrix[num_class].sum().item<int>() - tp;
+        if (tp + fp == 0) return 0; // Avoid division by zero (0/0 = 0
+        return float(tp) / (tp + fp);
+    }
+    float Scores::recall(int num_class)
+    {
+        int tp = confusion_matrix[num_class][num_class].item<int>();
+        int fp = confusion_matrix.index({ "...", num_class }).sum().item<int>() - tp;
+        int fn = confusion_matrix[num_class].sum().item<int>() - tp;
+        if (tp + fn == 0) return 0; // Avoid division by zero (0/0 = 0
+        return float(tp) / (tp + fn);
+    }
+    std::string Scores::classification_report_line(std::string label, float precision, float recall, float f1_score, int support)
+    {
+        std::stringstream oss;
+        oss << std::right << std::setw(label_len) << label << " ";
+        if (precision == 0) {
+            oss << std::string(dlen, ' ') << " ";
+        } else {
+            oss << std::setw(dlen) << std::setprecision(ndec) << std::fixed << precision << " ";
+        }
+        if (recall == 0) {
+            oss << std::string(dlen, ' ') << " ";
+        } else {
+            oss << std::setw(dlen) << std::setprecision(ndec) << std::fixed << recall << " ";
+        }
+        oss << std::setw(dlen) << std::setprecision(ndec) << std::fixed << f1_score << " "
+            << std::setw(dlen) << std::right << support;
+        return oss.str();
+    }
+    std::tuple<float, float, float, float> Scores::compute_averages()
+    {
+        float precision_avg = 0;
+        float recall_avg = 0;
+        float precision_wavg = 0;
+        float recall_wavg = 0;
+        for (int i = 0; i < num_classes; i++) {
+            int support = confusion_matrix[i].sum().item<int>();
+            precision_avg += precision(i);
+            precision_wavg += precision(i) * support;
+            recall_avg += recall(i);
+            recall_wavg += recall(i) * support;
+        }
+        precision_wavg /= total;
+        recall_wavg /= total;
+        precision_avg /= num_classes;
+        recall_avg /= num_classes;
+        return { precision_avg, recall_avg, precision_wavg, recall_wavg };
+    }
+    std::vector<std::string> Scores::classification_report(std::string color, std::string title)
+    {
+        std::stringstream oss;
+        std::vector<std::string> report;
+        for (int i = 0; i < num_classes; i++) {
+            label_len = std::max(label_len, (int)labels[i].size());
+        }
+        report.push_back("Classification Report using " + title + " dataset");
+        report.push_back("=========================================");
+        oss << std::string(label_len, ' ') << " precision recall    f1-score  support";
+        report.push_back(oss.str()); oss.str("");
+        oss << std::string(label_len, ' ') << " ========= ========= ========= =========";
+        report.push_back(oss.str()); oss.str("");
+        for (int i = 0; i < num_classes; i++) {
+            report.push_back(classification_report_line(labels[i], precision(i), recall(i), f1_score(i), confusion_matrix[i].sum().item<int>()));
+        }
+        report.push_back(" ");
+        oss << classification_report_line("accuracy", 0, 0, accuracy(), total);
+        report.push_back(oss.str()); oss.str("");
+        auto [precision_avg, recall_avg, precision_wavg, recall_wavg] = compute_averages();
+        report.push_back(classification_report_line("macro avg", precision_avg, recall_avg, f1_macro(), total));
+        report.push_back(classification_report_line("weighted avg", precision_wavg, recall_wavg, f1_weighted(), total));
+        report.push_back("");
+        report.push_back("Confusion Matrix");
+        report.push_back("================");
+        auto number = total > 1000 ? 4 : 3;
+        for (int i = 0; i < num_classes; i++) {
+            oss << std::right << std::setw(label_len) << labels[i] << " ";
+            for (int j = 0; j < num_classes; j++) {
+                if (i == j) oss << Colors::GREEN();
+                oss << std::setw(number) << confusion_matrix[i][j].item<int>() << " ";
+                if (i == j) oss << color;
+            }
+            report.push_back(oss.str()); oss.str("");
+        }
+        return report;
+    }
+    json Scores::classification_report_json(std::string title)
+    {
+        json output;
+        output["title"] = "Classification Report using " + title + " dataset";
+        output["headers"] = { " ", "precision", "recall", "f1-score", "support" };
+        output["body"] = {};
+        for (int i = 0; i < num_classes; i++) {
+            output["body"].push_back({ labels[i], precision(i), recall(i), f1_score(i), confusion_matrix[i].sum().item<int>() });
+        }
+        output["accuracy"] = { "accuracy", 0, 0, accuracy(), total };
+        auto [precision_avg, recall_avg, precision_wavg, recall_wavg] = compute_averages();
+        output["averages"] = { "macro avg", precision_avg, recall_avg, f1_macro(), total };
+        output["weighted"] = { "weighted avg", precision_wavg, recall_wavg, f1_weighted(), total };
+        output["confusion_matrix"] = get_confusion_matrix_json();
+        return output;
+    }
+    json Scores::get_confusion_matrix_json(bool labels_as_keys)
+    {
+        json output;
+        for (int i = 0; i < num_classes; i++) {
+            auto r_ptr = confusion_matrix[i].data_ptr<int>();
+            if (labels_as_keys) {
+                output[labels[i]] = std::vector<int>(r_ptr, r_ptr + num_classes);
+            } else {
+                output[i] = std::vector<int>(r_ptr, r_ptr + num_classes);
+            }
+        }
+        return output;
+    }
+}

src/main/Scores.h

@@ -0,0 +1,46 @@
+#ifndef SCORES_H
+#define SCORES_H
+#include <torch/torch.h>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+    class Scores {
+    public:
+        Scores(torch::Tensor& y_test, torch::Tensor& y_proba, int num_classes, std::vector<std::string> labels = {});
+        explicit Scores(const json& confusion_matrix_);
+        static Scores create_aggregate(const json& data, const std::string key);
+        float accuracy();
+        float auc();
+        float f1_score(int num_class);
+        float f1_weighted();
+        float f1_macro();
+        float precision(int num_class);
+        float recall(int num_class);
+        torch::Tensor get_confusion_matrix() { return confusion_matrix; }
+        std::vector<std::string> classification_report(std::string color = "", std::string title = "");
+        json classification_report_json(std::string title = "");
+        json get_confusion_matrix_json(bool labels_as_keys = false);
+        void aggregate(const Scores& a);
+    private:
+        std::string classification_report_line(std::string label, float precision, float recall, float f1_score, int support);
+        void init_confusion_matrix();
+        void init_default_labels();
+        void compute_accuracy_value();
+        std::tuple<float, float, float, float> compute_averages();
+        int num_classes;
+        float accuracy_value;
+        int total;
+        std::vector<std::string> labels;
+        torch::Tensor confusion_matrix; // Rows ar actual, columns are predicted
+        torch::Tensor null_t; // Covenient null tensor needed when confusion_matrix constructor is used
+        torch::Tensor& y_test = null_t; // for ROC AUC
+        torch::Tensor& y_proba = null_t; // for ROC AUC
+        int label_len = 16;
+        int dlen = 9;
+        int ndec = 7;
+    };
+}
+#endif

src/main/b_main.cpp

@@ -1,148 +0,0 @@
-#include <iostream>
-#include <argparse/argparse.hpp>
-#include <nlohmann/json.hpp>
-#include "Experiment.h"
-#include "common/Datasets.h"
-#include "common/DotEnv.h"
-#include "common/Paths.h"
-#include "Models.h"
-#include "modelRegister.h"
-#include "config.h"
-
-
-using json = nlohmann::json;
-
-void manageArguments(argparse::ArgumentParser& program)
-{
-    auto env = platform::DotEnv();
-    auto datasets = platform::Datasets(false, platform::Paths::datasets());
-    program.add_argument("-d", "--dataset")
-        .help("Dataset file name: " + datasets.toString())
-        .action([](const std::string& value) {
-        auto datasets = platform::Datasets(false, platform::Paths::datasets());
-        static const std::vector<std::string> choices_datasets(datasets.getNames());
-        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());
-            }
-    );
-    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("-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);
-    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("-s", "--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;
-    json hyperparameters_json;
-    bool discretize_dataset, stratified, saveResults, quiet, no_train_score;
-    std::vector<int> seeds;
-    std::vector<std::string> filesToTest;
-    int n_folds;
-    try {
-        program.parse_args(argc, argv);
-        file_name = program.get<std::string>("dataset");
-        model_name = program.get<std::string>("model");
-        discretize_dataset = program.get<bool>("discretize");
-        stratified = program.get<bool>("stratified");
-        quiet = program.get<bool>("quiet");
-        n_folds = program.get<int>("folds");
-        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");
-        if (hyperparameters_file != "" && hyperparameters != "{}") {
-            throw runtime_error("hyperparameters and hyper_file are mutually exclusive");
-        }
-        title = program.get<std::string>("title");
-        if (title == "" && file_name == "") {
-            throw runtime_error("title is mandatory if dataset is not provided");
-        }
-        saveResults = program.get<bool>("save");
-    }
-    catch (const exception& err) {
-        cerr << err.what() << std::endl;
-        cerr << program;
-        exit(1);
-    }
-    auto datasets = platform::Datasets(discretize_dataset, platform::Paths::datasets());
-    if (file_name != "") {
-        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);
-    } else {
-        test_hyperparams = platform::HyperParameters(datasets.getNames(), hyperparameters_json);
-    }
-
-    /*
-     * Begin Processing
-     */
-    auto env = platform::DotEnv();
-    auto experiment = platform::Experiment();
-    experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("14.0.3");
-    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
-    experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
-    experiment.setHyperparameters(test_hyperparams);
-    for (auto seed : seeds) {
-        experiment.addRandomSeed(seed);
-    }
-    platform::Timer timer;
-    timer.start();
-    experiment.go(filesToTest, quiet, no_train_score);
-    experiment.setDuration(timer.getDuration());
-    if (saveResults) {
-        experiment.saveResult();
-    }
-    if (!quiet)
-        experiment.report();
-    std::cout << "Done!" << std::endl;
-    return 0;
-}

src/main/modelRegister.h

@@ -1,4 +1,5 @@
-#pragma once
+#ifndef MODELREGISTER_H
+#define MODELREGISTER_H
 
 static platform::Registrar registrarT("TAN",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
@@ -6,6 +7,8 @@ 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",
@@ -14,10 +17,14 @@ 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",
@@ -27,4 +34,6 @@ static platform::Registrar registrarSvc("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();});
+    [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
+
+#endif

src/manage/CommandParser.cpp

@@ -1,87 +0,0 @@
-#include "CommandParser.h"
-#include <iostream>
-#include <sstream>
-#include <algorithm>
-#include "common/Colors.h"
-#include "common/Utils.h"
-
-namespace platform {
-    void CommandParser::messageError(const std::string& message)
-    {
-        std::cout << Colors::RED() << message << Colors::RESET() << std::endl;
-    }
-    std::pair<char, int> CommandParser::parse(const std::string& color, const std::vector<std::tuple<std::string, char, bool>>& options, const char defaultCommand, const int maxIndex)
-    {
-        bool finished = false;
-        while (!finished) {
-            std::stringstream oss;
-            std::string line;
-            oss << color << "Choose option (";
-            bool first = true;
-            for (auto& option : options) {
-                if (first) {
-                    first = false;
-                } else {
-                    oss << ", ";
-                }
-                oss << std::get<char>(option) << "=" << std::get<std::string>(option);
-            }
-            oss << "): ";
-            std::cout << oss.str();
-            getline(std::cin, line);
-            std::cout << Colors::RESET();
-            line = trim(line);
-            if (line.size() == 0)
-                continue;
-            if (all_of(line.begin(), line.end(), ::isdigit)) {
-                command = defaultCommand;
-                index = stoi(line);
-                if (index > maxIndex || index < 0) {
-                    messageError("Index out of range");
-                    continue;
-                }
-                finished = true;
-                break;
-            }
-            bool found = false;
-            for (auto& option : options) {
-                if (line[0] == std::get<char>(option)) {
-                    found = true;
-                    // it's a match
-                    line.erase(line.begin());
-                    line = trim(line);
-                    if (std::get<bool>(option)) {
-                        // The option requires a value
-                        if (line.size() == 0) {
-                            messageError("Option " + std::get<std::string>(option) + " requires a value");
-                            break;
-                        }
-                        try {
-                            index = stoi(line);
-                            if (index > maxIndex || index < 0) {
-                                messageError("Index out of range");
-                                break;
-                            }
-                        }
-                        catch (const std::invalid_argument& ia) {
-                            messageError("Invalid value: " + line);
-                            break;
-                        }
-                    } else {
-                        if (line.size() > 0) {
-                            messageError("option " + std::get<std::string>(option) + " doesn't accept values");
-                            break;
-                        }
-                    }
-                    command = std::get<char>(option);
-                    finished = true;
-                    break;
-                }
-            }
-            if (!found) {
-                messageError("I don't know " + line);
-            }
-        }
-        return { command, index };
-    }
-} /* namespace platform */

src/manage/CommandParser.h

@@ -1,19 +0,0 @@
-#pragma once
-
-#include <string>
-#include <vector>
-#include <tuple>
-
-namespace platform {
-    class CommandParser {
-    public:
-        CommandParser() = default;
-        std::pair<char, int> parse(const std::string& color, const std::vector<std::tuple<std::string, char, bool>>& options, const char defaultCommand, const int maxIndex);
-        char getCommand() const { return command; };
-        int getIndex() const { return index; };
-    private:
-        void messageError(const std::string& message);
-        char command;
-        int index;
-    };
-} /* namespace platform */

src/manage/ManageResults.cpp

@@ -1,273 +0,0 @@
-#include <filesystem>
-#include <tuple>
-#include "common/Colors.h"
-#include "common/CLocale.h"
-#include "common/Paths.h"
-#include "reports/ReportConsole.h"
-#include "reports/ReportExcel.h"
-#include "reports/ReportExcelCompared.h"
-#include "CommandParser.h"
-#include "ManageResults.h"
-
-namespace platform {
-
-    ManageResults::ManageResults(int numFiles, const std::string& model, const std::string& score, bool complete, bool partial, bool compare) :
-        numFiles{ numFiles }, complete{ complete }, partial{ partial }, compare{ compare }, results(ResultsManager(model, score, complete, partial))
-    {
-        indexList = true;
-        openExcel = false;
-        workbook = NULL;
-        if (numFiles == 0) {
-            this->numFiles = results.size();
-        }
-    }
-    void ManageResults::doMenu()
-    {
-        if (results.empty()) {
-            std::cout << Colors::MAGENTA() << "No results found!" << Colors::RESET() << std::endl;
-            return;
-        }
-        results.sortDate();
-        list();
-        menu();
-        if (openExcel) {
-            workbook_close(workbook);
-        }
-        std::cout << Colors::RESET() << "Done!" << std::endl;
-    }
-    void ManageResults::list()
-    {
-        auto temp = ConfigLocale();
-        std::string suffix = numFiles != results.size() ? " of " + std::to_string(results.size()) : "";
-        std::stringstream oss;
-        oss << "Results on screen: " << numFiles << suffix;
-        std::cout << Colors::GREEN() << oss.str() << std::endl;
-        std::cout << std::string(oss.str().size(), '-') << std::endl;
-        if (complete) {
-            std::cout << Colors::MAGENTA() << "Only listing complete results" << std::endl;
-        }
-        if (partial) {
-            std::cout << Colors::MAGENTA() << "Only listing partial results" << std::endl;
-        }
-        auto i = 0;
-        int maxModel = results.maxModelSize();
-        std::cout << Colors::GREEN() << " #  Date       " << std::setw(maxModel) << std::left << "Model" << " Score Name  Score       C/P Duration  Title" << std::endl;
-        std::cout << "=== ========== " << std::string(maxModel, '=') << " =========== =========== === ========= =============================================================" << std::endl;
-        bool odd = true;
-        for (auto& result : results) {
-            auto color = odd ? Colors::BLUE() : Colors::CYAN();
-            std::cout << color << std::setw(3) << std::fixed << std::right << i++ << " ";
-            std::cout << result.to_string(maxModel) << std::endl;
-            if (i == numFiles) {
-                break;
-            }
-            odd = !odd;
-        }
-    }
-    bool ManageResults::confirmAction(const std::string& intent, const std::string& fileName) const
-    {
-        std::string color;
-        if (intent == "delete") {
-            color = Colors::RED();
-        } else {
-            color = Colors::YELLOW();
-        }
-        std::string line;
-        bool finished = false;
-        while (!finished) {
-            std::cout << color << "Really want to " << intent << " " << fileName << "? (y/n): ";
-            getline(std::cin, line);
-            finished = line.size() == 1 && (tolower(line[0]) == 'y' || tolower(line[0] == 'n'));
-        }
-        if (tolower(line[0]) == 'y') {
-            return true;
-        }
-        std::cout << "Not done!" << std::endl;
-        return false;
-    }
-    void ManageResults::report_compared(const int index_A, const int index_B)
-    {
-        std::cout << "Comparing " << results.at(index_A).getFilename() << " with " << results.at(index_B).getFilename() << std::endl;
-        auto data_A = results.at(index_A).getJson();
-        auto data_B = results.at(index_B).getJson();
-        ReportExcelCompared reporter(data_A, data_B);
-        reporter.report();
-    }
-    void ManageResults::report(const int index, const bool excelReport)
-    {
-        std::cout << Colors::YELLOW() << "Reporting " << results.at(index).getFilename() << std::endl;
-        auto data = results.at(index).getJson();
-        if (excelReport) {
-            ReportExcel reporter(data, compare, workbook);
-            reporter.show();
-            openExcel = true;
-            workbook = reporter.getWorkbook();
-            std::cout << "Adding sheet to " << Paths::excel() + Paths::excelResults() << std::endl;
-        } else {
-            ReportConsole reporter(data, compare);
-            reporter.show();
-        }
-    }
-    void ManageResults::showIndex(const int index, const int idx)
-    {
-        // Show a dataset result inside a report
-        auto data = results.at(index).getJson();
-        std::cout << Colors::YELLOW() << "Showing " << results.at(index).getFilename() << std::endl;
-        ReportConsole reporter(data, compare, idx);
-        reporter.show();
-    }
-    void ManageResults::sortList()
-    {
-        std::cout << Colors::YELLOW() << "Choose sorting field (date='d', score='s', duration='u', model='m'): ";
-        std::string line;
-        char option;
-        getline(std::cin, line);
-        if (line.size() == 0)
-            return;
-        if (line.size() > 1) {
-            std::cout << "Invalid option" << std::endl;
-            return;
-        }
-        option = line[0];
-        switch (option) {
-            case 'd':
-                results.sortDate();
-                break;
-            case 's':
-                results.sortScore();
-                break;
-            case 'u':
-                results.sortDuration();
-                break;
-            case 'm':
-                results.sortModel();
-                break;
-            default:
-                std::cout << "Invalid option" << std::endl;
-        }
-    }
-    void ManageResults::menu()
-    {
-        char option;
-        int index, subIndex, index_A = -1, index_B = -1;
-        bool finished = false;
-        std::string filename;
-        // tuple<Option, digit, requires value>
-        std::vector<std::tuple<std::string, char, bool>>  mainOptions = {
-            {"quit", 'q', false},
-            {"list", 'l', false},
-            {"delete", 'd', true},
-            {"hide", 'h', true},
-            {"sort", 's', false},
-            {"report", 'r', true},
-            {"excel", 'e', true},
-            {"title", 't', true},
-            {"set A", 'a', true},
-            {"set B", 'b', true},
-            {"compare A~B", 'c', false}
-        };
-        // tuple<Option, digit, requires value>
-        std::vector<std::tuple<std::string, char, bool>> listOptions = {
-            {"report", 'r', true},
-            {"list", 'l', false},
-            {"back", 'b', false},
-            {"quit", 'q', false}
-        };
-        auto parser = CommandParser();
-        while (!finished) {
-            if (indexList) {
-                std::tie(option, index) = parser.parse(Colors::GREEN(), mainOptions, 'r', numFiles - 1);
-            } else {
-                std::tie(option, subIndex) = parser.parse(Colors::BLUE(), listOptions, 'r', results.at(index).getJson()["results"].size() - 1);
-            }
-            switch (option) {
-                case 'q':
-                    finished = true;
-                    break;
-                case 'a':
-                    if (index == index_B) {
-                        std::cout << Colors::RED() << "A and B cannot be the same!" << Colors::RESET() << std::endl;
-                        break;
-                    }
-                    if (!results.at(index).isComplete()) {
-                        std::cout << Colors::RED() << "A must be a complete result!" << Colors::RESET() << std::endl;
-                        break;
-                    }
-                    index_A = index;
-                    break;
-                case 'b':
-                    if (indexList) {
-                        if (index == index_A) {
-                            std::cout << Colors::RED() << "A and B cannot be the same!" << Colors::RESET() << std::endl;
-                            break;
-                        }
-                        if (!results.at(index).isComplete()) {
-                            std::cout << Colors::RED() << "B must be a complete result!" << Colors::RESET() << std::endl;
-                            break;
-                        }
-                        index_B = index;
-                    } else {
-                        // back to show the report
-                        report(index, false);
-                    }
-                    break;
-                case 'c':
-                    if (index_A == -1 || index_B == -1) {
-                        std::cout << Colors::RED() << "Need to set A and B first!" << Colors::RESET() << std::endl;
-                        break;
-                    }
-                    report_compared(index_A, index_B);
-                    break;
-                case 'l':
-                    list();
-                    indexList = true;
-                    break;
-                case 'd':
-                    filename = results.at(index).getFilename();
-                    if (!confirmAction("delete", filename))
-                        break;
-                    std::cout << "Deleting " << filename << std::endl;
-                    results.deleteResult(index);
-                    std::cout << "File: " + filename + " deleted!" << std::endl;
-                    list();
-                    break;
-                case 'h':
-                    filename = results.at(index).getFilename();
-                    if (!confirmAction("hide", filename))
-                        break;
-                    filename = results.at(index).getFilename();
-                    std::cout << "Hiding " << filename << std::endl;
-                    results.hideResult(index, Paths::hiddenResults());
-                    std::cout << "File: " + filename + " hidden! (moved to " << Paths::hiddenResults() << ")" << std::endl;
-                    list();
-                    break;
-                case 's':
-                    sortList();
-                    list();
-                    break;
-                case 'r':
-                    if (indexList) {
-                        report(index, false);
-                        indexList = false;
-                    } else {
-                        showIndex(index, subIndex);
-                    }
-                    break;
-                case 'e':
-                    report(index, true);
-                    break;
-                case 't':
-                    std::cout << "Title: " << results.at(index).getTitle() << std::endl;
-                    std::cout << "New title: ";
-                    std::string newTitle;
-                    getline(std::cin, newTitle);
-                    if (!newTitle.empty()) {
-                        results.at(index).setTitle(newTitle);
-                        results.at(index).save();
-                        std::cout << "Title changed to " << newTitle << std::endl;
-                    }
-                    break;
-            }
-        }
-    }
-} /* namespace platform */

src/manage/ManageResults.h

@@ -1,29 +0,0 @@
-#pragma once
-
-#include <xlsxwriter.h>
-#include "ResultsManager.h"
-
-namespace platform {
-    class ManageResults {
-    public:
-        ManageResults(int numFiles, const std::string& model, const std::string& score, bool complete, bool partial, bool compare);
-        ~ManageResults() = default;
-        void doMenu();
-    private:
-        void list();
-        bool confirmAction(const std::string& intent, const std::string& fileName) const;
-        void report(const int index, const bool excelReport);
-        void report_compared(const int index_A, const int index_B);
-        void showIndex(const int index, const int idx);
-        void sortList();
-        void menu();
-        int numFiles;
-        bool indexList;
-        bool openExcel;
-        bool complete;
-        bool partial;
-        bool compare;
-        ResultsManager results;
-        lxw_workbook* workbook;
-    };
-}

src/manage/ManageScreen.cpp

@@ -0,0 +1,564 @@
+#include <filesystem>
+#include <tuple>
+#include <string>
+#include <algorithm>
+#include "folding.hpp"
+#include "common/CLocale.h"
+#include "common/Paths.h"
+#include "OptionsMenu.h"
+#include "ManageScreen.h"
+#include "reports/DatasetsConsole.h"
+#include "reports/ReportConsole.h"
+#include "reports/ReportExcel.h"
+#include "reports/ReportExcelCompared.h"
+#include <bayesnet/classifiers/TAN.h>
+#include <fimdlp/CPPFImdlp.h>
+
+namespace platform {
+    const std::string STATUS_OK = "Ok.";
+    const std::string STATUS_COLOR = Colors::GREEN();
+
+    ManageScreen::ManageScreen(int rows, int cols, const std::string& model, const std::string& score, const std::string& platform, bool complete, bool partial, bool compare) :
+        rows{ rows }, cols{ cols }, complete{ complete }, partial{ partial }, compare{ compare }, didExcel(false), results(ResultsManager(model, score, platform, complete, partial))
+    {
+        results.load();
+        openExcel = false;
+        workbook = NULL;
+        maxModel = results.maxModelSize();
+        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" };
+        updateSize(rows, cols);
+        // Initializes the paginator for each output type (experiments, datasets, result)
+        for (int i = 0; i < static_cast<int>(OutputType::Count); i++) {
+            paginator.push_back(Paginator(this->rows, results.size()));
+        }
+        index_A = -1;
+        index_B = -1;
+        index = -1;
+        subIndex = -1;
+        output_type = OutputType::EXPERIMENTS;
+    }
+    void ManageScreen::computeSizes()
+    {
+        int minTitle = 10;
+        // set 10 chars as minimum for Title
+        auto header_title = header_lengths[header_lengths.size() - 1];
+        min_columns = std::accumulate(header_lengths.begin(), header_lengths.end(), 0) + header_lengths.size() - header_title + minTitle;
+        maxTitle = minTitle + cols - min_columns;
+        header_lengths[header_lengths.size() - 1] = maxTitle;
+        cols = std::min(cols, min_columns + maxTitle);
+        for (auto& paginator_ : paginator) {
+            paginator_.setPageSize(rows);
+        }
+    }
+    bool ManageScreen::checkWrongColumns()
+    {
+        if (min_columns > cols) {
+            std::cerr << Colors::MAGENTA() << "Make screen bigger to fit the results! " + std::to_string(min_columns - cols) + " columns needed! " << std::endl;
+            return true;
+        }
+        return false;
+    }
+    void ManageScreen::updateSize(int rows_, int cols_)
+    {
+        rows = std::max(6, rows_ - 6); // 6 is the number of lines used by the menu & header
+        cols = cols_;
+        computeSizes();
+    }
+    void ManageScreen::doMenu()
+    {
+        if (results.empty()) {
+            std::cerr << Colors::MAGENTA() << "No results found!" << Colors::RESET() << std::endl;
+            return;
+        }
+        if (checkWrongColumns())
+            return;
+        results.sortResults(sort_field, sort_type);
+        list(STATUS_OK, STATUS_COLOR);
+        menu();
+        if (openExcel) {
+            workbook_close(workbook);
+        }
+        if (didExcel) {
+            std::cout << Colors::MAGENTA() << "Excel file created: " << Paths::excel() + Paths::excelResults() << std::endl;
+        }
+        std::cout << Colors::RESET() << "Done!" << std::endl;
+    }
+    std::string ManageScreen::getVersions()
+    {
+        std::string kfold_version = folding::KFold(5, 100).version();
+        std::string bayesnet_version = bayesnet::TAN().getVersion();
+        std::string mdlp_version = mdlp::CPPFImdlp::version();
+        return " BayesNet: " + bayesnet_version + " Folding: " + kfold_version + " MDLP: " + mdlp_version + " ";
+    }
+    void ManageScreen::header()
+    {
+        auto [index_from, index_to] = paginator[static_cast<int>(output_type)].getOffset();
+        std::string suffix = "";
+        if (complete) {
+            suffix = " Only listing complete results ";
+        }
+        if (partial) {
+            suffix = " Only listing partial results ";
+        }
+        auto page = paginator[static_cast<int>(output_type)].getPage();
+        auto pages = paginator[static_cast<int>(output_type)].getPages();
+        auto lines = paginator[static_cast<int>(output_type)].getLines();
+        auto total = paginator[static_cast<int>(output_type)].getTotal();
+        std::string header = " Lines " + std::to_string(lines) + " of "
+            + std::to_string(total) + " - Page " + std::to_string(page) + " of "
+            + std::to_string(pages) + " ";
+        std::string versions = getVersions();
+        int filler = std::max(cols - versions.size() - suffix.size() - header.size(), size_t(0));
+        std::string prefix = std::string(filler, ' ');
+        std::cout << Colors::CLRSCR() << Colors::REVERSE() << Colors::WHITE() << header
+            << prefix << Colors::GREEN() << versions << Colors::MAGENTA() << suffix << Colors::RESET() << std::endl;
+    }
+    void ManageScreen::footer(const std::string& status, const std::string& status_color)
+    {
+        std::stringstream oss;
+        oss << " A: " << (index_A == -1 ? "<notset>" : std::to_string(index_A)) <<
+            " B: " << (index_B == -1 ? "<notset>" : std::to_string(index_B)) << " ";
+        int status_length = std::max(oss.str().size(), cols - oss.str().size());
+        auto status_message = status.substr(0, status_length - 1);
+        std::string status_line = status_message + std::string(std::max(size_t(0), status_length - status_message.size() - 1), ' ');
+        auto color = (index_A != -1 && index_B != -1) ? Colors::IGREEN() : Colors::IYELLOW();
+        std::cout << color << Colors::REVERSE() << oss.str() << Colors::RESET() << Colors::WHITE()
+            << Colors::REVERSE() << status_color << " " << status_line << Colors::IWHITE()
+            << Colors::RESET() << std::endl;
+    }
+    void ManageScreen::list(const std::string& status_message, const std::string& status_color)
+    {
+        switch (static_cast<int>(output_type)) {
+            case static_cast<int>(OutputType::RESULT):
+                list_result(status_message, status_color);
+                break;
+            case static_cast<int>(OutputType::DETAIL):
+                list_detail(status_message, status_color);
+                break;
+            case static_cast<int>(OutputType::DATASETS):
+                list_datasets(status_message, status_color);
+                break;
+            case static_cast<int>(OutputType::EXPERIMENTS):
+                list_experiments(status_message, status_color);
+                break;
+        }
+    }
+    void ManageScreen::list_result(const std::string& status_message, const std::string& status_color)
+    {
+        auto data = results.at(index).getJson();
+        ReportConsole report(data, compare);
+        auto header_text = report.getHeader();
+        auto body = report.getBody();
+        paginator[static_cast<int>(output_type)].setTotal(body.size());
+        // We need to subtract 8 from the page size to make room for the extra header in report
+        auto page_size = paginator[static_cast<int>(OutputType::EXPERIMENTS)].getPageSize();
+        paginator[static_cast<int>(output_type)].setPageSize(page_size - 8);
+        //
+        // header
+        //
+        header();
+        //
+        // Results
+        //
+        std::cout << header_text;
+        auto [index_from, index_to] = paginator[static_cast<int>(output_type)].getOffset();
+        for (int i = index_from; i <= index_to; i++) {
+            std::cout << body[i];
+        }
+        //
+        // Status Area
+        //
+        footer(status_message, status_color);
+    }
+    void ManageScreen::list_detail(const std::string& status_message, const std::string& status_color)
+    {
+        auto data = results.at(index).getJson();
+        ReportConsole report(data, compare, subIndex);
+        auto header_text = report.getHeader();
+        auto body = report.getBody();
+        paginator[static_cast<int>(output_type)].setTotal(body.size());
+        // We need to subtract 8 from the page size to make room for the extra header in report
+        auto page_size = paginator[static_cast<int>(OutputType::EXPERIMENTS)].getPageSize();
+        paginator[static_cast<int>(output_type)].setPageSize(page_size - 8);
+        //
+        // header
+        //
+        header();
+        //
+        // Results
+        //
+        std::cout << header_text;
+        auto [index_from, index_to] = paginator[static_cast<int>(output_type)].getOffset();
+        for (int i = index_from; i <= index_to; i++) {
+            std::cout << body[i];
+        }
+        //
+        // Status Area
+        //
+        footer(status_message, status_color);
+    }
+    void ManageScreen::list_datasets(const std::string& status_message, const std::string& status_color)
+    {
+        auto report = DatasetsConsole();
+        report.report();
+        paginator[static_cast<int>(output_type)].setTotal(report.getNumLines());
+        //
+        // header
+        //
+        header();
+        //
+        // Results
+        //
+        auto body = report.getBody();
+        std::cout << report.getHeader();
+        auto [index_from, index_to] = paginator[static_cast<int>(output_type)].getOffset();
+        for (int i = index_from; i <= index_to; i++) {
+            std::cout << body[i];
+        }
+        //
+        // Status Area
+        //
+        footer(status_message, status_color);
+    }
+    void ManageScreen::list_experiments(const std::string& status_message, const std::string& status_color)
+    {
+        //
+        // header
+        //
+        header();
+        std::cout << Colors::RESET();
+        std::string arrow_dn = Symbols::down_arrow + " ";
+        std::string arrow_up = Symbols::up_arrow + " ";
+        for (int i = 0; i < header_labels.size(); i++) {
+            std::string suffix = "", color = Colors::GREEN();
+            int diff = 0;
+            if (header_labels[i] == sort_fields[static_cast<int>(sort_field)]) {
+                color = Colors::YELLOW();
+                diff = 2;
+                suffix = sort_type == SortType::ASC ? arrow_up : arrow_dn;
+            }
+            std::cout << color << std::setw(header_lengths[i] + diff) << std::left << std::string(header_labels[i] + suffix) << " ";
+        }
+        std::cout << std::endl;
+        for (int i = 0; i < header_labels.size(); i++) {
+            std::cout << std::string(header_lengths[i], '=') << " ";
+        }
+        std::cout << Colors::RESET() << std::endl;
+        //
+        // Results
+        //
+        if (results.empty()) {
+            std::cout << "No results found!" << std::endl;
+            return;
+        }
+        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;
+        }
+        //
+        // Status Area
+        //
+        footer(status_message, status_color);
+    }
+    bool ManageScreen::confirmAction(const std::string& intent, const std::string& fileName) const
+    {
+        std::string color;
+        if (intent == "delete") {
+            color = Colors::RED();
+        } else {
+            color = Colors::YELLOW();
+        }
+        std::string line;
+        bool finished = false;
+        while (!finished) {
+            std::cout << color << "Really want to " << intent << " " << fileName << "? (y/n): ";
+            getline(std::cin, line);
+            finished = line.size() == 1 && (tolower(line[0]) == 'y' || tolower(line[0]) == 'n');
+        }
+        if (tolower(line[0]) == 'y') {
+            return true;
+        }
+        std::cout << "Not done!" << std::endl;
+        return false;
+    }
+    std::string ManageScreen::report_compared()
+    {
+        auto data_A = results.at(index_A).getJson();
+        auto data_B = results.at(index_B).getJson();
+        ReportExcelCompared reporter(data_A, data_B);
+        reporter.report();
+        didExcel = true;
+        return results.at(index_A).getFilename() + " Vs " + results.at(index_B).getFilename();
+    }
+    std::string ManageScreen::report(const int index, const bool excelReport)
+    {
+        auto data = results.at(index).getJson();
+        if (excelReport) {
+            didExcel = true;
+            ReportExcel reporter(data, compare, workbook);
+            reporter.show();
+            openExcel = true;
+            workbook = reporter.getWorkbook();
+            return results.at(index).getFilename() + "->" + Paths::excel() + Paths::excelResults();
+        } else {
+            ReportConsole reporter(data, compare);
+            std::cout << Colors::CLRSCR() << reporter.fileReport();
+            return "Reporting " + results.at(index).getFilename();
+        }
+    }
+    std::pair<std::string, std::string> ManageScreen::sortList()
+    {
+        std::vector<std::tuple<std::string, char, bool>>  sortOptions = {
+            {"date", 'd', false},
+            {"score", 's', false},
+            {"time", 't', false},
+            {"model", 'm', false},
+            {"ascending+", '+', false},
+            {"descending-", '-', false}
+        };
+        auto sortMenu = OptionsMenu(sortOptions, Colors::YELLOW(), Colors::RED(), cols);
+        std::string invalid_option = "Invalid sorting option";
+        char option;
+        bool parserError = true; // force the first iteration
+        while (parserError) {
+            if (checkWrongColumns())
+                return { Colors::RED(), "Invalid column size" };
+            auto [min_index, max_index] = paginator[static_cast<int>(output_type)].getOffset();
+            std::tie(option, index, parserError) = sortMenu.parse(' ', 0, 0);
+            sortMenu.updateColumns(cols);
+            if (parserError) {
+                return { Colors::RED(), invalid_option };
+            }
+        }
+        switch (option) {
+            case 'd':
+                sort_field = SortField::DATE;
+                break;
+            case 's':
+                sort_field = SortField::SCORE;
+                break;
+            case 't':
+                sort_field = SortField::DURATION;
+                break;
+            case 'm':
+                sort_field = SortField::MODEL;
+                break;
+            case '+':
+                sort_type = SortType::ASC;
+                break;
+            case '-':
+                sort_type = SortType::DESC;
+                break;
+            default:
+                return { Colors::RED(), invalid_option };
+        }
+        results.sortResults(sort_field, sort_type);
+        return { Colors::GREEN(), "Sorted by " + sort_fields[static_cast<int>(sort_field)] + " " + (sort_type == SortType::ASC ? "ascending" : "descending") };
+    }
+    void ManageScreen::menu()
+    {
+        char option;
+        bool finished = false;
+        std::string filename;
+        // tuple<Option, digit, requires value>
+        std::vector<std::tuple<std::string, char, bool>>  mainOptions = {
+            {"quit", 'q', false},
+            {"list", 'l', false},
+            {"Delete", 'D', true},
+            {"datasets", 'd', false},
+            {"hide", 'h', true},
+            {"sort", 's', false},
+            {"report", 'r', true},
+            {"excel", 'e', true},
+            {"title", 't', true},
+            {"set A", 'A', true},
+            {"set B", 'B', true},
+            {"compare A~B", 'c', false},
+            {"page", 'p', true},
+            {"Page+", '+', false },
+            {"Page-", '-', false}
+        };
+        // tuple<Option, digit, requires value>
+        std::vector<std::tuple<std::string, char, bool>> listOptions = {
+            {"quit", 'q', false},
+            {"report", 'r', true},
+            {"list", 'l', false},
+            {"excel", 'e', true},
+            {"back", 'b', false},
+            {"page", 'p', true},
+            {"Page+", '+', false},
+            {"Page-", '-', false}
+        };
+        while (!finished) {
+            auto main_menu = OptionsMenu(mainOptions, Colors::IGREEN(), Colors::YELLOW(), cols);
+            auto list_menu = OptionsMenu(listOptions, Colors::IBLUE(), Colors::YELLOW(), cols);
+            OptionsMenu& menu = output_type == OutputType::EXPERIMENTS ? main_menu : list_menu;
+            bool parserError = true; // force the first iteration
+            while (parserError) {
+                int index_menu;
+                auto [min_index, max_index] = paginator[static_cast<int>(output_type)].getOffset();
+                std::tie(option, index_menu, parserError) = menu.parse('r', min_index, max_index);
+                if (output_type == OutputType::EXPERIMENTS) {
+                    index = index_menu;
+                } else {
+                    subIndex = index_menu;
+                }
+                if (min_columns > cols) {
+                    std::cerr << "Make screen bigger to fit the results! " + std::to_string(min_columns - cols) + " columns needed! " << std::endl;
+                    return;
+                }
+                menu.updateColumns(cols);
+                if (parserError) {
+                    list(menu.getErrorMessage(), Colors::RED());
+                }
+            }
+            switch (option) {
+                case 'd':
+                    output_type = OutputType::DATASETS;
+                    list_datasets(STATUS_OK, STATUS_COLOR);
+                    break;
+                case 'p':
+                    {
+                        auto page = output_type == OutputType::EXPERIMENTS ? index : subIndex;
+                        if (paginator[static_cast<int>(output_type)].setPage(page)) {
+                            list(STATUS_OK, STATUS_COLOR);
+                        } else {
+                            list("Invalid page! (" + std::to_string(page) + ")", Colors::RED());
+                        }
+                    }
+                    break;
+                case '+':
+                    if (paginator[static_cast<int>(output_type)].addPage()) {
+                        list(STATUS_OK, STATUS_COLOR);
+                    } else {
+                        list("No more pages!", Colors::RED());
+                    }
+                    break;
+                case '-':
+                    if (paginator[static_cast<int>(output_type)].subPage()) {
+                        list(STATUS_OK, STATUS_COLOR);
+                    } else {
+                        list("First page already!", Colors::RED());
+                    }
+                    break;
+                case 'q':
+                    finished = true;
+                    break;
+                case 'A':
+                    if (index == index_B) {
+                        list("A and B cannot be the same!", Colors::RED());
+                        break;
+                    }
+                    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);
+                    }
+                    break;
+                case 'c':
+                    if (index_A == -1 || index_B == -1) {
+                        list("Need to set A and B first!", Colors::RED());
+                        break;
+                    }
+                    list(report_compared(), Colors::GREEN());
+                    break;
+                case 'l':
+                    output_type = OutputType::EXPERIMENTS;
+                    paginator[static_cast<int>(OutputType::DATASETS)].setPage(1);
+                    paginator[static_cast<int>(OutputType::RESULT)].setPage(1);
+                    paginator[static_cast<int>(OutputType::DETAIL)].setPage(1);
+                    list(STATUS_OK, STATUS_COLOR);
+                    break;
+                case 'D':
+                    filename = results.at(index).getFilename();
+                    if (!confirmAction("delete", filename)) {
+                        list(filename + " not deleted!", Colors::YELLOW());
+                        break;
+                    }
+                    std::cout << "Deleting " << filename << std::endl;
+                    results.deleteResult(index);
+                    paginator[static_cast<int>(OutputType::EXPERIMENTS)].setTotal(results.size());
+                    list(filename + " deleted!", Colors::RED());
+                    break;
+                case 'h':
+                    {
+                        std::string status_message;
+                        filename = results.at(index).getFilename();
+                        if (!confirmAction("hide", filename)) {
+                            list(filename + " not hidden!", Colors::YELLOW());
+                            break;
+                        }
+                        filename = results.at(index).getFilename();
+                        std::cout << "Hiding " << filename << std::endl;
+                        results.hideResult(index, Paths::hiddenResults());
+                        status_message = filename + " hidden! (moved to " + Paths::hiddenResults() + ")";
+                        paginator[static_cast<int>(OutputType::EXPERIMENTS)].setTotal(results.size());
+                        list(status_message, Colors::YELLOW());
+                    }
+                    break;
+                case 's':
+                    {
+                        std::string status_message, status_color;
+                        tie(status_color, status_message) = sortList();
+                        list(status_message, status_color);
+                    }
+                    break;
+                case 'r':
+                    if (output_type == OutputType::DATASETS) {
+                        list(STATUS_OK, STATUS_COLOR);
+                        break;
+                    }
+                    if (output_type == OutputType::EXPERIMENTS) {
+                        output_type = OutputType::RESULT;
+                        paginator[static_cast<int>(OutputType::DETAIL)].setPage(1);
+                        list(STATUS_OK, STATUS_COLOR);
+                    } else {
+                        output_type = OutputType::DETAIL;
+                        list(STATUS_OK, STATUS_COLOR);
+                    }
+                    break;
+                case 'e':
+                    if (output_type == OutputType::EXPERIMENTS) {
+                        list(report(index, true), Colors::GREEN());
+                        break;
+                    }
+                    list(report(subIndex, true), Colors::GREEN());
+                    break;
+                case 't':
+                    {
+                        std::string status_message;
+                        std::cout << "Title: " << results.at(index).getTitle() << std::endl;
+                        std::cout << "New title: ";
+                        std::string newTitle;
+                        getline(std::cin, newTitle);
+                        if (!newTitle.empty()) {
+                            results.at(index).setTitle(newTitle);
+                            results.at(index).save();
+                            status_message = "Title changed to " + newTitle;
+                            list(status_message, Colors::GREEN());
+                            break;
+                        }
+                        list("No title change!", Colors::YELLOW());
+                    }
+                    break;
+            }
+        }
+    }
+} /* namespace platform */

src/manage/ManageScreen.h

@@ -0,0 +1,62 @@
+#ifndef MANAGE_SCREEN_H
+#define MANAGE_SCREEN_H
+#include <xlsxwriter.h>
+#include "ResultsManager.h"
+#include "common/Colors.h"
+#include "Paginator.hpp"
+
+namespace platform {
+    enum class OutputType {
+        EXPERIMENTS = 0,
+        DATASETS = 1,
+        RESULT = 2,
+        DETAIL = 3,
+        Count
+    };
+    class ManageScreen {
+    public:
+        ManageScreen(int rows, int cols, const std::string& model, const std::string& score, const std::string& platform, bool complete, bool partial, bool compare);
+        ~ManageScreen() = default;
+        void doMenu();
+        void updateSize(int rows, int cols);
+    private:
+        void list(const std::string& status, const std::string& color);
+        void list_experiments(const std::string& status, const std::string& color);
+        void list_result(const std::string& status, const std::string& color);
+        void list_detail(const std::string& status, const std::string& color);
+        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);
+        std::string report_compared();
+        std::pair<std::string, std::string> sortList();
+        std::string getVersions();
+        void computeSizes();
+        bool checkWrongColumns();
+        void menu();
+        void header();
+        void footer(const std::string& status, const std::string& color);
+        OutputType output_type;
+        int rows;
+        int cols;
+        int min_columns;
+        int index;
+        int subIndex;
+        int index_A, index_B; // used for comparison of experiments
+        bool indexList;
+        bool openExcel;
+        bool didExcel;
+        bool complete;
+        bool partial;
+        bool compare;
+        int maxModel, maxTitle;
+        std::vector<std::string> header_labels;
+        std::vector<int> header_lengths;
+        std::vector<std::string> sort_fields;
+        SortField sort_field = SortField::DATE;
+        SortType sort_type = SortType::DESC;
+        std::vector<Paginator> paginator;
+        ResultsManager results;
+        lxw_workbook* workbook;
+    };
+}
+#endif

src/manage/OptionsMenu.cpp

@@ -0,0 +1,102 @@
+#include "OptionsMenu.h"
+#include <iostream>
+#include <sstream>
+#include <algorithm>
+#include "common/Utils.h"
+
+namespace platform {
+    std::string OptionsMenu::to_string()
+    {
+        bool first = true;
+        std::string result = color_normal + "Options: (";
+        size_t size = 10; // Size of "Options: ("
+        for (auto& option : options) {
+            if (!first) {
+                result += ", ";
+                size += 2;
+            }
+            std::string title = std::get<0>(option);
+            auto pos = title.find(std::get<1>(option));
+            result += color_normal + title.substr(0, pos) + color_bold + title.substr(pos, 1) + color_normal + title.substr(pos + 1);
+            size += title.size();
+            first = false;
+        }
+        if (size + 3 > cols) { // 3 is the size of the "): " at the end
+            result = "";
+            first = true;
+            for (auto& option : options) {
+                if (!first) {
+                    result += color_normal + ", ";
+                }
+                result += color_bold + std::get<1>(option);
+                first = false;
+            }
+        }
+        result += "): ";
+        return result;
+    }
+    std::tuple<char, int, bool> OptionsMenu::parse(char defaultCommand, int minIndex, int maxIndex)
+    {
+        bool finished = false;
+        while (!finished) {
+            std::cout << to_string();
+            std::string line;
+            getline(std::cin, line);
+            line = trim(line);
+            if (line.size() == 0) {
+                errorMessage = "No command";
+                return { defaultCommand, 0, true };
+            }
+            if (all_of(line.begin(), line.end(), ::isdigit)) {
+                command = defaultCommand;
+                index = stoi(line);
+                if (index > maxIndex || index < minIndex) {
+                    errorMessage = "Index out of range";
+                    return { ' ', -1, true };
+                }
+                finished = true;
+                break;
+            }
+            bool found = false;
+            for (auto& option : options) {
+                if (line[0] == std::get<char>(option)) {
+                    found = true;
+                    // it's a match
+                    line.erase(line.begin());
+                    line = trim(line);
+                    if (std::get<bool>(option)) {
+                        // The option requires a value
+                        if (line.size() == 0) {
+                            errorMessage = "Option " + std::get<std::string>(option) + " requires a value";
+                            return { command, index, true };
+                        }
+                        try {
+                            index = stoi(line);
+                            if (index > maxIndex || index < 0) {
+                                errorMessage = "Index out of range";
+                                return { command, index, true };
+                            }
+                        }
+                        catch (const std::invalid_argument& ia) {
+                            errorMessage = "Invalid value: " + line;
+                            return { command, index, true };
+                        }
+                    } else {
+                        if (line.size() > 0) {
+                            errorMessage = "option " + std::get<std::string>(option) + " doesn't accept values";
+                            return { command, index, true };
+                        }
+                    }
+                    command = std::get<char>(option);
+                    finished = true;
+                    break;
+                }
+            }
+            if (!found) {
+                errorMessage = "I don't know " + line;
+                return { command, index, true };
+            }
+        }
+        return { command, index, false };
+    }
+} /* namespace platform */

src/manage/OptionsMenu.h

@@ -0,0 +1,26 @@
+#ifndef OPTIONS_MENU_H
+#define OPTIONS_MENU_H
+#include <string>
+#include <vector>
+#include <tuple>
+
+namespace platform {
+    class OptionsMenu {
+    public:
+        OptionsMenu(std::vector<std::tuple<std::string, char, bool>>& options, std::string color_normal, std::string color_bold, int cols) : options(options), color_normal(color_normal), color_bold(color_bold), cols(cols) {}
+        std::string to_string();
+        std::tuple<char, int, bool> parse(char defaultCommand, int minIndex, int maxIndex);
+        char getCommand() const { return command; };
+        int getIndex() const { return index; };
+        std::string getErrorMessage() const { return errorMessage; };
+        void updateColumns(int cols) { this->cols = cols; }
+    private:
+        std::vector<std::tuple<std::string, char, bool>>& options;
+        std::string color_normal, color_bold;
+        int cols;
+        std::string errorMessage;
+        char command;
+        int index;
+    };
+} /* namespace platform */
+#endif

src/manage/Paginator.hpp

@@ -0,0 +1,57 @@
+#ifndef PAGINATOR_HPP
+#define PAGINATOR_HPP
+#include <utility>
+
+class Paginator {
+public:
+    Paginator() = default;
+    Paginator(int pageSize, int total, int page = 1) : pageSize(pageSize), total(total), page(page)
+    {
+        computePages();
+    };
+    ~Paginator() = default;
+    // Getters
+    int getPageSize() const { return pageSize; }
+    int getLines() const
+    {
+        auto [start, end] = getOffset();
+        return std::min(pageSize, end - start + 1);
+    }
+    int getPage() const { return page; }
+    int getTotal() const { return total; }
+    int getPages() const { return numPages; }
+    std::pair<int, int> getOffset() const
+    {
+        return { (page - 1) * pageSize, std::min(total - 1, page * pageSize - 1) };
+    }
+    // Setters
+    void setTotal(int total) { this->total = total; computePages(); }
+    void setPageSize(int page) { this->pageSize = page; computePages(); }
+    bool setPage(int page) { return valid(page) ? this->page = page, true : false; }
+    // Utils
+    bool valid(int page) const { return page > 0 && page <= numPages; }
+    bool hasPrev(int page) const { return page > 1; }
+    bool hasNext(int page) const { return page < getPages(); }
+    bool addPage() { return page < numPages ? ++page, true : false; }
+    bool subPage() { return page > 1 ? --page, true : false; }
+    std::string to_string() const
+    {
+        auto offset = getOffset();
+        return "Paginator: { pageSize: " + std::to_string(pageSize) + ", total: " + std::to_string(total)
+            + ", page: " + std::to_string(page) + ", numPages: " + std::to_string(numPages)
+            + " Offset [" + std::to_string(offset.first) + ", " + std::to_string(offset.second) + "]}";
+    }
+private:
+    void computePages()
+    {
+        numPages = pageSize > 0 ? (total + pageSize - 1) / pageSize : 0;
+        if (page > numPages) {
+            page = numPages;
+        }
+    }
+    int pageSize;
+    int total;
+    int page;
+    int numPages;
+};
+#endif

src/manage/ResultsManager.cpp

@@ -3,31 +3,36 @@
 #include "ResultsManager.h"
 
 namespace platform {
-    ResultsManager::ResultsManager(const std::string& model, const std::string& score, bool complete, bool partial) :
-        path(Paths::results()), model(model), scoreName(score), complete(complete), partial(partial)
+    ResultsManager::ResultsManager(const std::string& model, const std::string& score, const std::string& platform, bool complete, bool partial) :
+        path(Paths::results()), model(model), scoreName(score), platform(platform), complete(complete), partial(partial), maxModel(0), maxTitle(0)
     {
-        load();
-        if (!files.empty()) {
-            maxModel = (*max_element(files.begin(), files.end(), [](const Result& a, const Result& b) { return a.getModel().size() < b.getModel().size(); })).getModel().size();
-        } else {
-            maxModel = 0;
-        }
     }
     void ResultsManager::load()
     {
         using std::filesystem::directory_iterator;
+        bool found = false;
         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);
                 bool addResult = true;
-                if (model != "any" && result.getModel() != model || scoreName != "any" && scoreName != result.getScoreName() || complete && !result.isComplete() || partial && result.isComplete())
+                if (platform != "any" && result.getPlatform() != platform
+                    || model != "any" && result.getModel() != model
+                    || scoreName != "any" && scoreName != result.getScoreName()
+                    || complete && !result.isComplete()
+                    || partial && result.isComplete())
                     addResult = false;
-                if (addResult)
+                if (addResult) {
                     files.push_back(result);
+                    found = true;
+                }
             }
         }
+        if (found) {
+            maxModel = std::max(size_t(5), (*max_element(files.begin(), files.end(), [](const Result& a, const Result& b) { return a.getModel().size() < b.getModel().size(); })).getModel().size());
+            maxTitle = std::max(size_t(5), (*max_element(files.begin(), files.end(), [](const Result& a, const Result& b) { return a.getTitle().size() < b.getTitle().size(); })).getTitle().size());
+        }
     }
     void ResultsManager::hideResult(int index, const std::string& pathHidden)
     {
@@ -45,30 +50,79 @@ namespace platform {
     {
         return files.size();
     }
-    void ResultsManager::sortDate()
+    void ResultsManager::sortDate(SortType type)
     {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+        if (empty())
+            return;
+        sort(files.begin(), files.end(), [type](const Result& a, const Result& b) {
+            if (a.getDate() == b.getDate()) {
+                if (type == SortType::ASC)
+                    return a.getModel() < b.getModel();
+                return a.getModel() > b.getModel();
+            }
+            if (type == SortType::ASC)
+                return a.getDate() < b.getDate();
             return a.getDate() > b.getDate();
             });
     }
-    void ResultsManager::sortModel()
+    void ResultsManager::sortModel(SortType type)
     {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+        if (empty())
+            return;
+        sort(files.begin(), files.end(), [type](const Result& a, const Result& b) {
+            if (a.getModel() == b.getModel()) {
+                if (type == SortType::ASC)
+                    return a.getDate() < b.getDate();
+                return a.getDate() > b.getDate();
+            }
+            if (type == SortType::ASC)
+                return a.getModel() < b.getModel();
             return a.getModel() > b.getModel();
             });
     }
-    void ResultsManager::sortDuration()
+    void ResultsManager::sortDuration(SortType type)
     {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+        if (empty())
+            return;
+        sort(files.begin(), files.end(), [type](const Result& a, const Result& b) {
+            if (type == SortType::ASC)
+                return a.getDuration() < b.getDuration();
             return a.getDuration() > b.getDuration();
             });
     }
-    void ResultsManager::sortScore()
+    void ResultsManager::sortScore(SortType type)
     {
-        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+        if (empty())
+            return;
+        sort(files.begin(), files.end(), [type](const Result& a, const Result& b) {
+            if (a.getScore() == b.getScore()) {
+                if (type == SortType::ASC)
+                    return a.getDate() < b.getDate();
+                return a.getDate() > b.getDate();
+            }
+            if (type == SortType::ASC)
+                return a.getScore() < b.getScore();
             return a.getScore() > b.getScore();
             });
     }
+
+    void ResultsManager::sortResults(SortField field, SortType type)
+    {
+        switch (field) {
+            case SortField::DATE:
+                sortDate(type);
+                break;
+            case SortField::MODEL:
+                sortModel(type);
+                break;
+            case SortField::SCORE:
+                sortScore(type);
+                break;
+            case SortField::DURATION:
+                sortDuration(type);
+                break;
+        }
+    }
     bool ResultsManager::empty() const
     {
         return files.empty();

src/manage/ResultsManager.h

@@ -1,20 +1,32 @@
-#pragma once
-
-#include <map>
+#ifndef RESULTSMANAGER_H
+#define RESULTSMANAGER_H
 #include <vector>
 #include <string>
 #include <nlohmann/json.hpp>
-#include "main/Result.h"
+#include "results/Result.h"
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
+    enum class SortType {
+        ASC = 0,
+        DESC = 1,
+    };
+    enum class SortField {
+        DATE = 0,
+        MODEL = 1,
+        SCORE = 2,
+        DURATION = 3,
+    };
     class ResultsManager {
     public:
-        ResultsManager(const std::string& model, const std::string& score, bool complete, bool partial);
-        void sortDate();
-        void sortScore();
-        void sortModel();
-        void sortDuration();
+        ResultsManager(const std::string& model, const std::string& score, const std::string& platform, bool complete, bool partial);
+        void load(); // Loads the list of results
+        void sortResults(SortField field, SortType type); // Sorts the list of results
+        void sortDate(SortType type);
+        void sortScore(SortType type);
+        void sortModel(SortType type);
+        void sortDuration(SortType type);
         int maxModelSize() const { return maxModel; };
+        int maxTitleSize() const { return maxTitle; };
         void hideResult(int index, const std::string& pathHidden);
         void deleteResult(int index);
         int size() const;
@@ -26,10 +38,12 @@ namespace platform {
         std::string path;
         std::string model;
         std::string scoreName;
+        std::string platform;
         bool complete;
         bool partial;
         int maxModel;
+        int maxTitle;
         std::vector<Result> files;
-        void load(); // Loads the list of results
     };
-};
+};
+#endif

src/reports/DatasetsConsole.cpp

@@ -0,0 +1,79 @@
+#include <algorithm>
+#include "common/Colors.h"
+#include "common/Datasets.h"
+#include "common/Paths.h"
+#include "DatasetsConsole.h"
+
+namespace platform {
+    const int DatasetsConsole::BALANCE_LENGTH = 75;
+    void DatasetsConsole::split_lines(int name_len, std::string line, const std::string& balance)
+    {
+        auto temp = std::string(balance);
+        while (temp.size() > DatasetsConsole::BALANCE_LENGTH - 1) {
+            auto part = temp.substr(0, DatasetsConsole::BALANCE_LENGTH);
+            line += part + "\n";
+            body.push_back(line);
+            line = string(name_len + 28, ' ');
+            temp = temp.substr(DatasetsConsole::BALANCE_LENGTH);
+        }
+        line += temp + "\n";
+        body.push_back(line);
+    }
+    void DatasetsConsole::report()
+    {
+        header.clear();
+        body.clear();
+        auto datasets = platform::Datasets(false, platform::Paths::datasets());
+        std::stringstream sheader;
+        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 };
+        sheader << Colors::GREEN();
+        for (int i = 0; i < header_labels.size(); i++) {
+            sheader << setw(header_lengths[i]) << left << header_labels[i] << " ";
+        }
+        sheader << std::endl;
+        header.push_back(sheader.str());
+        std::string sline;
+        for (int i = 0; i < header_labels.size(); i++) {
+            sline += std::string(header_lengths[i], '=') + " ";
+        }
+        sline += "\n";
+        header.push_back(sline);
+        int num = 0;
+        for (const auto& dataset_name : datasets.getNames()) {
+            std::stringstream line;
+            line.imbue(loc);
+            auto color = num % 2 ? Colors::CYAN() : Colors::BLUE();
+            line << color << setw(3) << right << num++ << " ";
+            line << setw(maxName) << left << dataset_name << " ";
+            auto& dataset = datasets.getDataset(dataset_name);
+            dataset.load();
+            auto nSamples = dataset.getNSamples();
+            line << setw(6) << right << nSamples << " ";
+            auto nFeatures = dataset.getFeatures().size();
+            line << setw(5) << right << nFeatures << " ";
+            auto numericFeatures = dataset.getNumericFeatures();
+            auto num = std::count(numericFeatures.begin(), numericFeatures.end(), true);
+            line << setw(5) << right << num << " ";
+            auto nClasses = dataset.getNClasses();
+            line << setw(3) << right << nClasses << " ";
+            std::string sep = "";
+            oss.str("");
+            for (auto number : dataset.getClassesCounts()) {
+                oss << sep << std::setprecision(2) << fixed << (float)number / nSamples * 100.0 << "% (" << number << ")";
+                sep = " / ";
+            }
+            split_lines(maxName, line.str(), oss.str());
+            // Store data for Excel report
+            data[dataset_name] = json::object();
+            data[dataset_name]["samples"] = nSamples;
+            data[dataset_name]["features"] = nFeatures;
+            data[dataset_name]["numericFeatures"] = num;
+            data[dataset_name]["classes"] = nClasses;
+            data[dataset_name]["balance"] = oss.str();
+        }
+    }
+}
+

src/reports/DatasetsConsole.h

@@ -0,0 +1,21 @@
+#ifndef DATASETSCONSOLE_H
+#define DATASETSCONSOLE_H
+#include <locale>
+#include <sstream>
+#include <nlohmann/json.hpp>
+#include "ReportsPaged.h"
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+
+    class DatasetsConsole : public ReportsPaged {
+    public:
+        static const int BALANCE_LENGTH;
+        DatasetsConsole() = default;
+        ~DatasetsConsole() = default;
+        void report();
+    private:
+        void split_lines(int name_len, std::string line, const std::string& balance);
+    };
+}
+#endif

src/reports/DatasetsExcel.cpp

@@ -1,8 +1,4 @@
-#include <sstream>
-#include "common/Paths.h"
 #include "DatasetsExcel.h"
-
-
 namespace platform {
     DatasetsExcel::DatasetsExcel()
     {
@@ -21,11 +17,11 @@ namespace platform {
         int balanceSize = 75; // Min size of the column
         worksheet = workbook_add_worksheet(workbook, "Datasets");
         // Header
-        worksheet_merge_range(worksheet, 0, 0, 0, 5, "Datasets", styles["headerFirst"]);
+        worksheet_merge_range(worksheet, 0, 0, 0, 6, "Datasets", styles["headerFirst"]);
         // Body header
         row = 2;
         int col = 0;
-        for (const auto& name : { "Nº", "Dataset", "Samples", "Features", "Classes", "Balance" }) {
+        for (const auto& name : { "#", "Dataset", "Samples", "Features", "#Numer.", "Classes", "Balance" }) {
             writeString(row, col++, name, "bodyHeader");
         }
         // Body
@@ -38,12 +34,13 @@ namespace platform {
             writeString(row, 1, key.c_str(), "text");
             writeInt(row, 2, value["samples"], "ints");
             writeInt(row, 3, value["features"], "ints");
-            writeInt(row, 4, value["classes"], "ints");
-            writeString(row, 5, value["balance"].get<std::string>().c_str(), "text");
+            writeInt(row, 4, value["numericFeatures"], "ints");
+            writeInt(row, 5, value["classes"], "ints");
+            writeString(row, 6, value["balance"].get<std::string>().c_str(), "text");
         }
         // Format columns
         worksheet_freeze_panes(worksheet, 3, 2);
-        std::vector<int> columns_sizes = { 5, datasetNameSize, 10, 10, 10, balanceSize };
+        std::vector<int> columns_sizes = { 5, datasetNameSize, 10, 10, 10, 10, balanceSize };
         for (int i = 0; i < columns_sizes.size(); ++i) {
             worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
         }

src/reports/DatasetsExcel.h

@@ -1,14 +1,11 @@
-#pragma once
-
-#include <vector>
-#include <map>
+#ifndef DATASETSEXCEL_H
+#define DATASETSEXCEL_H
 #include <nlohmann/json.hpp>
 #include "reports/ExcelFile.h"
 
-using json = nlohmann::json;
 
 namespace platform {
-
+    using json = nlohmann::ordered_json;
     class DatasetsExcel : public ExcelFile {
     public:
         DatasetsExcel();
@@ -16,3 +13,4 @@ namespace platform {
         void report(json& data);
     };
 }
+#endif

src/reports/ExcelFile.cpp

@@ -22,6 +22,27 @@ namespace platform {
         colorOdd = 0xDCE6F1;
         colorEven = 0xFDE9D9;
     }
+    lxw_worksheet* ExcelFile::createWorksheet(const std::string& name)
+    {
+        lxw_worksheet* sheet;
+        std::string suffix = "";
+        std::string efectiveName;
+        int num = 1;
+        // Create a sheet with the name of the model
+        while (true) {
+            efectiveName = name + suffix;
+            if (workbook_get_worksheet_by_name(workbook, efectiveName.c_str())) {
+                suffix = std::to_string(++num);
+            } else {
+                sheet = workbook_add_worksheet(workbook, efectiveName.c_str());
+                break;
+            }
+            if (num > 100) {
+                throw std::invalid_argument("Couldn't create sheet " + efectiveName);
+            }
+        }
+        return sheet;
+    }
 
     lxw_workbook* ExcelFile::getWorkbook()
     {
@@ -64,6 +85,27 @@ namespace platform {
         }
         return efectiveStyle;
     }
+    void ExcelFile::boldFontColor(const uint32_t color)
+    {
+        createFormats();
+        for (const std::string& style : { "text", "ints", "result" }) {
+            for (const std::string& suffix : { "_odd", "_even" }) {
+                format_set_font_color(styles[style + "_bold" + suffix], lxw_color_t(color));
+            }
+        }
+    }
+    void ExcelFile::boldGreen()
+    {
+        boldFontColor(0x009900);
+    }
+    void ExcelFile::boldRed()
+    {
+        boldFontColor(0xFF0000);
+    }
+    void ExcelFile::boldBlue()
+    {
+        boldFontColor(0x0000FF);
+    }
     void ExcelFile::writeString(int row, int col, const std::string& text, const std::string& style)
     {
         worksheet_write_string(worksheet, row, col, text.c_str(), efectiveStyle(style));
@@ -84,6 +126,7 @@ namespace platform {
     void ExcelFile::createStyle(const std::string& name, lxw_format* style, bool odd)
     {
         addColor(style, odd);
+        auto color_bold = 0xFF0000;
         if (name == "textCentered") {
             format_set_align(style, LXW_ALIGN_CENTER);
             format_set_font_size(style, normalSize);
@@ -94,6 +137,13 @@ namespace platform {
             format_set_border(style, LXW_BORDER_THIN);
             format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
             format_set_text_wrap(style);
+        } else if (name == "text_bold") {
+            format_set_font_size(style, normalSize);
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
+            format_set_font_color(style, lxw_color_t(color_bold));
+            format_set_bold(style);
+            format_set_text_wrap(style);
         } else if (name == "bodyHeader") {
             format_set_bold(style);
             format_set_font_size(style, normalSize);
@@ -106,6 +156,13 @@ namespace platform {
             format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
             format_set_border(style, LXW_BORDER_THIN);
             format_set_num_format(style, "0.0000000");
+        } else if (name == "result_bold") {
+            format_set_font_size(style, normalSize);
+            format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_bold(style);
+            format_set_font_color(style, lxw_color_t(color_bold));
+            format_set_num_format(style, "0.0000000");
         } else if (name == "time") {
             format_set_font_size(style, normalSize);
             format_set_border(style, LXW_BORDER_THIN);
@@ -116,6 +173,13 @@ namespace platform {
             format_set_num_format(style, "###,##0");
             format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
             format_set_border(style, LXW_BORDER_THIN);
+        } else if (name == "ints_bold") {
+            format_set_font_size(style, normalSize);
+            format_set_num_format(style, "###,##0");
+            format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
+            format_set_bold(style);
+            format_set_font_color(style, lxw_color_t(color_bold));
+            format_set_border(style, LXW_BORDER_THIN);
         } else if (name == "floats") {
             format_set_border(style, LXW_BORDER_THIN);
             format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
@@ -131,7 +195,7 @@ namespace platform {
 
     void ExcelFile::createFormats()
     {
-        auto styleNames = { "text", "textCentered", "bodyHeader", "result", "time", "ints", "floats", "percentage" };
+        auto styleNames = { "text", "text_bold", "textCentered", "bodyHeader", "result", "result_bold", "time", "ints", "ints_bold", "floats", "percentage" };
         lxw_format* style;
         for (std::string name : styleNames) {
             lxw_format* style = workbook_add_format(workbook);

src/reports/ExcelFile.h

@@ -1,18 +1,11 @@
-#pragma once
-
+#ifndef EXCELFILE_H
+#define EXCELFILE_H
 #include <locale>
 #include <string>
 #include <map>
 #include <xlsxwriter.h>
 
 namespace platform {
-    struct separated : std::numpunct<char> {
-        char do_decimal_point() const { return ','; }
-
-        char do_thousands_sep() const { return '.'; }
-
-        std::string do_grouping() const { return "\03"; }
-    };
     class ExcelFile {
     public:
         ExcelFile();
@@ -26,7 +19,12 @@ namespace platform {
         void writeInt(int row, int col, const int number, const std::string& style = "");
         void writeDouble(int row, int col, const double number, const std::string& style = "");
         void createFormats();
+        void boldFontColor(const uint32_t color); // the same color for bold styles
+        void boldRed(); //set red color for the bold styles
+        void boldBlue(); //set blue color for the bold styles
+        void boldGreen(); //set green color for the bold styles
         void createStyle(const std::string& name, lxw_format* style, bool odd);
+        lxw_worksheet* createWorksheet(const std::string& name);
         void addColor(lxw_format* style, bool odd);
         lxw_format* efectiveStyle(const std::string& name);
         lxw_workbook* workbook;
@@ -42,3 +40,4 @@ namespace platform {
         void setDefault();
     };
 }
+#endif

src/reports/ReportBase.cpp

@@ -15,6 +15,10 @@ namespace platform {
             {Symbols::cross, "Less than or equal to ZeroR"},
             {Symbols::upward_arrow, oss.str()}
         };
+        auto env = DotEnv();
+        nodes_label = env.get("nodes");
+        leaves_label = env.get("leaves");
+        depth_label = env.get("depth");
     }
     std::string ReportBase::fromVector(const std::string& key)
     {
@@ -57,12 +61,13 @@ namespace platform {
             }
         } else {
             if (data["score_name"].get<std::string>() == "accuracy") {
-                auto dt = Datasets(false, Paths::datasets());
-                dt.loadDataset(dataset);
-                auto numClasses = dt.getNClasses(dataset);
+                auto datasets = Datasets(false, Paths::datasets());
+                auto& dt = datasets.getDataset(dataset);
+                dt.load();
+                auto numClasses = dt.getNClasses();
                 if (numClasses == 2) {
-                    std::vector<int> distribution = dt.getClassesCounts(dataset);
-                    double nSamples = dt.getNSamples(dataset);
+                    std::vector<int> distribution = dt.getClassesCounts();
+                    double nSamples = dt.getNSamples();
                     std::vector<int>::iterator maxValue = max_element(distribution.begin(), distribution.end());
                     double mark = *maxValue / nSamples * (1 + margin);
                     if (mark > 1) {

src/reports/ReportBase.h

@@ -1,14 +1,13 @@
-#pragma once
-
+#ifndef REPORTBASE_H
+#define REPORTBASE_H
 #include <string>
-#include <iostream>
+#include <map>
 #include <nlohmann/json.hpp>
 #include "common/Paths.h"
 #include "common/Symbols.h"
 
-using json = nlohmann::json;
 namespace platform {
-
+    using json = nlohmann::ordered_json;
     class ReportBase {
     public:
         explicit ReportBase(json data_, bool compare);
@@ -27,9 +26,13 @@ namespace platform {
         double margin;
         std::map<std::string, std::string> meaning;
         bool compare;
+        std::string nodes_label;
+        std::string leaves_label;
+        std::string depth_label;
     private:
         double bestResult(const std::string& dataset, const std::string& model);
         json bestResults;
         bool existBestFile = true;
     };
 };
+#endif

src/reports/ReportConsole.cpp

@@ -1,9 +1,10 @@
 #include <iostream>
-#include <sstream>
 #include <locale>
 #include "best/BestScore.h"
 #include "common/CLocale.h"
+#include "common/Timer.h"
 #include "ReportConsole.h"
+#include "main/Scores.h"
 
 namespace platform {
     std::string ReportConsole::headerLine(const std::string& text, int utf = 0)
@@ -12,92 +13,173 @@ namespace platform {
         n = n < 0 ? 0 : n;
         return "* " + text + std::string(n + utf, ' ') + "*\n";
     }
-
-    void ReportConsole::header()
+    void ReportConsole::do_header()
     {
+        sheader.str("");
         std::stringstream oss;
-        std::cout << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
-        std::cout << headerLine(
+        sheader << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
+        sheader << headerLine(
             "Report " + data["model"].get<std::string>() + " ver. " + data["version"].get<std::string>()
             + " with " + std::to_string(data["folds"].get<int>()) + " Folds cross validation and " + std::to_string(data["seeds"].size())
             + " random seeds. " + data["date"].get<std::string>() + " " + data["time"].get<std::string>()
         );
-        std::cout << headerLine(data["title"].get<std::string>());
-        std::cout << headerLine(
-            "Random seeds: " + fromVector("seeds") + " Discretized: " + (data["discretized"].get<bool>() ? "True" : "False")
-            + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False")
+        sheader << headerLine(data["title"].get<std::string>());
+        std::string discretize_algo = data.find("discretization_algorithm") != data.end() ? data["discretization_algorithm"].get<std::string>() : "ORIGINAL";
+        std::string algorithm = data["discretized"].get<bool>() ? " (" + discretize_algo + ")" : "";
+        std::string smooth = data.find("smooth_strategy") != data.end() ? data["smooth_strategy"].get<std::string>() : "ORIGINAL";
+        std::string stratified;
+        try {
+            stratified = data["stratified"].get<bool>() ? "True" : "False";
+        }
+        catch (nlohmann::json::type_error) {
+            stratified = data["stratified"].get<int>() == 1 ? "True" : "False";
+        }
+        std::string discretized;
+        try {
+            discretized = data["discretized"].get<bool>() ? "True" : "False";
+        }
+        catch (nlohmann::json::type_error) {
+            discretized = data["discretized"].get<int>() == 1 ? "True" : "False";
+        }
+        sheader << headerLine(
+            "Random seeds: " + fromVector("seeds") + " Discretized: " + discretized + " " + algorithm
+            + " Stratified: " + stratified + " Smooth Strategy: " + smooth
         );
-        oss << "Execution took  " << std::setprecision(2) << std::fixed << data["duration"].get<float>()
-            << " seconds,   " << data["duration"].get<float>() / 3600 << " hours, on " << data["platform"].get<std::string>();
-        std::cout << headerLine(oss.str());
-        std::cout << headerLine("Score is " + data["score_name"].get<std::string>());
-        std::cout << std::string(MAXL, '*') << std::endl;
-        std::cout << std::endl;
+        Timer timer;
+        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>());
+        sheader << std::string(MAXL, '*') << std::endl;
+        sheader << std::endl;
+    }
+    void ReportConsole::header()
+    {
+        do_header();
     }
     void ReportConsole::body()
     {
+        do_body();
+        std::cout << sbody.str();
+    }
+    std::string ReportConsole::fileReport()
+    {
+        do_header();
+        do_body();
+        std::stringstream oss;
+        oss << sheader.str() << sbody.str();
+        return oss.str();
+    }
+    void ReportConsole::do_body()
+    {
+        sbody.str("");
+        vbody.clear();
         auto tmp = ConfigLocale();
         int maxHyper = 15;
         int maxDataset = 7;
         for (const auto& r : data["results"]) {
             maxHyper = std::max(maxHyper, (int)r["hyperparameters"].dump().size());
             maxDataset = std::max(maxDataset, (int)r["dataset"].get<std::string>().size());
-
         }
-        std::cout << Colors::GREEN() << " #  " << std::setw(maxDataset) << std::left << "Dataset" << " Sampl. Feat. Cls Nodes     Edges     States    Score           Time                Hyperparameters" << std::endl;
-        std::cout << "=== " << std::string(maxDataset, '=') << " ====== ===== === ========= ========= ========= =============== =================== " << std::string(maxHyper, '=') << std::endl;
+        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 };
+        for (int i = 0; i < header_labels.size(); i++) {
+            sheader << std::setw(header_lengths[i]) << std::left << header_labels[i] << " ";
+        }
+        sheader << std::endl;
+        for (int i = 0; i < header_labels.size(); i++) {
+            sheader << std::string(header_lengths[i], '=') << " ";
+        }
+        sheader << std::endl;
+        std::cout << sheader.str();
         json lastResult;
         double totalScore = 0.0;
-        bool odd = true;
         int index = 0;
         for (const auto& r : data["results"]) {
             if (selectedIndex != -1 && index != selectedIndex) {
                 index++;
                 continue;
             }
-            auto color = odd ? Colors::CYAN() : Colors::BLUE();
-            std::cout << color;
-            std::string separator{ " " };
-            if (r.find("notes") != r.end()) {
-                separator = r["notes"].size() > 0 ? Colors::YELLOW() + Symbols::notebook + color : " ";
-            }
-            std::cout << std::setw(3) << std::right << index++ << separator;
-            std::cout << std::setw(maxDataset) << std::left << r["dataset"].get<std::string>() << " ";
-            std::cout << std::setw(6) << std::right << r["samples"].get<int>() << " ";
-            std::cout << std::setw(5) << std::right << r["features"].get<int>() << " ";
-            std::cout << std::setw(3) << std::right << r["classes"].get<int>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["nodes"].get<float>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["leaves"].get<float>() << " ";
-            std::cout << std::setw(9) << std::setprecision(2) << std::fixed << r["depth"].get<float>() << " ";
-            std::cout << 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>();
+            auto color = (index % 2) ? Colors::CYAN() : Colors::BLUE();
+            std::stringstream line;
+            line << color;
+            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(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(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>());
-            std::cout << status;
-            std::cout << std::setw(12) << std::right << std::setprecision(6) << std::fixed << r["time"].get<double>() << "±" << std::setw(6) << std::setprecision(4) << std::fixed << r["time_std"].get<double>() << " ";
-            std::cout << r["hyperparameters"].dump();
-            std::cout << std::endl;
-            std::cout << std::flush;
+            line << status;
+            line << std::setw(12) << std::right << std::setprecision(6) << std::fixed << r["time"].get<double>() << "±" << std::setw(7) << std::setprecision(4) << std::fixed << r["time_std"].get<double>() << " ";
+            line << r["hyperparameters"].dump();
+            line << std::endl;
+            vbody.push_back(line.str());
+            sbody << line.str();
             lastResult = r;
             totalScore += r["score"].get<double>();
-            odd = !odd;
         }
         if (data["results"].size() == 1 || selectedIndex != -1) {
-            std::cout << std::string(MAXL, '*') << std::endl;
+            std::stringstream line;
+            line << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
+            vbody.push_back(line.str());
+            sbody << line.str();
             if (lastResult.find("notes") != lastResult.end()) {
                 if (lastResult["notes"].size() > 0) {
-                    std::cout << headerLine("Notes: ");
+                    sbody << headerLine("Notes: ");
+                    vbody.push_back(headerLine("Notes: "));
                     for (const auto& note : lastResult["notes"]) {
-                        std::cout << headerLine(note.get<std::string>());
+                        line.str("");
+                        line << headerLine(note.get<std::string>());
+                        vbody.push_back(line.str());
+                        sbody << line.str();
                     }
                 }
             }
-            std::cout << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
-            std::cout << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));
-            std::cout << headerLine(fVector("Train  times: ", lastResult["times_train"], 10, 3));
-            std::cout << headerLine(fVector("Test   times: ", lastResult["times_test"], 10, 3));
+            line.str("");
+            if (lastResult.find("score_train") == lastResult.end()) {
+                line << headerLine("Train  score: -");
+            } else {
+                line << headerLine("Train  score: " + std::to_string(lastResult["score_train"].get<double>()));
+            }
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str(""); line << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str(""); line << headerLine("Test   score: " + std::to_string(lastResult["score"].get<double>()));
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str(""); line << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str("");
+            if (lastResult.find("train_time") == lastResult.end()) {
+                line << headerLine("Train  time: -");
+            } else {
+                line << headerLine("Train  time: " + std::to_string(lastResult["train_time"].get<double>()));
+            }
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str(""); line << headerLine(fVector("Train  times: ", lastResult["times_train"], 10, 3));
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str("");
+            if (lastResult.find("test_time") == lastResult.end()) {
+                line << headerLine("Test  time: -");
+            } else {
+                line << headerLine("Test  time: " + std::to_string(lastResult["test_time"].get<double>()));
+            }
+            vbody.push_back(line.str()); sbody << line.str();
+            line.str(""); line << headerLine(fVector("Test   times: ", lastResult["times_test"], 10, 3));
+            vbody.push_back(line.str()); sbody << line.str();
+
         } else {
             footer(totalScore);
         }
-        std::cout << std::string(MAXL, '*') << Colors::RESET() << std::endl;
+        sbody << std::string(MAXL, '*') << Colors::RESET() << std::endl;
+        vbody.push_back(std::string(MAXL, '*') + Colors::RESET() + "\n");
+        if (data["results"].size() == 1 || selectedIndex != -1) {
+            vbody.push_back(buildClassificationReport(lastResult, Colors::BLUE()));
+        }
     }
     void ReportConsole::showSummary()
     {
@@ -106,23 +188,88 @@ namespace platform {
             oss << std::setw(3) << std::left << item.first;
             oss << std::setw(3) << std::right << item.second << " ";
             oss << std::left << meaning.at(item.first);
-            std::cout << headerLine(oss.str(), 2);
+            sbody << headerLine(oss.str(), 2);
+            vbody.push_back(headerLine(oss.str(), 2));
         }
     }
 
     void ReportConsole::footer(double totalScore)
     {
-        std::cout << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
+        std::stringstream linea;
+        linea << Colors::MAGENTA() << std::string(MAXL, '*') << std::endl;
+        vbody.push_back(linea.str()); sbody << linea.str();
         showSummary();
         auto score = data["score_name"].get<std::string>();
         auto best = BestScore::getScore(score);
         if (best.first != "") {
             std::stringstream oss;
             oss << score << " compared to " << best.first << " .:  " << totalScore / best.second;
-            std::cout << headerLine(oss.str());
+            sbody << headerLine(oss.str());
+            vbody.push_back(headerLine(oss.str()));
         }
         if (!getExistBestFile() && compare) {
             std::cout << headerLine("*** Best Results File not found. Couldn't compare any result!");
         }
     }
+    Scores ReportConsole::aggregateScore(json& result, std::string key)
+    {
+        auto scores = Scores(result[key][0]);
+        for (int i = 1; i < result[key].size(); i++) {
+            auto score = Scores(result[key][i]);
+            scores.aggregate(score);
+        }
+        return scores;
+    }
+    std::string ReportConsole::buildClassificationReport(json& result, std::string color)
+    {
+        std::stringstream oss;
+        if (result.find("confusion_matrices") == result.end())
+            return "";
+        bool second_header = false;
+        int lines_header = 0;
+        std::string color_line;
+        std::string suffix = "";
+        auto scores = Scores::create_aggregate(result, "confusion_matrices");
+        auto output_test = scores.classification_report(color, "Test");
+        int maxLine = (*std::max_element(output_test.begin(), output_test.end(), [](const std::string& a, const std::string& b) { return a.size() < b.size(); })).size();
+        bool train_data = result.find("confusion_matrices_train") != result.end();
+        std::vector<std::string> output_train;
+        if (train_data) {
+            auto scores_train = Scores::create_aggregate(result, "confusion_matrices_train");
+            output_train = scores_train.classification_report(color, "Train");
+        }
+        oss << Colors::BLUE();
+        for (int i = 0; i < output_test.size(); i++) {
+            if (i < 2 || second_header) {
+                color_line = Colors::GREEN();
+            } else {
+                color_line = Colors::BLUE();
+                if (lines_header > 1)
+                    suffix = std::string(14, ' '); // compensate for the color
+            }
+            if (train_data) {
+                oss << color_line << std::left << std::setw(maxLine) << output_train[i]
+                    << suffix << Colors::BLUE() << " | " << color_line << std::left << std::setw(maxLine)
+                    << output_test[i] << std::endl;
+            } else {
+                oss << color_line << output_test[i] << std::endl;
+            }
+            if (output_test[i] == "" || (second_header && lines_header < 2)) {
+                lines_header++;
+                second_header = true;
+            } else {
+                second_header = false;
+            }
+        }
+        oss << Colors::RESET();
+        return oss.str();
+    }
+    std::string ReportConsole::showClassificationReport(std::string color)
+    {
+        std::stringstream oss;
+        for (auto& result : data["results"]) {
+            oss << buildClassificationReport(result, color);
+        }
+        return oss.str();
+    }
 }

src/reports/ReportConsole.h

@@ -1,8 +1,10 @@
-#pragma once
-
+#ifndef REPORT_CONSOLE_H
+#define REPORT_CONSOLE_H
 #include <string>
 #include "common/Colors.h"
+#include <sstream>
 #include "ReportBase.h"
+#include "main/Scores.h"
 
 namespace platform {
     const int MAXL = 133;
@@ -10,12 +12,24 @@ namespace platform {
     public:
         explicit ReportConsole(json data_, bool compare = false, int index = -1) : ReportBase(data_, compare), selectedIndex(index) {};
         virtual ~ReportConsole() = default;
+        std::string fileReport();
+        std::string getHeader() { do_header(); do_body(); return sheader.str(); }
+        std::vector<std::string>& getBody() { return vbody; }
+        std::string showClassificationReport(std::string color);
     private:
         int selectedIndex;
         std::string headerLine(const std::string& text, int utf);
+        std::string buildClassificationReport(json& result, std::string color);
         void header() override;
+        void do_header();
         void body() override;
+        void do_body();
         void footer(double totalScore);
         void showSummary() override;
+        Scores aggregateScore(json& result, std::string key);
+        std::stringstream sheader;
+        std::stringstream sbody;
+        std::vector<std::string> vbody;
     };
 };
+#endif

src/reports/ReportExcel.cpp

@@ -2,10 +2,7 @@
 #include <locale>
 #include "best/BestScore.h"
 #include "ReportExcel.h"
-
-
 namespace platform {
-
     ReportExcel::ReportExcel(json data_, bool compare, lxw_workbook* workbook, lxw_worksheet* worksheet) : ReportBase(data_, compare), ExcelFile(workbook, worksheet)
     {
         createFile();
@@ -20,26 +17,7 @@ namespace platform {
             worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
         }
     }
-    void ReportExcel::createWorksheet()
-    {
-        const std::string name = data["model"].get<std::string>();
-        std::string suffix = "";
-        std::string efectiveName;
-        int num = 1;
-        // Create a sheet with the name of the model
-        while (true) {
-            efectiveName = name + suffix;
-            if (workbook_get_worksheet_by_name(workbook, efectiveName.c_str())) {
-                suffix = std::to_string(++num);
-            } else {
-                worksheet = workbook_add_worksheet(workbook, efectiveName.c_str());
-                break;
-            }
-            if (num > 100) {
-                throw std::invalid_argument("Couldn't create sheet " + efectiveName);
-            }
-        }
-    }
+
 
     void ReportExcel::createFile()
     {
@@ -47,7 +25,8 @@ namespace platform {
             workbook = workbook_new((Paths::excel() + Paths::excelResults()).c_str());
         }
         if (worksheet == NULL) {
-            createWorksheet();
+            const std::string name = data["model"].get<std::string>();
+            worksheet = createWorksheet(name);
         }
         setProperties(data["title"].get<std::string>());
         formatColumns();
@@ -60,9 +39,8 @@ namespace platform {
 
     void ReportExcel::header()
     {
-        std::locale mylocale(std::cout.getloc(), new separated);
-        std::locale::global(mylocale);
-        std::cout.imbue(mylocale);
+        auto loc = std::locale("es_ES");
+        std::cout.imbue(loc);
         std::stringstream oss;
         std::string message = data["model"].get<std::string>() + " ver. " + data["version"].get<std::string>() + " " +
             data["language"].get<std::string>() + " ver. " + data["language_version"].get<std::string>() +
@@ -71,7 +49,10 @@ namespace platform {
         worksheet_merge_range(worksheet, 0, 0, 0, 12, message.c_str(), styles["headerFirst"]);
         worksheet_merge_range(worksheet, 1, 0, 1, 12, data["title"].get<std::string>().c_str(), styles["headerRest"]);
         worksheet_merge_range(worksheet, 2, 0, 3, 0, ("Score is " + data["score_name"].get<std::string>()).c_str(), styles["headerRest"]);
-        worksheet_merge_range(worksheet, 2, 1, 3, 3, "Execution time", styles["headerRest"]);
+        writeString(2, 1, "Smooth", "headerRest");
+        std::string smooth = data.find("smooth_strategy") != data.end() ? data["smooth_strategy"].get<std::string>() : "ORIGINAL";
+        writeString(3, 1, smooth, "headerSmall");
+        worksheet_merge_range(worksheet, 2, 2, 3, 3, "Execution time", styles["headerRest"]);
         oss << std::setprecision(2) << std::fixed << data["duration"].get<float>() << " s";
         worksheet_merge_range(worksheet, 2, 4, 2, 5, oss.str().c_str(), styles["headerRest"]);
         oss.str("");
@@ -87,7 +68,9 @@ namespace platform {
         worksheet_merge_range(worksheet, 3, 10, 3, 11, oss.str().c_str(), styles["headerSmall"]);
         oss.str("");
         oss.clear();
-        oss << "Discretized: " << (data["discretized"].get<bool>() ? "True" : "False");
+        std::string discretize_algo = data.find("discretization_algorithm") != data.end() ? data["discretization_algorithm"].get<std::string>() : "mdlp";
+        std::string algorithm = data["discretized"].get<bool>() ? " (" + discretize_algo + ")" : "";
+        oss << "Discretized: " << (data["discretized"].get<bool>() ? "True" : "False") << algorithm;
         worksheet_write_string(worksheet, 3, 12, oss.str().c_str(), styles["headerSmall"]);
     }
     void ReportExcel::header_notes(int row)
@@ -197,6 +180,10 @@ namespace platform {
                     writeDouble(row, ++col, item, style);
                 }
             }
+            // Classificacion report
+            if (lastResult.find("confusion_matrices") != lastResult.end()) {
+                create_classification_report(lastResult);
+            }
             // Set with of columns to show those totals completely
             worksheet_set_column(worksheet, 1, 1, 12, NULL);
             for (int i = 2; i < 7; ++i) {
@@ -207,7 +194,129 @@ namespace platform {
             footer(totalScore, row);
         }
     }
+    void ReportExcel::create_classification_report(const json& result)
+    {
 
+        auto matrix_sheet = createWorksheet("clf_report");
+        lxw_worksheet* tmp = worksheet;
+        worksheet = matrix_sheet;
+        if (matrix_sheet == NULL) {
+            throw std::invalid_argument("Couldn't create sheet classif_report");
+        }
+        row = 1;
+        int col = 0;
+        if (result.find("confusion_matrices_train") != result.end()) {
+            // Train classification report
+            auto score = Scores::create_aggregate(result, "confusion_matrices_train");
+            auto train = score.classification_report_json("Train");
+            std::tie(row, col) = write_classification_report(train, row, 0);
+            int new_row = 0;
+            int new_col = col + 1;
+            for (int i = 0; i < result["confusion_matrices_train"].size(); ++i) {
+                auto item = result["confusion_matrices_train"][i];
+                auto score_item = Scores(item);
+                auto title = "Train Fold " + std::to_string(i);
+                std::tie(new_row, new_col) = write_classification_report(score_item.classification_report_json(title), 1, new_col);
+                new_col++;
+            }
+            col = new_col;
+            worksheet_merge_range(matrix_sheet, 0, 0, 0, col - 1, "Train Classification Report", efectiveStyle("headerRest"));
+        }
+        // Test classification report
+        worksheet_merge_range(matrix_sheet, row, 0, row, col - 1, "Test Classification Report", efectiveStyle("headerRest"));
+        auto score = Scores::create_aggregate(result, "confusion_matrices");
+        auto test = score.classification_report_json("Test");
+        int init_row = ++row;
+        std::tie(row, col) = write_classification_report(test, init_row, 0);
+        int new_row = 0;
+        int new_col = col + 1;
+        for (int i = 0; i < result["confusion_matrices"].size(); ++i) {
+            auto item = result["confusion_matrices"][i];
+            auto score_item = Scores(item);
+            auto title = "Test Fold " + std::to_string(i);
+            std::tie(new_row, new_col) = write_classification_report(score_item.classification_report_json(title), init_row, new_col);
+            new_col++;
+        }
+        // Format columns (change size to fit the content)
+        for (int i = 0; i < new_col; ++i) {
+            // doesn't work with from col to col, so...
+            worksheet_set_column(worksheet, i, i, 12, NULL);
+        }
+        worksheet = tmp;
+    }
+    std::pair<int, int> ReportExcel::write_classification_report(const json& result, int init_row, int init_col)
+    {
+        row = init_row;
+        auto text = result["title"].get<std::string>();
+        worksheet_merge_range(worksheet, row, init_col, row + 1, init_col + 5, text.c_str(), efectiveStyle("bodyHeader"));
+        row += 2;
+        int col = init_col + 2;
+        // Headers
+        bool first_item = true;
+        for (const auto& item : result["headers"]) {
+            auto text = item.get<std::string>();
+            if (first_item) {
+                first_item = false;
+                worksheet_merge_range(worksheet, row, init_col, row, init_col + 1, text.c_str(), efectiveStyle("bodyHeader"));
+            } else {
+                writeString(row, col++, text, "bodyHeader");
+            }
+        }
+        row++;
+        // Classes f1-score
+        for (const auto& item : result["body"]) {
+            col = init_col + 2;
+            for (const auto& value : item) {
+                if (value.is_string()) {
+                    worksheet_merge_range(worksheet, row, init_col, row, init_col + 1, value.get<std::string>().c_str(), efectiveStyle("text"));
+                } else {
+                    if (value.is_number_integer()) {
+                        writeInt(row, col++, value.get<int>(), "ints");
+                    } else {
+                        writeDouble(row, col++, value.get<double>(), "result");
+                    }
+                }
+            }
+            row++;
+        }
+        // Accuracy and average f1-score
+        for (const auto& item : { "accuracy", "averages", "weighted" }) {
+            col = init_col + 2;
+            for (const auto& value : result[item]) {
+                if (value.is_string()) {
+                    worksheet_merge_range(worksheet, row, init_col, row, init_col + 1, value.get<std::string>().c_str(), efectiveStyle("text"));
+                } else {
+                    if (value.is_number_integer()) {
+                        writeInt(row, col++, value.get<int>(), "ints");
+                    } else {
+                        writeDouble(row, col++, value.get<double>(), "result");
+                    }
+                }
+            }
+            row++;
+        }
+        // Confusion matrix
+        auto n_items = result["confusion_matrix"].size();
+        worksheet_merge_range(worksheet, row, init_col, row, init_col + n_items + 1, "Confusion Matrix", efectiveStyle("bodyHeader"));
+        row++;
+        boldGreen();
+        for (int i = 0; i < n_items; ++i) {
+            col = init_col + 2;
+            auto label = result["body"][i][0].get<std::string>();
+            worksheet_merge_range(worksheet, row, init_col, row, init_col + 1, label.c_str(), efectiveStyle("text"));
+            for (int j = 0; j < result["confusion_matrix"][i].size(); ++j) {
+                auto value = result["confusion_matrix"][i][j];
+                if (i == j) {
+                    writeInt(row, col++, value.get<int>(), "ints_bold");
+                } else {
+                    writeInt(row, col++, value.get<int>(), "ints");
+                }
+            }
+            row++;
+        }
+        int maxcol = std::max(init_col + 5, int(init_col + n_items + 1));
+        return { row, maxcol };
+    }
     void ReportExcel::showSummary()
     {
         for (const auto& item : summary) {
@@ -217,7 +326,6 @@ namespace platform {
             row += 1;
         }
     }
-
     void ReportExcel::footer(double totalScore, int row)
     {
         showSummary();

src/reports/ReportExcel.h

@@ -1,11 +1,13 @@
-#pragma once
-
-#include <map>
-#include <xlsxwriter.h>
+#ifndef REPORT_EXCEL_H
+#define REPORT_EXCEL_H
+#include <algorithm>
+#include "main/Scores.h"
 #include "common/Colors.h"
 #include "ReportBase.h"
 #include "ExcelFile.h"
 namespace platform {
+    using json = nlohmann::ordered_json;
+
     class ReportExcel : public ReportBase, public ExcelFile {
     public:
         explicit ReportExcel(json data_, bool compare, lxw_workbook* workbook, lxw_worksheet* worksheet = NULL);
@@ -13,12 +15,14 @@ namespace platform {
     private:
         void formatColumns();
         void createFile();
-        void createWorksheet();
         void header() override;
         void body() override;
         void showSummary() override;
         void footer(double totalScore, int row);
         void append_notes(const json& r, int row);
+        void create_classification_report(const json& result);
+        std::pair<int, int> write_classification_report(const json& result, int init_row, int init_col);
         void header_notes(int row);
     };
 };
+#endif

src/reports/ReportExcelCompared.cpp

@@ -4,6 +4,10 @@ namespace platform {
 
     ReportExcelCompared::ReportExcelCompared(json& data_A, json& data_B) : data_A(data_A), data_B(data_B), ExcelFile(NULL, NULL)
     {
+        auto env = DotEnv();
+        nodes_label = env.get("nodes");
+        leaves_label = env.get("leaves");
+        depth_label = env.get("depth");
     }
     ReportExcelCompared::~ReportExcelCompared()
     {
@@ -35,7 +39,7 @@ namespace platform {
     }
     double diff(double a, double b)
     {
-        return (a - b) / b;
+        return b != 0 ? (a - b) / b : 0.0;
     }
     float compute_model_number(json& rA)
     {
@@ -52,7 +56,7 @@ namespace platform {
                 average = true;
             }
         }
-        if (average)
+        if (average && num > 0)
             result = models / num;
         return result;
     }
@@ -61,7 +65,7 @@ namespace platform {
         // Body Header
         auto sizes = std::vector<int>({ 22, 10, 9, 7, 12, 12, 9, 12, 12, 9, 12, 12, 9, 12, 12, 9, 12, 12, 9, 15, 15, 9, 15, 15 });
         auto head_a = std::vector<std::string>({ "Dataset", "Samples", "Features", "Classes" });
-        auto head_b = std::vector<std::string>({ "Models", "Nodes", "Edges", "States", "Score", "Time" });
+        auto head_b = std::vector<std::string>({ "Models", nodes_label, leaves_label, depth_label, "Score", "Time" });
         int headerRow = 3;
         int col = 0;
         for (const auto& item : head_a) {
@@ -104,6 +108,10 @@ namespace platform {
                 totals_A[j] += r_A[key].get<double>();
                 totals_B[j] += r_B[key].get<double>();
             }
+            std::cout << "After comparing data " << std::endl;
+            if (r_A["dataset"].get<std::string>() != r_B["dataset"].get<std::string>()) {
+                throw std::runtime_error("Datasets are not the same [" + r_A["dataset"].get<std::string>() + "] vs [" + r_B["dataset"].get<std::string>() + "]");
+            }
             writeString(row, col++, r_A["dataset"].get<std::string>(), "text");
             writeInt(row, col++, r_A["samples"].get<int>(), "ints");
             writeInt(row, col++, r_A["features"].get<int>(), "ints");

src/reports/ReportExcelCompared.h

@@ -1,4 +1,5 @@
-#pragma once
+#ifndef REPORT_EXCEL_COMPARED_H
+#define REPORT_EXCEL_COMPARED_H
 #include "ReportExcel.h"
 namespace platform {
     class ReportExcelCompared : public ExcelFile {
@@ -12,5 +13,9 @@ namespace platform {
         void footer(std::vector<double>& totals_A, std::vector<double>& totals_B, int row);
         json& data_A;
         json& data_B;
+        std::string nodes_label;
+        std::string leaves_label;
+        std::string depth_label;
     };
-};
+};
+#endif

src/reports/ReportsPaged.cpp

@@ -0,0 +1,23 @@
+#include "common/Colors.h"
+#include "ReportsPaged.h"
+
+namespace platform {
+    ReportsPaged::ReportsPaged()
+    {
+        loc = std::locale("es_ES.UTF-8");
+        oss.imbue(loc);
+    }
+    std::string ReportsPaged::getOutput() const
+    {
+        std::string s;
+        for (const auto& piece : header) s += piece;
+        for (const auto& piece : body) s += piece;
+        return s;
+    }
+    std::string ReportsPaged::getHeader() const
+    {
+        std::string s;
+        for (const auto& piece : header) s += piece;
+        return s;
+    }
+}

src/reports/ReportsPaged.h

@@ -0,0 +1,26 @@
+#ifndef REPORTS_PAGED_H
+#define REPORTS_PAGED_H
+#include <locale>
+#include <sstream>
+#include <nlohmann/json.hpp>
+
+namespace platform {
+    using json = nlohmann::ordered_json;
+
+    class ReportsPaged {
+    public:
+        ReportsPaged();
+        ~ReportsPaged() = default;
+        std::string getOutput() const;
+        std::string getHeader() const;
+        std::vector<std::string>& getBody() { return body; }
+        int getNumLines() const { return body.size(); }
+        json& getData() { return data; }
+    protected:
+        std::vector<std::string> header, body;
+        json data;
+        std::stringstream oss;
+        std::locale loc;
+    };
+}
+#endif

src/results/Result.cpp

@@ -6,6 +6,7 @@
 #include "common/DotEnv.h"
 #include "common/CLocale.h"
 #include "common/Paths.h"
+#include "common/Symbols.h"
 #include "Result.h"
 
 namespace platform {
@@ -64,35 +65,63 @@ namespace platform {
 
     void Result::save()
     {
-        std::ofstream file(Paths::results() + "/" + getFilename());
+        std::ofstream file(Paths::results() + getFilename());
         file << data;
         file.close();
     }
     std::string Result::getFilename() const
     {
         std::ostringstream oss;
-        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>() << "_" << (data["stratified"] ? "1" : "0") << ".json";
+        std::string stratified;
+        try {
+            stratified = data["stratified"].get<bool>() ? "1" : "0";
+        }
+        catch (nlohmann::json_abi_v3_11_3::detail::type_error) {
+            stratified = data["stratified"].get<int>() == 1 ? "1" : "0";
+        }
+        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";
         return oss.str();
     }
-
-
-    std::string Result::to_string(int maxModel) const
+    std::string Result::to_string(int maxModel, int maxTitle) const
     {
         auto tmp = ConfigLocale();
         std::stringstream oss;
+        std::string s, d;
+        try {
+            s = data["stratified"].get<bool>() ? "S" : " ";
+        }
+        catch (nlohmann::json_abi_v3_11_3::detail::type_error) {
+            s = data["stratified"].get<int>() == 1 ? "S" : " ";
+        }
+        try {
+            d = data["discretized"].get<bool>() ? "D" : " ";
+        }
+        catch (nlohmann::json_abi_v3_11_3::detail::type_error) {
+            d = data["discretized"].get<int>() == 1 ? "D" : " ";
+        }
         auto duration = data["duration"].get<double>();
         double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
         std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
         oss << data["date"].get<std::string>() << " ";
         oss << std::setw(maxModel) << std::left << data["model"].get<std::string>() << " ";
         oss << std::setw(11) << std::left << data["score_name"].get<std::string>() << " ";
-        oss << std::right << std::setw(11) << std::setprecision(7) << std::fixed << score << " ";
+        oss << std::right << std::setw(10) << std::setprecision(7) << std::fixed << score << " ";
+        oss << std::left << std::setw(12) << data["platform"].get<std::string>() << " ";
+        oss << s << d << " ";
         auto completeString = isComplete() ? "C" : "P";
         oss << std::setw(1) << " " << completeString << "  ";
-        oss << std::setw(7) << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit << " ";
-        oss << std::setw(50) << std::left << data["title"].get<std::string>() << " ";
+        oss << std::setw(5) << std::right << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit << " ";
+        auto title = data["title"].get<std::string>();
+        if (title.size() > maxTitle) {
+            title = title.substr(0, maxTitle - 1) + Symbols::ellipsis;
+        }
+        oss << std::setw(maxTitle) << std::left << title;
         return  oss.str();
     }
 }

src/results/Result.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef RESULT_H
+#define RESULT_H
 #include <map>
 #include <vector>
 #include <string>
@@ -9,7 +9,7 @@
 #include "main/PartialResult.h"
 
 namespace platform {
-    using json = nlohmann::json;
+    using json = nlohmann::ordered_json;
 
     class Result {
     public:
@@ -18,17 +18,23 @@ namespace platform {
         void save();
         // Getters
         json getJson();
-        std::string to_string(int maxModel) const;
+        std::string to_string(int maxModel, int maxTitle) const;
         std::string getFilename() const;
         std::string getDate() const { return data["date"].get<std::string>(); };
+        std::string getTime() const { return data["time"].get<std::string>(); };
         double getScore() const { return score; };
         std::string getTitle() const { return data["title"].get<std::string>(); };
         double getDuration() const { return data["duration"]; };
         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>(); };
+
         bool isComplete() const { return complete; };
+        json getData() const { return data; }
         // Setters
         void setTitle(const std::string& title) { data["title"] = title; };
+        void setSmoothStrategy(const std::string& smooth_strategy) { data["smooth_strategy"] = smooth_strategy; };
+        void setDiscretizationAlgorithm(const std::string& discretization_algo) { data["discretization_algorithm"] = discretization_algo; };
         void setLanguage(const std::string& language) { data["language"] = language; };
         void setLanguageVersion(const std::string& language_version) { data["language_version"] = language_version; };
         void setDuration(double duration) { data["duration"] = duration; };
@@ -41,10 +47,10 @@ namespace platform {
         void setStratified(bool stratified) { data["stratified"] = stratified; };
         void setNFolds(int nfolds) { data["folds"] = nfolds; };
         void setPlatform(const std::string& platform_name) { data["platform"] = platform_name; };
-
     private:
         json data;
         bool complete;
         double score = 0.0;
     };
 };
+#endif