Add boost info to README

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

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

.clang-tidy

@@ -13,5 +13,4 @@ HeaderFilterRegex: 'src/*'
 AnalyzeTemporaryDtors: false
 WarningsAsErrors: ''
 FormatStyle: file
-FormatStyleOptions: ''
 ...

.clang-uml

@@ -0,0 +1,31 @@
+compilation_database_dir: build
+output_directory: puml
+diagrams:
+  BayesNet:
+    type: class
+    glob:
+      - src/BayesNet/*.cc
+      - src/Platform/*.cc
+    using_namespace: bayesnet
+    include:
+      namespaces:
+        - bayesnet
+        - platform
+    plantuml:
+      after:
+        - "note left of {{ alias(\"MyProjectMain\") }}: Main class of myproject library."
+  sequence:
+    type: sequence
+    glob:
+      - src/Platform/main.cc
+    combine_free_functions_into_file_participants: true
+    using_namespace:
+      - std
+      - bayesnet
+      - platform
+    include:
+      paths:
+        - src/BayesNet
+        - src/Platform
+    start_from:
+      - function: main(int,const char **)

.gitignore

@@ -35,3 +35,5 @@ build/
 *.dSYM/**
 cmake-build*/**
 .idea
+puml/**
+.vscode/settings.json

.gitmodules

@@ -10,3 +10,6 @@
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
+[submodule "lib/libxlsxwriter"]
+	path = lib/libxlsxwriter
+	url = https://github.com/jmcnamara/libxlsxwriter.git

.vscode/launch.json

@@ -10,7 +10,7 @@
                 "-d",
                 "iris",
                 "-m",
-                "KDB",
+                "TANLd",
                 "-s",
                 "271",
                 "-p",
@@ -25,15 +25,51 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "AODELd",
+                "BoostAODE",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
                 "-d",
-                "iris"
+                "mfeat-morphological",
+                "--discretize"
+                // "--hyperparameters",
+                // "{\"repeatSparent\": true, \"maxModels\": 12}"
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "best",
+            "program": "${workspaceFolder}/build/src/Platform/best",
+            "args": [
+                "-m",
+                "BoostAODE",
+                "-s",
+                "accuracy",
+                "--build",
+            ],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "manage",
+            "program": "${workspaceFolder}/build/src/Platform/manage",
+            "args": [
+                "-n",
+                "20"
+            ],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
+        {
+            "type": "lldb",
+            "request": "launch",
+            "name": "list",
+            "program": "${workspaceFolder}/build/src/Platform/list",
+            "args": [],
+            "cwd": "/Users/rmontanana/Code/discretizbench",
+        },
         {
             "name": "Build & debug active file",
             "type": "cppdbg",

.vscode/settings.json

@@ -1,109 +0,0 @@
-{
-    "files.associations": {
-        "*.rmd": "markdown",
-        "*.py": "python",
-        "vector": "cpp",
-        "__bit_reference": "cpp",
-        "__bits": "cpp",
-        "__config": "cpp",
-        "__debug": "cpp",
-        "__errc": "cpp",
-        "__hash_table": "cpp",
-        "__locale": "cpp",
-        "__mutex_base": "cpp",
-        "__node_handle": "cpp",
-        "__nullptr": "cpp",
-        "__split_buffer": "cpp",
-        "__string": "cpp",
-        "__threading_support": "cpp",
-        "__tuple": "cpp",
-        "array": "cpp",
-        "atomic": "cpp",
-        "bitset": "cpp",
-        "cctype": "cpp",
-        "chrono": "cpp",
-        "clocale": "cpp",
-        "cmath": "cpp",
-        "compare": "cpp",
-        "complex": "cpp",
-        "concepts": "cpp",
-        "cstdarg": "cpp",
-        "cstddef": "cpp",
-        "cstdint": "cpp",
-        "cstdio": "cpp",
-        "cstdlib": "cpp",
-        "cstring": "cpp",
-        "ctime": "cpp",
-        "cwchar": "cpp",
-        "cwctype": "cpp",
-        "exception": "cpp",
-        "initializer_list": "cpp",
-        "ios": "cpp",
-        "iosfwd": "cpp",
-        "istream": "cpp",
-        "limits": "cpp",
-        "locale": "cpp",
-        "memory": "cpp",
-        "mutex": "cpp",
-        "new": "cpp",
-        "optional": "cpp",
-        "ostream": "cpp",
-        "ratio": "cpp",
-        "sstream": "cpp",
-        "stdexcept": "cpp",
-        "streambuf": "cpp",
-        "string": "cpp",
-        "string_view": "cpp",
-        "system_error": "cpp",
-        "tuple": "cpp",
-        "type_traits": "cpp",
-        "typeinfo": "cpp",
-        "unordered_map": "cpp",
-        "variant": "cpp",
-        "algorithm": "cpp",
-        "iostream": "cpp",
-        "iomanip": "cpp",
-        "numeric": "cpp",
-        "set": "cpp",
-        "__tree": "cpp",
-        "deque": "cpp",
-        "list": "cpp",
-        "map": "cpp",
-        "unordered_set": "cpp",
-        "any": "cpp",
-        "condition_variable": "cpp",
-        "forward_list": "cpp",
-        "fstream": "cpp",
-        "stack": "cpp",
-        "thread": "cpp",
-        "__memory": "cpp",
-        "filesystem": "cpp",
-        "*.toml": "toml",
-        "utility": "cpp",
-        "__verbose_abort": "cpp",
-        "bit": "cpp",
-        "random": "cpp",
-        "*.tcc": "cpp",
-        "functional": "cpp",
-        "iterator": "cpp",
-        "memory_resource": "cpp",
-        "format": "cpp",
-        "valarray": "cpp",
-        "regex": "cpp",
-        "span": "cpp",
-        "cfenv": "cpp",
-        "cinttypes": "cpp",
-        "csetjmp": "cpp",
-        "future": "cpp",
-        "queue": "cpp",
-        "typeindex": "cpp",
-        "shared_mutex": "cpp",
-        "*.ipp": "cpp",
-        "cassert": "cpp",
-        "charconv": "cpp",
-        "source_location": "cpp",
-        "ranges": "cpp"
-    },
-    "cmake.configureOnOpen": false,
-    "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools"
-}

.vscode/tasks.json

@@ -32,6 +32,29 @@
             ],
             "group": "build",
             "detail": "Task generated by Debugger."
+        },
+        {
+            "type": "cppbuild",
+            "label": "C/C++: g++ build active file",
+            "command": "/usr/bin/g++",
+            "args": [
+                "-fdiagnostics-color=always",
+                "-g",
+                "${file}",
+                "-o",
+                "${fileDirname}/${fileBasenameNoExtension}"
+            ],
+            "options": {
+                "cwd": "${fileDirname}"
+            },
+            "problemMatcher": [
+                "$gcc"
+            ],
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "detail": "Task generated by Debugger."
         }
     ]
 }

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 0.1.0
+  VERSION 0.2.0
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -31,6 +31,17 @@ option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
 option(ENABLE_TESTING "Unit testing build"                        OFF)
 option(CODE_COVERAGE "Collect coverage from test library"         OFF)
 
+# Boost Library
+set(Boost_USE_STATIC_LIBS OFF) 
+set(Boost_USE_MULTITHREADED ON)  
+set(Boost_USE_STATIC_RUNTIME OFF) 
+find_package(Boost 1.78.0 REQUIRED) 
+if(Boost_FOUND)
+    message("Boost_INCLUDE_DIRS=${Boost_INCLUDE_DIRS}")
+    include_directories(${Boost_INCLUDE_DIRS}) 
+endif()
+
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
 # CMakes modules
 # --------------
 set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
@@ -40,8 +51,7 @@ if (CODE_COVERAGE)
     enable_testing()
     include(CodeCoverage)
     MESSAGE("Code coverage enabled")
-    set(CMAKE_C_FLAGS " ${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
+    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
-    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
     SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -55,6 +65,7 @@ endif (ENABLE_CLANG_TIDY)
 add_git_submodule("lib/mdlp")
 add_git_submodule("lib/argparse")
 add_git_submodule("lib/json")
+find_library(XLSXWRITER_LIB libxlsxwriter.dylib PATHS /usr/local/lib)
 
 # Subdirectories
 # --------------
@@ -73,8 +84,7 @@ file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/Platform
 
 if (ENABLE_TESTING)
   MESSAGE("Testing enabled")
-  add_git_submodule("lib/catch2")
+  add_git_submodule("lib/catch2")  
-  
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)

Makefile

@@ -11,23 +11,37 @@ setup: ## Install dependencies for tests and coverage
 		pip install gcovr; \
 	fi
 
+dest ?= ../discretizbench
+copy: ## Copy binary files to selected folder
+	@echo "Destination folder: $(dest)"
+	make build
+	@echo ">>> Copying files to $(dest)"
+	@cp build/src/Platform/main $(dest)
+	@cp build/src/Platform/list $(dest)
+	@cp build/src/Platform/manage $(dest)
+	@cp build/src/Platform/best $(dest)
+	@echo ">>> Done"
+
 dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
 	cd build && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
 build: ## Build the main and BayesNetSample
-	cmake --build build -t main -t BayesNetSample -j 32
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -j 32
 
 clean: ## Clean the debug info
 	@echo ">>> Cleaning Debug BayesNet ...";
 	find . -name "*.gcda" -print0 | xargs -0 rm
 	@echo ">>> Done";
 
+clang-uml: ## Create uml class and sequence diagrams
+	clang-uml -p --add-compile-flag -I /usr/lib/gcc/x86_64-redhat-linux/8/include/
+
 debug: ## Build a debug version of the project
 	@echo ">>> Building Debug BayesNet ...";
 	@if [ -d ./build ]; then rm -rf ./build; fi
 	@mkdir build; 
 	cmake -S . -B build -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON; \
-	cmake --build build -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -t unit_tests -j 32;
 	@echo ">>> Done";
 
 release: ## Build a Release version of the project
@@ -35,7 +49,7 @@ release: ## Build a Release version of the project
 	@if [ -d ./build ]; then rm -rf ./build; fi
 	@mkdir build; 
 	cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; \
-	cmake --build build -t main -t BayesNetSample -j 32;
+	cmake --build build -t main -t BayesNetSample -t manage -t list -t best -j 32;
 	@echo ">>> Done";	
 
 test: ## Run tests

README.md

@@ -2,4 +2,40 @@
 
 Bayesian Network Classifier with libtorch from scratch
 
+## 0. Setup
+
+Before compiling BayesNet.
+
+### boost library
+
+[Getting Started](<https://www.boost.org/doc/libs/1_83_0/more/getting_started/index.html>)
+
+### libxlswriter
+
+```bash
+cd lib/libxlsxwriter
+make
+sudo make install
+```
+
+It has to be installed in /usr/local/lib otherwise CMakeLists.txt has to be modified accordingly
+
+Environment variable has to be set:
+
+```bash
+ export LD_LIBRARY_PATH=/usr/local/lib
+ ```
+
+### Release
+
+```bash
+make release
+```
+
+### Debug & Tests
+
+```bash
+make debug
+```
+
 ## 1. Introduction

TAN_iris.dot

@@ -1,12 +0,0 @@
-digraph BayesNet {
-label=<BayesNet >
-fontsize=30
-fontcolor=blue
-labelloc=t
-layout=circo
- class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] 
- class -> sepallength class -> sepalwidth class -> petallength class -> petalwidth petallength [shape=circle] 
- petallength -> sepallength petalwidth [shape=circle] 
- sepallength [shape=circle] 
- sepallength -> sepalwidth sepalwidth [shape=circle] 
- sepalwidth -> petalwidth }

data/_TAN_cpp_accuracy__.json

@@ -1 +0,0 @@
-null

lib/Files/CMakeLists.txt

@@ -1,2 +1 @@
-add_library(ArffFiles ArffFiles.cc)
+add_library(ArffFiles ArffFiles.cc)
-#target_link_libraries(BayesNet "${TORCH_LIBRARIES}")

sample/CMakeLists.txt

@@ -3,5 +3,6 @@ include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
+include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
 add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc) 
 target_link_libraries(BayesNetSample BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

sample/sample.cc

@@ -3,13 +3,14 @@
 #include <string>
 #include <map>
 #include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
 #include "ArffFiles.h"
 #include "BayesMetrics.h"
 #include "CPPFImdlp.h"
 #include "Folding.h"
 #include "Models.h"
 #include "modelRegister.h"
-
+#include <fstream>
 
 using namespace std;
 
@@ -57,6 +58,52 @@ pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vect
 
 int main(int argc, char** argv)
 {
+    torch::Tensor weights_ = torch::full({ 10 }, 1.0 / 10, torch::kFloat64);
+    torch::Tensor y_ = torch::tensor({ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, torch::kInt32);
+    torch::Tensor ypred = torch::tensor({ 1, 1, 1, 0, 0, 1, 1, 1, 1, 0 }, torch::kInt32);
+    cout << "Initial weights_: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << weights_.index({ i }).item<double>() << ", ";
+    }
+    cout << "end." << endl;
+    cout << "y_: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << y_.index({ i }).item<int>() << ", ";
+    }
+    cout << "end." << endl;
+    cout << "ypred: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << ypred.index({ i }).item<int>() << ", ";
+    }
+    cout << "end." << endl;
+    auto mask_wrong = ypred != y_;
+    auto mask_right = ypred == y_;
+    auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
+    double epsilon_t = masked_weights.sum().item<double>();
+    cout << "epsilon_t: " << epsilon_t << endl;
+    double wt = (1 - epsilon_t) / epsilon_t;
+    cout << "wt: " << wt << endl;
+    double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+    cout << "alpha_t: " << alpha_t << endl;
+    // Step 3.2: Update weights for next classifier
+    // Step 3.2.1: Update weights of wrong samples
+    cout << "exp(alpha_t): " << exp(alpha_t) << endl;
+    cout << "exp(-alpha_t): " << exp(-alpha_t) << endl;
+    weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
+    // Step 3.2.2: Update weights of right samples
+    weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
+    // Step 3.3: Normalise the weights
+    double totalWeights = torch::sum(weights_).item<double>();
+    cout << "totalWeights: " << totalWeights << endl;
+    cout << "Before normalization: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << weights_.index({ i }).item<double>() << endl;
+    }
+    weights_ = weights_ / totalWeights;
+    cout << "After normalization: " << endl;
+    for (int i = 0; i < 10; i++) {
+        cout << weights_.index({ i }).item<double>() << endl;
+    }
     map<string, bool> datasets = {
             {"diabetes",           true},
             {"ecoli",              true},
@@ -178,59 +225,59 @@ int main(int argc, char** argv)
     cout << "end." << endl;
     auto score = clf->score(Xd, y);
     cout << "Score: " << score << endl;
-    // auto graph = clf->graph();
+    auto graph = clf->graph();
-    // auto dot_file = model_name + "_" + file_name;
+    auto dot_file = model_name + "_" + file_name;
-    // ofstream file(dot_file + ".dot");
+    ofstream file(dot_file + ".dot");
-    // file << graph;
+    file << graph;
-    // file.close();
+    file.close();
-    // cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
+    cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
-    // cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
+    cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
-    // string stratified_string = stratified ? " Stratified" : "";
+    string stratified_string = stratified ? " Stratified" : "";
-    // cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
+    cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
-    // cout << "==========================================" << endl;
+    cout << "==========================================" << endl;
-    // torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
+    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);
+    torch::Tensor yt = torch::tensor(y, torch::kInt32);
-    // for (int i = 0; i < features.size(); ++i) {
+    for (int i = 0; i < features.size(); ++i) {
-    //     Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
+        Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
-    // }
+    }
-    // float total_score = 0, total_score_train = 0, score_train, score_test;
+    float total_score = 0, total_score_train = 0, score_train, score_test;
-    // Fold* fold;
+    platform::Fold* fold;
-    // if (stratified)
+    if (stratified)
-    //     fold = new StratifiedKFold(nFolds, y, seed);
+        fold = new platform::StratifiedKFold(nFolds, y, seed);
-    // else
+    else
-    //     fold = new KFold(nFolds, y.size(), seed);
+        fold = new platform::KFold(nFolds, y.size(), seed);
-    // for (auto i = 0; i < nFolds; ++i) {
+    for (auto i = 0; i < nFolds; ++i) {
-    //     auto [train, test] = fold->getFold(i);
+        auto [train, test] = fold->getFold(i);
-    //     cout << "Fold: " << i + 1 << endl;
+        cout << "Fold: " << i + 1 << endl;
-    //     if (tensors) {
+        if (tensors) {
-    //         auto ttrain = torch::tensor(train, torch::kInt64);
+            auto ttrain = torch::tensor(train, torch::kInt64);
-    //         auto ttest = torch::tensor(test, torch::kInt64);
+            auto ttest = torch::tensor(test, torch::kInt64);
-    //         torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
+            torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
-    //         torch::Tensor ytraint = yt.index({ ttrain });
+            torch::Tensor ytraint = yt.index({ ttrain });
-    //         torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
+            torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
-    //         torch::Tensor ytestt = yt.index({ ttest });
+            torch::Tensor ytestt = yt.index({ ttest });
-    //         clf->fit(Xtraint, ytraint, features, className, states);
+            clf->fit(Xtraint, ytraint, features, className, states);
-    //         auto temp = clf->predict(Xtraint);
+            auto temp = clf->predict(Xtraint);
-    //         score_train = clf->score(Xtraint, ytraint);
+            score_train = clf->score(Xtraint, ytraint);
-    //         score_test = clf->score(Xtestt, ytestt);
+            score_test = clf->score(Xtestt, ytestt);
-    //     } else {
+        } else {
-    //         auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
+            auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
-    //         auto [Xtest, ytest] = extract_indices(test, Xd, y);
+            auto [Xtest, ytest] = extract_indices(test, Xd, y);
-    //         clf->fit(Xtrain, ytrain, features, className, states);
+            clf->fit(Xtrain, ytrain, features, className, states);
-    //         score_train = clf->score(Xtrain, ytrain);
+            score_train = clf->score(Xtrain, ytrain);
-    //         score_test = clf->score(Xtest, ytest);
+            score_test = clf->score(Xtest, ytest);
-    //     }
+        }
-    //     if (dump_cpt) {
+        if (dump_cpt) {
-    //         cout << "--- CPT Tables ---" << endl;
+            cout << "--- CPT Tables ---" << endl;
-    //         clf->dump_cpt();
+            clf->dump_cpt();
-    //     }
+        }
-    //     total_score_train += score_train;
+        total_score_train += score_train;
-    //     total_score += score_test;
+        total_score += score_test;
-    //     cout << "Score Train: " << score_train << endl;
+        cout << "Score Train: " << score_train << endl;
-    //     cout << "Score Test : " << score_test << endl;
+        cout << "Score Test : " << score_test << endl;
-    //     cout << "-------------------------------------------------------------------------------" << endl;
+        cout << "-------------------------------------------------------------------------------" << endl;
-    // }
+    }
-    // cout << "**********************************************************************************" << endl;
+    cout << "**********************************************************************************" << endl;
-    // cout << "Average Score Train: " << total_score_train / nFolds << endl;
+    cout << "Average Score Train: " << total_score_train / nFolds << endl;
-    // cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
+    cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
 }

src/BayesNet/AODE.cc

@@ -2,14 +2,16 @@
 
 namespace bayesnet {
     AODE::AODE() : Ensemble() {}
-    void AODE::train()
+    void AODE::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODE>(i));
         }
+        n_models = models.size();
+        significanceModels = vector<double>(n_models, 1.0);
     }
-    vector<string> AODE::graph(const string& title)
+    vector<string> AODE::graph(const string& title) const
     {
         return Ensemble::graph(title);
     }

src/BayesNet/AODE.h

@@ -5,11 +5,11 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODE();
         virtual ~AODE() {};
-        vector<string> graph(const string& title = "AODE") override;
+        vector<string> graph(const string& title = "AODE") const override;
     };
 }
 #endif

src/BayesNet/AODELd.cc

@@ -1,33 +1,40 @@
 #include "AODELd.h"
+#include "Models.h"
 
 namespace bayesnet {
     using namespace std;
-    AODELd::AODELd() : Ensemble(), Proposal(Ensemble::Xv, Ensemble::yv, features, className) {}
+    AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
-    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
+        checkInput(X_, y_);
         features = features_;
         className = className_;
-        states = states_;
+        Xf = X_;
-        train();
+        y = y_;
-        for (const auto& model : models) {
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-            model->fit(X_, y_, features_, className_, states_);
+        states = fit_local_discretization(y);
-        }
+        // We have discretized the input data
-        n_models = models.size();
+        // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        fitted = true;
+        Ensemble::fit(dataset, features, className, states);
         return *this;
+
     }
-    void AODELd::train()
+    void AODELd::buildModel(const torch::Tensor& weights)
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODELd>(i));
         }
+        n_models = models.size();
+        significanceModels = vector<double>(n_models, 1.0);
     }
-    Tensor AODELd::predict(Tensor& X)
+    void AODELd::trainModel(const torch::Tensor& weights)
     {
-        return Ensemble::predict(X);
+        for (const auto& model : models) {
+            model->fit(Xf, y, features, className, states);
+        }
     }
-    vector<string> AODELd::graph(const string& name)
+    vector<string> AODELd::graph(const string& name) const
     {
         return Ensemble::graph(name);
     }

src/BayesNet/AODELd.h

@@ -7,13 +7,14 @@
 namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
+    protected:
+        void trainModel(const torch::Tensor& weights) override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODELd();
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_) override;
         virtual ~AODELd() = default;
-        AODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "AODELd") const override;
-        vector<string> graph(const string& name = "AODE") override;
-        Tensor predict(Tensor& X) override;
-        void train() override;
         static inline string version() { return "0.0.1"; };
     };
 }

src/BayesNet/BaseClassifier.h

@@ -1,28 +1,36 @@
 #ifndef BASE_H
 #define BASE_H
 #include <torch/torch.h>
+#include <nlohmann/json.hpp>
 #include <vector>
 namespace bayesnet {
     using namespace std;
+    enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
+    protected:
+        virtual void trainModel(const torch::Tensor& weights) = 0;
     public:
         // X is nxm vector, y is nx1 vector
-        virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;
+        status_t virtual getStatus() const = 0;
         float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
         float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
-        int virtual getNumberOfNodes() = 0;
+        int virtual getNumberOfNodes()const = 0;
-        int virtual getNumberOfEdges() = 0;
+        int virtual getNumberOfEdges()const = 0;
-        int virtual getNumberOfStates() = 0;
+        int virtual getNumberOfStates() const = 0;
-        vector<string> virtual show() = 0;
+        vector<string> virtual show() const = 0;
-        vector<string> virtual graph(const string& title = "") = 0;
+        vector<string> virtual graph(const string& title = "") const = 0;
-        const string inline getVersion() const { return "0.1.0"; };
+        const string inline getVersion() const { return "0.2.0"; };
         vector<string> virtual topological_order() = 0;
-        void virtual dump_cpt() = 0;
+        void virtual dump_cpt()const = 0;
+        virtual void setHyperparameters(nlohmann::json& hyperparameters) = 0;
     };
 }
 #endif

src/BayesNet/BayesMetrics.cc

@@ -2,7 +2,7 @@
 #include "Mst.h"
 namespace bayesnet {
     //samples is nxm tensor used to fit the model
-    Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
+    Metrics::Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates)
         : samples(samples)
         , features(features)
         , className(className)
@@ -21,6 +21,45 @@ namespace bayesnet {
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
     }
+    vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
+    {
+        // Return the K Best features 
+        auto n = samples.size(0) - 1;
+        if (k == 0) {
+            k = n;
+        }
+        // compute scores
+        scoresKBest.clear();
+        featuresKBest.clear();
+        auto label = samples.index({ -1, "..." });
+        for (int i = 0; i < n; ++i) {
+            scoresKBest.push_back(mutualInformation(label, samples.index({ i, "..." }), weights));
+            featuresKBest.push_back(i);
+        }
+        // sort & reduce scores and features
+        if (ascending) {
+            sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
+                { return scoresKBest[i] < scoresKBest[j]; });
+            sort(scoresKBest.begin(), scoresKBest.end(), std::less<double>());
+            if (k < n) {
+                for (int i = 0; i < n - k; ++i) {
+                    featuresKBest.erase(featuresKBest.begin());
+                    scoresKBest.erase(scoresKBest.begin());
+                }
+            }
+        } else {
+            sort(featuresKBest.begin(), featuresKBest.end(), [&](int i, int j)
+                { return scoresKBest[i] > scoresKBest[j]; });
+            sort(scoresKBest.begin(), scoresKBest.end(), std::greater<double>());
+            featuresKBest.resize(k);
+            scoresKBest.resize(k);
+        }
+        return featuresKBest;
+    }
+    vector<double> Metrics::getScoresKBest() const
+    {
+        return scoresKBest;
+    }
     vector<pair<string, string>> Metrics::doCombinations(const vector<string>& source)
     {
         vector<pair<string, string>> result;
@@ -32,17 +71,17 @@ namespace bayesnet {
         }
         return result;
     }
-    torch::Tensor Metrics::conditionalEdge()
+    torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights)
     {
         auto result = vector<double>();
         auto source = vector<string>(features);
         source.push_back(className);
         auto combinations = doCombinations(source);
         // Compute class prior
-        auto margin = torch::zeros({ classNumStates });
+        auto margin = torch::zeros({ classNumStates }, torch::kFloat);
         for (int value = 0; value < classNumStates; ++value) {
             auto mask = samples.index({ -1,  "..." }) == value;
-            margin[value] = mask.sum().item<float>() / samples.size(1);
+            margin[value] = mask.sum().item<double>() / samples.size(1);
         }
         for (auto [first, second] : combinations) {
             int index_first = find(features.begin(), features.end(), first) - features.begin();
@@ -52,8 +91,9 @@ namespace bayesnet {
                 auto mask = samples.index({ -1, "..." }) == value;
                 auto first_dataset = samples.index({ index_first, mask });
                 auto second_dataset = samples.index({ index_second, mask });
-                auto mi = mutualInformation(first_dataset, second_dataset);
+                auto weights_dataset = weights.index({ mask });
-                auto pb = margin[value].item<float>();
+                auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset);
+                auto pb = margin[value].item<double>();
                 accumulated += pb * mi;
             }
             result.push_back(accumulated);
@@ -70,31 +110,32 @@ namespace bayesnet {
         return matrix;
     }
     // To use in Python
-    vector<float> Metrics::conditionalEdgeWeights()
+    vector<float> Metrics::conditionalEdgeWeights(vector<float>& weights_)
     {
-        auto matrix = conditionalEdge();
+        const torch::Tensor weights = torch::tensor(weights_);
+        auto matrix = conditionalEdge(weights);
         std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
         return v;
     }
-    double Metrics::entropy(torch::Tensor& feature)
+    double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
-        torch::Tensor counts = feature.bincount();
+        torch::Tensor counts = feature.bincount(weights);
-        int totalWeight = counts.sum().item<int>();
+        double totalWeight = counts.sum().item<double>();
         torch::Tensor probs = counts.to(torch::kFloat) / totalWeight;
         torch::Tensor logProbs = torch::log(probs);
         torch::Tensor entropy = -probs * logProbs;
         return entropy.nansum().item<double>();
     }
     // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
-    double Metrics::conditionalEntropy(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
         int numSamples = firstFeature.sizes()[0];
-        torch::Tensor featureCounts = secondFeature.bincount();
+        torch::Tensor featureCounts = secondFeature.bincount(weights);
         unordered_map<int, unordered_map<int, double>> jointCounts;
         double totalWeight = 0;
         for (auto i = 0; i < numSamples; i++) {
-            jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += 1;
+            jointCounts[secondFeature[i].item<int>()][firstFeature[i].item<int>()] += weights[i].item<double>();
-            totalWeight += 1;
+            totalWeight += weights[i].item<float>();
         }
         if (totalWeight == 0)
             return 0;
@@ -115,16 +156,16 @@ namespace bayesnet {
         return entropyValue;
     }
     // I(X;Y) = H(Y) - H(Y|X)
-    double Metrics::mutualInformation(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
+    double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
-        return entropy(firstFeature) - conditionalEntropy(firstFeature, secondFeature);
+        return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
     }
     /*
     Compute the maximum spanning tree considering the weights as distances
     and the indices of the weights as nodes of this square matrix using
     Kruskal algorithm
     */
-    vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
+    vector<pair<int, int>> Metrics::maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root)
     {
         auto mst = MST(features, weights, root);
         return mst.maximumSpanningTree();

src/BayesNet/BayesMetrics.h

@@ -12,17 +12,21 @@ namespace bayesnet {
         vector<string> features;
         string className;
         int classNumStates = 0;
+        vector<double> scoresKBest;
+        vector<int> featuresKBest; // sorted indices of the features
+        double entropy(const Tensor& feature, const Tensor& weights);
+        double conditionalEntropy(const Tensor& firstFeature, const Tensor& secondFeature, const Tensor& weights);
+        vector<pair<string, string>> doCombinations(const vector<string>&);
     public:
         Metrics() = default;
-        Metrics(Tensor&, vector<string>&, string&, int);
+        Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates);
-        Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
+        Metrics(const vector<vector<int>>& vsamples, const vector<int>& labels, const vector<string>& features, const string& className, const int classNumStates);
-        double entropy(Tensor&);
+        vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending=false, unsigned k = 0);
-        double conditionalEntropy(Tensor&, Tensor&);
+        vector<double> getScoresKBest() const;
-        double mutualInformation(Tensor&, Tensor&);
+        double mutualInformation(const Tensor& firstFeature, const Tensor& secondFeature, const Tensor& weights);
-        vector<float> conditionalEdgeWeights(); // To use in Python
+        vector<float> conditionalEdgeWeights(vector<float>& weights); // To use in Python
-        Tensor conditionalEdge();
+        Tensor conditionalEdge(const torch::Tensor& weights);
-        vector<pair<string, string>> doCombinations(const vector<string>&);
+        vector<pair<int, int>> maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root);
-        vector<pair<int, int>> maximumSpanningTree(vector<string> features, Tensor& weights, int root);
     };
 }
 #endif

src/BayesNet/BoostAODE.cc

@@ -0,0 +1,148 @@
+#include "BoostAODE.h"
+#include <set>
+#include "BayesMetrics.h"
+#include "Colors.h"
+#include "Folding.h"
+#include <limits.h>
+
+namespace bayesnet {
+    BoostAODE::BoostAODE() : Ensemble() {}
+    void BoostAODE::buildModel(const torch::Tensor& weights)
+    {
+        // Models shall be built in trainModel
+    }
+    void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid
+        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending", "convergence" };
+        checkHyperparameters(validKeys, hyperparameters);
+        if (hyperparameters.contains("repeatSparent")) {
+            repeatSparent = hyperparameters["repeatSparent"];
+        }
+        if (hyperparameters.contains("maxModels")) {
+            maxModels = hyperparameters["maxModels"];
+        }
+        if (hyperparameters.contains("ascending")) {
+            ascending = hyperparameters["ascending"];
+        }
+        if (hyperparameters.contains("convergence")) {
+            convergence = hyperparameters["convergence"];
+        }
+    }
+    void BoostAODE::validationInit()
+    {
+        auto y_ = dataset.index({ -1, "..." });
+        if (convergence) {
+            // Prepare train & validation sets from train data
+            auto fold = platform::StratifiedKFold(5, y_, 271);
+            dataset_ = torch::clone(dataset);
+            // save input dataset
+            auto [train, test] = fold.getFold(0);
+            auto train_t = torch::tensor(train);
+            auto test_t = torch::tensor(test);
+            // Get train and validation sets
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+            y_train = dataset.index({ -1, train_t });
+            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+            y_test = dataset.index({ -1, test_t });
+            dataset = X_train;
+            m = X_train.size(1);
+            auto n_classes = states.at(className).size();
+            metrics = Metrics(dataset, features, className, n_classes);
+            // Build dataset with train data
+            buildDataset(y_train);
+        } else {
+            // Use all data to train
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+            y_train = y_;
+        }
+
+    }
+    void BoostAODE::trainModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        n_models = 0;
+        if (maxModels == 0)
+            maxModels = .1 * n > 10 ? .1 * n : n;
+        validationInit();
+        Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        bool exitCondition = false;
+        unordered_set<int> featuresUsed;
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double delta = 1.0;
+        double threshold = 1e-4;
+        int tolerance = 5; // number of times the accuracy can be lower than the threshold
+        int count = 0; // number of times the accuracy is lower than the threshold
+        fitted = true; // to enable predict
+        // Step 0: Set the finish condition
+        // if not repeatSparent a finish condition is run out of features
+        // n_models == maxModels
+        // epsiolon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        while (!exitCondition) {
+            // Step 1: Build ranking with mutual information
+            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
+            unique_ptr<Classifier> model;
+            auto feature = featureSelection[0];
+            if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
+                bool found = false;
+                for (auto feat : featureSelection) {
+                    if (find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
+                        continue;
+                    }
+                    found = true;
+                    feature = feat;
+                    break;
+                }
+                if (!found) {
+                    exitCondition = true;
+                    continue;
+                }
+            }
+            featuresUsed.insert(feature);
+            model = std::make_unique<SPODE>(feature);
+            model->fit(dataset, features, className, states, weights_);
+            auto ypred = model->predict(X_train);
+            // Step 3.1: Compute the classifier amout of say
+            auto mask_wrong = ypred != y_train;
+            auto mask_right = ypred == y_train;
+            auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
+            double epsilon_t = masked_weights.sum().item<double>();
+            double wt = (1 - epsilon_t) / epsilon_t;
+            double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+            // Step 3.2: Update weights for next classifier
+            // Step 3.2.1: Update weights of wrong samples
+            weights_ += mask_wrong.to(weights_.dtype()) * exp(alpha_t) * weights_;
+            // Step 3.2.2: Update weights of right samples
+            weights_ += mask_right.to(weights_.dtype()) * exp(-alpha_t) * weights_;
+            // Step 3.3: Normalise the weights
+            double totalWeights = torch::sum(weights_).item<double>();
+            weights_ = weights_ / totalWeights;
+            // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            models.push_back(std::move(model));
+            significanceModels.push_back(alpha_t);
+            n_models++;
+            if (convergence) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    delta = accuracy - priorAccuracy;
+                }
+                if (delta < threshold) {
+                    count++;
+                }
+            }
+            exitCondition = n_models == maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
+        }
+        if (featuresUsed.size() != features.size()) {
+            status = WARNING;
+        }
+    }
+    vector<string> BoostAODE::graph(const string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

src/BayesNet/BoostAODE.h

@@ -0,0 +1,25 @@
+#ifndef BOOSTAODE_H
+#define BOOSTAODE_H
+#include "Ensemble.h"
+#include "SPODE.h"
+namespace bayesnet {
+    class BoostAODE : public Ensemble {
+    public:
+        BoostAODE();
+        virtual ~BoostAODE() {};
+        vector<string> graph(const string& title = "BoostAODE") const override;
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights) override;
+    private:
+        torch::Tensor dataset_;
+        torch::Tensor X_train, y_train, X_test, y_test;
+        void validationInit();
+        bool repeatSparent = false;
+        int maxModels = 0;
+        bool ascending = false; //Process KBest features ascending or descending order
+        bool convergence = false; //if true, stop when the model does not improve
+    };
+}
+#endif

src/BayesNet/CMakeLists.txt

@@ -1,5 +1,9 @@
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
+include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
+include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
+include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
 add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
-    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc)
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc BoostAODE.cc 
-target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")
+    Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -5,63 +5,77 @@ namespace bayesnet {
     using namespace torch;
 
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    Classifier& Classifier::build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
     {
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
         this->features = features;
         this->className = className;
         this->states = states;
+        m = dataset.size(1);
+        n = dataset.size(0) - 1;
         checkFitParameters();
-        auto n_classes = states[className].size();
+        auto n_classes = states.at(className).size();
-        metrics = Metrics(samples, features, className, n_classes);
+        metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        train();
+        buildModel(weights);
-        if (Xv.empty()) {
+        trainModel(weights);
-            // fit with tensors
-            model.fit(X, y, features, className);
-        } else {
-            // fit with vectors
-            model.fit(Xv, yv, features, className);
-        }
         fitted = true;
         return *this;
     }
-    // X is nxm where n is the number of features and m the number of samples
+    void Classifier::buildDataset(Tensor& ytmp)
-    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        this->X = X;
+        try {
-        this->y = y;
+            auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
-        Xv = vector<vector<int>>();
+            dataset = torch::cat({ dataset, yresized }, 0);
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        }
-        return build(features, className, states);
+        catch (const std::exception& e) {
-    }
+            std::cerr << e.what() << '\n';
-    void Classifier::generateTensorXFromVector()
+            cout << "X dimensions: " << dataset.sizes() << "\n";
-    {
+            cout << "y dimensions: " << ytmp.sizes() << "\n";
-        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
+            exit(1);
-        for (int i = 0; i < Xv.size(); ++i) {
-            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
         }
     }
-    // X is nxm where n is the number of features and m the number of samples
+    void Classifier::trainModel(const torch::Tensor& weights)
-    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        Xv = X;
+        model.fit(dataset, weights, features, className, states);
-        generateTensorXFromVector();
+    }
-        this->y = torch::tensor(y, kInt32);
+    // X is nxm where n is the number of features and m the number of samples
-        yv = y;
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
-        return build(features, className, states);
+    {
+        dataset = X;
+        buildDataset(y);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
+    }
+    // X is nxm where n is the number of features and m the number of samples
+    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states)
+    {
+        dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
+        for (int i = 0; i < X.size(); ++i) {
+            dataset.index_put_({ i, "..." }, torch::tensor(X[i], kInt32));
+        }
+        auto ytmp = torch::tensor(y, kInt32);
+        buildDataset(ytmp);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states)
+    {
+        this->dataset = dataset;
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
+        return build(features, className, states, weights);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights)
+    {
+        this->dataset = dataset;
+        return build(features, className, states, weights);
     }
     void Classifier::checkFitParameters()
     {
-        auto sizes = X.sizes();
+        if (torch::is_floating_point(dataset)) {
-        m = sizes[1];
+            throw invalid_argument("dataset (X, y) must be of type Integer");
-        n = sizes[0];
-        if (m != y.size(0)) {
-            throw invalid_argument("X and y must have the same number of samples");
         }
         if (n != features.size()) {
-            throw invalid_argument("X and features must have the same number of features");
+            throw invalid_argument("Classifier: X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
             throw invalid_argument("className not found in states");
@@ -108,7 +122,7 @@ namespace bayesnet {
         }
         return model.score(X, y);
     }
-    vector<string> Classifier::show()
+    vector<string> Classifier::show() const
     {
         return model.show();
     }
@@ -120,16 +134,16 @@ namespace bayesnet {
         }
         model.addNode(className);
     }
-    int Classifier::getNumberOfNodes()
+    int Classifier::getNumberOfNodes() const
     {
         // Features does not include class
         return fitted ? model.getFeatures().size() + 1 : 0;
     }
-    int Classifier::getNumberOfEdges()
+    int Classifier::getNumberOfEdges() const
     {
-        return fitted ? model.getEdges().size() : 0;
+        return fitted ? model.getNumEdges() : 0;
     }
-    int Classifier::getNumberOfStates()
+    int Classifier::getNumberOfStates() const
     {
         return fitted ? model.getStates() : 0;
     }
@@ -137,9 +151,22 @@ namespace bayesnet {
     {
         return model.topological_sort();
     }
-    void Classifier::dump_cpt()
+    void Classifier::dump_cpt() const
     {
         model.dump_cpt();
     }
-
+    void Classifier::checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters)
+    {
+        for (const auto& item : hyperparameters.items()) {
+            if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
+                throw invalid_argument("Hyperparameter " + item.key() + " is not valid");
+            }
+        }
+    }
+    void Classifier::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid, default is no hyperparameters
+        const vector<string> validKeys = { };
+        checkHyperparameters(validKeys, hyperparameters);
+    }
 }

src/BayesNet/Classifier.h

@@ -10,39 +10,42 @@ using namespace torch;
 namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
-        bool fitted;
+        Classifier& build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights);
-        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
-        Network model;
+        bool fitted;
         int m, n; // m: number of samples, n: number of features
-        Tensor X; // nxm tensor
+        Network model;
-        vector<vector<int>> Xv; // nxm vector
-        Tensor y;
-        vector<int> yv;
-        Tensor samples; // (n+1)xm tensor
         Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
+        Tensor dataset; // (n+1)xm tensor
+        status_t status = NORMAL;
         void checkFitParameters();
-        void generateTensorXFromVector();
+        virtual void buildModel(const torch::Tensor& weights) = 0;
-        virtual void train() = 0;
+        void trainModel(const torch::Tensor& weights) override;
+        void checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters);
+        void buildDataset(torch::Tensor& y);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(vector<vector<int>>& X, vector<int>& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights) override;
         void addNodes();
-        int getNumberOfNodes() override;
+        int getNumberOfNodes() const override;
-        int getNumberOfEdges() override;
+        int getNumberOfEdges() const override;
-        int getNumberOfStates() override;
+        int getNumberOfStates() const override;
         Tensor predict(Tensor& X) override;
+        status_t getStatus() const override { return status; }
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        vector<string> show() override;
+        vector<string> show() const override;
-        vector<string> topological_order() override;
+        vector<string> topological_order()  override;
-        void dump_cpt() override;
+        void dump_cpt() const override;
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 }
 #endif

src/BayesNet/Ensemble.cc

@@ -3,63 +3,28 @@
 namespace bayesnet {
     using namespace torch;
 
-    Ensemble::Ensemble() : n_models(0), metrics(Metrics()), fitted(false) {}
+    Ensemble::Ensemble() : Classifier(Network()), n_models(0) {}
-    Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
+
+    void Ensemble::trainModel(const torch::Tensor& weights)
     {
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        this->features = features;
-        this->className = className;
-        this->states = states;
-        auto n_classes = states[className].size();
-        metrics = Metrics(samples, features, className, n_classes);
-        // Build models
-        train();
-        // Train models
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
-            if (Xv.empty()) {
+            // fit with vectors
-                // fit with tensors
+            models[i]->fit(dataset, features, className, states);
-                models[i]->fit(X, y, features, className, states);
-            } else {
-                // fit with vectors
-                models[i]->fit(Xv, yv, features, className, states);
-            }
         }
-        fitted = true;
-        return *this;
-    }
-    void Ensemble::generateTensorXFromVector()
-    {
-        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
-        for (int i = 0; i < Xv.size(); ++i) {
-            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
-        }
-    }
-    Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
-    {
-        this->X = X;
-        this->y = y;
-        Xv = vector<vector<int>>();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-        return build(features, className, states);
-    }
-    Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
-    {
-        Xv = X;
-        generateTensorXFromVector();
-        this->y = torch::tensor(y, kInt32);
-        yv = y;
-        return build(features, className, states);
     }
     vector<int> Ensemble::voting(Tensor& y_pred)
     {
         auto y_pred_ = y_pred.accessor<int, 2>();
         vector<int> y_pred_final;
+        int numClasses = states.at(className).size();
+        // y_pred is m x n_models with the prediction of every model for each sample
         for (int i = 0; i < y_pred.size(0); ++i) {
-            vector<float> votes(y_pred.size(1), 0);
+            // votes store in each index (value of class) the significance added by each model
-            for (int j = 0; j < y_pred.size(1); ++j) {
+            // i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
-                votes[y_pred_[i][j]] += 1;
+            vector<double> votes(numClasses, 0.0);
+            for (int j = 0; j < n_models; ++j) {
+                votes[y_pred_[i][j]] += significanceModels.at(j);
             }
             // argsort in descending order
             auto indices = argsort(votes);
@@ -73,7 +38,6 @@ namespace bayesnet {
             throw logic_error("Ensemble has not been fitted");
         }
         Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
-        //Create a threadpool
         auto threads{ vector<thread>() };
         mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
@@ -132,9 +96,8 @@ namespace bayesnet {
             }
         }
         return (double)correct / y_pred.size();
-
     }
-    vector<string> Ensemble::show()
+    vector<string> Ensemble::show() const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -143,7 +106,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Ensemble::graph(const string& title)
+    vector<string> Ensemble::graph(const string& title) const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -152,7 +115,7 @@ namespace bayesnet {
         }
         return result;
     }
-    int Ensemble::getNumberOfNodes()
+    int Ensemble::getNumberOfNodes() const
     {
         int nodes = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -160,7 +123,7 @@ namespace bayesnet {
         }
         return nodes;
     }
-    int Ensemble::getNumberOfEdges()
+    int Ensemble::getNumberOfEdges() const
     {
         int edges = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -168,7 +131,7 @@ namespace bayesnet {
         }
         return edges;
     }
-    int Ensemble::getNumberOfStates()
+    int Ensemble::getNumberOfStates() const
     {
         int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {

src/BayesNet/Ensemble.h

@@ -8,44 +8,32 @@ using namespace std;
 using namespace torch;
 
 namespace bayesnet {
-    class Ensemble : public BaseClassifier {
+    class Ensemble : public Classifier {
     private:
         Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
         unsigned n_models;
-        bool fitted;
         vector<unique_ptr<Classifier>> models;
-        Tensor X;
+        vector<double> significanceModels;
-        vector<vector<int>> Xv;
+        void trainModel(const torch::Tensor& weights) override;
-        Tensor y;
-        vector<int> yv;
-        Tensor samples;
-        Metrics metrics;
-        vector<string> features;
-        string className;
-        map<string, vector<int>> states;
-        void virtual train() = 0;
         vector<int> voting(Tensor& y_pred);
-        void generateTensorXFromVector();
     public:
         Ensemble();
         virtual ~Ensemble() = default;
-        Ensemble& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        Ensemble& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Tensor predict(Tensor& X) override;
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        int getNumberOfNodes() override;
+        int getNumberOfNodes() const override;
-        int getNumberOfEdges() override;
+        int getNumberOfEdges() const override;
-        int getNumberOfStates() override;
+        int getNumberOfStates() const override;
-        vector<string> show() override;
+        vector<string> show() const override;
-        vector<string> graph(const string& title) override;
+        vector<string> graph(const string& title) const override;
-        vector<string> topological_order() override
+        vector<string> topological_order()  override
         {
             return vector<string>();
         }
-        void dump_cpt() override
+        void dump_cpt() const override
         {
         }
     };

src/BayesNet/KDB.cc

@@ -4,7 +4,19 @@ namespace bayesnet {
     using namespace torch;
 
     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::train()
+    void KDB::setHyperparameters(nlohmann::json& hyperparameters)
+    {
+        // Check if hyperparameters are valid
+        const vector<string> validKeys = { "k", "theta" };
+        checkHyperparameters(validKeys, hyperparameters);
+        if (hyperparameters.contains("k")) {
+            k = hyperparameters["k"];
+        }
+        if (hyperparameters.contains("theta")) {
+            theta = hyperparameters["theta"];
+        }
+    }
+    void KDB::buildModel(const torch::Tensor& weights)
     {
         /*
         1. For each feature Xi, compute mutual information, I(X;C),
@@ -28,13 +40,14 @@ namespace bayesnet {
         // 1. For each feature Xi, compute mutual information, I(X;C),
         // where C is the class.
         addNodes();
-        vector <float> mi;
+        const Tensor& y = dataset.index({ -1, "..." });
+        vector<double> mi;
         for (auto i = 0; i < features.size(); i++) {
-            Tensor firstFeature = X.index({ i, "..." });
+            Tensor firstFeature = dataset.index({ i, "..." });
-            mi.push_back(metrics.mutualInformation(firstFeature, y));
+            mi.push_back(metrics.mutualInformation(firstFeature, y, weights));
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
-        auto conditionalEdgeWeights = metrics.conditionalEdge();
+        auto conditionalEdgeWeights = metrics.conditionalEdge(weights);
         // 3. Let the used variable list, S, be empty.
         vector<int> S;
         // 4. Let the DAG network being constructed, BN, begin with a single
@@ -78,7 +91,7 @@ namespace bayesnet {
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::graph(const string& title)
+    vector<string> KDB::graph(const string& title) const
     {
         string header{ title };
         if (title == "KDB") {

src/BayesNet/KDB.h

@@ -1,5 +1,6 @@
 #ifndef KDB_H
 #define KDB_H
+#include <torch/torch.h>
 #include "Classifier.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
@@ -11,11 +12,12 @@ namespace bayesnet {
         float theta;
         void add_m_edges(int idx, vector<int>& S, Tensor& weights);
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};
-        vector<string> graph(const string& name = "KDB") override;
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
+        vector<string> graph(const string& name = "KDB") const override;
     };
 }
 #endif

src/BayesNet/KDBLd.cc

@@ -2,25 +2,20 @@
 
 namespace bayesnet {
     using namespace std;
-    KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
-        generateTensorXFromVector();
         // We have discretized the input data
         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
-        KDB::fit(KDB::Xv, KDB::yv, features, className, states);
+        KDB::fit(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
+        states = localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(KDB::Xv, KDB::yv, features, className);
         return *this;
     }
     Tensor KDBLd::predict(Tensor& X)
@@ -28,7 +23,7 @@ namespace bayesnet {
         auto Xt = prepareX(X);
         return KDB::predict(Xt);
     }
-    vector<string> KDBLd::graph(const string& name)
+    vector<string> KDBLd::graph(const string& name) const
     {
         return KDB::graph(name);
     }

src/BayesNet/KDBLd.h

@@ -10,8 +10,8 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "KDB") override;
+        vector<string> graph(const string& name = "KDB") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/BayesNet/Mst.cc

@@ -94,7 +94,7 @@ namespace bayesnet {
         return result;
     }
 
-    MST::MST(vector<string>& features, Tensor& weights, int root) : features(features), weights(weights), root(root) {}
+    MST::MST(const vector<string>& features, const Tensor& weights, const int root) : features(features), weights(weights), root(root) {}
     vector<pair<int, int>> MST::maximumSpanningTree()
     {
         auto num_features = features.size();

src/BayesNet/Mst.h

@@ -13,7 +13,7 @@ namespace bayesnet {
         int root = 0;
     public:
         MST() = default;
-        MST(vector<string>& features, Tensor& weights, int root);
+        MST(const vector<string>& features, const Tensor& weights, const int root);
         vector<pair<int, int>> maximumSpanningTree();
     };
     class Graph {

src/BayesNet/Network.cc

@@ -3,9 +3,8 @@
 #include "Network.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
-    Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false) {}
+    Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
+    Network::Network(float maxT) : features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
     Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
         getmaxThreads()), fitted(other.fitted)
     {
@@ -20,7 +19,6 @@ namespace bayesnet {
         classNumStates = 0;
         fitted = false;
         nodes.clear();
-        dataset.clear();
         samples = torch::Tensor();
     }
     float Network::getmaxThreads()
@@ -44,15 +42,15 @@ namespace bayesnet {
         }
         nodes[name] = std::make_unique<Node>(name);
     }
-    vector<string> Network::getFeatures()
+    vector<string> Network::getFeatures() const
     {
         return features;
     }
-    int Network::getClassNumStates()
+    int Network::getClassNumStates() const
     {
         return classNumStates;
     }
-    int Network::getStates()
+    int Network::getStates() const
     {
         int result = 0;
         for (auto& node : nodes) {
@@ -60,7 +58,7 @@ namespace bayesnet {
         }
         return result;
     }
-    string Network::getClassName()
+    string Network::getClassName() const
     {
         return className;
     }
@@ -105,8 +103,11 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className)
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states, const torch::Tensor& weights)
     {
+        if (weights.size(0) != n_samples) {
+            throw invalid_argument("Weights (" + to_string(weights.size(0)) + ") must have the same number of elements as samples (" + to_string(n_samples) + ") in Network::fit");
+        }
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");
         }
@@ -123,81 +124,61 @@ namespace bayesnet {
             if (find(features.begin(), features.end(), feature) == features.end()) {
                 throw invalid_argument("Feature " + feature + " not found in Network::features");
             }
+            if (states.find(feature) == states.end()) {
+                throw invalid_argument("Feature " + feature + " not found in states");
+            }
         }
     }
-    void Network::setStates()
+    void Network::setStates(const map<string, vector<int>>& states)
     {
         // Set states to every Node in the network
-        for (int i = 0; i < features.size(); ++i) {
+        for_each(features.begin(), features.end(), [this, &states](const string& feature) {
-            nodes[features[i]]->setNumStates(static_cast<int>(torch::max(samples.index({ i, "..." })).item<int>()) + 1);
+            nodes.at(feature)->setNumStates(states.at(feature).size());
-        }
+            });
-        classNumStates = nodes[className]->getNumStates();
+        classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
-        dataset.clear();
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
         samples = torch::cat({ X , ytmp }, 0);
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
-            dataset[featureNames[i]] = vector<int>(row_feature.data_ptr<int>(), row_feature.data_ptr<int>() + row_feature.size(0));;
         }
-        dataset[className] = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        completeFit(states, weights);
-        completeFit();
+    }
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    {
+        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
+        this->className = className;
+        this->samples = samples;
+        completeFit(states, weights);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className)
+    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<float>& weights_, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
+        const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
+        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
         this->className = className;
-        dataset.clear();
+        // Build tensor of samples (nxm) (n+1 because of the class)
-        // Build dataset & tensor of samples (nxm) (n+1 because of the class)
         samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
         for (int i = 0; i < featureNames.size(); ++i) {
-            dataset[featureNames[i]] = input_data[i];
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
-        dataset[className] = labels;
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit();
+        completeFit(states, weights);
     }
-    void Network::completeFit()
+    void Network::completeFit(const map<string, vector<int>>& states, const torch::Tensor& weights)
     {
-        setStates();
+        setStates(states);
-        int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
+        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
-        if (maxThreadsRunning < 1) {
-            maxThreadsRunning = 1;
-        }
         vector<thread> threads;
-        mutex mtx;
+        for (auto& node : nodes) {
-        condition_variable cv;
+            threads.emplace_back([this, &node, &weights]() {
-        int activeThreads = 0;
+                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
-        int nextNodeIndex = 0;
-        while (nextNodeIndex < nodes.size()) {
-            unique_lock<mutex> lock(mtx);
-            cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
-            threads.emplace_back([this, &nextNodeIndex, &mtx, &cv, &activeThreads]() {
-                while (true) {
-                    unique_lock<mutex> lock(mtx);
-                    if (nextNodeIndex >= nodes.size()) {
-                        break;  // No more work remaining
-                    }
-                    auto& pair = *std::next(nodes.begin(), nextNodeIndex);
-                    ++nextNodeIndex;
-                    lock.unlock();
-                    pair.second->computeCPT(dataset, laplaceSmoothing);
-                    lock.lock();
-                    nodes[pair.first] = std::move(pair.second);
-                    lock.unlock();
-                }
-                lock_guard<mutex> lock(mtx);
-                --activeThreads;
-                cv.notify_one();
                 });
-            ++activeThreads;
         }
         for (auto& thread : threads) {
             thread.join();
@@ -212,7 +193,7 @@ namespace bayesnet {
         torch::Tensor result;
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         for (int i = 0; i < samples.size(1); ++i) {
-            auto sample = samples.index({ "...", i });
+            const Tensor sample = samples.index({ "...", i });
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
             //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
@@ -328,22 +309,22 @@ namespace bayesnet {
         mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
             threads.emplace_back([this, &result, &evidence, i, &mtx]() {
-            auto completeEvidence = map<string, int>(evidence);
+                auto completeEvidence = map<string, int>(evidence);
-            completeEvidence[getClassName()] = i;
+                completeEvidence[getClassName()] = i;
                 double factor = computeFactor(completeEvidence);
                 lock_guard<mutex> lock(mtx);
                 result[i] = factor;
-            });
+                });
         }
         for (auto& thread : threads) {
             thread.join();
         }
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
-        transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
+        transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }
-    vector<string> Network::show()
+    vector<string> Network::show() const
     {
         vector<string> result;
         // Draw the network
@@ -356,7 +337,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(const string& title)
+    vector<string> Network::graph(const string& title) const
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
@@ -370,7 +351,7 @@ namespace bayesnet {
         output.push_back("}\n");
         return output;
     }
-    vector<pair<string, string>> Network::getEdges()
+    vector<pair<string, string>> Network::getEdges() const
     {
         auto edges = vector<pair<string, string>>();
         for (const auto& node : nodes) {
@@ -382,13 +363,16 @@ namespace bayesnet {
         }
         return edges;
     }
+    int Network::getNumEdges() const
+    {
+        return getEdges().size();
+    }
     vector<string> Network::topological_sort()
     {
         /* Check if al the fathers of every node are before the node */
         auto result = features;
         result.erase(remove(result.begin(), result.end(), className), result.end());
         bool ending{ false };
-        int idx = 0;
         while (!ending) {
             ending = true;
             for (auto feature : features) {
@@ -420,10 +404,11 @@ namespace bayesnet {
         }
         return result;
     }
-    void Network::dump_cpt()
+    void Network::dump_cpt() const
     {
         for (auto& node : nodes) {
             cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << endl;
+            cout << node.second->getCPT() << endl;
         }
     }
 }

src/BayesNet/Network.h

@@ -8,57 +8,52 @@ namespace bayesnet {
     class Network {
     private:
         map<string, unique_ptr<Node>> nodes;
-        map<string, vector<int>> dataset;
         bool fitted;
         float maxThreads = 0.95;
         int classNumStates;
-        vector<string> features; // Including class
+        vector<string> features; // Including classname
         string className;
-        int laplaceSmoothing = 1;
+        double laplaceSmoothing;
         torch::Tensor samples; // nxm tensor used to fit the model
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         vector<double> predict_sample(const vector<int>&);
         vector<double> predict_sample(const torch::Tensor&);
         vector<double> exactInference(map<string, int>&);
         double computeFactor(map<string, int>&);
-        double mutual_info(torch::Tensor&, torch::Tensor&);
+        void completeFit(const map<string, vector<int>>& states, const torch::Tensor& weights);
-        double entropy(torch::Tensor&);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states, const torch::Tensor& weights);
-        double conditionalEntropy(torch::Tensor&, torch::Tensor&);
+        void setStates(const map<string, vector<int>>&);
-        double mutualInformation(torch::Tensor&, torch::Tensor&);
-        void completeFit();
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className);
-        void setStates();
     public:
         Network();
-        explicit Network(float, int);
         explicit Network(float);
         explicit Network(Network&);
+        ~Network() = default;
         torch::Tensor& getSamples();
         float getmaxThreads();
         void addNode(const string&);
         void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
-        vector<string> getFeatures();
+        vector<string> getFeatures() const;
-        int getStates();
+        int getStates() const;
-        vector<pair<string, string>> getEdges();
+        vector<pair<string, string>> getEdges() const;
-        int getClassNumStates();
+        int getNumEdges() const;
-        string getClassName();
+        int getClassNumStates() const;
-        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
+        string getClassName() const;
-        void fit(torch::Tensor&, torch::Tensor&, const vector<string>&, const string&);
+        void fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<float>& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
         vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
-        //Computes the conditional edge weight of variable index u and v conditioned on class_node
-        torch::Tensor conditionalEdgeWeight();
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
         vector<vector<double>> predict_proba(const vector<vector<int>>&); // Return mxn vector of probabilities
         torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
         double score(const vector<vector<int>>&, const vector<int>&);
         vector<string> topological_sort();
-        vector<string> show();
+        vector<string> show() const;
-        vector<string> graph(const string& title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& title) const; // Returns a vector of strings representing the graph in graphviz format
         void initialize();
-        void dump_cpt();
+        void dump_cpt() const;
-        inline string version() { return "0.1.0"; }
+        inline string version() { return "0.2.0"; }
     };
 }
 #endif

src/BayesNet/Node.cc

@@ -84,7 +84,7 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(map<string, vector<int>>& dataset, const int laplaceSmoothing)
+    void Node::computeCPT(const torch::Tensor& dataset, const vector<string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
         // Get dimensions of the CPT
@@ -94,22 +94,34 @@ namespace bayesnet {
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts
-        for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
+        auto pos = find(features.begin(), features.end(), name);
-            torch::List<c10::optional<torch::Tensor>> coordinates;
+        if (pos == features.end()) {
-            coordinates.push_back(torch::tensor(dataset[name][n_sample]));
+            throw logic_error("Feature " + name + " not found in dataset");
-            transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](const auto& parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
+        }
+        int name_index = pos - features.begin();
+        for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
+            c10::List<c10::optional<at::Tensor>> coordinates;
+            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            for (auto parent : parents) {
+                pos = find(features.begin(), features.end(), parent->getName());
+                if (pos == features.end()) {
+                    throw logic_error("Feature parent " + parent->getName() + " not found in dataset");
+                }
+                int parent_index = pos - features.begin();
+                coordinates.push_back(dataset.index({ parent_index, n_sample }));
+            }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
+            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
         }
         // Normalize the counts
         cpTable = cpTable / cpTable.sum(0);
     }
     float Node::getFactorValue(map<string, int>& evidence)
     {
-        torch::List<c10::optional<torch::Tensor>> coordinates;
+        c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
-        coordinates.push_back(torch::tensor(evidence[name]));
+        coordinates.push_back(at::tensor(evidence[name]));
-        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return torch::tensor(evidence[parent->getName()]); });
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
         return cpTable.index({ coordinates }).item<float>();
     }
     vector<string> Node::graph(const string& className)

src/BayesNet/Node.h

@@ -26,7 +26,7 @@ namespace bayesnet {
         vector<Node*>& getParents();
         vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(map<string, vector<int>>&, const int);
+        void computeCPT(const torch::Tensor& dataset, const vector<string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();

src/BayesNet/Proposal.cc

@@ -2,21 +2,30 @@
 #include "ArffFiles.h"
 
 namespace bayesnet {
-    Proposal::Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_) : Xv(Xv_), yv(yv_), pFeatures(features_), pClassName(className_) {}
+    Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
     Proposal::~Proposal()
     {
         for (auto& [key, value] : discretizers) {
             delete value;
         }
     }
-    void Proposal::localDiscretizationProposal(map<string, vector<int>>& states, Network& model)
+    void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
+    {
+        if (!torch::is_floating_point(X)) {
+            throw std::invalid_argument("X must be a floating point tensor");
+        }
+        if (torch::is_floating_point(y)) {
+            throw std::invalid_argument("y must be an integer tensor");
+        }
+    }
+    map<string, vector<int>> Proposal::localDiscretizationProposal(const map<string, vector<int>>& oldStates, Network& model)
     {
         // order of local discretization is important. no good 0, 1, 2...
         // although we rediscretize features after the local discretization of every feature
         auto order = model.topological_sort();
         auto& nodes = model.getNodes();
+        map<string, vector<int>> states = oldStates;
         vector<int> indicesToReDiscretize;
-        auto n_samples = Xf.size(1);
         bool upgrade = false; // Flag to check if we need to upgrade the model
         for (auto feature : order) {
             auto nodeParents = nodes[feature]->getParents();
@@ -30,13 +39,13 @@ namespace bayesnet {
             parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
             // Get the indices of the parents
             vector<int> indices;
+            indices.push_back(-1); // Add class index
             transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
             // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
-            vector<string> yJoinParents;
+            vector<string> yJoinParents(Xf.size(1));
-            transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
             for (auto idx : indices) {
-                for (int i = 0; i < n_samples; ++i) {
+                for (int i = 0; i < Xf.size(1); ++i) {
-                    yJoinParents[i] += to_string(Xv[idx][i]);
+                    yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
                 }
             }
             auto arff = ArffFiles();
@@ -44,41 +53,38 @@ namespace bayesnet {
             auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
             auto xvf = vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
             discretizers[feature]->fit(xvf, yxv);
-            //
-            //
-            //
-            // auto tmp = discretizers[feature]->transform(xvf);
-            // Xv[index] = tmp;
-            // auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
-            // iota(xStates.begin(), xStates.end(), 0);
-            // //Update new states of the feature/node
-            // states[feature] = xStates;
         }
         if (upgrade) {
             // Discretize again X (only the affected indices) with the new fitted discretizers
             for (auto index : indicesToReDiscretize) {
                 auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
                 auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-                Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
+                pDataset.index_put_({ index, "..." }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));
                 auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
                 iota(xStates.begin(), xStates.end(), 0);
                 //Update new states of the feature/node
                 states[pFeatures[index]] = xStates;
             }
+            const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
+            model.fit(pDataset, weights, pFeatures, pClassName, states);
         }
+        return states;
     }
-    void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
+    map<string, vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
     {
-        // Sharing Xv and yv with Classifier
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
-        Xv = vector<vector<int>>();
+        int m = Xf.size(1);
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        int n = Xf.size(0);
+        map<string, vector<int>> states;
+        pDataset = torch::zeros({ n + 1, m }, kInt32);
+        auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
         // discretize input data by feature(row)
-        for (int i = 0; i < pFeatures.size(); ++i) {
+        for (auto i = 0; i < pFeatures.size(); ++i) {
             auto* discretizer = new mdlp::CPPFImdlp();
             auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
             auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
             discretizer->fit(Xt, yv);
-            Xv.push_back(discretizer->transform(Xt));
+            pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
             auto xStates = vector<int>(discretizer->getCutPoints().size() + 1);
             iota(xStates.begin(), xStates.end(), 0);
             states[pFeatures[i]] = xStates;
@@ -88,6 +94,8 @@ namespace bayesnet {
         auto yStates = vector<int>(n_classes);
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
+        pDataset.index_put_({ n, "..." }, y);
+        return states;
     }
     torch::Tensor Proposal::prepareX(torch::Tensor& X)
     {

src/BayesNet/Proposal.h

@@ -10,20 +10,21 @@
 namespace bayesnet {
     class Proposal {
     public:
-        Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
+        Proposal(torch::Tensor& pDataset, vector<string>& features_, string& className_);
         virtual ~Proposal();
     protected:
+        void checkInput(const torch::Tensor& X, const torch::Tensor& y);
         torch::Tensor prepareX(torch::Tensor& X);
-        void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
+        map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
-        void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
+        map<string, vector<int>> fit_local_discretization(const torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
+        torch::Tensor y; // y discrete nx1 tensor
         map<string, mdlp::CPPFImdlp*> discretizers;
     private:
+        torch::Tensor& pDataset; // (n+1)xm tensor
         vector<string>& pFeatures;
         string& pClassName;
-        vector<vector<int>>& Xv; // X discrete nxm vector
-        vector<int>& yv;
     };
 }
 
-#endif
+#endif  

src/BayesNet/SPODE.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
 
     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
 
-    void SPODE::train()
+    void SPODE::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();
@@ -17,7 +17,7 @@ namespace bayesnet {
             }
         }
     }
-    vector<string> SPODE::graph(const string& name)
+    vector<string> SPODE::graph(const string& name) const
     {
         return model.graph(name);
     }

src/BayesNet/SPODE.h

@@ -7,11 +7,11 @@ namespace bayesnet {
     private:
         int root;
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() {};
-        vector<string> graph(const string& name = "SPODE") override;
+        vector<string> graph(const string& name = "SPODE") const override;
     };
 }
 #endif

src/BayesNet/SPODELd.cc

@@ -2,33 +2,46 @@
 
 namespace bayesnet {
     using namespace std;
-    SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
-        generateTensorXFromVector();
         // We have discretized the input data
         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
-        SPODE::fit(SPODE::Xv, SPODE::yv, features, className, states);
+        SPODE::fit(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
+        states = localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(SPODE::Xv, SPODE::yv, features, className);
         return *this;
     }
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
+    {
+        if (!torch::is_floating_point(dataset)) {
+            throw std::runtime_error("Dataset must be a floating point tensor");
+        }
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone();
+        features = features_;
+        className = className_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
+        SPODE::fit(dataset, features, className, states);
+        states = localDiscretizationProposal(states, model);
+        return *this;
+    }
+
     Tensor SPODELd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return SPODE::predict(Xt);
     }
-    vector<string> SPODELd::graph(const string& name)
+    vector<string> SPODELd::graph(const string& name) const
     {
         return SPODE::graph(name);
     }

src/BayesNet/SPODELd.h

@@ -6,12 +6,12 @@
 namespace bayesnet {
     using namespace std;
     class SPODELd : public SPODE, public Proposal {
-    private:
     public:
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
-        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "SPODE") override;
+        SPODELd& fit(torch::Tensor& dataset, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "SPODE") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/BayesNet/TAN.cc

@@ -5,25 +5,25 @@ namespace bayesnet {
 
     TAN::TAN() : Classifier(Network()) {}
 
-    void TAN::train()
+    void TAN::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();
         // 1. Compute mutual information between each feature and the class and set the root node
         // as the highest mutual information with the class
         auto mi = vector <pair<int, float >>();
-        Tensor class_dataset = samples.index({ -1, "..." });
+        Tensor class_dataset = dataset.index({ -1, "..." });
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
-            Tensor feature_dataset = samples.index({ i, "..." });
+            Tensor feature_dataset = dataset.index({ i, "..." });
-            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
+            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);
             mi.push_back({ i, mi_value });
         }
         sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
         auto root = mi[mi.size() - 1].first;
         // 2. Compute mutual information between each feature and the class
-        auto weights = metrics.conditionalEdge();
+        auto weights_matrix = metrics.conditionalEdge(weights);
         // 3. Compute the maximum spanning tree
-        auto mst = metrics.maximumSpanningTree(features, weights, root);
+        auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);
         // 4. Add edges from the maximum spanning tree to the model
         for (auto i = 0; i < mst.size(); ++i) {
             auto [from, to] = mst[i];
@@ -34,7 +34,7 @@ namespace bayesnet {
             model.addEdge(className, feature);
         }
     }
-    vector<string> TAN::graph(const string& title)
+    vector<string> TAN::graph(const string& title) const
     {
         return model.graph(title);
     }

src/BayesNet/TAN.h

@@ -3,15 +3,14 @@
 #include "Classifier.h"
 namespace bayesnet {
     using namespace std;
-    using namespace torch;
     class TAN : public Classifier {
     private:
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
         virtual ~TAN() {};
-        vector<string> graph(const string& name = "TAN") override;
+        vector<string> graph(const string& name = "TAN") const override;
     };
 }
 #endif

src/BayesNet/TANLd.cc

@@ -2,33 +2,29 @@
 
 namespace bayesnet {
     using namespace std;
-    TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
+    TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const vector<string>& features_, const string& className_, map<string, vector<int>>& states_)
     {
-        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        checkInput(X_, y_);
         features = features_;
         className = className_;
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
-        generateTensorXFromVector();
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        TAN::fit(TAN::Xv, TAN::yv, features, className, states);
+        TAN::fit(dataset, features, className, states);
-        localDiscretizationProposal(states, model);
+        states = localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(TAN::Xv, TAN::yv, features, className);
         return *this;
+
     }
     Tensor TANLd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return TAN::predict(Xt);
     }
-    vector<string> TANLd::graph(const string& name)
+    vector<string> TANLd::graph(const string& name) const
     {
         return TAN::graph(name);
     }

src/BayesNet/TANLd.h

@@ -10,8 +10,8 @@ namespace bayesnet {
     public:
         TANLd();
         virtual ~TANLd() = default;
-        TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "TAN") override;
+        vector<string> graph(const string& name = "TAN") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };

src/BayesNet/bayesnetUtils.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
     // Return the indices in descending order
-    vector<int> argsort(vector<float>& nums)
+    vector<int> argsort(vector<double>& nums)
     {
         int n = nums.size();
         vector<int> indices(n);

src/BayesNet/bayesnetUtils.h

@@ -5,7 +5,7 @@
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    vector<int> argsort(vector<float>& nums);
+    vector<int> argsort(vector<double>& nums);
     vector<vector<int>> tensorToVector(Tensor& tensor);
 }
 #endif //BAYESNET_UTILS_H

src/Platform/BestResults.cc

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

src/Platform/BestResults.h

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

src/Platform/BestScore.h

@@ -0,0 +1,10 @@
+#ifndef BESTSCORE_H
+#define BESTSCORE_H
+#include <string>
+class BestScore {
+public:
+    static std::string title() { return "STree_default (linear-ovo)"; }
+    static double score() { return 22.109799; }
+    static std::string scoreName() { return "accuracy"; }
+};
+#endif

src/Platform/CMakeLists.txt

@@ -4,5 +4,17 @@ include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
 include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
-add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc Report.cc)
+include_directories(${BayesNet_SOURCE_DIR}/lib/libxlsxwriter/include)
-target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
+add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc ReportConsole.cc ReportBase.cc)
+add_executable(manage manage.cc Results.cc Result.cc ReportConsole.cc ReportExcel.cc ReportBase.cc Datasets.cc platformUtils.cc)
+add_executable(list list.cc platformUtils Datasets.cc)
+add_executable(best best.cc BestResults.cc Result.cc Statistics.cc)
+target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
+if (${CMAKE_HOST_SYSTEM_NAME} MATCHES "Linux")
+    target_link_libraries(manage "${TORCH_LIBRARIES}" libxlsxwriter.so ArffFiles mdlp stdc++fs)
+    target_link_libraries(best Boost::boost stdc++fs)
+else()
+    target_link_libraries(manage "${TORCH_LIBRARIES}" "${XLSXWRITER_LIB}" ArffFiles mdlp)
+    target_link_libraries(best Boost::boost)
+endif()
+target_link_libraries(list ArffFiles mdlp "${TORCH_LIBRARIES}")

src/Platform/Colors.h

@@ -0,0 +1,14 @@
+#ifndef COLORS_H
+#define COLORS_H
+class Colors {
+public:
+    static std::string MAGENTA() { return "\033[1;35m"; }
+    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 RESET() { return "\033[0m"; }
+};
+#endif // COLORS_H

src/Platform/Datasets.cc

@@ -1,6 +1,7 @@
 #include "Datasets.h"
 #include "platformUtils.h"
 #include "ArffFiles.h"
+#include <fstream>
 namespace platform {
     void Datasets::load()
     {
@@ -24,75 +25,110 @@ namespace platform {
         transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
         return result;
     }
-    vector<string> Datasets::getFeatures(string name)
+    vector<string> Datasets::getFeatures(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getFeatures();
+            return datasets.at(name)->getFeatures();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    map<string, vector<int>> Datasets::getStates(string name)
+    map<string, vector<int>> Datasets::getStates(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getStates();
+            return datasets.at(name)->getStates();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    string Datasets::getClassName(string name)
+    void Datasets::loadDataset(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getClassName();
+            return;
+        } else {
+            datasets.at(name)->load();
+        }
+    }
+    string Datasets::getClassName(const string& name) const
+    {
+        if (datasets.at(name)->isLoaded()) {
+            return datasets.at(name)->getClassName();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Datasets::getNSamples(string name)
+    int Datasets::getNSamples(const string& name) const
     {
-        if (datasets[name]->isLoaded()) {
+        if (datasets.at(name)->isLoaded()) {
-            return datasets[name]->getNSamples();
+            return datasets.at(name)->getNSamples();
         } else {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(string name)
+    int Datasets::getNClasses(const 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 *max_element(yv.begin(), yv.end()) + 1;
+        } else {
+            throw invalid_argument("Dataset not loaded.");
+        }
+    }
+    vector<int> Datasets::getClassesCounts(const string& name) const
+    {
+        if (datasets.at(name)->isLoaded()) {
+            auto [Xv, yv] = datasets.at(name)->getVectors();
+            vector<int> counts(*max_element(yv.begin(), yv.end()) + 1);
+            for (auto y : yv) {
+                counts[y]++;
+            }
+            return counts;
+        } else {
+            throw invalid_argument("Dataset not loaded.");
+        }
+    }
+    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectors();
     }
-    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(string name)
+    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectorsDiscretized();
     }
-    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(string name)
+    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getTensors();
     }
-    bool Datasets::isDataset(const string& name)
+    bool Datasets::isDataset(const string& name) const
     {
         return datasets.find(name) != datasets.end();
     }
     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)
     {
     }
-    string Dataset::getName()
+    string Dataset::getName() const
     {
         return name;
     }
-    string Dataset::getClassName()
+    string Dataset::getClassName() const
     {
         return className;
     }
-    vector<string> Dataset::getFeatures()
+    vector<string> Dataset::getFeatures() const
     {
         if (loaded) {
             return features;
@@ -100,7 +136,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNFeatures()
+    int Dataset::getNFeatures() const
     {
         if (loaded) {
             return n_features;
@@ -108,7 +144,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNSamples()
+    int Dataset::getNSamples() const
     {
         if (loaded) {
             return n_samples;
@@ -116,7 +152,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    map<string, vector<int>> Dataset::getStates()
+    map<string, vector<int>> Dataset::getStates() const
     {
         if (loaded) {
             return states;
@@ -177,10 +213,11 @@ namespace platform {
     {
         for (int i = 0; i < features.size(); ++i) {
             states[features[i]] = vector<int>(*max_element(Xd[i].begin(), Xd[i].end()) + 1);
-            iota(begin(states[features[i]]), end(states[features[i]]), 0);
+            auto item = states.at(features[i]);
+            iota(begin(item), end(item), 0);
         }
         states[className] = vector<int>(*max_element(yv.begin(), yv.end()) + 1);
-        iota(begin(states[className]), end(states[className]), 0);
+        iota(begin(states.at(className)), end(states.at(className)), 0);
     }
     void Dataset::load_arff()
     {

src/Platform/Datasets.h

@@ -29,15 +29,15 @@ namespace platform {
     public:
         Dataset(const string& path, const string& name, const string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
         explicit Dataset(const Dataset&);
-        string getName();
+        string getName() const;
-        string getClassName();
+        string getClassName() const;
-        vector<string> getFeatures();
+        vector<string> getFeatures() const;
-        map<string, vector<int>> getStates();
+        map<string, vector<int>> getStates() const;
         pair<vector<vector<float>>&, vector<int>&> getVectors();
         pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized();
         pair<torch::Tensor&, torch::Tensor&> getTensors();
-        int getNFeatures();
+        int getNFeatures() const;
-        int getNSamples();
+        int getNSamples() const;
         void load();
         const bool inline isLoaded() const { return loaded; };
     };
@@ -51,14 +51,17 @@ namespace platform {
     public:
         explicit Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
         vector<string> getNames();
-        vector<string> getFeatures(string name);
+        vector<string> getFeatures(const string& name) const;
-        int getNSamples(string name);
+        int getNSamples(const string& name) const;
-        string getClassName(string name);
+        string getClassName(const string& name) const;
-        map<string, vector<int>> getStates(string name);
+        int getNClasses(const string& name);
-        pair<vector<vector<float>>&, vector<int>&> getVectors(string name);
+        vector<int> getClassesCounts(const string& name) const;
-        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(string name);
+        map<string, vector<int>> getStates(const string& name) const;
-        pair<torch::Tensor&, torch::Tensor&> getTensors(string name);
+        pair<vector<vector<float>>&, vector<int>&> getVectors(const string& name);
-        bool isDataset(const string& name);
+        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(const string& name);
+        pair<torch::Tensor&, torch::Tensor&> getTensors(const string& name);
+        bool isDataset(const string& name) const;
+        void loadDataset(const string& name) const;
     };
 };
 

src/Platform/Experiment.cc

@@ -1,8 +1,8 @@
 #include "Experiment.h"
 #include "Datasets.h"
 #include "Models.h"
-#include "Report.h"
+#include "ReportConsole.h"
-
+#include <fstream>
 namespace platform {
     using json = nlohmann::json;
     string get_date()
@@ -25,6 +25,7 @@ namespace platform {
         oss << std::put_time(timeinfo, "%H:%M:%S");
         return oss.str();
     }
+    Experiment::Experiment() : hyperparameters(json::parse("{}")) {}
     string Experiment::get_file_name()
     {
         string result = "results_" + score_name + "_" + model + "_" + platform + "_" + get_date() + "_" + get_time() + "_" + (stratified ? "1" : "0") + ".json";
@@ -90,7 +91,7 @@ namespace platform {
     void Experiment::report()
     {
         json data = build_json();
-        Report report(data);
+        ReportConsole report(data);
         report.show();
     }
 
@@ -110,6 +111,26 @@ namespace platform {
         }
     }
 
+    string getColor(bayesnet::status_t status)
+    {
+        switch (status) {
+            case bayesnet::NORMAL:
+                return Colors::GREEN();
+            case bayesnet::WARNING:
+                return Colors::YELLOW();
+            case bayesnet::ERROR:
+                return Colors::RED();
+            default:
+                return Colors::RESET();
+        }
+    }
+
+    void showProgress(int fold, const string& color, const string& phase)
+    {
+        string prefix = phase == "a" ? "" : "\b\b\b\b";
+        cout << prefix << color << fold << Colors::RESET() << "(" << color << phase << Colors::RESET() << ")" << flush;
+
+    }
     void Experiment::cross_validation(const string& path, const string& fileName)
     {
         auto datasets = platform::Datasets(path, discretized, platform::ARFF);
@@ -124,6 +145,8 @@ namespace platform {
         auto result = Result();
         auto [values, counts] = at::_unique(y);
         result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
+        result.setHyperparameters(hyperparameters);
+        // Initialize results 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);
@@ -144,6 +167,10 @@ namespace platform {
             for (int nfold = 0; nfold < nfolds; nfold++) {
                 auto clf = Models::instance()->create(model);
                 setModelVersion(clf->getVersion());
+                if (hyperparameters.size() != 0) {
+                    clf->setHyperparameters(hyperparameters);
+                }
+                // Split train - test dataset
                 train_timer.start();
                 auto [train, test] = fold->getFold(nfold);
                 auto train_t = torch::tensor(train);
@@ -152,24 +179,31 @@ namespace platform {
                 auto y_train = y.index({ train_t });
                 auto X_test = X.index({ "...", test_t });
                 auto y_test = y.index({ test_t });
-                cout << nfold + 1 << ", " << flush;
+                showProgress(nfold + 1, getColor(clf->getStatus()), "a");
+                // Train model
                 clf->fit(X_train, y_train, features, className, states);
+                showProgress(nfold + 1, getColor(clf->getStatus()), "b");
                 nodes[item] = clf->getNumberOfNodes();
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();
                 train_time[item] = train_timer.getDuration();
+                // Score train
                 auto accuracy_train_value = clf->score(X_train, y_train);
+                // Test model
+                showProgress(nfold + 1, getColor(clf->getStatus()), "c");
                 test_timer.start();
                 auto accuracy_test_value = clf->score(X_test, y_test);
                 test_time[item] = test_timer.getDuration();
                 accuracy_train[item] = accuracy_train_value;
                 accuracy_test[item] = accuracy_test_value;
+                cout << "\b\b\b, " << flush;
                 // Store results and times in vector
                 result.addScoreTrain(accuracy_train_value);
                 result.addScoreTest(accuracy_test_value);
                 result.addTimeTrain(train_time[item].item<double>());
                 result.addTimeTest(test_time[item].item<double>());
                 item++;
+                clf.reset();
             }
             cout << "end. " << flush;
             delete fold;
@@ -177,6 +211,7 @@ namespace platform {
         result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
         result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
         result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
+        result.setTestTimeStd(torch::std(test_time).item<double>()).setTrainTimeStd(torch::std(train_time).item<double>());
         result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
         result.setDataset(fileName);
         addResult(result);

src/Platform/Experiment.h

@@ -29,7 +29,8 @@ namespace platform {
     };
     class Result {
     private:
-        string dataset, hyperparameters, model_version;
+        string dataset, model_version;
+        json hyperparameters;
         int samples{ 0 }, features{ 0 }, classes{ 0 };
         double score_train{ 0 }, score_test{ 0 }, score_train_std{ 0 }, score_test_std{ 0 }, train_time{ 0 }, train_time_std{ 0 }, test_time{ 0 }, test_time_std{ 0 };
         float nodes{ 0 }, leaves{ 0 }, depth{ 0 };
@@ -37,7 +38,7 @@ namespace platform {
     public:
         Result() = default;
         Result& setDataset(const string& dataset) { this->dataset = dataset; return *this; }
-        Result& setHyperparameters(const string& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
+        Result& setHyperparameters(const json& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
         Result& setSamples(int samples) { this->samples = samples; return *this; }
         Result& setFeatures(int features) { this->features = features; return *this; }
         Result& setClasses(int classes) { this->classes = classes; return *this; }
@@ -59,7 +60,7 @@ namespace platform {
         const float get_score_train() const { return score_train; }
         float get_score_test() { return score_test; }
         const string& getDataset() const { return dataset; }
-        const string& getHyperparameters() const { return hyperparameters; }
+        const json& getHyperparameters() const { return hyperparameters; }
         const int getSamples() const { return samples; }
         const int getFeatures() const { return features; }
         const int getClasses() const { return classes; }
@@ -85,11 +86,12 @@ namespace platform {
         bool discretized{ false }, stratified{ false };
         vector<Result> results;
         vector<int> randomSeeds;
+        json hyperparameters = "{}";
         int nfolds{ 0 };
         float duration{ 0 };
         json build_json();
     public:
-        Experiment() = default;
+        Experiment();
         Experiment& setTitle(const string& title) { this->title = title; return *this; }
         Experiment& setModel(const string& model) { this->model = model; return *this; }
         Experiment& setPlatform(const string& platform) { this->platform = platform; return *this; }
@@ -103,6 +105,7 @@ namespace platform {
         Experiment& addResult(Result result) { results.push_back(result); return *this; }
         Experiment& addRandomSeed(int randomSeed) { randomSeeds.push_back(randomSeed); return *this; }
         Experiment& setDuration(float duration) { this->duration = duration; return *this; }
+        Experiment& setHyperparameters(const json& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
         string get_file_name();
         void save(const string& path);
         void cross_validation(const string& path, const string& fileName);

src/Platform/Folding.cc

@@ -1,95 +1,97 @@
 #include "Folding.h"
 #include <algorithm>
 #include <map>
-Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
+namespace platform {
-{
+    Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
-    random_device rd;
+    {
-    random_seed = default_random_engine(seed == -1 ? rd() : seed);
+        random_device rd;
-    srand(seed == -1 ? time(0) : seed);
+        random_seed = default_random_engine(seed == -1 ? rd() : seed);
-}
+        srand(seed == -1 ? time(0) : seed);
-KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
-{
-    iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
-    shuffle(indices.begin(), indices.end(), random_seed);
-}
-pair<vector<int>, vector<int>> KFold::getFold(int nFold)
-{
-    if (nFold >= k || nFold < 0) {
-        throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
     }
-    int nTest = n / k;
+    KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
-    auto train = vector<int>();
+    {
-    auto test = vector<int>();
+        iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
-    for (int i = 0; i < n; i++) {
-        if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
-            test.push_back(indices[i]);
-        } else {
-            train.push_back(indices[i]);
-        }
-    }
-    return { train, test };
-}
-StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
-{
-    n = y.numel();
-    this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
-    build();
-}
-StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
-    : Fold(k, y.size(), seed)
-{
-    this->y = y;
-    n = y.size();
-    build();
-}
-void StratifiedKFold::build()
-{
-    stratified_indices = vector<vector<int>>(k);
-    int fold_size = n / k;
-    // Compute class counts and indices
-    auto class_indices = map<int, vector<int>>();
-    vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
-    for (auto i = 0; i < n; ++i) {
-        class_counts[y[i]]++;
-        class_indices[y[i]].push_back(i);
-    }
-    // Shuffle class indices
-    for (auto& [cls, indices] : class_indices) {
         shuffle(indices.begin(), indices.end(), random_seed);
     }
-    // Assign indices to folds
+    pair<vector<int>, vector<int>> KFold::getFold(int nFold)
-    for (auto label = 0; label < class_counts.size(); ++label) {
+    {
-        auto num_samples_to_take = class_counts[label] / k;
+        if (nFold >= k || nFold < 0) {
-        if (num_samples_to_take == 0)
+            throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
-            continue;
-        auto remainder_samples_to_take = class_counts[label] % k;
-        for (auto fold = 0; fold < k; ++fold) {
-            auto it = next(class_indices[label].begin(), num_samples_to_take);
-            move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold]));  // ##
-            class_indices[label].erase(class_indices[label].begin(), it);
         }
-        while (remainder_samples_to_take > 0) {
+        int nTest = n / k;
-            int fold = (rand() % static_cast<int>(k));
+        auto train = vector<int>();
-            if (stratified_indices[fold].size() == fold_size + 1) {
+        auto test = vector<int>();
-                continue;
+        for (int i = 0; i < n; i++) {
+            if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
+                test.push_back(indices[i]);
+            } else {
+                train.push_back(indices[i]);
+            }
+        }
+        return { train, test };
+    }
+    StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
+    {
+        n = y.numel();
+        this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
+        build();
+    }
+    StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
+        : Fold(k, y.size(), seed)
+    {
+        this->y = y;
+        n = y.size();
+        build();
+    }
+    void StratifiedKFold::build()
+    {
+        stratified_indices = vector<vector<int>>(k);
+        int fold_size = n / k;
+        // Compute class counts and indices
+        auto class_indices = map<int, vector<int>>();
+        vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
+        for (auto i = 0; i < n; ++i) {
+            class_counts[y[i]]++;
+            class_indices[y[i]].push_back(i);
+        }
+        // Shuffle class indices
+        for (auto& [cls, indices] : class_indices) {
+            shuffle(indices.begin(), indices.end(), random_seed);
+        }
+        // Assign indices to folds
+        for (auto label = 0; label < class_counts.size(); ++label) {
+            auto num_samples_to_take = class_counts[label] / k;
+            if (num_samples_to_take == 0)
+                continue;
+            auto remainder_samples_to_take = class_counts[label] % k;
+            for (auto fold = 0; fold < k; ++fold) {
+                auto it = next(class_indices[label].begin(), num_samples_to_take);
+                move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold]));  // ##
+                class_indices[label].erase(class_indices[label].begin(), it);
+            }
+            while (remainder_samples_to_take > 0) {
+                int fold = (rand() % static_cast<int>(k));
+                if (stratified_indices[fold].size() == fold_size + 1) {
+                    continue;
+                }
+                auto it = next(class_indices[label].begin(), 1);
+                stratified_indices[fold].push_back(*class_indices[label].begin());
+                class_indices[label].erase(class_indices[label].begin(), it);
+                remainder_samples_to_take--;
             }
-            auto it = next(class_indices[label].begin(), 1);
-            stratified_indices[fold].push_back(*class_indices[label].begin());
-            class_indices[label].erase(class_indices[label].begin(), it);
-            remainder_samples_to_take--;
         }
     }
-}
+    pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
-pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
+    {
-{
+        if (nFold >= k || nFold < 0) {
-    if (nFold >= k || nFold < 0) {
+            throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
-        throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
+        }
+        vector<int> test_indices = stratified_indices[nFold];
+        vector<int> train_indices;
+        for (int i = 0; i < k; ++i) {
+            if (i == nFold) continue;
+            train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
+        }
+        return { train_indices, test_indices };
     }
-    vector<int> test_indices = stratified_indices[nFold];
-    vector<int> train_indices;
-    for (int i = 0; i < k; ++i) {
-        if (i == nFold) continue;
-        train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
-    }
-    return { train_indices, test_indices };
 }

src/Platform/Folding.h

@@ -4,34 +4,35 @@
 #include <vector>
 #include <random> 
 using namespace std;
-
+namespace platform {
-class Fold {
+    class Fold {
-protected:
+    protected:
-    int k;
+        int k;
-    int n;
+        int n;
-    int seed;
+        int seed;
-    default_random_engine random_seed;
+        default_random_engine random_seed;
-public:
+    public:
-    Fold(int k, int n, int seed = -1);
+        Fold(int k, int n, int seed = -1);
-    virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
+        virtual pair<vector<int>, vector<int>> getFold(int nFold) = 0;
-    virtual ~Fold() = default;
+        virtual ~Fold() = default;
-    int getNumberOfFolds() { return k; }
+        int getNumberOfFolds() { return k; }
-};
+    };
-class KFold : public Fold {
+    class KFold : public Fold {
-private:
+    private:
-    vector<int> indices;
+        vector<int> indices;
-public:
+    public:
-    KFold(int k, int n, int seed = -1);
+        KFold(int k, int n, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold) override;
+        pair<vector<int>, vector<int>> getFold(int nFold) override;
-};
+    };
-class StratifiedKFold : public Fold {
+    class StratifiedKFold : public Fold {
-private:
+    private:
-    vector<int> y;
+        vector<int> y;
-    vector<vector<int>> stratified_indices;
+        vector<vector<int>> stratified_indices;
-    void build();
+        void build();
-public:
+    public:
-    StratifiedKFold(int k, const vector<int>& y, int seed = -1);
+        StratifiedKFold(int k, const vector<int>& y, int seed = -1);
-    StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
+        StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold) override;
+        pair<vector<int>, vector<int>> getFold(int nFold) override;
-};
+    };
+}
 #endif

src/Platform/Models.cc

@@ -26,7 +26,7 @@ namespace platform {
             instance = it->second();
         // wrap instance in a shared ptr and return
         if (instance != nullptr)
-            return shared_ptr<bayesnet::BaseClassifier>(instance);
+            return unique_ptr<bayesnet::BaseClassifier>(instance);
         else
             return nullptr;
     }

src/Platform/Models.h

@@ -10,6 +10,7 @@
 #include "KDBLd.h"
 #include "SPODELd.h"
 #include "AODELd.h"
+#include "BoostAODE.h"
 namespace platform {
     class Models {
     private:

src/Platform/Paths.h

@@ -0,0 +1,12 @@
+#ifndef PATHS_H
+#define PATHS_H
+#include <string>
+namespace platform {
+    class Paths {
+    public:
+        static std::string datasets() { return "datasets/"; }
+        static std::string results() { return "results/"; }
+        static std::string excel() { return "excel/"; }
+    };
+}
+#endif

src/Platform/Report.cc

@@ -1,66 +0,0 @@
-#include "Report.h"
-
-namespace platform {
-    string headerLine(const string& text)
-    {
-        int n = MAXL - text.length() - 3;
-        return "* " + text + string(n, ' ') + "*\n";
-    }
-    string Report::fromVector(const string& key)
-    {
-        string result = "";
-
-        for (auto& item : data[key]) {
-            result += to_string(item) + ", ";
-        }
-        return "[" + result.substr(0, result.length() - 2) + "]";
-    }
-    string fVector(const json& data)
-    {
-        string result = "";
-        for (const auto& item : data) {
-            result += to_string(item) + ", ";
-        }
-        return "[" + result.substr(0, result.length() - 2) + "]";
-    }
-    void Report::show()
-    {
-        header();
-        body();
-    }
-    void Report::header()
-    {
-        cout << string(MAXL, '*') << endl;
-        cout << headerLine("Report " + data["model"].get<string>() + " ver. " + data["version"].get<string>() + " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) + " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>());
-        cout << headerLine(data["title"].get<string>());
-        cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
-        cout << headerLine("Execution took  " + to_string(data["duration"].get<float>()) + " seconds,   " + to_string(data["duration"].get<float>() / 3600) + " hours, on " + data["platform"].get<string>());
-        cout << headerLine("Score is " + data["score_name"].get<string>());
-        cout << string(MAXL, '*') << endl;
-        cout << endl;
-    }
-    void Report::body()
-    {
-        cout << "Dataset                        Sampl. Feat. Cls Nodes   Edges   States  Score           Time              Hyperparameters" << endl;
-        cout << "============================== ====== ===== === ======= ======= ======= =============== ================= ===============" << endl;
-        for (const auto& r : data["results"]) {
-            cout << setw(30) << left << r["dataset"].get<string>() << " ";
-            cout << setw(6) << right << r["samples"].get<int>() << " ";
-            cout << setw(5) << right << r["features"].get<int>() << " ";
-            cout << setw(3) << right << r["classes"].get<int>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
-            cout << setw(7) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
-            cout << setw(8) << right << setprecision(6) << fixed << r["score_test"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_test_std"].get<double>() << " ";
-            cout << setw(10) << right << setprecision(6) << fixed << r["test_time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["test_time_std"].get<double>() << " ";
-            cout << " " << r["hyperparameters"].get<string>();
-            cout << endl;
-            cout << string(MAXL, '*') << endl;
-            cout << headerLine("Train scores: " + fVector(r["scores_train"]));
-            cout << headerLine("Test  scores: " + fVector(r["scores_test"]));
-            cout << headerLine("Train  times: " + fVector(r["times_train"]));
-            cout << headerLine("Test   times: " + fVector(r["times_test"]));
-            cout << string(MAXL, '*') << endl;
-        }
-    }
-}

src/Platform/Report.h

@@ -1,23 +0,0 @@
-#ifndef REPORT_H
-#define REPORT_H
-#include <string>
-#include <iostream>
-#include <nlohmann/json.hpp>
-
-using json = nlohmann::json;
-const int MAXL = 121;
-namespace platform {
-    using namespace std;
-    class Report {
-    public:
-        explicit Report(json data_) { data = data_; };
-        virtual ~Report() = default;
-        void show();
-    private:
-        void header();
-        void body();
-        string fromVector(const string& key);
-        json data;
-    };
-};
-#endif

src/Platform/ReportBase.cc

@@ -0,0 +1,114 @@
+#include <sstream>
+#include <locale>
+#include "Datasets.h"
+#include "ReportBase.h"
+#include "BestScore.h"
+
+
+namespace platform {
+    ReportBase::ReportBase(json data_, bool compare) : data(data_), compare(compare), margin(0.1)
+    {
+        stringstream oss;
+        oss << "Better than ZeroR + " << setprecision(1) << fixed << margin * 100 << "%";
+        meaning = {
+            {Symbols::equal_best, "Equal to best"},
+            {Symbols::better_best, "Better than best"},
+            {Symbols::cross, "Less than or equal to ZeroR"},
+            {Symbols::upward_arrow, oss.str()}
+        };
+    }
+    string ReportBase::fromVector(const string& key)
+    {
+        stringstream oss;
+        string sep = "";
+        oss << "[";
+        for (auto& item : data[key]) {
+            oss << sep << item.get<double>();
+            sep = ", ";
+        }
+        oss << "]";
+        return oss.str();
+    }
+    string ReportBase::fVector(const string& title, const json& data, const int width, const int precision)
+    {
+        stringstream oss;
+        string sep = "";
+        oss << title << "[";
+        for (const auto& item : data) {
+            oss << sep << fixed << setw(width) << setprecision(precision) << item.get<double>();
+            sep = ", ";
+        }
+        oss << "]";
+        return oss.str();
+    }
+    void ReportBase::show()
+    {
+        header();
+        body();
+    }
+    string ReportBase::compareResult(const string& dataset, double result)
+    {
+        string status = " ";
+        if (compare) {
+            double best = bestResult(dataset, data["model"].get<string>());
+            if (result == best) {
+                status = Symbols::equal_best;
+            } else if (result > best) {
+                status = Symbols::better_best;
+            }
+        } else {
+            if (data["score_name"].get<string>() == "accuracy") {
+                auto dt = Datasets(Paths::datasets(), false);
+                dt.loadDataset(dataset);
+                auto numClasses = dt.getNClasses(dataset);
+                if (numClasses == 2) {
+                    vector<int> distribution = dt.getClassesCounts(dataset);
+                    double nSamples = dt.getNSamples(dataset);
+                    vector<int>::iterator maxValue = max_element(distribution.begin(), distribution.end());
+                    double mark = *maxValue / nSamples * (1 + margin);
+                    if (mark > 1) {
+                        mark = 0.9995;
+                    }
+                    status = result < mark ? Symbols::cross : result > mark ? Symbols::upward_arrow : "=";
+                }
+            }
+        }
+        if (status != " ") {
+            auto item = summary.find(status);
+            if (item != summary.end()) {
+                summary[status]++;
+            } else {
+                summary[status] = 1;
+            }
+        }
+        return status;
+    }
+    double ReportBase::bestResult(const string& dataset, const string& model)
+    {
+        double value = 0.0;
+        if (bestResults.size() == 0) {
+            // try to load the best results
+            string score = data["score_name"];
+            replace(score.begin(), score.end(), '_', '-');
+            string fileName = "best_results_" + score + "_" + model + ".json";
+            ifstream resultData(Paths::results() + "/" + fileName);
+            if (resultData.is_open()) {
+                bestResults = json::parse(resultData);
+            } else {
+                existBestFile = false;
+            }
+        }
+        try {
+            value = bestResults.at(dataset).at(0);
+        }
+        catch (exception) {
+            value = 1.0;
+
+        }
+        return value;
+    }
+    bool ReportBase::getExistBestFile()
+    {
+        return existBestFile;
+    }
+}

src/Platform/ReportBase.h

@@ -0,0 +1,37 @@
+#ifndef REPORTBASE_H
+#define REPORTBASE_H
+#include <string>
+#include <iostream>
+#include "Paths.h"
+#include "Symbols.h"
+#include <nlohmann/json.hpp>
+
+using json = nlohmann::json;
+namespace platform {
+    using namespace std;
+
+    class ReportBase {
+    public:
+        explicit ReportBase(json data_, bool compare);
+        virtual ~ReportBase() = default;
+        void show();
+    protected:
+        json data;
+        string fromVector(const string& key);
+        string fVector(const string& title, const json& data, const int width, const int precision);
+        bool getExistBestFile();
+        virtual void header() = 0;
+        virtual void body() = 0;
+        virtual void showSummary() = 0;
+        string compareResult(const string& dataset, double result);
+        map<string, int> summary;
+        double margin;
+        map<string, string> meaning;
+        bool compare;
+    private:
+        double bestResult(const string& dataset, const string& model);
+        json bestResults;
+        bool existBestFile = true;
+    };
+};
+#endif

src/Platform/ReportConsole.cc

@@ -0,0 +1,112 @@
+#include <sstream>
+#include <locale>
+#include "ReportConsole.h"
+#include "BestScore.h"
+
+
+namespace platform {
+    struct separated : numpunct<char> {
+        char do_decimal_point() const { return ','; }
+        char do_thousands_sep() const { return '.'; }
+        string do_grouping() const { return "\03"; }
+    };
+
+    string ReportConsole::headerLine(const string& text, int utf = 0)
+    {
+        int n = MAXL - text.length() - 3;
+        n = n < 0 ? 0 : n;
+        return "* " + text + string(n + utf, ' ') + "*\n";
+    }
+
+    void ReportConsole::header()
+    {
+        locale mylocale(cout.getloc(), new separated);
+        locale::global(mylocale);
+        cout.imbue(mylocale);
+        stringstream oss;
+        cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
+        cout << headerLine("Report " + data["model"].get<string>() + " ver. " + data["version"].get<string>() + " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) + " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>());
+        cout << headerLine(data["title"].get<string>());
+        cout << headerLine("Random seeds: " + fromVector("seeds") + " Stratified: " + (data["stratified"].get<bool>() ? "True" : "False"));
+        oss << "Execution took  " << setprecision(2) << fixed << data["duration"].get<float>() << " seconds,   " << data["duration"].get<float>() / 3600 << " hours, on " << data["platform"].get<string>();
+        cout << headerLine(oss.str());
+        cout << headerLine("Score is " + data["score_name"].get<string>());
+        cout << string(MAXL, '*') << endl;
+        cout << endl;
+    }
+    void ReportConsole::body()
+    {
+        cout << Colors::GREEN() << " #  Dataset                   Sampl. Feat. Cls Nodes     Edges     States    Score           Time                Hyperparameters" << endl;
+        cout << "=== ========================= ====== ===== === ========= ========= ========= =============== =================== ====================" << endl;
+        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();
+            cout << color;
+            cout << setw(3) << index++ << " ";
+            cout << setw(25) << left << r["dataset"].get<string>() << " ";
+            cout << setw(6) << right << r["samples"].get<int>() << " ";
+            cout << setw(5) << right << r["features"].get<int>() << " ";
+            cout << setw(3) << right << r["classes"].get<int>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["nodes"].get<float>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["leaves"].get<float>() << " ";
+            cout << setw(9) << setprecision(2) << fixed << r["depth"].get<float>() << " ";
+            cout << setw(8) << right << setprecision(6) << fixed << r["score"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["score_std"].get<double>();
+            const string status = compareResult(r["dataset"].get<string>(), r["score"].get<double>());
+            cout << status;
+            cout << setw(12) << right << setprecision(6) << fixed << r["time"].get<double>() << "±" << setw(6) << setprecision(4) << fixed << r["time_std"].get<double>() << " ";
+            try {
+                cout << r["hyperparameters"].get<string>();
+            }
+            catch (const exception& err) {
+                cout << r["hyperparameters"];
+            }
+            cout << endl;
+            lastResult = r;
+            totalScore += r["score"].get<double>();
+            odd = !odd;
+        }
+        if (data["results"].size() == 1 || selectedIndex != -1) {
+            cout << string(MAXL, '*') << endl;
+            cout << headerLine(fVector("Train scores: ", lastResult["scores_train"], 14, 12));
+            cout << headerLine(fVector("Test  scores: ", lastResult["scores_test"], 14, 12));
+            cout << headerLine(fVector("Train  times: ", lastResult["times_train"], 10, 3));
+            cout << headerLine(fVector("Test   times: ", lastResult["times_test"], 10, 3));
+            cout << string(MAXL, '*') << endl;
+        } else {
+            footer(totalScore);
+        }
+    }
+    void ReportConsole::showSummary()
+    {
+        for (const auto& item : summary) {
+            stringstream oss;
+            oss << setw(3) << left << item.first;
+            oss << setw(3) << right << item.second << " ";
+            oss << left << meaning.at(item.first);
+            cout << headerLine(oss.str(), 2);
+        }
+    }
+
+    void ReportConsole::footer(double totalScore)
+    {
+        cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
+        showSummary();
+        auto score = data["score_name"].get<string>();
+        if (score == BestScore::scoreName()) {
+            stringstream oss;
+            oss << score << " compared to " << BestScore::title() << " .:  " << totalScore / BestScore::score();
+            cout << headerLine(oss.str());
+        }
+        if (!getExistBestFile() && compare) {
+            cout << headerLine("*** Best Results File not found. Couldn't compare any result!");
+        }
+        cout << string(MAXL, '*') << endl << Colors::RESET();
+    }
+}

src/Platform/ReportConsole.h

@@ -0,0 +1,24 @@
+#ifndef REPORTCONSOLE_H
+#define REPORTCONSOLE_H
+#include <string>
+#include <iostream>
+#include "ReportBase.h"
+#include "Colors.h"
+
+namespace platform {
+    using namespace std;
+    const int MAXL = 133;
+    class ReportConsole : public ReportBase {
+    public:
+        explicit ReportConsole(json data_, bool compare = false, int index = -1) : ReportBase(data_, compare), selectedIndex(index) {};
+        virtual ~ReportConsole() = default;
+    private:
+        int selectedIndex;
+        string headerLine(const string& text, int utf);
+        void header() override;
+        void body() override;
+        void footer(double totalScore);
+        void showSummary() override;
+    };
+};
+#endif

src/Platform/ReportExcel.cc

@@ -0,0 +1,333 @@
+#include <sstream>
+#include <locale>
+#include "ReportExcel.h"
+#include "BestScore.h"
+
+
+namespace platform {
+    struct separated : numpunct<char> {
+        char do_decimal_point() const { return ','; }
+
+        char do_thousands_sep() const { return '.'; }
+
+        string do_grouping() const { return "\03"; }
+    };
+
+    ReportExcel::ReportExcel(json data_, bool compare, lxw_workbook* workbook) : ReportBase(data_, compare), row(0), workbook(workbook)
+    {
+        normalSize = 14; //font size for report body
+        colorTitle = 0xB1A0C7;
+        colorOdd = 0xDCE6F1;
+        colorEven = 0xFDE9D9;
+        createFile();
+    }
+
+    lxw_workbook* ReportExcel::getWorkbook()
+    {
+        return workbook;
+    }
+
+    lxw_format* ReportExcel::efectiveStyle(const string& style)
+    {
+        lxw_format* efectiveStyle;
+        if (style == "") {
+            efectiveStyle = NULL;
+        } else {
+            string suffix = row % 2 ? "_odd" : "_even";
+            efectiveStyle = styles.at(style + suffix);
+        }
+        return efectiveStyle;
+    }
+
+    void ReportExcel::writeString(int row, int col, const string& text, const string& style)
+    {
+        worksheet_write_string(worksheet, row, col, text.c_str(), efectiveStyle(style));
+    }
+    void ReportExcel::writeInt(int row, int col, const int number, const string& style)
+    {
+        worksheet_write_number(worksheet, row, col, number, efectiveStyle(style));
+    }
+    void ReportExcel::writeDouble(int row, int col, const double number, const string& style)
+    {
+        worksheet_write_number(worksheet, row, col, number, efectiveStyle(style));
+    }
+
+    void ReportExcel::formatColumns()
+    {
+        worksheet_freeze_panes(worksheet, 6, 1);
+        vector<int> columns_sizes = { 22, 10, 9, 7, 12, 12, 12, 12, 12, 3, 15, 12, 23 };
+        for (int i = 0; i < columns_sizes.size(); ++i) {
+            worksheet_set_column(worksheet, i, i, columns_sizes.at(i), NULL);
+        }
+    }
+
+    void ReportExcel::addColor(lxw_format* style, bool odd)
+    {
+        uint32_t efectiveColor = odd ? colorEven : colorOdd;
+        format_set_bg_color(style, lxw_color_t(efectiveColor));
+    }
+    void ReportExcel::createStyle(const string& name, lxw_format* style, bool odd)
+    {
+        addColor(style, odd);
+        if (name == "textCentered") {
+            format_set_align(style, LXW_ALIGN_CENTER);
+            format_set_font_size(style, normalSize);
+            format_set_border(style, LXW_BORDER_THIN);
+        } else if (name == "text") {
+            format_set_font_size(style, normalSize);
+            format_set_border(style, LXW_BORDER_THIN);
+        } else if (name == "bodyHeader") {
+            format_set_bold(style);
+            format_set_font_size(style, normalSize);
+            format_set_align(style, LXW_ALIGN_CENTER);
+            format_set_align(style, LXW_ALIGN_VERTICAL_CENTER);
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_bg_color(style, lxw_color_t(colorTitle));
+        } else if (name == "result") {
+            format_set_font_size(style, normalSize);
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_num_format(style, "0.0000000");
+        } else if (name == "time") {
+            format_set_font_size(style, normalSize);
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_num_format(style, "#,##0.000000");
+        } else if (name == "ints") {
+            format_set_font_size(style, normalSize);
+            format_set_num_format(style, "###,##0");
+            format_set_border(style, LXW_BORDER_THIN);
+        } else if (name == "floats") {
+            format_set_border(style, LXW_BORDER_THIN);
+            format_set_font_size(style, normalSize);
+            format_set_num_format(style, "#,##0.00");
+        }
+    }
+
+    void ReportExcel::createFormats()
+    {
+        auto styleNames = { "text", "textCentered", "bodyHeader", "result", "time", "ints", "floats" };
+        lxw_format* style;
+        for (string name : styleNames) {
+            lxw_format* style = workbook_add_format(workbook);
+            style = workbook_add_format(workbook);
+            createStyle(name, style, true);
+            styles[name + "_odd"] = style;
+            style = workbook_add_format(workbook);
+            createStyle(name, style, false);
+            styles[name + "_even"] = style;
+        }
+
+        // Header 1st line
+        lxw_format* headerFirst = workbook_add_format(workbook);
+        format_set_bold(headerFirst);
+        format_set_font_size(headerFirst, 18);
+        format_set_align(headerFirst, LXW_ALIGN_CENTER);
+        format_set_align(headerFirst, LXW_ALIGN_VERTICAL_CENTER);
+        format_set_border(headerFirst, LXW_BORDER_THIN);
+        format_set_bg_color(headerFirst, lxw_color_t(colorTitle));
+
+        // Header rest
+        lxw_format* headerRest = workbook_add_format(workbook);
+        format_set_bold(headerRest);
+        format_set_align(headerRest, LXW_ALIGN_CENTER);
+        format_set_font_size(headerRest, 16);
+        format_set_align(headerRest, LXW_ALIGN_VERTICAL_CENTER);
+        format_set_border(headerRest, LXW_BORDER_THIN);
+        format_set_bg_color(headerRest, lxw_color_t(colorOdd));
+
+        // Header small
+        lxw_format* headerSmall = workbook_add_format(workbook);
+        format_set_bold(headerSmall);
+        format_set_align(headerSmall, LXW_ALIGN_LEFT);
+        format_set_font_size(headerSmall, 12);
+        format_set_border(headerSmall, LXW_BORDER_THIN);
+        format_set_align(headerSmall, LXW_ALIGN_VERTICAL_CENTER);
+        format_set_bg_color(headerSmall, lxw_color_t(colorOdd));
+
+        // Summary style
+        lxw_format* summaryStyle = workbook_add_format(workbook);
+        format_set_bold(summaryStyle);
+        format_set_font_size(summaryStyle, 16);
+        format_set_border(summaryStyle, LXW_BORDER_THIN);
+        format_set_align(summaryStyle, LXW_ALIGN_VERTICAL_CENTER);
+
+        styles["headerFirst"] = headerFirst;
+        styles["headerRest"] = headerRest;
+        styles["headerSmall"] = headerSmall;
+        styles["summaryStyle"] = summaryStyle;
+    }
+
+    void ReportExcel::setProperties()
+    {
+        char line[data["title"].get<string>().size() + 1];
+        strcpy(line, data["title"].get<string>().c_str());
+        lxw_doc_properties properties = {
+            .title = line,
+            .subject = (char*)"Machine learning results",
+            .author = (char*)"Ricardo Montañana Gómez",
+            .manager = (char*)"Dr. J. A. Gámez, Dr. J. M. Puerta",
+            .company = (char*)"UCLM",
+            .comments = (char*)"Created with libxlsxwriter and c++",
+        };
+        workbook_set_properties(workbook, &properties);
+    }
+
+    void ReportExcel::createFile()
+    {
+        if (workbook == NULL) {
+            workbook = workbook_new((Paths::excel() + fileName).c_str());
+        }
+        const string name = data["model"].get<string>();
+        string suffix = "";
+        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 = to_string(++num);
+            } else {
+                worksheet = workbook_add_worksheet(workbook, efectiveName.c_str());
+                break;
+            }
+            if (num > 100) {
+                throw invalid_argument("Couldn't create sheet " + efectiveName);
+            }
+        }
+        cout << "Adding sheet " << efectiveName << " to " << Paths::excel() + fileName << endl;
+        setProperties();
+        createFormats();
+        formatColumns();
+    }
+
+    void ReportExcel::closeFile()
+    {
+        workbook_close(workbook);
+    }
+
+    void ReportExcel::header()
+    {
+        locale mylocale(cout.getloc(), new separated);
+        locale::global(mylocale);
+        cout.imbue(mylocale);
+        stringstream oss;
+        string message = data["model"].get<string>() + " ver. " + data["version"].get<string>() + " " +
+            data["language"].get<string>() + " ver. " + data["language_version"].get<string>() +
+            " with " + to_string(data["folds"].get<int>()) + " Folds cross validation and " + to_string(data["seeds"].size()) +
+            " random seeds. " + data["date"].get<string>() + " " + data["time"].get<string>();
+        worksheet_merge_range(worksheet, 0, 0, 0, 12, message.c_str(), styles["headerFirst"]);
+        worksheet_merge_range(worksheet, 1, 0, 1, 12, data["title"].get<string>().c_str(), styles["headerRest"]);
+        worksheet_merge_range(worksheet, 2, 0, 3, 0, ("Score is " + data["score_name"].get<string>()).c_str(), styles["headerRest"]);
+        worksheet_merge_range(worksheet, 2, 1, 3, 3, "Execution time", styles["headerRest"]);
+        oss << setprecision(2) << fixed << data["duration"].get<float>() << " s";
+        worksheet_merge_range(worksheet, 2, 4, 2, 5, oss.str().c_str(), styles["headerRest"]);
+        oss.str("");
+        oss.clear();
+        oss << setprecision(2) << fixed << data["duration"].get<float>() / 3600 << " h";
+        worksheet_merge_range(worksheet, 3, 4, 3, 5, oss.str().c_str(), styles["headerRest"]);
+        worksheet_merge_range(worksheet, 2, 6, 3, 7, "Platform", styles["headerRest"]);
+        worksheet_merge_range(worksheet, 2, 8, 3, 9, data["platform"].get<string>().c_str(), styles["headerRest"]);
+        worksheet_merge_range(worksheet, 2, 10, 2, 12, ("Random seeds: " + fromVector("seeds")).c_str(), styles["headerSmall"]);
+        oss.str("");
+        oss.clear();
+        oss << "Stratified: " << (data["stratified"].get<bool>() ? "True" : "False");
+        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");
+        worksheet_write_string(worksheet, 3, 12, oss.str().c_str(), styles["headerSmall"]);
+    }
+
+    void ReportExcel::body()
+    {
+        auto head = vector<string>(
+            { "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "St.", "Time",
+             "Time Std.", "Hyperparameters" });
+        int col = 0;
+        for (const auto& item : head) {
+            writeString(5, col++, item, "bodyHeader");
+        }
+        row = 6;
+        col = 0;
+        int hypSize = 22;
+        json lastResult;
+        double totalScore = 0.0;
+        string hyperparameters;
+        for (const auto& r : data["results"]) {
+            writeString(row, col, r["dataset"].get<string>(), "text");
+            writeInt(row, col + 1, r["samples"].get<int>(), "ints");
+            writeInt(row, col + 2, r["features"].get<int>(), "ints");
+            writeInt(row, col + 3, r["classes"].get<int>(), "ints");
+            writeDouble(row, col + 4, r["nodes"].get<float>(), "floats");
+            writeDouble(row, col + 5, r["leaves"].get<float>(), "floats");
+            writeDouble(row, col + 6, r["depth"].get<double>(), "floats");
+            writeDouble(row, col + 7, r["score"].get<double>(), "result");
+            writeDouble(row, col + 8, r["score_std"].get<double>(), "result");
+            const string status = compareResult(r["dataset"].get<string>(), r["score"].get<double>());
+            writeString(row, col + 9, status, "textCentered");
+            writeDouble(row, col + 10, r["time"].get<double>(), "time");
+            writeDouble(row, col + 11, r["time_std"].get<double>(), "time");
+            try {
+                hyperparameters = r["hyperparameters"].get<string>();
+            }
+            catch (const exception& err) {
+                stringstream oss;
+                oss << r["hyperparameters"];
+                hyperparameters = oss.str();
+            }
+            if (hyperparameters.size() > hypSize) {
+                hypSize = hyperparameters.size();
+            }
+            writeString(row, col + 12, hyperparameters, "text");
+            lastResult = r;
+            totalScore += r["score"].get<double>();
+            row++;
+
+        }
+        // Set the right column width of hyperparameters with the maximum length
+        worksheet_set_column(worksheet, 12, 12, hypSize + 5, NULL);
+        // Show totals if only one dataset is present in the result
+        if (data["results"].size() == 1) {
+            for (const string& group : { "scores_train", "scores_test", "times_train", "times_test" }) {
+                row++;
+                col = 1;
+                writeString(row, col, group, "text");
+                for (double item : lastResult[group]) {
+                    string style = group.find("scores") != string::npos ? "result" : "time";
+                    writeDouble(row, ++col, item, style);
+                }
+            }
+            // Set with of columns to show those totals completely
+            worksheet_set_column(worksheet, 1, 1, 12, NULL);
+            for (int i = 2; i < 7; ++i) {
+                // doesn't work with from col to col, so...
+                worksheet_set_column(worksheet, i, i, 15, NULL);
+            }
+        } else {
+            footer(totalScore, row);
+        }
+    }
+
+    void ReportExcel::showSummary()
+    {
+        for (const auto& item : summary) {
+            worksheet_write_string(worksheet, row + 2, 1, item.first.c_str(), styles["summaryStyle"]);
+            worksheet_write_number(worksheet, row + 2, 2, item.second, styles["summaryStyle"]);
+            worksheet_merge_range(worksheet, row + 2, 3, row + 2, 5, meaning.at(item.first).c_str(), styles["summaryStyle"]);
+            row += 1;
+        }
+    }
+
+    void ReportExcel::footer(double totalScore, int row)
+    {
+        showSummary();
+        row += 4 + summary.size();
+        auto score = data["score_name"].get<string>();
+        if (score == BestScore::scoreName()) {
+            worksheet_merge_range(worksheet, row, 1, row, 5, (score + " compared to " + BestScore::title() + " .:").c_str(), efectiveStyle("text"));
+            writeDouble(row, 6, totalScore / BestScore::score(), "result");
+        }
+        if (!getExistBestFile() && compare) {
+            worksheet_write_string(worksheet, row + 1, 0, "*** Best Results File not found. Couldn't compare any result!", styles["summaryStyle"]);
+        }
+    }
+}

src/Platform/ReportExcel.h

@@ -0,0 +1,42 @@
+#ifndef REPORTEXCEL_H
+#define REPORTEXCEL_H
+#include<map>
+#include "xlsxwriter.h"
+#include "ReportBase.h"
+#include "Colors.h"
+namespace platform {
+    using namespace std;
+    const int MAXLL = 128;
+
+    class ReportExcel : public ReportBase {
+    public:
+        explicit ReportExcel(json data_, bool compare, lxw_workbook* workbook);
+        lxw_workbook* getWorkbook();
+    private:
+        void writeString(int row, int col, const string& text, const string& style = "");
+        void writeInt(int row, int col, const int number, const string& style = "");
+        void writeDouble(int row, int col, const double number, const string& style = "");
+        void formatColumns();
+        void createFormats();
+        void setProperties();
+        void createFile();
+        void closeFile();
+        lxw_workbook* workbook;
+        lxw_worksheet* worksheet;
+        map<string, lxw_format*> styles;
+        int row;
+        int normalSize; //font size for report body
+        uint32_t colorTitle;
+        uint32_t colorOdd;
+        uint32_t colorEven;
+        const string fileName = "some_results.xlsx";
+        void header() override;
+        void body() override;
+        void showSummary() override;
+        void footer(double totalScore, int row);
+        void createStyle(const string& name, lxw_format* style, bool odd);
+        void addColor(lxw_format* style, bool odd);
+        lxw_format* efectiveStyle(const string& name);
+    };
+};
+#endif // !REPORTEXCEL_H

src/Platform/Result.cc

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

src/Platform/Result.h

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

src/Platform/Results.cc

@@ -0,0 +1,268 @@
+#include <filesystem>
+#include "platformUtils.h"
+#include "Results.h"
+#include "ReportConsole.h"
+#include "ReportExcel.h"
+#include "BestScore.h"
+#include "Colors.h"
+namespace platform {
+    void Results::load()
+    {
+        using std::filesystem::directory_iterator;
+        for (const auto& file : directory_iterator(path)) {
+            auto filename = file.path().filename().string();
+            if (filename.find(".json") != string::npos && filename.find("results_") == 0) {
+                auto result = Result(path, filename);
+                bool addResult = true;
+                if (model != "any" && result.getModel() != model || scoreName != "any" && scoreName != result.getScoreName() || complete && !result.isComplete() || partial && result.isComplete())
+                    addResult = false;
+                if (addResult)
+                    files.push_back(result);
+            }
+        }
+        if (max == 0) {
+            max = files.size();
+        }
+    }
+    void Results::show() const
+    {
+        cout << Colors::GREEN() << "Results found: " << files.size() << endl;
+        cout << "-------------------" << endl;
+        if (complete) {
+            cout << Colors::MAGENTA() << "Only listing complete results" << endl;
+        }
+        if (partial) {
+            cout << Colors::MAGENTA() << "Only listing partial results" << endl;
+        }
+        auto i = 0;
+        cout << Colors::GREEN() << " #  Date       Model        Score Name  Score       C/P Duration  Title" << endl;
+        cout << "=== ========== ============ =========== =========== === ========= =============================================================" << endl;
+        bool odd = true;
+        for (const auto& result : files) {
+            auto color = odd ? Colors::BLUE() : Colors::CYAN();
+            cout << color << setw(3) << fixed << right << i++ << " ";
+            cout << result.to_string() << endl;
+            if (i == max && max != 0) {
+                break;
+            }
+            odd = !odd;
+        }
+    }
+    int Results::getIndex(const string& intent) const
+    {
+        string color;
+        if (intent == "delete") {
+            color = Colors::RED();
+        } else {
+            color = Colors::YELLOW();
+        }
+        cout << color << "Choose result to " << intent << " (cancel=-1): ";
+        string line;
+        getline(cin, line);
+        int index = stoi(line);
+        if (index >= -1 && index < static_cast<int>(files.size())) {
+            return index;
+        }
+        cout << "Invalid index" << endl;
+        return -1;
+    }
+    void Results::report(const int index, const bool excelReport)
+    {
+        cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl;
+        auto data = files.at(index).load();
+        if (excelReport) {
+            ReportExcel reporter(data, compare, workbook);
+            reporter.show();
+            openExcel = true;
+            workbook = reporter.getWorkbook();
+        } else {
+            ReportConsole reporter(data, compare);
+            reporter.show();
+        }
+    }
+    void Results::showIndex(const int index, const int idx) const
+    {
+        auto data = files.at(index).load();
+        if (idx < 0 or idx >= static_cast<int>(data["results"].size())) {
+            cout << "Invalid index" << endl;
+            return;
+        }
+        cout << Colors::YELLOW() << "Showing " << files.at(index).getFilename() << endl;
+        ReportConsole reporter(data, compare, idx);
+        reporter.show();
+    }
+    void Results::menu()
+    {
+        char option;
+        int index;
+        bool finished = false;
+        string color, context;
+        string filename, line, options = "qldhsre";
+        while (!finished) {
+            if (indexList) {
+                color = Colors::GREEN();
+                context = " (quit='q', list='l', delete='d', hide='h', sort='s', report='r', excel='e'): ";
+                options = "qldhsre";
+            } else {
+                color = Colors::MAGENTA();
+                context = " (quit='q', list='l'): ";
+                options = "ql";
+            }
+            cout << Colors::RESET() << color;
+
+            cout << "Choose option " << context;
+            getline(cin, line);
+            if (line.size() == 0)
+                continue;
+            if (options.find(line[0]) != string::npos) {
+                if (line.size() > 1) {
+                    cout << "Invalid option" << endl;
+                    continue;
+                }
+                option = line[0];
+            } else {
+                if (all_of(line.begin(), line.end(), ::isdigit)) {
+                    int idx = stoi(line);
+                    if (indexList) {
+                        // The value is about the files list
+                        index = idx;
+                        if (index >= 0 && index < max) {
+                            report(index, false);
+                            indexList = false;
+                            continue;
+                        }
+                    } else {
+                        // The value is about the result showed on screen
+                        showIndex(index, idx);
+                        continue;
+                    }
+                }
+                cout << "Invalid option" << endl;
+                continue;
+            }
+            switch (option) {
+                case 'q':
+                    finished = true;
+                    break;
+                case 'l':
+                    show();
+                    indexList = true;
+                    break;
+                case 'd':
+                    index = getIndex("delete");
+                    if (index == -1)
+                        break;
+                    filename = files[index].getFilename();
+                    cout << "Deleting " << filename << endl;
+                    remove((path + "/" + filename).c_str());
+                    files.erase(files.begin() + index);
+                    cout << "File: " + filename + " deleted!" << endl;
+                    show();
+                    indexList = true;
+                    break;
+                case 'h':
+                    index = getIndex("hide");
+                    if (index == -1)
+                        break;
+                    filename = files[index].getFilename();
+                    cout << "Hiding " << filename << endl;
+                    rename((path + "/" + filename).c_str(), (path + "/." + filename).c_str());
+                    files.erase(files.begin() + index);
+                    show();
+                    menu();
+                    indexList = true;
+                    break;
+                case 's':
+                    sortList();
+                    indexList = true;
+                    show();
+                    break;
+                case 'r':
+                    index = getIndex("report");
+                    if (index == -1)
+                        break;
+                    indexList = false;
+                    report(index, false);
+                    break;
+                case 'e':
+                    index = getIndex("excel");
+                    if (index == -1)
+                        break;
+                    indexList = true;
+                    report(index, true);
+                    break;
+                default:
+                    cout << "Invalid option" << endl;
+            }
+        }
+    }
+    void Results::sortList()
+    {
+        cout << Colors::YELLOW() << "Choose sorting field (date='d', score='s', duration='u', model='m'): ";
+        string line;
+        char option;
+        getline(cin, line);
+        if (line.size() == 0)
+            return;
+        if (line.size() > 1) {
+            cout << "Invalid option" << endl;
+            return;
+        }
+        option = line[0];
+        switch (option) {
+            case 'd':
+                sortDate();
+                break;
+            case 's':
+                sortScore();
+                break;
+            case 'u':
+                sortDuration();
+                break;
+            case 'm':
+                sortModel();
+                break;
+            default:
+                cout << "Invalid option" << endl;
+        }
+    }
+    void Results::sortDate()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getDate() > b.getDate();
+            });
+    }
+    void Results::sortModel()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getModel() > b.getModel();
+            });
+    }
+    void Results::sortDuration()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getDuration() > b.getDuration();
+            });
+    }
+    void Results::sortScore()
+    {
+        sort(files.begin(), files.end(), [](const Result& a, const Result& b) {
+            return a.getScore() > b.getScore();
+            });
+    }
+    void Results::manage()
+    {
+        if (files.size() == 0) {
+            cout << "No results found!" << endl;
+            exit(0);
+        }
+        sortDate();
+        show();
+        menu();
+        if (openExcel) {
+            workbook_close(workbook);
+        }
+        cout << Colors::RESET() << "Done!" << endl;
+    }
+
+}

src/Platform/Results.h

@@ -0,0 +1,47 @@
+#ifndef RESULTS_H
+#define RESULTS_H
+#include "xlsxwriter.h"
+#include <map>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+#include "Result.h"
+namespace platform {
+    using namespace std;
+    using json = nlohmann::json;
+
+    class Results {
+    public:
+        Results(const string& path, const int max, const string& model, const string& score, bool complete, bool partial, bool compare) :
+            path(path), max(max), model(model), scoreName(score), complete(complete), partial(partial), compare(compare)
+        {
+            load();
+        };
+        void manage();
+    private:
+        string path;
+        int max;
+        string model;
+        string scoreName;
+        bool complete;
+        bool partial;
+        bool indexList = true;
+        bool openExcel = false;
+        bool compare;
+        lxw_workbook* workbook = NULL;
+        vector<Result> files;
+        void load(); // Loads the list of results
+        void show() const;
+        void report(const int index, const bool excelReport);
+        void showIndex(const int index, const int idx) const;
+        int getIndex(const string& intent) const;
+        void menu();
+        void sortList();
+        void sortDate();
+        void sortScore();
+        void sortModel();
+        void sortDuration();
+    };
+};
+
+#endif

src/Platform/Statistics.cc

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

src/Platform/Statistics.h

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

src/Platform/Symbols.h

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

src/Platform/best.cc

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

src/Platform/list.cc

@@ -0,0 +1,57 @@
+#include <iostream>
+#include <locale>
+#include "Paths.h"
+#include "Colors.h"
+#include "Datasets.h"
+
+using namespace std;
+const int BALANCE_LENGTH = 75;
+
+struct separated : numpunct<char> {
+    char do_decimal_point() const { return ','; }
+    char do_thousands_sep() const { return '.'; }
+    string do_grouping() const { return "\03"; }
+};
+
+void outputBalance(const string& balance)
+{
+    auto temp = string(balance);
+    while (temp.size() > BALANCE_LENGTH - 1) {
+        auto part = temp.substr(0, BALANCE_LENGTH);
+        cout << part << endl;
+        cout << setw(48) << " ";
+        temp = temp.substr(BALANCE_LENGTH);
+    }
+    cout << temp << endl;
+}
+
+int main(int argc, char** argv)
+{
+    auto data = platform::Datasets(platform::Paths().datasets(), false);
+    locale mylocale(cout.getloc(), new separated);
+    locale::global(mylocale);
+    cout.imbue(mylocale);
+    cout << Colors::GREEN() << "Dataset                        Sampl. Feat. Cls. Balance" << endl;
+    string balanceBars = string(BALANCE_LENGTH, '=');
+    cout << "============================== ====== ===== === " << balanceBars << endl;
+    bool odd = true;
+    for (const auto& dataset : data.getNames()) {
+        auto color = odd ? Colors::CYAN() : Colors::BLUE();
+        cout << color << setw(30) << left << dataset << " ";
+        data.loadDataset(dataset);
+        auto nSamples = data.getNSamples(dataset);
+        cout << setw(6) << right << nSamples << " ";
+        cout << setw(5) << right << data.getFeatures(dataset).size() << " ";
+        cout << setw(3) << right << data.getNClasses(dataset) << " ";
+        stringstream oss;
+        string sep = "";
+        for (auto number : data.getClassesCounts(dataset)) {
+            oss << sep << setprecision(2) << fixed << (float)number / nSamples * 100.0 << "% (" << number << ")";
+            sep = " / ";
+        }
+        outputBalance(oss.str());
+        odd = !odd;
+    }
+    cout << Colors::RESET() << endl;
+    return 0;
+}

src/Platform/main.cc

@@ -1,25 +1,27 @@
 #include <iostream>
 #include <argparse/argparse.hpp>
+#include <nlohmann/json.hpp>
 #include "platformUtils.h"
 #include "Experiment.h"
 #include "Datasets.h"
 #include "DotEnv.h"
 #include "Models.h"
 #include "modelRegister.h"
+#include "Paths.h"
+
 
 using namespace std;
-const string PATH_RESULTS = "results";
+using json = nlohmann::json;
-const string PATH_DATASETS = "datasets";
 
 argparse::ArgumentParser manageArguments(int argc, char** argv)
 {
     auto env = platform::DotEnv();
-    argparse::ArgumentParser program("BayesNetSample");
+    argparse::ArgumentParser program("main");
     program.add_argument("-d", "--dataset").default_value("").help("Dataset file name");
+    program.add_argument("--hyperparameters").default_value("{}").help("Hyperparamters passed to the model in Experiment");
     program.add_argument("-p", "--path")
         .help("folder where the data files are located, default")
-        .default_value(string{ PATH_DATASETS }
+        .default_value(string{ platform::Paths::datasets() });
-    );
     program.add_argument("-m", "--model")
         .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
@@ -32,6 +34,7 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
     );
     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("--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 string& value) {
         try {
@@ -60,6 +63,8 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         auto seeds = program.get<vector<int>>("seeds");
         auto complete_file_name = path + file_name + ".arff";
         auto title = program.get<string>("title");
+        auto hyperparameters = program.get<string>("hyperparameters");
+        auto saveResults = program.get<bool>("save");
         if (title == "" && file_name == "") {
             throw runtime_error("title is mandatory if dataset is not provided");
         }
@@ -75,7 +80,6 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
 int main(int argc, char** argv)
 {
     auto program = manageArguments(argc, argv);
-    bool saveResults = false;
     auto file_name = program.get<string>("dataset");
     auto path = program.get<string>("path");
     auto model_name = program.get<string>("model");
@@ -83,9 +87,11 @@ int main(int argc, char** argv)
     auto stratified = program.get<bool>("stratified");
     auto n_folds = program.get<int>("folds");
     auto seeds = program.get<vector<int>>("seeds");
+    auto hyperparameters = program.get<string>("hyperparameters");
     vector<string> filesToTest;
     auto datasets = platform::Datasets(path, true, platform::ARFF);
     auto title = program.get<string>("title");
+    auto saveResults = program.get<bool>("save");
     if (file_name != "") {
         if (!datasets.isDataset(file_name)) {
             cerr << "Dataset " << file_name << " not found" << endl;
@@ -96,7 +102,7 @@ int main(int argc, char** argv)
         }
         filesToTest.push_back(file_name);
     } else {
-        filesToTest = platform::Datasets(path, true, platform::ARFF).getNames();
+        filesToTest = datasets.getNames();
         saveResults = true;
     }
     /*
@@ -104,9 +110,10 @@ int main(int argc, char** argv)
     */
     auto env = platform::DotEnv();
     auto experiment = platform::Experiment();
-    experiment.setTitle(title).setLanguage("cpp").setLanguageVersion("1.0.0");
+    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(json::parse(hyperparameters));
     for (auto seed : seeds) {
         experiment.addRandomSeed(seed);
     }
@@ -114,10 +121,10 @@ int main(int argc, char** argv)
     timer.start();
     experiment.go(filesToTest, path);
     experiment.setDuration(timer.getDuration());
-    if (saveResults)
+    if (saveResults) {
-        experiment.save(PATH_RESULTS);
+        experiment.save(platform::Paths::results());
-    else
+    }
-        experiment.report();
+    experiment.report();
     cout << "Done!" << endl;
     return 0;
 }

src/Platform/manage.cc

@@ -0,0 +1,52 @@
+#include <iostream>
+#include <argparse/argparse.hpp>
+#include "platformUtils.h"
+#include "Paths.h"
+#include "Results.h"
+
+using namespace std;
+
+argparse::ArgumentParser manageArguments(int argc, char** argv)
+{
+    argparse::ArgumentParser program("manage");
+    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("--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 runtime_error("Number of results must be greater than or equal to 0");
+        }
+        auto model = program.get<string>("model");
+        auto score = program.get<string>("score");
+        auto complete = program.get<bool>("complete");
+        auto partial = program.get<bool>("partial");
+        auto compare = program.get<bool>("compare");
+    }
+    catch (const exception& err) {
+        cerr << err.what() << endl;
+        cerr << program;
+        exit(1);
+    }
+    return program;
+}
+
+int main(int argc, char** argv)
+{
+    auto program = manageArguments(argc, argv);
+    auto number = program.get<int>("number");
+    auto model = program.get<string>("model");
+    auto score = program.get<string>("score");
+    auto complete = program.get<bool>("complete");
+    auto partial = program.get<bool>("partial");
+    auto compare = program.get<bool>("compare");
+    if (complete)
+        partial = false;
+    auto results = platform::Results(platform::Paths::results(), number, model, score, complete, partial, compare);
+    results.manage();
+    return 0;
+}

src/Platform/modelRegister.h

@@ -16,4 +16,6 @@ static platform::Registrar registrarA("AODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
 static platform::Registrar registrarALD("AODELd",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODELd();});
+static platform::Registrar registrarBA("BoostAODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::BoostAODE();});
 #endif

src/Platform/platformUtils.cc

@@ -1,4 +1,5 @@
 #include "platformUtils.h"
+#include "Paths.h"
 
 using namespace torch;
 
@@ -68,11 +69,12 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
         Xd = torch::zeros({ static_cast<int>(Xr[0].size()), static_cast<int>(Xr.size()) }, torch::kInt32);
         for (int i = 0; i < features.size(); ++i) {
             states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
-            iota(begin(states[features[i]]), end(states[features[i]]), 0);
+            auto item = states.at(features[i]);
+            iota(begin(item), end(item), 0);
             Xd.index_put_({ "...", i }, torch::tensor(Xr[i], torch::kInt32));
         }
         states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
-        iota(begin(states[className]), end(states[className]), 0);
+        iota(begin(states.at(className)), end(states.at(className)), 0);
     } else {
         Xd = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, torch::kFloat32);
         for (int i = 0; i < features.size(); ++i) {
@@ -85,7 +87,7 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
 tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
 {
     auto handler = ArffFiles();
-    handler.load(PATH + static_cast<string>(name) + ".arff");
+    handler.load(platform::Paths::datasets() + static_cast<string>(name) + ".arff");
     // Get Dataset X, y
     vector<mdlp::samples_t>& X = handler.getX();
     mdlp::labels_t& y = handler.getY();

src/Platform/platformUtils.h

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

tests/CMakeLists.txt

@@ -4,6 +4,7 @@ if(ENABLE_TESTING)
     include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
     include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
     include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
+    include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
     set(TEST_SOURCES BayesModels.cc BayesNetwork.cc ${BayesNet_SOURCE_DIR}/src/Platform/platformUtils.cc ${BayesNet_SOURCES})
     add_executable(${TEST_MAIN} ${TEST_SOURCES})
     target_link_libraries(${TEST_MAIN} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)