Upgrade models version and Add class diagram

Reviewed-on: https://gitea.rmontanana.es:11000/rmontanana/BayesNet/pulls/6

.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 +35,4 @@ build/
 *.dSYM/**
 cmake-build*/**
 .idea
+puml/**

.gitmodules

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

.vscode/launch.json

@@ -10,12 +10,13 @@
                 "-d",
                 "iris",
                 "-m",
-                "TAN",
+                "TANLd",
+                "-s",
+                "271",
                 "-p",
-                "../../data/",
+                "/Users/rmontanana/Code/discretizbench/datasets/",
-                "--tensors"
             ],
-            "cwd": "${workspaceFolder}/build/sample/",
+            //"cwd": "${workspaceFolder}/build/sample/",
         },
         {
             "type": "lldb",
@@ -24,17 +25,36 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "TAN",
+                "AODE",
                 "-p",
-                "/Users/rmontanana/Code/discretizbench/datasets",
+                "/home/rmontanana/Code/discretizbench/datasets",
-                "--discretize",
                 "--stratified",
-                "--title",
-                "Debug test",
                 "-d",
-                "ionosphere"
+                "mfeat-morphological",
+                "--discretize"
+                // "--hyperparameters",
+                // "{\"repeatSparent\": true, \"maxModels\": 12}"
             ],
-            "cwd": "${workspaceFolder}/build/src/Platform",
+            "cwd": "/home/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",

.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

@@ -1,16 +1,20 @@
 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
 )
 
+if (CODE_COVERAGE AND NOT ENABLE_TESTING)
+  MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
+endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
+
 find_package(Torch REQUIRED)
 
 if (POLICY CMP0135)
-    cmake_policy(SET CMP0135 NEW)
+  cmake_policy(SET CMP0135 NEW)
 endif ()
 
 # Global CMake variables
@@ -24,26 +28,33 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 # Options
 # -------
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
-option(ENABLE_TESTING "Unit testing build"                        ON)
+option(ENABLE_TESTING "Unit testing build"                        OFF)
-option(CODE_COVERAGE "Collect coverage from test library"         ON)
+option(CODE_COVERAGE "Collect coverage from test library"         OFF)
-
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
-set(CMAKE_BUILD_TYPE "Debug")
-
 # CMakes modules
 # --------------
 set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_PATH})
 
 include(AddGitSubmodule)
-include(StaticAnalyzers) # clang-tidy
+if (CODE_COVERAGE)
-include(CodeCoverage)
+    enable_testing()
+    include(CodeCoverage)
+    MESSAGE("Code coverage enabled")
+    set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0")
+    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
+endif (CODE_COVERAGE)
+
+if (ENABLE_CLANG_TIDY)
+  include(StaticAnalyzers) # clang-tidy
+endif (ENABLE_CLANG_TIDY)
 
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
 # include(FetchContent)
 add_git_submodule("lib/mdlp")
-add_git_submodule("lib/catch2")
 add_git_submodule("lib/argparse")
 add_git_submodule("lib/json")
+add_git_submodule("lib/openXLSX")
 
 # Subdirectories
 # --------------
@@ -59,18 +70,11 @@ file(GLOB Platform_SOURCES CONFIGURE_DEPENDS ${BayesNet_SOURCE_DIR}/src/Platform
 
 # Testing
 # -------
+
 if (ENABLE_TESTING)
   MESSAGE("Testing enabled")
-  enable_testing()
+  add_git_submodule("lib/catch2")
-  if (CODE_COVERAGE)
+  
-    #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")
-    SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
-  endif (CODE_COVERAGE)
-  #find_package(Catch2 3 REQUIRED)
   include(CTest)
-  #include(Catch)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)

Makefile

@@ -11,19 +11,46 @@ 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)
+	@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 project
+build: ## Build the main and BayesNetSample
-	@echo ">>> Building BayesNet ...";
+	cmake --build build -t main -t BayesNetSample -t manage -t list -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; \
+	cmake -S . -B build -D CMAKE_BUILD_TYPE=Debug -D ENABLE_TESTING=ON -D CODE_COVERAGE=ON; \
-	cd build; \
+	cmake --build build -j 32;
-	make; \
-	
 	@echo ">>> Done";
 
+release: ## Build a Release version of the project
+	@echo ">>> Building Release BayesNet ...";
+	@if [ -d ./build ]; then rm -rf ./build; fi
+	@mkdir build; 
+	cmake -S . -B build -D CMAKE_BUILD_TYPE=Release; \
+	cmake --build build -t main -t BayesNetSample -t manage -t list -j 32;
+	@echo ">>> Done";	
+
 test: ## Run tests
 	@echo "* Running tests...";
 	find . -name "*.gcda" -print0 | xargs -0 rm

data/_TAN_cpp_accuracy__.json

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

gcovr.cfg

@@ -1,5 +1,4 @@
 filter = src/
-exclude = external/
+exclude-directories = build/lib/
-exclude = tests/
 print-summary = yes
 sort-percentage = yes

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)
-add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc) 
+include_directories(${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

@@ -1,20 +1,16 @@
 #include <iostream>
 #include <torch/torch.h>
 #include <string>
-#include <thread>
 #include <map>
 #include <argparse/argparse.hpp>
-#include "BaseClassifier.h"
+#include <nlohmann/json.hpp>
 #include "ArffFiles.h"
-#include "Network.h"
 #include "BayesMetrics.h"
 #include "CPPFImdlp.h"
-#include "KDB.h"
-#include "SPODE.h"
-#include "AODE.h"
-#include "TAN.h"
 #include "Folding.h"
-
+#include "Models.h"
+#include "modelRegister.h"
+#include <fstream>
 
 using namespace std;
 
@@ -46,7 +42,7 @@ bool file_exists(const std::string& name)
 }
 pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vector<vector<int>> X, vector<int> y)
 {
-    vector<vector<int>> Xr;
+    vector<vector<int>> Xr; // nxm
     vector<int> yr;
     for (int col = 0; col < X.size(); ++col) {
         Xr.push_back(vector<int>());
@@ -73,9 +69,8 @@ int main(int argc, char** argv)
             {"mfeat-factors",      true},
     };
     auto valid_datasets = vector<string>();
-    for (auto dataset : datasets) {
+    transform(datasets.begin(), datasets.end(), back_inserter(valid_datasets),
-        valid_datasets.push_back(dataset.first);
+        [](const pair<string, bool>& pair) { return pair.first; });
-    }
     argparse::ArgumentParser program("BayesNetSample");
     program.add_argument("-d", "--dataset")
         .help("Dataset file name")
@@ -91,16 +86,17 @@ int main(int argc, char** argv)
         .default_value(string{ PATH }
     );
     program.add_argument("-m", "--model")
-        .help("Model to use {AODE, KDB, SPODE, TAN}")
+        .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
-        static const vector<string> choices = { "AODE", "KDB", "SPODE", "TAN" };
+        static const vector<string> choices = platform::Models::instance()->getNames();
         if (find(choices.begin(), choices.end(), value) != choices.end()) {
             return value;
         }
-        throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
+        throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
             }
     );
     program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
+    program.add_argument("--dumpcpt").help("Dump CPT Tables").default_value(false).implicit_value(true);
     program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
     program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
     program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
@@ -118,7 +114,7 @@ int main(int argc, char** argv)
             throw runtime_error("Number of folds must be an integer");
         }});
     program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
-    bool class_last, stratified, tensors;
+    bool class_last, stratified, tensors, dump_cpt;
     string model_name, file_name, path, complete_file_name;
     int nFolds, seed;
     try {
@@ -131,6 +127,7 @@ int main(int argc, char** argv)
         tensors = program.get<bool>("tensors");
         nFolds = program.get<int>("folds");
         seed = program.get<int>("seed");
+        dump_cpt = program.get<bool>("dumpcpt");
         class_last = datasets[file_name];
         if (!file_exists(complete_file_name)) {
             throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
@@ -153,9 +150,9 @@ int main(int argc, char** argv)
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
+    auto attributes = handler.getAttributes();
-        features.push_back(feature.first);
+    transform(attributes.begin(), attributes.end(), back_inserter(features),
-    }
+        [](const pair<string, string>& item) { return item.first; });
     // Discretize Dataset
     auto [Xd, maxes] = discretize(X, y, features);
     maxes[className] = *max_element(y.begin(), y.end()) + 1;
@@ -164,20 +161,25 @@ int main(int argc, char** argv)
         states[feature] = vector<int>(maxes[feature]);
     }
     states[className] = vector<int>(maxes[className]);
-    auto classifiers = map<string, bayesnet::BaseClassifier*>({
+    auto clf = platform::Models::instance()->create(model_name);
-        { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
-        { "SPODE",  new bayesnet::SPODE(2) }, { "TAN",  new bayesnet::TAN() }
-        }
-    );
-    bayesnet::BaseClassifier* clf = classifiers[model_name];
     clf->fit(Xd, y, features, className, states);
-    auto score = clf->score(Xd, y);
+    if (dump_cpt) {
+        cout << "--- CPT Tables ---" << endl;
+        clf->dump_cpt();
+    }
     auto lines = clf->show();
-    auto graph = clf->graph();
     for (auto line : lines) {
         cout << line << endl;
     }
+    cout << "--- Topological Order ---" << endl;
+    auto order = clf->topological_order();
+    for (auto name : order) {
+        cout << name << ", ";
+    }
+    cout << "end." << endl;
+    auto score = clf->score(Xd, y);
     cout << "Score: " << score << endl;
+    auto graph = clf->graph();
     auto dot_file = model_name + "_" + file_name;
     ofstream file(dot_file + ".dot");
     file << graph;
@@ -193,11 +195,11 @@ int main(int argc, char** argv)
         Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
     }
     float total_score = 0, total_score_train = 0, score_train, score_test;
-    Fold* fold;
+    platform::Fold* fold;
     if (stratified)
-        fold = new StratifiedKFold(nFolds, y, seed);
+        fold = new platform::StratifiedKFold(nFolds, y, seed);
     else
-        fold = new KFold(nFolds, y.size(), seed);
+        fold = new platform::KFold(nFolds, y.size(), seed);
     for (auto i = 0; i < nFolds; ++i) {
         auto [train, test] = fold->getFold(i);
         cout << "Fold: " << i + 1 << endl;
@@ -209,6 +211,7 @@ int main(int argc, char** argv)
             torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
             torch::Tensor ytestt = yt.index({ ttest });
             clf->fit(Xtraint, ytraint, features, className, states);
+            auto temp = clf->predict(Xtraint);
             score_train = clf->score(Xtraint, ytraint);
             score_test = clf->score(Xtestt, ytestt);
         } else {
@@ -218,6 +221,10 @@ int main(int argc, char** argv)
             score_train = clf->score(Xtrain, ytrain);
             score_test = clf->score(Xtest, ytest);
         }
+        if (dump_cpt) {
+            cout << "--- CPT Tables ---" << endl;
+            clf->dump_cpt();
+        }
         total_score_train += score_train;
         total_score += score_test;
         cout << "Score Train: " << score_train << endl;
@@ -226,6 +233,5 @@ int main(int argc, char** argv)
     }
     cout << "**********************************************************************************" << endl;
     cout << "Average Score Train: " << total_score_train / nFolds << endl;
-    cout << "Average Score Test : " << total_score / nFolds << endl;
+    cout << "Average Score Test : " << total_score / nFolds << endl;return 0;
-    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(string title)
+    vector<string> AODE::graph(const string& title) const
     {
         return Ensemble::graph(title);
     }

src/BayesNet/AODE.h

@@ -5,10 +5,11 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
         AODE();
-        vector<string> graph(string title = "AODE") override;
+        virtual ~AODE() {};
+        vector<string> graph(const string& title = "AODE") const override;
     };
 }
 #endif

src/BayesNet/AODELd.cc

@@ -0,0 +1,41 @@
+#include "AODELd.h"
+#include "Models.h"
+
+namespace bayesnet {
+    using namespace std;
+    AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
+    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_;
+        Xf = X_;
+        y = y_;
+        // 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 TAN structure, TAN::fit initializes the base Bayesian network
+        Ensemble::fit(dataset, features, className, states);
+        return *this;
+
+    }
+    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);
+    }
+    void AODELd::trainModel(const torch::Tensor& weights)
+    {
+        for (const auto& model : models) {
+            model->fit(Xf, y, features, className, states);
+        }
+    }
+    vector<string> AODELd::graph(const string& name) const
+    {
+        return Ensemble::graph(name);
+    }
+}

src/BayesNet/AODELd.h

@@ -0,0 +1,21 @@
+#ifndef AODELD_H
+#define AODELD_H
+#include "Ensemble.h"
+#include "Proposal.h"
+#include "SPODELd.h"
+
+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;
+        vector<string> graph(const string& name = "AODELd") const override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !AODELD_H

src/BayesNet/BaseClassifier.h

@@ -1,23 +1,34 @@
 #ifndef BASE_H
 #define BASE_H
 #include <torch/torch.h>
+#include <nlohmann/json.hpp>
 #include <vector>
 namespace bayesnet {
     using namespace std;
     class BaseClassifier {
+    protected:
+        virtual void trainModel(const torch::Tensor& weights) = 0;
     public:
-        virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        // X is nxm vector, y is nx1 vector
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& 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, 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;
         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(string title = "") = 0;
+        vector<string> virtual graph(const string& title = "") const = 0;
-        virtual ~BaseClassifier() = default;
+        const string inline getVersion() const { return "0.2.0"; };
-        const string inline getVersion() const { return "0.1.0"; };
+        vector<string> virtual topological_order() = 0;
+        void virtual dump_cpt()const = 0;
+        virtual void setHyperparameters(nlohmann::json& hyperparameters) = 0;
     };
 }
 #endif

src/BayesNet/BayesMetrics.cc

@@ -1,23 +1,64 @@
 #include "BayesMetrics.h"
 #include "Mst.h"
 namespace bayesnet {
-    Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
+    //samples is nxm tensor used to fit the model
+    Metrics::Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates)
         : samples(samples)
         , features(features)
         , className(className)
         , classNumStates(classNumStates)
     {
     }
+    //samples is nxm vector used to fit the model
     Metrics::Metrics(const vector<vector<int>>& vsamples, const vector<int>& labels, const vector<string>& features, const string& className, const int classNumStates)
         : features(features)
         , className(className)
         , classNumStates(classNumStates)
+        , samples(torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
     {
-        samples = torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32);
         for (int i = 0; i < vsamples.size(); ++i) {
-            samples.index_put_({ "...", i }, torch::tensor(vsamples[i], torch::kInt32));
+            samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
         }
-        samples.index_put_({ "...", -1 }, torch::tensor(labels, torch::kInt32));
+        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)
     {
@@ -30,28 +71,29 @@ 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;
+            auto mask = samples.index({ -1,  "..." }) == value;
-            margin[value] = mask.sum().item<float>() / samples.sizes()[0];
+            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();
             int index_second = find(features.begin(), features.end(), second) - features.begin();
             double accumulated = 0;
             for (int value = 0; value < classNumStates; ++value) {
-                auto mask = samples.index({ "...", -1 }) == value;
+                auto mask = samples.index({ -1, "..." }) == value;
-                auto first_dataset = samples.index({ mask, index_first });
+                auto first_dataset = samples.index({ index_first, mask });
-                auto second_dataset = samples.index({ mask, index_second });
+                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);
@@ -67,31 +109,33 @@ namespace bayesnet {
         }
         return matrix;
     }
-    vector<float> Metrics::conditionalEdgeWeights()
+    // To use in Python
+    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;
@@ -112,18 +156,17 @@ 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 result = vector<pair<int, int>>();
         auto mst = MST(features, weights, root);
         return mst.maximumSpanningTree();
     }

src/BayesNet/BayesMetrics.h

@@ -8,21 +8,25 @@ namespace bayesnet {
     using namespace torch;
     class Metrics {
     private:
-        Tensor samples;
+        Tensor samples; // nxm tensor used to fit the model
         vector<string> features;
         string className;
-        int classNumStates;
+        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();
+        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 +1,90 @@
+#include "BoostAODE.h"
+#include <set>
+#include "BayesMetrics.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" };
+        checkHyperparameters(validKeys, hyperparameters);
+        if (hyperparameters.contains("repeatSparent")) {
+            repeatSparent = hyperparameters["repeatSparent"];
+        }
+        if (hyperparameters.contains("maxModels")) {
+            maxModels = hyperparameters["maxModels"];
+        }
+        if (hyperparameters.contains("ascending")) {
+            ascending = hyperparameters["ascending"];
+        }
+    }
+    void BoostAODE::trainModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        n_models = 0;
+        if (maxModels == 0)
+            maxModels = .1 * n > 10 ? .1 * n : n;
+        Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        auto X_ = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+        auto y_ = dataset.index({ -1, "..." });
+        bool exitCondition = false;
+        unordered_set<int> featuresUsed;
+        // Step 0: Set the finish condition
+        // if not repeatSparent a finish condition is run out of features
+        // n_models == maxModels
+        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);
+            n_models++;
+            model->fit(dataset, features, className, states, weights_);
+            auto ypred = model->predict(X_);
+            // Step 3.1: Compute the classifier amout of say
+            auto mask_wrong = ypred != y_;
+            auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
+            double wrongWeights = masked_weights.sum().item<double>();
+            double significance = wrongWeights == 0 ? 1 : 0.5 * log((1 - wrongWeights) / wrongWeights);
+            // Step 3.2: Update weights for next classifier
+            // Step 3.2.1: Update weights of wrong samples
+            weights_ += mask_wrong.to(weights_.dtype()) * exp(significance) * 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(significance);
+            exitCondition = n_models == maxModels && repeatSparent;
+        }
+        if (featuresUsed.size() != features.size()) {
+            cout << "Warning: BoostAODE did not use all the features" << endl;
+        }
+        weights.copy_(weights_);
+    }
+    vector<string> BoostAODE::graph(const string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

src/BayesNet/BoostAODE.h

@@ -0,0 +1,21 @@
+#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:
+        bool repeatSparent=false;
+        int maxModels=0;
+        bool ascending=false; //Process KBest features ascending or descending order
+    };
+}
+#endif

src/BayesNet/CMakeLists.txt

@@ -1,2 +1,9 @@
-add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc Mst.cc)
+include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
-target_link_libraries(BayesNet "${TORCH_LIBRARIES}")
+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 BoostAODE.cc 
+    Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
+target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")

src/BayesNet/Classifier.cc

@@ -5,56 +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)
     {
-        dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
         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(dataset, features, className, n_classes);
-        train();
+        model.initialize();
-        if (Xv == vector<vector<int>>()) {
+        buildModel(weights);
-            // fit with tensors
+        trainModel(weights);
-            model.fit(X, y, features, className);
-        } else {
-            // fit with vectors
-            model.fit(Xv, yv, features, className);
-        }
         fitted = true;
         return *this;
     }
-    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    void Classifier::buildDataset(Tensor& ytmp)
     {
-        this->X = torch::transpose(X, 0, 1);
+        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);
-    }
-
-    Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
-    {
-        this->X = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, kInt32);
-        Xv = X;
-        for (int i = 0; i < X.size(); ++i) {
-            this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt32));
         }
-        this->y = torch::tensor(y, kInt32);
+        catch (const std::exception& e) {
-        yv = y;
+            std::cerr << e.what() << '\n';
-        return build(features, className, states);
+            cout << "X dimensions: " << dataset.sizes() << "\n";
+            cout << "y dimensions: " << ytmp.sizes() << "\n";
+            exit(1);
+        }
+    }
+    void Classifier::trainModel(const torch::Tensor& weights)
+    {
+        model.fit(dataset, weights, features, className, states);
+    }
+    // X is nxm where n is the number of features and m the number of samples
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& 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[0];
+            throw invalid_argument("dataset (X, y) must be of type Integer");
-        n = sizes[1];
-        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("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");
@@ -65,23 +86,12 @@ namespace bayesnet {
             }
         }
     }
-
     Tensor Classifier::predict(Tensor& X)
     {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
         }
-        auto m_ = X.size(0);
+        return model.predict(X);
-        auto n_ = X.size(1);
-        //auto Xt = torch::transpose(X, 0, 1);
-        vector<vector<int>> Xd(n_, vector<int>(m_, 0));
-        for (auto i = 0; i < n_; i++) {
-            auto temp = X.index({ "...", i });
-            Xd[i] = vector<int>(temp.data_ptr<int>(), temp.data_ptr<int>() + temp.numel());
-        }
-        auto yp = model.predict(Xd);
-        auto ypred = torch::tensor(yp, torch::kInt32);
-        return ypred;
     }
     vector<int> Classifier::predict(vector<vector<int>>& X)
     {
@@ -102,8 +112,7 @@ namespace bayesnet {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
         }
-        auto Xt = torch::transpose(X, 0, 1);
+        Tensor y_pred = predict(X);
-        Tensor y_pred = predict(Xt);
         return (y_pred == y).sum().item<float>() / y.size(0);
     }
     float Classifier::score(vector<vector<int>>& X, vector<int>& y)
@@ -111,38 +120,53 @@ namespace bayesnet {
         if (!fitted) {
             throw logic_error("Classifier has not been fitted");
         }
-        auto m_ = X[0].size();
+        return model.score(X, y);
-        auto n_ = X.size();
-        vector<vector<int>> Xd(n_, vector<int>(m_, 0));
-        for (auto i = 0; i < n_; i++) {
-            Xd[i] = vector<int>(X[i].begin(), X[i].end());
-        }
-        return model.score(Xd, y);
     }
-    vector<string> Classifier::show()
+    vector<string> Classifier::show() const
     {
         return model.show();
     }
     void Classifier::addNodes()
     {
-        auto test = model.getEdges();
         // Add all nodes to the network
-        for (auto feature : features) {
+        for (const auto& feature : features) {
-            model.addNode(feature, states[feature].size());
+            model.addNode(feature);
         }
-        model.addNode(className, states[className].size());
+        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;
     }
+    vector<string> Classifier::topological_order()
+    {
+        return model.topological_sort();
+    }
+    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

@@ -10,36 +10,40 @@ using namespace torch;
 namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
-        bool fitted;
+        void buildDataset(torch::Tensor& y);
-        Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
+        Classifier& build(const vector<string>& features, const string& className, map<string, vector<int>>& states, const torch::Tensor& weights);
     protected:
-        Network model;
+        bool fitted;
         int m, n; // m: number of samples, n: number of features
-        Tensor X;
+        Network model;
-        vector<vector<int>> Xv;
-        Tensor y;
-        vector<int> yv;
-        Tensor dataset;
         Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
+        Tensor dataset; // (n+1)xm tensor
         void checkFitParameters();
-        virtual void train() = 0;
+        virtual void buildModel(const torch::Tensor& weights) = 0;
+        void trainModel(const torch::Tensor& weights) override;
+        void checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters);
     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);
+        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;
-        vector<string> show() override;
+        vector<string> show() const override;
+        vector<string> topological_order()  override;
+        void dump_cpt() const override;
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
     };
 }
 #endif

src/BayesNet/Ensemble.cc

@@ -3,69 +3,55 @@
 namespace bayesnet {
     using namespace torch;
 
-    Ensemble::Ensemble() : m(0), n(0), 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)
     {
-        dataset = cat({ X, y.view({y.size(0), 1}) }, 1);
-        this->features = features;
-        this->className = className;
-        this->states = states;
-        auto n_classes = states[className].size();
-        metrics = Metrics(dataset, features, className, n_classes);
-        // Build models
-        train();
-        // Train models
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
-            models[i]->fit(Xv, yv, features, className, states);
+            // fit with vectors
+            models[i]->fit(dataset, features, className, states);
         }
-        fitted = true;
-        return *this;
     }
-    Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
+    vector<int> Ensemble::voting(Tensor& y_pred)
     {
-        this->X = X;
+        auto y_pred_ = y_pred.accessor<int, 2>();
-        this->y = y;
+        vector<int> y_pred_final;
-        Xv = vector<vector<int>>();
+        int numClasses = states.at(className).size();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        // y_pred is m x n_models with the prediction of every model for each sample
-        return build(features, className, states);
+        for (int i = 0; i < y_pred.size(0); ++i) {
-    }
+            // votes store in each index (value of class) the significance added by each model
-    Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
+            // i.e. votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
-    {
+            vector<double> votes(numClasses, 0.0);
-        this->X = torch::zeros({ static_cast<int>(X[0].size()), static_cast<int>(X.size()) }, kInt32);
+            for (int j = 0; j < n_models; ++j) {
-        Xv = X;
+                votes[y_pred_[i][j]] += significanceModels[j];
-        for (int i = 0; i < X.size(); ++i) {
+            }
-            this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt32));
+            // argsort in descending order
+            auto indices = argsort(votes);
+            y_pred_final.push_back(indices[0]);
         }
-        this->y = torch::tensor(y, kInt32);
+        return y_pred_final;
-        yv = y;
-        return build(features, className, states);
     }
     Tensor Ensemble::predict(Tensor& X)
     {
         if (!fitted) {
             throw logic_error("Ensemble has not been fitted");
         }
-        Tensor y_pred = torch::zeros({ X.size(0), n_models }, kInt32);
+        Tensor y_pred = torch::zeros({ X.size(1), n_models }, kInt32);
+        auto threads{ vector<thread>() };
+        mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            y_pred.index_put_({ "...", i }, models[i]->predict(X));
+            threads.push_back(thread([&, i]() {
+                auto ypredict = models[i]->predict(X);
+                lock_guard<mutex> lock(mtx);
+                y_pred.index_put_({ "...", i }, ypredict);
+                }));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return torch::tensor(voting(y_pred));
     }
-    vector<int> Ensemble::voting(Tensor& y_pred)
-    {
-        auto y_pred_ = y_pred.accessor<int, 2>();
-        vector<int> y_pred_final;
-        for (int i = 0; i < y_pred.size(0); ++i) {
-            vector<float> votes(states[className].size(), 0);
-            for (int j = 0; j < y_pred.size(1); ++j) {
-                votes[y_pred_[i][j]] += 1;
-            }
-            auto indices = argsort(votes);
-            y_pred_final.push_back(indices[0]);
-        }
-        return y_pred_final;
-    }
     vector<int> Ensemble::predict(vector<vector<int>>& X)
     {
         if (!fitted) {
@@ -110,9 +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) {
@@ -121,7 +106,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Ensemble::graph(string title)
+    vector<string> Ensemble::graph(const string& title) const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -130,7 +115,7 @@ namespace bayesnet {
         }
         return result;
     }
-    int Ensemble::getNumberOfNodes()
+    int Ensemble::getNumberOfNodes() const
     {
         int nodes = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -138,7 +123,7 @@ namespace bayesnet {
         }
         return nodes;
     }
-    int Ensemble::getNumberOfEdges()
+    int Ensemble::getNumberOfEdges() const
     {
         int edges = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -146,12 +131,12 @@ namespace bayesnet {
         }
         return edges;
     }
-    int Ensemble::getNumberOfStates()
+    int Ensemble::getNumberOfStates() const
     {
-        int states = 0;
+        int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {
-            states += models[i]->getNumberOfStates();
+            nstates += models[i]->getNumberOfStates();
         }
-        return states;
+        return nstates;
     }
 }

src/BayesNet/Ensemble.h

@@ -8,39 +8,34 @@ using namespace std;
 using namespace torch;
 
 namespace bayesnet {
-    class Ensemble : public BaseClassifier {
+    class Ensemble : public Classifier {
     private:
-        bool fitted;
-        long n_models;
         Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
+        unsigned n_models;
         vector<unique_ptr<Classifier>> models;
-        int m, n; // m: number of samples, n: number of features
+        vector<double> significanceModels;
-        Tensor X;
+        void trainModel(const torch::Tensor& weights) override;
-        vector<vector<int>> Xv;
-        Tensor y;
-        vector<int> yv;
-        Tensor dataset;
-        Metrics metrics;
-        vector<string> features;
-        string className;
-        map<string, vector<int>> states;
-        void virtual train() = 0;
         vector<int> voting(Tensor& y_pred);
     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;
+        Tensor predict(Tensor& X) override;
-        Ensemble& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        Tensor predict(Tensor& X);
         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(string title) override;
+        vector<string> graph(const string& title) const override;
+        vector<string> topological_order()  override
+        {
+            return vector<string>();
+        }
+        void dump_cpt() const override
+        {
+        }
     };
 }
 #endif

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),
@@ -27,25 +39,25 @@ namespace bayesnet {
         */
         // 1. For each feature Xi, compute mutual information, I(X;C),
         // where C is the class.
-        vector <float> mi;
+        addNodes();
+        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
         // class node, C.
-        model.addNode(className, states[className].size());
         // 5. Repeat until S includes all domain features
         // 5.1. Select feature Xmax which is not in S and has the largest value
         // I(Xmax;C).
         auto order = argsort(mi);
         for (auto idx : order) {
             // 5.2. Add a node to BN representing Xmax.
-            model.addNode(features[idx], states[features[idx]].size());
             // 5.3. Add an arc from C to Xmax in BN.
             model.addEdge(className, features[idx]);
             // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
@@ -79,11 +91,12 @@ namespace bayesnet {
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::graph(string title)
+    vector<string> KDB::graph(const string& title) const
     {
+        string header{ title };
         if (title == "KDB") {
-            title += " (k=" + to_string(k) + ", theta=" + to_string(theta) + ")";
+            header += " (k=" + to_string(k) + ", theta=" + to_string(theta) + ")";
         }
-        return model.graph(title);
+        return model.graph(header);
     }
 }

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 {
@@ -11,10 +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:
-        KDB(int k, float theta = 0.03);
+        explicit KDB(int k, float theta = 0.03);
-        vector<string> graph(string name = "KDB") override;
+        virtual ~KDB() {};
+        void setHyperparameters(nlohmann::json& hyperparameters) override;
+        vector<string> graph(const string& name = "KDB") const override;
     };
 }
 #endif

src/BayesNet/KDBLd.cc

@@ -0,0 +1,30 @@
+#include "KDBLd.h"
+
+namespace bayesnet {
+    using namespace std;
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
+    KDBLd& KDBLd::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_;
+        Xf = X_;
+        y = y_;
+        // 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 KDB structure, KDB::fit initializes the base Bayesian network
+        KDB::fit(dataset, features, className, states);
+        states = localDiscretizationProposal(states, model);
+        return *this;
+    }
+    Tensor KDBLd::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return KDB::predict(Xt);
+    }
+    vector<string> KDBLd::graph(const string& name) const
+    {
+        return KDB::graph(name);
+    }
+}

src/BayesNet/KDBLd.h

@@ -0,0 +1,19 @@
+#ifndef KDBLD_H
+#define KDBLD_H
+#include "KDB.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class KDBLd : public KDB, public Proposal {
+    private:
+    public:
+        explicit KDBLd(int k);
+        virtual ~KDBLd() = default;
+        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") const override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !KDBLD_H

src/BayesNet/Mst.cc

@@ -7,9 +7,8 @@
 
 namespace bayesnet {
     using namespace std;
-    Graph::Graph(int V)
+    Graph::Graph(int V) : V(V), parent(vector<int>(V))
     {
-        parent = vector<int>(V);
         for (int i = 0; i < V; i++)
             parent[i] = i;
         G.clear();
@@ -34,10 +33,10 @@ namespace bayesnet {
     }
     void Graph::kruskal_algorithm()
     {
-        int i, uSt, vEd;
         // sort the edges ordered on decreasing weight
-        sort(G.begin(), G.end(), [](auto& left, auto& right) {return left.first > right.first;});
+        sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
-        for (i = 0; i < G.size(); i++) {
+        for (int i = 0; i < G.size(); i++) {
+            int uSt, vEd;
             uSt = find_set(G[i].second.first);
             vEd = find_set(G[i].second.second);
             if (uSt != vEd) {
@@ -95,7 +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

@@ -10,10 +10,10 @@ namespace bayesnet {
     private:
         Tensor weights;
         vector<string> features;
-        int root;
+        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 {
@@ -23,7 +23,7 @@ namespace bayesnet {
         vector <pair<float, pair<int, int>>> T; // vector for mst
         vector<int> parent;
     public:
-        Graph(int V);
+        explicit Graph(int V);
         void addEdge(int u, int v, float wt);
         int find_set(int i);
         void union_set(int u, int v);

src/BayesNet/Network.cc

@@ -3,15 +3,24 @@
 #include "Network.h"
 #include "bayesnetUtils.h"
 namespace bayesnet {
-    Network::Network() : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(0.8), fitted(false) {}
+    Network::Network() : features(vector<string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT) : laplaceSmoothing(1), 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.
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads()), fitted(other.fitted)
+        getmaxThreads()), fitted(other.fitted)
     {
-        for (auto& pair : other.nodes) {
+        for (const auto& pair : other.nodes) {
             nodes[pair.first] = std::make_unique<Node>(*pair.second);
         }
     }
+    void Network::initialize()
+    {
+        features = vector<string>();
+        className = "";
+        classNumStates = 0;
+        fitted = false;
+        nodes.clear();
+        samples = torch::Tensor();
+    }
     float Network::getmaxThreads()
     {
         return maxThreads;
@@ -20,28 +29,28 @@ namespace bayesnet {
     {
         return samples;
     }
-    void Network::addNode(string name, int numStates)
+    void Network::addNode(const string& name)
     {
+        if (name == "") {
+            throw invalid_argument("Node name cannot be empty");
+        }
+        if (nodes.find(name) != nodes.end()) {
+            return;
+        }
         if (find(features.begin(), features.end(), name) == features.end()) {
             features.push_back(name);
         }
-        if (nodes.find(name) != nodes.end()) {
+        nodes[name] = std::make_unique<Node>(name);
-            // if node exists update its number of states and remove parents, children and CPT
-            nodes[name]->clear();
-            nodes[name]->setNumStates(numStates);
-            return;
-        }
-        nodes[name] = std::make_unique<Node>(name, numStates);
     }
-    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) {
@@ -49,7 +58,7 @@ namespace bayesnet {
         }
         return result;
     }
-    string Network::getClassName()
+    string Network::getClassName() const
     {
         return className;
     }
@@ -69,7 +78,7 @@ namespace bayesnet {
         recStack.erase(nodeId); // remove node from recursion stack before function ends
         return false;
     }
-    void Network::addEdge(const string parent, const string child)
+    void Network::addEdge(const string& parent, const string& child)
     {
         if (nodes.find(parent) == nodes.end()) {
             throw invalid_argument("Parent node " + parent + " does not exist");
@@ -94,86 +103,115 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::fit(torch::Tensor& X, torch::Tensor& 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)
     {
-        features = featureNames;
+        if (weights.size(0) != n_samples) {
-        this->className = className;
+            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");
-        dataset.clear();
+        }
-        // Specific part
+        if (n_samples != n_samples_y) {
-        classNumStates = torch::max(y).item<int>() + 1;
+            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) + ")");
-        samples = torch::cat({ X, y.view({ y.size(0), 1 }) }, 1);
+        }
-        for (int i = 0; i < featureNames.size(); ++i) {
+        if (n_features != featureNames.size()) {
-            auto column = torch::flatten(X.index({ "...", i }));
+            throw invalid_argument("X and features must have the same number of features in Network::fit (" + to_string(n_features) + " != " + to_string(featureNames.size()) + ")");
-            auto k = vector<int>();
+        }
-            for (auto i = 0; i < X.size(0); ++i) {
+        if (n_features != features.size() - 1) {
-                k.push_back(column[i].item<int>());
+            throw invalid_argument("X and local features must have the same number of features in Network::fit (" + to_string(n_features) + " != " + to_string(features.size() - 1) + ")");
+        }
+        if (find(features.begin(), features.end(), className) == features.end()) {
+            throw invalid_argument("className not found in Network::features");
+        }
+        for (auto& feature : featureNames) {
+            if (find(features.begin(), features.end(), feature) == features.end()) {
+                throw invalid_argument("Feature " + feature + " not found in Network::features");
             }
-            dataset[featureNames[i]] = k;
+            if (states.find(feature) == states.end()) {
-        }
+                throw invalid_argument("Feature " + feature + " not found in states");
-        dataset[className] = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-        completeFit();
-    }
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className)
-    {
-        features = featureNames;
-        this->className = className;
-        dataset.clear();
-        // Specific part
-        classNumStates = *max_element(labels.begin(), labels.end()) + 1;
-        // Build dataset & tensor of samples
-        samples = torch::zeros({ static_cast<int>(input_data[0].size()), static_cast<int>(input_data.size() + 1) }, 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();
-    }
-    void Network::completeFit()
-    {
-
-        int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
-        if (maxThreadsRunning < 1) {
-            maxThreadsRunning = 1;
-        }
-        vector<thread> threads;
-        mutex mtx;
-        condition_variable cv;
-        int activeThreads = 0;
-        int nextNodeIndex = 0;
-        while (nextNodeIndex < nodes.size()) {
-            unique_lock<mutex> lock(mtx);
-            cv.wait(lock, [&activeThreads, &maxThreadsRunning]() { return activeThreads < maxThreadsRunning; });
-            if (nextNodeIndex >= nodes.size()) {
-                break;  // No more work remaining
             }
-            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();
+    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) {
+            nodes[features[i]]->setNumStates(states.at(features[i]).size());
         }
-        fitted = true;
+        classNumStates = nodes[className]->getNumStates();
+    }
+    // X comes in nxm, where n is the number of features and m the number of samples
+    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, states, weights);
+        this->className = className;
+        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, "..." });
+        }
+        completeFit(states, weights);
+    }
+    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<float>& weights_, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    {
+        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;
+        // Build 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) {
+            samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
+        }
+        samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
+        completeFit(states, weights);
+    }
+    void Network::completeFit(const map<string, vector<int>>& states, const torch::Tensor& weights)
+    {
+        setStates(states);
+        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
+        for (auto& node : nodes) {
+            node.second->computeCPT(samples, features, laplaceSmoothing, weights);
+            fitted = true;
+        }
+    }
+    torch::Tensor Network::predict_tensor(const torch::Tensor& samples, const bool proba)
+    {
+        if (!fitted) {
+            throw logic_error("You must call fit() before calling predict()");
+        }
+        torch::Tensor result;
+        result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
+        for (int i = 0; i < samples.size(1); ++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));
+            result.index_put_({ i, "..." }, temp);
+        }
+        if (proba)
+            return result;
+        else
+            return result.argmax(1);
+    }
+    // Return mxn tensor of probabilities
+    Tensor Network::predict_proba(const Tensor& samples)
+    {
+        return predict_tensor(samples, true);
     }
 
+    // Return mxn tensor of probabilities
+    Tensor Network::predict(const Tensor& samples)
+    {
+        return predict_tensor(samples, false);
+    }
+
+    // Return mx1 vector of predictions
+    // tsamples is nxm vector of samples
     vector<int> Network::predict(const vector<vector<int>>& tsamples)
     {
         if (!fitted) {
@@ -194,6 +232,7 @@ namespace bayesnet {
         }
         return predictions;
     }
+    // Return mxn vector of probabilities
     vector<vector<double>> Network::predict_proba(const vector<vector<int>>& tsamples)
     {
         if (!fitted) {
@@ -221,12 +260,13 @@ namespace bayesnet {
         }
         return (double)correct / y_pred.size();
     }
+    // Return 1xn vector of probabilities
     vector<double> Network::predict_sample(const vector<int>& sample)
     {
         // Ensure the sample size is equal to the number of features
-        if (sample.size() != features.size()) {
+        if (sample.size() != features.size() - 1) {
             throw invalid_argument("Sample size (" + to_string(sample.size()) +
-                ") does not match the number of features (" + to_string(features.size()) + ")");
+                ") does not match the number of features (" + to_string(features.size() - 1) + ")");
         }
         map<string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
@@ -234,6 +274,20 @@ namespace bayesnet {
         }
         return exactInference(evidence);
     }
+    // Return 1xn vector of probabilities
+    vector<double> Network::predict_sample(const Tensor& sample)
+    {
+        // Ensure the sample size is equal to the number of features
+        if (sample.size(0) != features.size() - 1) {
+            throw invalid_argument("Sample size (" + to_string(sample.size(0)) +
+                ") does not match the number of features (" + to_string(features.size() - 1) + ")");
+        }
+        map<string, int> evidence;
+        for (int i = 0; i < sample.size(0); ++i) {
+            evidence[features[i]] = sample[i].item<int>();
+        }
+        return exactInference(evidence);
+    }
     double Network::computeFactor(map<string, int>& completeEvidence)
     {
         double result = 1.0;
@@ -259,15 +313,12 @@ namespace bayesnet {
         for (auto& thread : threads) {
             thread.join();
         }
-
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
-        for (double& value : result) {
+        transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
-            value /= sum;
-        }
         return result;
     }
-    vector<string> Network::show()
+    vector<string> Network::show() const
     {
         vector<string> result;
         // Draw the network
@@ -280,7 +331,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(string title)
+    vector<string> Network::graph(const string& title) const
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
@@ -294,7 +345,7 @@ 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) {
@@ -306,4 +357,52 @@ 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 };
+        while (!ending) {
+            ending = true;
+            for (auto feature : features) {
+                auto fathers = nodes[feature]->getParents();
+                for (const auto& father : fathers) {
+                    auto fatherName = father->getName();
+                    if (fatherName == className) {
+                        continue;
+                    }
+                    // Check if father is placed before the actual feature
+                    auto it = find(result.begin(), result.end(), fatherName);
+                    if (it != result.end()) {
+                        auto it2 = find(result.begin(), result.end(), feature);
+                        if (it2 != result.end()) {
+                            if (distance(it, it2) < 0) {
+                                // if it is not, insert it before the feature
+                                result.erase(remove(result.begin(), result.end(), fatherName), result.end());
+                                result.insert(it2, fatherName);
+                                ending = false;
+                            }
+                        } else {
+                            throw logic_error("Error in topological sort because of node " + feature + " is not in result");
+                        }
+                    } else {
+                        throw logic_error("Error in topological sort because of node father " + fatherName + " is not in result");
+                    }
+                }
+            }
+        }
+        return result;
+    }
+    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

@@ -8,48 +8,52 @@ namespace bayesnet {
     class Network {
     private:
         map<string, unique_ptr<Node>> nodes;
-        map<string, vector<int>> dataset;
         bool fitted;
-        float maxThreads;
+        float maxThreads = 0.95;
         int classNumStates;
-        vector<string> features;
+        vector<string> features; // Including classname
         string className;
-        int laplaceSmoothing;
+        double laplaceSmoothing;
-        torch::Tensor samples;
+        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();
     public:
         Network();
-        Network(float, int);
+        explicit Network(float);
-        Network(float);
+        explicit Network(Network&);
-        Network(Network&);
+        ~Network() = default;
         torch::Tensor& getSamples();
         float getmaxThreads();
-        void addNode(string, int);
+        void addNode(const string&);
-        void addEdge(const string, 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);
-        vector<int> predict(const vector<vector<int>>&);
+        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);
-        //Computes the conditional edge weight of variable index u and v conditioned on class_node
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states);
-        torch::Tensor conditionalEdgeWeight();
+        vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
-        vector<vector<double>> predict_proba(const vector<vector<int>>&);
+        torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
+        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> show();
+        vector<string> topological_sort();
-        vector<string> graph(string title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> show() const;
-        inline string version() { return "0.1.0"; }
+        vector<string> graph(const string& title) const; // Returns a vector of strings representing the graph in graphviz format
+        void initialize();
+        void dump_cpt() const;
+        inline string version() { return "0.2.0"; }
     };
 }
 #endif

src/BayesNet/Node.cc

@@ -2,8 +2,8 @@
 
 namespace bayesnet {
 
-    Node::Node(const std::string& name, int numStates)
+    Node::Node(const std::string& name)
-        : name(name), numStates(numStates), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
+        : name(name), numStates(0), cpTable(torch::Tensor()), parents(vector<Node*>()), children(vector<Node*>())
     {
     }
     void Node::clear()
@@ -84,46 +84,52 @@ 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
         dimensions.push_back(numStates);
-        for (auto father : getParents()) {
+        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-            dimensions.push_back(father->getNumStates());
+
-        }
         // 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");
-            for (auto father : getParents()) {
+        }
-                coordinates.push_back(torch::tensor(dataset[father->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]));
-        for (auto parent : getParents()) {
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-            coordinates.push_back(torch::tensor(evidence[parent->getName()]));
-        }
         return cpTable.index({ coordinates }).item<float>();
     }
-    vector<string> Node::graph(string className)
+    vector<string> Node::graph(const string& className)
     {
         auto output = vector<string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
         output.push_back(name + " [shape=circle" + suffix + "] \n");
-        for (auto& child : children) {
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
-            output.push_back(name + " -> " + child->getName());
-        }
         return output;
     }
 }

src/BayesNet/Node.h

@@ -16,7 +16,7 @@ namespace bayesnet {
         vector<int64_t> dimensions; // dimensions of the cpTable
     public:
         vector<pair<string, string>> combinations(const vector<string>&);
-        Node(const std::string&, int);
+        explicit Node(const string&);
         void clear();
         void addParent(Node*);
         void addChild(Node*);
@@ -26,11 +26,11 @@ 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();
-        vector<string> graph(string clasName); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& clasName); // Returns a vector of strings representing the graph in graphviz format
         float getFactorValue(map<string, int>&);
     };
 }

src/BayesNet/Proposal.cc

@@ -0,0 +1,110 @@
+#include "Proposal.h"
+#include "ArffFiles.h"
+
+namespace bayesnet {
+    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::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;
+        bool upgrade = false; // Flag to check if we need to upgrade the model
+        for (auto feature : order) {
+            auto nodeParents = nodes[feature]->getParents();
+            if (nodeParents.size() < 2) continue; // Only has class as parent
+            upgrade = true;
+            int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
+            indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
+            vector<string> parents;
+            transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
+            // Remove class as parent as it will be added later
+            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(Xf.size(1));
+            for (auto idx : indices) {
+                for (int i = 0; i < Xf.size(1); ++i) {
+                    yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
+                }
+            }
+            auto arff = ArffFiles();
+            auto yxv = arff.factorize(yJoinParents);
+            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);
+        }
+        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));
+                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;
+    }
+    map<string, vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
+    {
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
+        int m = Xf.size(1);
+        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 (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);
+            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;
+            discretizers[pFeatures[i]] = discretizer;
+        }
+        int n_classes = torch::max(y).item<int>() + 1;
+        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)
+    {
+        auto Xtd = torch::zeros_like(X, torch::kInt32);
+        for (int i = 0; i < X.size(0); ++i) {
+            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
+            auto Xd = discretizers[pFeatures[i]]->transform(Xt);
+            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
+        }
+        return Xtd;
+    }
+}

src/BayesNet/Proposal.h

@@ -0,0 +1,30 @@
+#ifndef PROPOSAL_H
+#define PROPOSAL_H
+#include <string>
+#include <map>
+#include <torch/torch.h>
+#include "Network.h"
+#include "CPPFImdlp.h"
+#include "Classifier.h"
+
+namespace bayesnet {
+    class Proposal {
+    public:
+        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);
+        map<string, vector<int>> localDiscretizationProposal(const map<string, vector<int>>& states, Network& model);
+        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;
+    };
+}
+
+#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(string name )
+    vector<string> SPODE::graph(const string& name) const
     {
         return model.graph(name);
     }

src/BayesNet/SPODE.h

@@ -1,15 +1,17 @@
 #ifndef SPODE_H
 #define SPODE_H
 #include "Classifier.h"
+
 namespace bayesnet {
     class SPODE : public Classifier {
     private:
         int root;
     protected:
-        void train() override;
+        void buildModel(const torch::Tensor& weights) override;
     public:
-        SPODE(int root);
+        explicit SPODE(int root);
-        vector<string> graph(string name = "SPODE") override;
+        virtual ~SPODE() {};
+        vector<string> graph(const string& name = "SPODE") const override;
     };
 }
 #endif

src/BayesNet/SPODELd.cc

@@ -0,0 +1,48 @@
+#include "SPODELd.h"
+
+namespace bayesnet {
+    using namespace std;
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
+    SPODELd& SPODELd::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_;
+        Xf = X_;
+        y = y_;
+        // 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;
+    }
+    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) const
+    {
+        return SPODE::graph(name);
+    }
+}

src/BayesNet/SPODELd.h

@@ -0,0 +1,19 @@
+#ifndef SPODELD_H
+#define SPODELD_H
+#include "SPODE.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class SPODELd : public SPODE, public Proposal {
+    public:
+        explicit SPODELd(int root);
+        virtual ~SPODELd() = default;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& features, const string& className, map<string, vector<int>>& states) 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"; };
+    };
+}
+#endif // !SPODELD_H

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 = dataset.index({ "...", -1 });
+        Tensor class_dataset = dataset.index({ -1, "..." });
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
-            Tensor feature_dataset = dataset.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(), [](auto& left, auto& right) {return left.second < right.second;});
+        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(string title)
+    vector<string> TAN::graph(const string& title) const
     {
         return model.graph(title);
     }

src/BayesNet/TAN.h

@@ -3,14 +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();
-        vector<string> graph(string name = "TAN") override;
+        virtual ~TAN() {};
+        vector<string> graph(const string& name = "TAN") const override;
     };
 }
 #endif

src/BayesNet/TANLd.cc

@@ -0,0 +1,31 @@
+#include "TANLd.h"
+
+namespace bayesnet {
+    using namespace std;
+    TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
+    TANLd& TANLd::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_;
+        Xf = X_;
+        y = y_;
+        // 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 TAN structure, TAN::fit initializes the base Bayesian network
+        TAN::fit(dataset, features, className, states);
+        states = localDiscretizationProposal(states, model);
+        return *this;
+
+    }
+    Tensor TANLd::predict(Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
+    }
+    vector<string> TANLd::graph(const string& name) const
+    {
+        return TAN::graph(name);
+    }
+}

src/BayesNet/TANLd.h

@@ -0,0 +1,19 @@
+#ifndef TANLD_H
+#define TANLD_H
+#include "TAN.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class TANLd : public TAN, public Proposal {
+    private:
+    public:
+        TANLd();
+        virtual ~TANLd() = default;
+        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") const override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !TANLD_H

src/BayesNet/bayesnetUtils.cc

@@ -3,7 +3,8 @@
 namespace bayesnet {
     using namespace std;
     using namespace torch;
-    vector<int> argsort(vector<float>& nums)
+    // Return the indices in descending order
+    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/BestResult.h

@@ -0,0 +1,10 @@
+#ifndef BESTRESULT_H
+#define BESTRESULT_H
+#include <string>
+class BestResult {
+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 +4,9 @@ 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)
+add_executable(main main.cc Folding.cc platformUtils.cc Experiment.cc Datasets.cc Models.cc ReportConsole.cc ReportBase.cc)
-target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
+add_executable(manage manage.cc Results.cc ReportConsole.cc ReportExcel.cc ReportBase.cc)
+add_executable(list list.cc platformUtils Datasets.cc)
+target_link_libraries(main BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(manage "${TORCH_LIBRARIES}" OpenXLSX::OpenXLSX)
+target_link_libraries(list ArffFiles mdlp "${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

@@ -1,22 +1,13 @@
 #include "Datasets.h"
 #include "platformUtils.h"
 #include "ArffFiles.h"
+#include <fstream>
 namespace platform {
-    vector<string> split(string text, char delimiter)
-    {
-        vector<string> result;
-        stringstream ss(text);
-        string token;
-        while (getline(ss, token, delimiter)) {
-            result.push_back(token);
-        }
-        return result;
-    }
     void Datasets::load()
     {
-        string line;
         ifstream catalog(path + "/all.txt");
         if (catalog.is_open()) {
+            string line;
             while (getline(catalog, line)) {
                 vector<string> tokens = split(line, ',');
                 string name = tokens[0];
@@ -31,95 +22,113 @@ namespace platform {
     vector<string> Datasets::getNames()
     {
         vector<string> result;
-        for (auto& d : datasets) {
+        transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
-            result.push_back(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(string name)
+    bool Datasets::isDataset(const string& name) const
     {
         return datasets.find(name) != datasets.end();
     }
-    Dataset::Dataset(Dataset& dataset)
+    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)
     {
-        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;
@@ -127,7 +136,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNFeatures()
+    int Dataset::getNFeatures() const
     {
         if (loaded) {
             return n_features;
@@ -135,7 +144,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Dataset::getNSamples()
+    int Dataset::getNSamples() const
     {
         if (loaded) {
             return n_samples;
@@ -143,7 +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;
@@ -178,9 +187,9 @@ namespace platform {
     }
     void Dataset::load_csv()
     {
-        string line;
         ifstream file(path + "/" + name + ".csv");
         if (file.is_open()) {
+            string line;
             getline(file, line);
             vector<string> tokens = split(line, ',');
             features = vector<string>(tokens.begin(), tokens.end() - 1);
@@ -204,10 +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()
     {
@@ -218,9 +228,8 @@ namespace platform {
         yv = arff.getY();
         // Get className & Features
         className = arff.getClassName();
-        for (auto feature : arff.getAttributes()) {
+        auto attributes = arff.getAttributes();
-            features.push_back(feature.first);
+        transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& attribute) { return attribute.first; });
-        }
     }
     void Dataset::load()
     {
@@ -235,9 +244,9 @@ namespace platform {
         if (discretize) {
             Xd = discretizeDataset(Xv, yv);
             computeStates();
-            n_samples = Xd[0].size();
-            n_features = Xd.size();
         }
+        n_samples = Xv[0].size();
+        n_features = Xv.size();
         loaded = true;
     }
     void Dataset::buildTensors()

src/Platform/Datasets.h

@@ -13,7 +13,7 @@ namespace platform {
         string name;
         fileType_t fileType;
         string className;
-        int n_samples, n_features;
+        int n_samples{ 0 }, n_features{ 0 };
         vector<string> features;
         map<string, vector<int>> states;
         bool loaded;
@@ -27,17 +27,17 @@ namespace platform {
         void load_arff();
         void computeStates();
     public:
-        Dataset(string path, string name, string className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
+        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) {};
-        Dataset(Dataset&);
+        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; };
     };
@@ -49,16 +49,19 @@ namespace platform {
         bool discretize;
         void load(); // Loads the list of datasets
     public:
-        Datasets(string path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
+        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(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/DotEnv.h

@@ -0,0 +1,62 @@
+#ifndef DOTENV_H
+#define DOTENV_H
+#include <string>
+#include <map>
+#include <fstream>
+#include <sstream>
+#include "platformUtils.h"
+namespace platform {
+    class DotEnv {
+    private:
+        std::map<std::string, std::string> env;
+        std::string trim(const std::string& str)
+        {
+            std::string result = str;
+            result.erase(result.begin(), std::find_if(result.begin(), result.end(), [](int ch) {
+                return !std::isspace(ch);
+                }));
+            result.erase(std::find_if(result.rbegin(), result.rend(), [](int ch) {
+                return !std::isspace(ch);
+                }).base(), result.end());
+            return result;
+        }
+    public:
+        DotEnv()
+        {
+            std::ifstream file(".env");
+            if (!file.is_open()) {
+                std::cerr << "File .env not found" << std::endl;
+                exit(1);
+            }
+            std::string line;
+            while (std::getline(file, line)) {
+                line = trim(line);
+                if (line.empty() || line[0] == '#') {
+                    continue;
+                }
+                std::istringstream iss(line);
+                std::string key, value;
+                if (std::getline(iss, key, '=') && std::getline(iss, value)) {
+                    env[key] = value;
+                }
+            }
+        }
+        std::string get(const std::string& key)
+        {
+            return env[key];
+        }
+        std::vector<int> getSeeds()
+        {
+            auto seeds = std::vector<int>();
+            auto seeds_str = env["seeds"];
+            seeds_str = trim(seeds_str);
+            seeds_str = seeds_str.substr(1, seeds_str.size() - 2);
+            auto seeds_str_split = split(seeds_str, ',');
+            transform(seeds_str_split.begin(), seeds_str_split.end(), back_inserter(seeds), [](const std::string& str) {
+                return stoi(str);
+                });
+            return seeds;
+        }
+    };
+}
+#endif

src/Platform/Experiment.cc

@@ -1,5 +1,8 @@
 #include "Experiment.h"
-
+#include "Datasets.h"
+#include "Models.h"
+#include "ReportConsole.h"
+#include <fstream>
 namespace platform {
     using json = nlohmann::json;
     string get_date()
@@ -22,6 +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";
@@ -43,10 +47,10 @@ namespace platform {
         result["discretized"] = discretized;
         result["stratified"] = stratified;
         result["folds"] = nfolds;
-        result["seeds"] = random_seeds;
+        result["seeds"] = randomSeeds;
         result["duration"] = duration;
         result["results"] = json::array();
-        for (auto& r : results) {
+        for (const auto& r : results) {
             json j;
             j["dataset"] = r.getDataset();
             j["hyperparameters"] = r.getHyperparameters();
@@ -65,6 +69,10 @@ namespace platform {
             j["test_time_std"] = r.getTestTimeStd();
             j["time"] = r.getTestTime() + r.getTrainTime();
             j["time_std"] = r.getTestTimeStd() + r.getTrainTimeStd();
+            j["scores_train"] = r.getScoresTrain();
+            j["scores_test"] = r.getScoresTest();
+            j["times_train"] = r.getTimesTrain();
+            j["times_test"] = r.getTimesTest();
             j["nodes"] = r.getNodes();
             j["leaves"] = r.getLeaves();
             j["depth"] = r.getDepth();
@@ -72,62 +80,116 @@ namespace platform {
         }
         return result;
     }
-    void Experiment::save(string path)
+    void Experiment::save(const string& path)
     {
         json data = build_json();
         ofstream file(path + "/" + get_file_name());
         file << data;
         file.close();
     }
-    Result cross_validation(Fold* fold, string model_name, torch::Tensor& Xt, torch::Tensor& y, vector<string> features, string className, map<string, vector<int>> states)
+
+    void Experiment::report()
     {
-        auto classifiers = map<string, bayesnet::BaseClassifier*>({
+        json data = build_json();
-           { "AODE", new bayesnet::AODE() }, { "KDB", new bayesnet::KDB(2) },
+        ReportConsole report(data);
-           { "SPODE",  new bayesnet::SPODE(2) }, { "TAN",  new bayesnet::TAN() }
+        report.show();
-            }
+    }
-        );
+
+    void Experiment::show()
+    {
+        json data = build_json();
+        cout << data.dump(4) << endl;
+    }
+
+    void Experiment::go(vector<string> filesToProcess, const string& path)
+    {
+        cout << "*** Starting experiment: " << title << " ***" << endl;
+        for (auto fileName : filesToProcess) {
+            cout << "- " << setw(20) << left << fileName << " " << right << flush;
+            cross_validation(path, fileName);
+            cout << endl;
+        }
+    }
+
+    void Experiment::cross_validation(const string& path, const string& fileName)
+    {
+        auto datasets = platform::Datasets(path, discretized, platform::ARFF);
+        // Get dataset
+        auto [X, y] = datasets.getTensors(fileName);
+        auto states = datasets.getStates(fileName);
+        auto features = datasets.getFeatures(fileName);
+        auto samples = datasets.getNSamples(fileName);
+        auto className = datasets.getClassName(fileName);
+        cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
+        // Prepare Result
         auto result = Result();
         auto [values, counts] = at::_unique(y);
-        result.setSamples(Xt.size(1)).setFeatures(Xt.size(0)).setClasses(values.size(0));
+        result.setSamples(X.size(1)).setFeatures(X.size(0)).setClasses(values.size(0));
-        auto k = fold->getNumberOfFolds();
+        result.setHyperparameters(hyperparameters);
-        auto accuracy_test = torch::zeros({ k }, torch::kFloat64);
+        // Initialize results vectors
-        auto accuracy_train = torch::zeros({ k }, torch::kFloat64);
+        int nResults = nfolds * static_cast<int>(randomSeeds.size());
-        auto train_time = torch::zeros({ k }, torch::kFloat64);
+        auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
-        auto test_time = torch::zeros({ k }, torch::kFloat64);
+        auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
-        auto nodes = torch::zeros({ k }, torch::kFloat64);
+        auto train_time = torch::zeros({ nResults }, torch::kFloat64);
-        auto edges = torch::zeros({ k }, torch::kFloat64);
+        auto test_time = torch::zeros({ nResults }, torch::kFloat64);
-        auto num_states = torch::zeros({ k }, torch::kFloat64);
+        auto nodes = torch::zeros({ nResults }, torch::kFloat64);
+        auto edges = torch::zeros({ nResults }, torch::kFloat64);
+        auto num_states = torch::zeros({ nResults }, torch::kFloat64);
         Timer train_timer, test_timer;
-        cout << "doing Fold: " << flush;
+        int item = 0;
-        for (int i = 0; i < k; i++) {
+        for (auto seed : randomSeeds) {
-            bayesnet::BaseClassifier* model = classifiers[model_name];
+            cout << "(" << seed << ") doing Fold: " << flush;
-            result.setModelVersion(model->getVersion());
+            Fold* fold;
-            train_timer.start();
+            if (stratified)
-            auto [train, test] = fold->getFold(i);
+                fold = new StratifiedKFold(nfolds, y, seed);
-            auto train_t = torch::tensor(train);
+            else
-            auto test_t = torch::tensor(test);
+                fold = new KFold(nfolds, y.size(0), seed);
-            auto X_train = Xt.index({ "...", train_t });
+            for (int nfold = 0; nfold < nfolds; nfold++) {
-            auto y_train = y.index({ train_t });
+                auto clf = Models::instance()->create(model);
-            auto X_test = Xt.index({ "...", test_t });
+                setModelVersion(clf->getVersion());
-            auto y_test = y.index({ test_t });
+                if (hyperparameters.size() != 0) {
-            cout << i + 1 << ", " << flush;
+                    clf->setHyperparameters(hyperparameters);
-            model->fit(X_train, y_train, features, className, states);
+                }
-            nodes[i] = model->getNumberOfNodes();
+                // Split train - test dataset
-            edges[i] = model->getNumberOfEdges();
+                train_timer.start();
-            num_states[i] = model->getNumberOfStates();
+                auto [train, test] = fold->getFold(nfold);
-            train_time[i] = train_timer.getDuration();
+                auto train_t = torch::tensor(train);
-            auto accuracy_train_value = model->score(X_train, y_train);
+                auto test_t = torch::tensor(test);
-            test_timer.start();
+                auto X_train = X.index({ "...", train_t });
-            auto accuracy_test_value = model->score(X_test, y_test);
+                auto y_train = y.index({ train_t });
-            test_time[i] = test_timer.getDuration();
+                auto X_test = X.index({ "...", test_t });
-            accuracy_train[i] = accuracy_train_value;
+                auto y_test = y.index({ test_t });
-            accuracy_test[i] = accuracy_test_value;
+                cout << nfold + 1 << ", " << flush;
+                // Train model
+                clf->fit(X_train, y_train, features, className, states);
+                nodes[item] = clf->getNumberOfNodes();
+                edges[item] = clf->getNumberOfEdges();
+                num_states[item] = clf->getNumberOfStates();
+                train_time[item] = train_timer.getDuration();
+                auto accuracy_train_value = clf->score(X_train, y_train);
+                // Test model
+                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;
+                // 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;
         }
-        cout << "end." << endl;
         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>());
-        return result;
+        result.setDataset(fileName);
+        addResult(result);
     }
 }

src/Platform/Experiment.h

@@ -29,14 +29,16 @@ namespace platform {
     };
     class Result {
     private:
-        string dataset, hyperparameters, model_version;
+        string dataset, model_version;
-        int samples, features, classes;
+        json hyperparameters;
-        double score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
+        int samples{ 0 }, features{ 0 }, classes{ 0 };
-        float nodes, leaves, depth;
+        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 };
+        vector<double> scores_train, scores_test, times_train, times_test;
     public:
         Result() = default;
-        Result& setDataset(string dataset) { this->dataset = dataset; return *this; }
+        Result& setDataset(const string& dataset) { this->dataset = dataset; return *this; }
-        Result& setHyperparameters(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; }
@@ -51,11 +53,14 @@ namespace platform {
         Result& setNodes(float nodes) { this->nodes = nodes; return *this; }
         Result& setLeaves(float leaves) { this->leaves = leaves; return *this; }
         Result& setDepth(float depth) { this->depth = depth; return *this; }
-        Result& setModelVersion(string model_version) { this->model_version = model_version; return *this; }
+        Result& addScoreTrain(double score) { scores_train.push_back(score); return *this; }
+        Result& addScoreTest(double score) { scores_test.push_back(score); return *this; }
+        Result& addTimeTrain(double time) { times_train.push_back(time); return *this; }
+        Result& addTimeTest(double time) { times_test.push_back(time); return *this; }
         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; }
@@ -70,36 +75,43 @@ namespace platform {
         const float getNodes() const { return nodes; }
         const float getLeaves() const { return leaves; }
         const float getDepth() const { return depth; }
-        const string& getModelVersion() const { return model_version; }
+        const vector<double>& getScoresTrain() const { return scores_train; }
+        const vector<double>& getScoresTest() const { return scores_test; }
+        const vector<double>& getTimesTrain() const { return times_train; }
+        const vector<double>& getTimesTest() const { return times_test; }
     };
     class Experiment {
     private:
         string title, model, platform, score_name, model_version, language_version, language;
-        bool discretized, stratified;
+        bool discretized{ false }, stratified{ false };
         vector<Result> results;
-        vector<int> random_seeds;
+        vector<int> randomSeeds;
-        int nfolds;
+        json hyperparameters = "{}";
-        float duration;
+        int nfolds{ 0 };
+        float duration{ 0 };
         json build_json();
     public:
-        Experiment() = default;
+        Experiment();
-        Experiment& setTitle(string title) { this->title = title; return *this; }
+        Experiment& setTitle(const string& title) { this->title = title; return *this; }
-        Experiment& setModel(string model) { this->model = model; return *this; }
+        Experiment& setModel(const string& model) { this->model = model; return *this; }
-        Experiment& setPlatform(string platform) { this->platform = platform; return *this; }
+        Experiment& setPlatform(const string& platform) { this->platform = platform; return *this; }
-        Experiment& setScoreName(string score_name) { this->score_name = score_name; return *this; }
+        Experiment& setScoreName(const string& score_name) { this->score_name = score_name; return *this; }
-        Experiment& setModelVersion(string model_version) { this->model_version = model_version; return *this; }
+        Experiment& setModelVersion(const string& model_version) { this->model_version = model_version; return *this; }
-        Experiment& setLanguage(string language) { this->language = language; return *this; }
+        Experiment& setLanguage(const string& language) { this->language = language; return *this; }
-        Experiment& setLanguageVersion(string language_version) { this->language_version = language_version; return *this; }
+        Experiment& setLanguageVersion(const string& language_version) { this->language_version = language_version; return *this; }
         Experiment& setDiscretized(bool discretized) { this->discretized = discretized; return *this; }
         Experiment& setStratified(bool stratified) { this->stratified = stratified; return *this; }
         Experiment& setNFolds(int nfolds) { this->nfolds = nfolds; return *this; }
         Experiment& addResult(Result result) { results.push_back(result); return *this; }
-        Experiment& addRandomSeed(int random_seed) { random_seeds.push_back(random_seed); 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(string path);
+        void save(const string& path);
-        void show() { cout << "Showing experiment..." << "Score Test: " << results[0].get_score_test() << " Score Train: " << results[0].get_score_train() << endl; }
+        void cross_validation(const string& path, const string& fileName);
+        void go(vector<string> filesToProcess, const string& path);
+        void show();
+        void report();
     };
-    Result cross_validation(Fold* fold, string model_name, torch::Tensor& X, torch::Tensor& y, vector<string> features, string className, map<string, vector<int>> states);
 }
 #endif

src/Platform/Folding.cc

@@ -1,96 +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);
+        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);
+        pair<vector<int>, vector<int>> getFold(int nFold) override;
-};
+    };
+}
 #endif

src/Platform/Models.cc

@@ -0,0 +1,54 @@
+#include "Models.h"
+namespace platform {
+    using namespace std;
+    // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
+    Models* Models::factory = nullptr;;
+    Models* Models::instance()
+    {
+        //manages singleton
+        if (factory == nullptr)
+            factory = new Models();
+        return factory;
+    }
+    void Models::registerFactoryFunction(const string& name,
+        function<bayesnet::BaseClassifier* (void)> classFactoryFunction)
+    {
+        // register the class factory function
+        functionRegistry[name] = classFactoryFunction;
+    }
+    shared_ptr<bayesnet::BaseClassifier> Models::create(const string& name)
+    {
+        bayesnet::BaseClassifier* instance = nullptr;
+
+        // find name in the registry and call factory method.
+        auto it = functionRegistry.find(name);
+        if (it != functionRegistry.end())
+            instance = it->second();
+        // wrap instance in a shared ptr and return
+        if (instance != nullptr)
+            return unique_ptr<bayesnet::BaseClassifier>(instance);
+        else
+            return nullptr;
+    }
+    vector<string> Models::getNames()
+    {
+        vector<string> names;
+        transform(functionRegistry.begin(), functionRegistry.end(), back_inserter(names),
+            [](const pair<string, function<bayesnet::BaseClassifier* (void)>>& pair) { return pair.first; });
+        return names;
+    }
+    string Models::toString()
+    {
+        string result = "";
+        for (const auto& pair : functionRegistry) {
+            result += pair.first + ", ";
+        }
+        return "{" + result.substr(0, result.size() - 2) + "}";
+    }
+
+    Registrar::Registrar(const string& name, function<bayesnet::BaseClassifier* (void)> classFactoryFunction)
+    {
+        // register the class factory function 
+        Models::instance()->registerFactoryFunction(name, classFactoryFunction);
+    }
+}

src/Platform/Models.h

@@ -0,0 +1,37 @@
+#ifndef MODELS_H
+#define MODELS_H
+#include <map>
+#include "BaseClassifier.h"
+#include "AODE.h"
+#include "TAN.h"
+#include "KDB.h"
+#include "SPODE.h"
+#include "TANLd.h"
+#include "KDBLd.h"
+#include "SPODELd.h"
+#include "AODELd.h"
+#include "BoostAODE.h"
+namespace platform {
+    class Models {
+    private:
+        map<string, function<bayesnet::BaseClassifier* (void)>> functionRegistry;
+        static Models* factory; //singleton
+        Models() {};
+    public:
+        Models(Models&) = delete;
+        void operator=(const Models&) = delete;
+        // Idea from: https://www.codeproject.com/Articles/567242/AplusC-2b-2bplusObjectplusFactory
+        static Models* instance();
+        shared_ptr<bayesnet::BaseClassifier> create(const string& name);
+        void registerFactoryFunction(const string& name,
+            function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
+        vector<string> getNames();
+        string toString();
+
+    };
+    class Registrar {
+    public:
+        Registrar(const string& className, function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
+    };
+}
+#endif

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/ReportBase.cc

@@ -0,0 +1,37 @@
+#include <sstream>
+#include <locale>
+#include "ReportBase.h"
+#include "BestResult.h"
+
+
+namespace platform {
+    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();
+    }
+}

src/Platform/ReportBase.h

@@ -0,0 +1,23 @@
+#ifndef REPORTBASE_H
+#define REPORTBASE_H
+#include <string>
+#include <iostream>
+#include <nlohmann/json.hpp>
+
+using json = nlohmann::json;
+namespace platform {
+    using namespace std;
+    class ReportBase {
+    public:
+        explicit ReportBase(json data_) { data = data_; };
+        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);
+        virtual void header() = 0;
+        virtual void body() = 0;
+    };
+};
+#endif

src/Platform/ReportConsole.cc

@@ -0,0 +1,88 @@
+#include <sstream>
+#include <locale>
+#include "ReportConsole.h"
+#include "BestResult.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 n = MAXL - text.length() - 3;
+        n = n < 0 ? 0 : n;
+        return "* " + text + string(n, ' ') + "*\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;
+        for (const auto& r : data["results"]) {
+            auto color = odd ? Colors::CYAN() : Colors::BLUE();
+            cout << color << 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(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>() << " ";
+            cout << setw(11) << 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) {
+            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::footer(double totalScore)
+    {
+        cout << Colors::MAGENTA() << string(MAXL, '*') << endl;
+        auto score = data["score_name"].get<string>();
+        if (score == BestResult::scoreName()) {
+            stringstream oss;
+            oss << score << " compared to " << BestResult::title() << " .:  " << totalScore / BestResult::score();
+            cout << headerLine(oss.str());
+        }
+        cout << string(MAXL, '*') << endl << Colors::RESET();
+    }
+}

src/Platform/ReportConsole.h

@@ -0,0 +1,22 @@
+#ifndef REPORTCONSOLE_H
+#define REPORTCONSOLE_H
+#include <string>
+#include <iostream>
+#include "ReportBase.h"
+#include "Colors.h"
+
+namespace platform {
+    using namespace std;
+    const int MAXL = 128;
+    class ReportConsole : public ReportBase{
+    public:
+        explicit ReportConsole(json data_) : ReportBase(data_) {};
+        virtual ~ReportConsole() = default;
+    private:
+        string headerLine(const string& text);
+        void header() override;
+        void body() override;
+        void footer(double totalScore);
+    };
+};
+#endif

src/Platform/ReportExcel.cc

@@ -0,0 +1,109 @@
+#include <sstream>
+#include <locale>
+#include "ReportExcel.h"
+#include "BestResult.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"; }
+    };
+
+    void ReportExcel::createFile()
+    {
+        doc.create(Paths::excel() + "some_results.xlsx");
+        wks = doc.workbook().worksheet("Sheet1");
+        wks.setName(data["model"].get<string>());
+    }
+
+    void ReportExcel::closeFile()
+    {
+        doc.save();
+        doc.close();
+    }
+
+    void ReportExcel::header()
+    {
+        locale mylocale(cout.getloc(), new separated);
+        locale::global(mylocale);
+        cout.imbue(mylocale);
+        stringstream oss;
+        wks.cell("A1").value().set(
+            "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>());
+        wks.cell("A2").value() = data["title"].get<string>();
+        wks.cell("A3").value() = "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>();
+        wks.cell("A4").value() = oss.str();
+        wks.cell("A5").value() = "Score is " + data["score_name"].get<string>();
+    }
+
+    void ReportExcel::body()
+    {
+        auto head = vector<string>(
+            { "Dataset", "Samples", "Features", "Classes", "Nodes", "Edges", "States", "Score", "Score Std.", "Time",
+             "Time Std.", "Hyperparameters" });
+        int col = 1;
+        for (const auto& item : head) {
+            wks.cell(8, col++).value() = item;
+        }
+        int row = 9;
+        col = 1;
+        json lastResult;
+        double totalScore = 0.0;
+        string hyperparameters;
+        for (const auto& r : data["results"]) {
+            wks.cell(row, col).value() = r["dataset"].get<string>();
+            wks.cell(row, col + 1).value() = r["samples"].get<int>();
+            wks.cell(row, col + 2).value() = r["features"].get<int>();
+            wks.cell(row, col + 3).value() = r["classes"].get<int>();
+            wks.cell(row, col + 4).value() = r["nodes"].get<float>();
+            wks.cell(row, col + 5).value() = r["leaves"].get<float>();
+            wks.cell(row, col + 6).value() = r["depth"].get<float>();
+            wks.cell(row, col + 7).value() = r["score"].get<double>();
+            wks.cell(row, col + 8).value() = r["score_std"].get<double>();
+            wks.cell(row, col + 9).value() = r["time"].get<double>();
+            wks.cell(row, col + 10).value() = r["time_std"].get<double>();
+            try {
+                hyperparameters = r["hyperparameters"].get<string>();
+            }
+            catch (const exception& err) {
+                stringstream oss;
+                oss << r["hyperparameters"];
+                hyperparameters = oss.str();
+            }
+            wks.cell(row, col + 11).value() = hyperparameters;
+            lastResult = r;
+            totalScore += r["score"].get<double>();
+            row++;
+        }
+        if (data["results"].size() == 1) {
+            for (const string& group : { "scores_train", "scores_test", "times_train", "times_test" }) {
+                row++;
+                col = 1;
+                wks.cell(row, col).value() = group;
+                for (double item : lastResult[group]) {
+                    wks.cell(row, ++col).value() = item;
+                }
+            }
+        } else {
+            footer(totalScore, row);
+        }
+    }
+
+    void ReportExcel::footer(double totalScore, int row)
+    {
+        auto score = data["score_name"].get<string>();
+        if (score == BestResult::scoreName()) {
+            wks.cell(row + 2, 1).value() = score + " compared to " + BestResult::title() + " .:  ";
+            wks.cell(row + 2, 5).value() = totalScore / BestResult::score();
+        }
+    }
+}

src/Platform/ReportExcel.h

@@ -0,0 +1,25 @@
+#ifndef REPORTEXCEL_H
+#define REPORTEXCEL_H
+#include <OpenXLSX.hpp>
+#include "ReportBase.h"
+#include "Paths.h"
+#include "Colors.h"
+namespace platform {
+    using namespace std;
+    using namespace OpenXLSX;
+    const int MAXLL = 128;
+    class ReportExcel : public ReportBase{
+    public:
+        explicit ReportExcel(json data_) : ReportBase(data_) {createFile();};
+        virtual ~ReportExcel() {closeFile();};
+    private:
+        void createFile();
+        void closeFile();
+        XLDocument doc;
+        XLWorksheet wks;
+        void header() override;
+        void body() override;
+        void footer(double totalScore, int row);
+    };
+};
+#endif // !REPORTEXCEL_H

src/Platform/Results.cc

@@ -0,0 +1,254 @@
+#include <filesystem>
+#include "platformUtils.h"
+#include "Results.h"
+#include "ReportConsole.h"
+#include "ReportExcel.h"
+#include "BestResult.h"
+#include "Colors.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 == BestResult::scoreName()) {
+            score /= BestResult::score();
+        }
+        title = data["title"];
+        duration = data["duration"];
+        model = data["model"];
+    }
+    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 + "]");
+    }
+    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())
+                    addResult = false;
+                if (addResult)
+                    files.push_back(result);
+            }
+        }
+    }
+    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 << " ";
+        oss << setw(9) << setprecision(3) << fixed << duration << " ";
+        oss << setw(50) << left << title << " ";
+        return  oss.str();
+    }
+    void Results::show() const
+    {
+        cout << Colors::GREEN() << "Results found: " << files.size() << endl;
+        cout << "-------------------" << endl;
+        auto i = 0;
+        cout << " #  Date       Model        Score Name  Score       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) const
+    {
+        cout << Colors::YELLOW() << "Reporting " << files.at(index).getFilename() << endl;
+        auto data = files.at(index).load();
+        if (excelReport) {
+            ReportExcel reporter(data);
+            reporter.show();
+        } else {
+            ReportConsole reporter(data);
+            reporter.show();
+        }
+    }
+    void Results::menu()
+    {
+        char option;
+        int index;
+        bool finished = false;
+        string filename, line, options = "qldhsre";
+        while (!finished) {
+            cout << Colors::RESET() << "Choose option (quit='q', list='l', delete='d', hide='h', sort='s', report='r', excel='e'): ";
+            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)) {
+                    index = stoi(line);
+                    if (index >= 0 && index < files.size()) {
+                        report(index, false);
+                        continue;
+                    }
+                }
+                cout << "Invalid option" << endl;
+                continue;
+            }
+            switch (option) {
+                case 'q':
+                    finished = true;
+                    break;
+                case 'l':
+                    show();
+                    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();
+                    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();
+                    break;
+                case 's':
+                    sortList();
+                    show();
+                    break;
+                case 'r':
+                    index = getIndex("report");
+                    if (index == -1)
+                        break;
+                    report(index, false);
+                    break;
+                case 'e':
+                    index = getIndex("excel");
+                    if (index == -1)
+                        break;
+                    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();
+        cout << "Done!" << endl;
+    }
+
+}

src/Platform/Results.h

@@ -0,0 +1,56 @@
+#ifndef RESULTS_H
+#define RESULTS_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; };
+    private:
+        string path;
+        string filename;
+        string date;
+        double score;
+        string title;
+        double duration;
+        string model;
+        string scoreName;
+    };
+    class Results {
+    public:
+        Results(const string& path, const int max, const string& model, const string& score) : path(path), max(max), model(model), scoreName(score) { load(); };
+        void manage();
+    private:
+        string path;
+        int max;
+        string model;
+        string scoreName;
+        vector<Result> files;
+        void load(); // Loads the list of results
+        void show() const;
+        void report(const int index, const bool excelReport) const;
+        int getIndex(const string& intent) const;
+        void menu();
+        void sortList();
+        void sortDate();
+        void sortScore();
+        void sortModel();
+        void sortDuration();
+    };
+};
+
+#endif

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

@@ -1,35 +1,42 @@
 #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;
 
 argparse::ArgumentParser manageArguments(int argc, char** argv)
 {
-    argparse::ArgumentParser program("BayesNetSample");
+    auto env = platform::DotEnv();
+    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 }
+        .default_value(string{ platform::Paths::datasets() });
-    );
     program.add_argument("-m", "--model")
-        .help("Model to use {AODE, KDB, SPODE, TAN}")
+        .help("Model to use " + platform::Models::instance()->toString())
         .action([](const std::string& value) {
-        static const vector<string> choices = { "AODE", "KDB", "SPODE", "TAN" };
+        static const vector<string> choices = platform::Models::instance()->getNames();
         if (find(choices.begin(), choices.end(), value) != choices.end()) {
             return value;
         }
-        throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
+        throw runtime_error("Model must be one of " + platform::Models::instance()->toString());
             }
     );
-    program.add_argument("--title").required().help("Experiment title");
+    program.add_argument("--title").default_value("").help("Experiment title");
-    program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
+    program.add_argument("--discretize").help("Discretize input dataset").default_value((bool)stoi(env.get("discretize"))).implicit_value(true);
-    program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
+    program.add_argument("--save").help("Save result (always save if no dataset is supplied)").default_value(false).implicit_value(true);
-    program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
+    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 {
             auto k = stoi(value);
             if (k < 2) {
@@ -43,22 +50,24 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         catch (...) {
             throw runtime_error("Number of folds must be an integer");
         }});
-    program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
+    auto seed_values = env.getSeeds();
-    bool class_last, discretize_dataset, stratified;
+    program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
-    int n_folds, seed;
-    string model_name, file_name, path, complete_file_name, title;
     try {
         program.parse_args(argc, argv);
-        file_name = program.get<string>("dataset");
+        auto file_name = program.get<string>("dataset");
-        path = program.get<string>("path");
+        auto path = program.get<string>("path");
-        model_name = program.get<string>("model");
+        auto model_name = program.get<string>("model");
-        discretize_dataset = program.get<bool>("discretize");
+        auto discretize_dataset = program.get<bool>("discretize");
-        stratified = program.get<bool>("stratified");
+        auto stratified = program.get<bool>("stratified");
-        n_folds = program.get<int>("folds");
+        auto n_folds = program.get<int>("folds");
-        seed = program.get<int>("seed");
+        auto seeds = program.get<vector<int>>("seeds");
-        complete_file_name = path + file_name + ".arff";
+        auto complete_file_name = path + file_name + ".arff";
-        class_last = false;//datasets[file_name];
+        auto title = program.get<string>("title");
-        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");
+        }
     }
     catch (const exception& err) {
         cerr << err.what() << endl;
@@ -77,51 +86,45 @@ int main(int argc, char** argv)
     auto discretize_dataset = program.get<bool>("discretize");
     auto stratified = program.get<bool>("stratified");
     auto n_folds = program.get<int>("folds");
-    auto seed = program.get<int>("seed");
+    auto seeds = program.get<vector<int>>("seeds");
-    vector<string> filesToProcess;
+    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;
             exit(1);
         }
-        filesToProcess.push_back(file_name);
+        if (title == "") {
+            title = "Test " + file_name + " " + model_name + " " + to_string(n_folds) + " folds";
+        }
+        filesToTest.push_back(file_name);
     } else {
-        filesToProcess = platform::Datasets(path, true, platform::ARFF).getNames();
+        filesToTest = platform::Datasets(path, true, platform::ARFF).getNames();
+        saveResults = true;
     }
-    auto title = program.get<string>("title");
-
     /*
     * Begin Processing
     */
+    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("BayesNet");
+    experiment.setDiscretized(discretize_dataset).setModel(model_name).setPlatform(env.get("platform"));
-    experiment.setStratified(stratified).setNFolds(n_folds).addRandomSeed(seed).setScoreName("accuracy");
+    experiment.setStratified(stratified).setNFolds(n_folds).setScoreName("accuracy");
-    platform::Timer timer;
+    experiment.setHyperparameters(json::parse(hyperparameters));
-    cout << "*** Starting experiment: " << title << " ***" << endl;
+    for (auto seed : seeds) {
-    timer.start();
+        experiment.addRandomSeed(seed);
-    for (auto fileName : filesToProcess) {
-        cout << "- " << setw(20) << left << fileName << " " << right << flush;
-        auto [X, y] = datasets.getTensors(fileName);
-        auto states = datasets.getStates(fileName);
-        auto features = datasets.getFeatures(fileName);
-        auto samples = datasets.getNSamples(fileName);
-        auto className = datasets.getClassName(fileName);
-        cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
-        Fold* fold;
-        if (stratified)
-            fold = new StratifiedKFold(n_folds, y, seed);
-        else
-            fold = new KFold(n_folds, samples, seed);
-        auto result = platform::cross_validation(fold, model_name, X, y, features, className, states);
-        result.setDataset(fileName);
-        experiment.setModelVersion(result.getModelVersion());
-        experiment.addResult(result);
-        delete fold;
     }
+    platform::Timer timer;
+    timer.start();
+    experiment.go(filesToTest, path);
     experiment.setDuration(timer.getDuration());
-    experiment.save(PATH_RESULTS);
+    if (saveResults) {
+        experiment.save(platform::Paths::results());
+    }
+    experiment.report();
     cout << "Done!" << endl;
     return 0;
 }

src/Platform/manage.cc

@@ -0,0 +1,41 @@
+#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");
+    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");
+    }
+    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 results = platform::Results(platform::Paths::results(), number, model, score);
+    results.manage();
+    return 0;
+}

src/Platform/modelRegister.h

@@ -0,0 +1,21 @@
+#ifndef MODEL_REGISTER_H
+#define MODEL_REGISTER_H
+static platform::Registrar registrarT("TAN",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
+static platform::Registrar registrarTLD("TANLd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
+static platform::Registrar registrarS("SPODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
+static platform::Registrar registrarSLD("SPODELd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
+static platform::Registrar registrarK("KDB",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
+static platform::Registrar registrarKLD("KDBLd",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
+static platform::Registrar registrarA("AODE",
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
+static platform::Registrar 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

@@ -1,7 +1,19 @@
 #include "platformUtils.h"
+#include "Paths.h"
 
 using namespace torch;
 
+vector<string> split(const string& text, char delimiter)
+{
+    vector<string> result;
+    stringstream ss(text);
+    string token;
+    while (getline(ss, token, delimiter)) {
+        result.push_back(token);
+    }
+    return result;
+}
+
 pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
 {
     vector<mdlp::labels_t> Xd;
@@ -28,7 +40,7 @@ vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::label
     return Xd;
 }
 
-bool file_exists(const std::string& name)
+bool file_exists(const string& name)
 {
     if (FILE* file = fopen(name.c_str(), "r")) {
         fclose(file);
@@ -38,7 +50,7 @@ bool file_exists(const std::string& name)
     }
 }
 
-tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset)
+tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset)
 {
     auto handler = ArffFiles();
     handler.load(path + static_cast<string>(name) + ".arff", class_last);
@@ -48,9 +60,8 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
+    auto attributes = handler.getAttributes();
-        features.push_back(feature.first);
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
-    }
     Tensor Xd;
     auto states = map<string, vector<int>>();
     if (discretize_dataset) {
@@ -58,11 +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) {
@@ -72,19 +84,18 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
 }
 
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name)
+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();
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
+    auto attributes = handler.getAttributes();
-        features.push_back(feature.first);
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
-    }
     // Discretize Dataset
     vector<mdlp::labels_t> Xd;
     map<string, int> maxes;

src/Platform/platformUtils.h

@@ -11,10 +11,11 @@ using namespace std;
 const string PATH = "../../data/";
 
 bool file_exists(const std::string& name);
+vector<string> split(const string& text, char delimiter);
 pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features);
 vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className);
+pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, const string& className);
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name);
+tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name);
-tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset);
+tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset);
 map<string, vector<int>> get_states(vector<string>& features, string className, map<string, int>& maxes);
 #endif //PLATFORM_UTILS_H

tests/BayesNetwork.cc

@@ -9,29 +9,21 @@ TEST_CASE("Test Bayesian Network")
 {
     auto [Xd, y, features, className, states] = loadFile("iris");
 
-    SECTION("Test Update Nodes")
-    {
-        auto net = bayesnet::Network();
-        net.addNode("A", 3);
-        REQUIRE(net.getStates() == 3);
-        net.addNode("A", 5);
-        REQUIRE(net.getStates() == 5);
-    }
     SECTION("Test get features")
     {
         auto net = bayesnet::Network();
-        net.addNode("A", 3);
+        net.addNode("A");
-        net.addNode("B", 5);
+        net.addNode("B");
         REQUIRE(net.getFeatures() == vector<string>{"A", "B"});
-        net.addNode("C", 2);
+        net.addNode("C");
         REQUIRE(net.getFeatures() == vector<string>{"A", "B", "C"});
     }
     SECTION("Test get edges")
     {
         auto net = bayesnet::Network();
-        net.addNode("A", 3);
+        net.addNode("A");
-        net.addNode("B", 5);
+        net.addNode("B");
-        net.addNode("C", 2);
+        net.addNode("C");
         net.addEdge("A", "B");
         net.addEdge("B", "C");
         REQUIRE(net.getEdges() == vector<pair<string, string>>{ {"A", "B"}, { "B", "C" } });