Add Cuda iniitialization in Classifier

Reviewed-on: #30

.devcontainer/Dockerfile

@@ -0,0 +1,57 @@
+FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
+
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.22.2"
+
+# Optionally install the cmake for vcpkg
+COPY ./reinstall-cmake.sh /tmp/
+
+RUN if [ "${REINSTALL_CMAKE_VERSION_FROM_SOURCE}" != "none" ]; then \
+    chmod +x /tmp/reinstall-cmake.sh && /tmp/reinstall-cmake.sh ${REINSTALL_CMAKE_VERSION_FROM_SOURCE}; \
+    fi \
+    && rm -f /tmp/reinstall-cmake.sh
+
+
+# [Optional] Uncomment this section to install additional vcpkg ports.
+# RUN su vscode -c "${VCPKG_ROOT}/vcpkg install <your-port-name-here>"
+
+# [Optional] Uncomment this section to install additional packages.
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -y install --no-install-recommends wget software-properties-common libdatetime-perl libcapture-tiny-perl libdatetime-format-dateparse-perl libgd-perl
+
+# Add PPA for GCC 13
+RUN add-apt-repository ppa:ubuntu-toolchain-r/test
+RUN apt-get update
+
+# Install GCC 13.1
+RUN apt-get install -y gcc-13 g++-13
+
+# Install lcov 2.1
+RUN wget --quiet https://github.com/linux-test-project/lcov/releases/download/v2.1/lcov-2.1.tar.gz && \
+    tar -xvf lcov-2.1.tar.gz && \
+    cd lcov-2.1 && \
+    make install
+RUN rm lcov-2.1.tar.gz
+RUN rm -fr lcov-2.1
+
+# Install Miniconda
+RUN mkdir -p /opt/conda 
+RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh" -O /opt/conda/miniconda.sh && \
+    bash /opt/conda/miniconda.sh -b -p /opt/miniconda
+
+# Add conda to PATH
+ENV PATH=/opt/miniconda/bin:$PATH
+
+# add CXX and CC to the environment with gcc 13
+ENV CXX=/usr/bin/g++-13
+ENV CC=/usr/bin/gcc-13
+
+# link the last gcov version
+RUN rm /usr/bin/gcov
+RUN ln -s /usr/bin/gcov-13 /usr/bin/gcov
+
+# change ownership of /opt/miniconda to vscode user
+RUN chown -R vscode:vscode /opt/miniconda
+
+USER vscode
+RUN conda init
+RUN conda install -y -c conda-forge yaml pytorch

.devcontainer/devcontainer.json

@@ -0,0 +1,37 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/cpp
+{
+	"name": "C++",
+	"build": {
+		"dockerfile": "Dockerfile"
+	},
+	// "features": {
+	// 	"ghcr.io/devcontainers/features/conda:1": {}
+	// }
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+	// Use 'postCreateCommand' to run commands after the container is created.
+	"postCreateCommand": "make release && make debug && echo 'Done!'",
+	// Configure tool-specific properties.
+	// "customizations": {},
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"ms-vscode.cpptools",
+				"ms-vscode.cpptools-extension-pack",
+				"ms-vscode.cpptools-themes",
+				"ms-vscode.cmake-tools",
+				"ms-azuretools.vscode-docker",
+				"jbenden.c-cpp-flylint",
+				"matepek.vscode-catch2-test-adapter",
+				"GitHub.copilot"
+			]
+		}
+	}
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

.devcontainer/reinstall-cmake.sh

@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+#-------------------------------------------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See https://go.microsoft.com/fwlink/?linkid=2090316 for license information.
+#-------------------------------------------------------------------------------------------------------------
+#
+set -e
+
+CMAKE_VERSION=${1:-"none"}
+
+if [ "${CMAKE_VERSION}" = "none" ]; then
+    echo "No CMake version specified, skipping CMake reinstallation"
+    exit 0
+fi
+
+# Cleanup temporary directory and associated files when exiting the script.
+cleanup() {
+    EXIT_CODE=$?
+    set +e
+    if [[ -n "${TMP_DIR}" ]]; then
+        echo "Executing cleanup of tmp files"
+        rm -Rf "${TMP_DIR}"
+    fi
+    exit $EXIT_CODE
+}
+trap cleanup EXIT
+
+
+echo "Installing CMake..."
+apt-get -y purge --auto-remove cmake
+mkdir -p /opt/cmake
+
+architecture=$(dpkg --print-architecture)
+case "${architecture}" in
+    arm64)
+        ARCH=aarch64 ;;
+    amd64)
+        ARCH=x86_64 ;;
+    *)
+        echo "Unsupported architecture ${architecture}."
+        exit 1
+        ;;
+esac
+
+CMAKE_BINARY_NAME="cmake-${CMAKE_VERSION}-linux-${ARCH}.sh"
+CMAKE_CHECKSUM_NAME="cmake-${CMAKE_VERSION}-SHA-256.txt"
+TMP_DIR=$(mktemp -d -t cmake-XXXXXXXXXX)
+
+echo "${TMP_DIR}"
+cd "${TMP_DIR}"
+
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_BINARY_NAME}" -O
+curl -sSL "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_CHECKSUM_NAME}" -O
+
+sha256sum -c --ignore-missing "${CMAKE_CHECKSUM_NAME}"
+sh "${TMP_DIR}/${CMAKE_BINARY_NAME}" --prefix=/opt/cmake --skip-license
+
+ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
+ln -s /opt/cmake/bin/ctest /usr/local/bin/ctest

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for more information:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# https://containers.dev/guide/dependabot
+
+version: 2
+updates:
+ - package-ecosystem: "devcontainers"
+   directory: "/"
+   schedule:
+     interval: weekly

.github/workflows/main.yml

@@ -1,12 +0,0 @@
-name: CI
-on: push
-
-jobs:
-  tests:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-    - run: sudo apt-get install ninja-build cmake
-    - run: ninja --version
-    - run: cmake --version
-    - run: g++ --version

.gitignore

@@ -39,4 +39,9 @@ cmake-build*/**
 puml/**
 .vscode/settings.json
 sample/build
+**/.DS_Store
+docs/manual
+docs/man3
+docs/man
+docs/Doxyfile
 

.gitmodules

@@ -3,11 +3,6 @@
 	url = https://github.com/rmontanana/mdlp
 	main = main
 	update = merge
-[submodule "lib/catch2"]
-	path = lib/catch2
-    main  = v2.x
-	update = merge
-	url = https://github.com/catchorg/Catch2.git
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
@@ -18,3 +13,11 @@
 	url = https://github.com/rmontanana/folding
 	main = main
 	update = merge
+[submodule "tests/lib/catch2"]
+	path = tests/lib/catch2
+        url = https://github.com/catchorg/Catch2.git
+	main = main
+	update = merge
+[submodule "tests/lib/Files"]
+	path = tests/lib/Files
+	url = https://github.com/rmontanana/ArffFiles

.sonarlint/connectedMode.json

@@ -0,0 +1,4 @@
+{
+    "sonarCloudOrganization": "rmontanana",
+    "projectKey": "rmontanana_BayesNet"
+}

.vscode/launch.json

@@ -14,11 +14,11 @@
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                "Block Update"
+                "[Network]"
             ],
-            "cwd": "${workspaceFolder}/build_debug/tests"
+            "cwd": "${workspaceFolder}/build_Debug/tests"
         },
         {
             "name": "(gdb) Launch",

CHANGELOG.md

@@ -5,6 +5,34 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- Library logo generated with <https://openart.ai> to README.md
+- Link to the coverage report in the README.md coverage label.
+- *convergence_best* hyperparameter to the BoostAODE class, to control the way the prior accuracy is computed if convergence is set. Default value is *false*.
+- SPnDE model.
+- A2DE model.
+- A2DE & SPnDE tests.
+- Add tests to reach 99% of coverage.
+- Add tests to check the correct version of the mdlp, folding and json libraries.
+- Library documentation generated with Doxygen.
+- Link to documentation in the README.md.
+- Three types of smoothing the Bayesian Network OLD_LAPLACE, LAPLACE and CESTNIK.
+
+### Internal
+
+- Create library ShuffleArffFile to limit the number of samples with a parameter and shuffle them.
+- Refactor catch2 library location to test/lib
+- Refactor loadDataset function in tests.
+- Remove conditionalEdgeWeights method in BayesMetrics.
+- Refactor Coverage Report generation.
+- Add devcontainer to work on apple silicon.
+- Change build cmake folder names to Debug & Release.
+- Add a Makefile target (doc) to generate the documentation.
+- Add a Makefile target (doc-install) to install the documentation.
+
 ## [1.0.5] 2024-04-20
 
 ### Added
@@ -25,6 +53,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - The worse model count in BoostAODE is reset to 0 every time a new model produces better accuracy, so the tolerance of the model is meant to be the number of **consecutive** models that produce worse accuracy.
 - Default hyperparameter values in BoostAODE: bisection is true, maxTolerance is 3, convergence is true
 
+### Removed
+
+- The 'predict_single' hyperparameter from the BoostAODE class.
+- The 'repeatSparent' hyperparameter from the BoostAODE class.
+
 ## [1.0.4] 2024-03-06
 
 ### Added

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.5
+  VERSION 1.0.6
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -25,8 +25,12 @@ set(CMAKE_CXX_EXTENSIONS                  OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS          ON)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
 SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
-set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -O0 -g")
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
+
 # Options
 # -------
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
@@ -47,10 +51,15 @@ endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
 
 if (CODE_COVERAGE)
+get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
+message("ALL LANGUAGES: ${LANGUAGES}")
+foreach(LANG ${LANGUAGES})
+    message("${LANG} compiler is \"${CMAKE_${LANG}_COMPILER_ID}\"")
+endforeach()
   enable_testing()
-  include(CodeCoverage)
-  MESSAGE("Code coverage enabled")
-  SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
+  #include(CodeCoverage)
+  #MESSAGE("Code coverage enabled")
+  #SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
 if (ENABLE_CLANG_TIDY)
@@ -60,20 +69,19 @@ endif (ENABLE_CLANG_TIDY)
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
 # include(FetchContent)
-add_git_submodule("lib/mdlp")
 add_git_submodule("lib/json")
+add_git_submodule("lib/mdlp")
 
 # Subdirectories
 # --------------
 add_subdirectory(config)
-add_subdirectory(lib/Files)
 add_subdirectory(bayesnet)
 
 # Testing
 # -------
 if (ENABLE_TESTING)
-  MESSAGE("Testing enabled")
-  add_git_submodule("lib/catch2")  
+MESSAGE("Testing enabled")
+  add_subdirectory(tests/lib/catch2)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)
@@ -86,3 +94,14 @@ install(TARGETS BayesNet
         CONFIGURATIONS Release)
 install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h")
 install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release)
+
+# Documentation
+# -------------
+find_package(Doxygen)
+set(DOC_DIR ${CMAKE_CURRENT_SOURCE_DIR}/docs)
+set(doxyfile_in ${DOC_DIR}/Doxyfile.in)
+set(doxyfile ${DOC_DIR}/Doxyfile)
+configure_file(${doxyfile_in} ${doxyfile} @ONLY)
+doxygen_add_docs(doxygen
+    WORKING_DIRECTORY ${DOC_DIR}
+    CONFIG_FILE ${doxyfile})

Makefile

@@ -1,16 +1,22 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge
+.PHONY: viewcoverage coverage setup help install uninstall diagrams buildr buildd test clean debug release sample updatebadge doc doc-install
 
-f_release = build_release
-f_debug = build_debug
+f_release = build_Release
+f_debug = build_Debug
 f_diagrams = diagrams
 app_targets = BayesNet
 test_targets = TestBayesNet
 clang-uml = clang-uml
 plantuml = plantuml
+lcov = lcov
+genhtml = genhtml
 dot = dot
-n_procs = -j 16
+docsrcdir = docs/manual
+mansrcdir = docs/man3
+mandestdir = /usr/local/share/man
+sed_command_link = 's/e">LCOV -/e"><a href="https:\/\/rmontanana.github.io\/bayesnet">Back to manual<\/a> LCOV -/g'
+sed_command_diagram = 's/Diagram"/Diagram" width="100%" height="100%" /g'
 
 define ClearTests
 	@for t in $(test_targets); do \
@@ -52,10 +58,10 @@ diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/
 	@$(dot) -Tsvg $(f_debug)/dependency.dot.BayesNet -o $(f_diagrams)/dependency.svg
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) --parallel
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug BayesNet tests...";
@@ -97,9 +103,9 @@ sample: ## Build sample
 
 opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
-	@echo ">>> Running BayesNet & Platform tests...";
+	@echo ">>> Running BayesNet tests...";
 	@$(MAKE) clean
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel
 	@for t in $(test_targets); do \
 		echo ">>> Running $$t...";\
 		if [ -f $(f_debug)/tests/$$t ]; then \
@@ -112,31 +118,70 @@ test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximu
 
 coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Building tests with coverage..."
-	@$(MAKE) test
-	@gcovr $(f_debug)/tests
-	@echo ">>> Done";	
-
-viewcoverage: ## Run tests, generate coverage report and upload it to codecov (build/index.html)
-	@echo ">>> Building tests with coverage..."
-	@$(MAKE) coverage
+	@which $(lcov) || (echo ">>ease install lcov"; exit 1)
+	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
 	@echo ">>> Building report..."
 	@cd $(f_debug)/tests; \
-	lcov --directory . --capture --output-file coverage.info >/dev/null 2>&1; \
-	lcov --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
-	lcov --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
-	lcov --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
-	lcov --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
-	lcov --remove coverage.info 'bayesnet/utils/loguru.*' --output-file coverage.info >/dev/null 2>&1; \
-	genhtml coverage.info --output-directory coverage >/dev/null 2>&1;
+	$(lcov) --directory CMakeFiles --capture --demangle-cpp --ignore-errors source,source --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/usr/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'lib/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'libtorch/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'tests/*' --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info 'bayesnet/utils/loguru.*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --remove coverage.info '/opt/miniconda/*' --ignore-errors unused --output-file coverage.info >/dev/null 2>&1; \
+	$(lcov) --summary coverage.info
 	@$(MAKE) updatebadge
-	@xdg-open $(f_debug)/tests/coverage/index.html || open $(f_debug)/tests/coverage/index.html 2>/dev/null
+	@echo ">>> Done";	
+
+viewcoverage: ## View the html coverage report
+	@which $(genhtml) >/dev/null || (echo ">>> Please install lcov (genhtml not found)"; exit 1)
+	@if [ ! -d $(docsrcdir)/coverage ]; then mkdir -p $(docsrcdir)/coverage; fi
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
+	@$(genhtml) $(f_debug)/tests/coverage.info --demangle-cpp --output-directory $(docsrcdir)/coverage --title "BayesNet Coverage Report" -s -k -f --legend >/dev/null 2>&1;
+	@xdg-open $(docsrcdir)/coverage/index.html || open $(docsrcdir)/coverage/index.html 2>/dev/null
 	@echo ">>> Done";
 
 updatebadge: ## Update the coverage badge in README.md
+	@which python || (echo ">>> Please install python"; exit 1)
+	@if [ ! -f $(f_debug)/tests/coverage.info ]; then \
+		echo ">>> No coverage.info file found. Run make coverage first!"; \
+		exit 1; \
+	fi
 	@echo ">>> Updating coverage badge..."
 	@env python update_coverage.py $(f_debug)/tests
 	@echo ">>> Done";
 
+doc: ## Generate documentation
+	@echo ">>> Generating documentation..."
+	@cmake --build $(f_release) -t doxygen
+	@cp -rp diagrams $(docsrcdir)
+	@
+	@if [ "$(shell uname)" = "Darwin" ]; then \
+		sed -i "" $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i "" $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	else \
+		sed -i $(sed_command_link) $(docsrcdir)/coverage/index.html ; \
+		sed -i $(sed_command_diagram) $(docsrcdir)/index.html ; \
+	fi
+	@echo ">>> Done";
+
+docdir = ""
+doc-install: ## Install documentation
+	@echo ">>> Installing documentation..."
+	@if [ "$(docdir)" = "" ]; then \
+		echo "docdir parameter has to be set when calling doc-install"; \
+		exit 1; \
+	fi
+	@if [ ! -d $(docdir) ]; then \
+		@$(MAKE) doc; \
+	fi
+	@cp -rp $(docsrcdir)/* $(docdir)
+	@sudo cp -rp $(mansrcdir) $(mandestdir)
+	@echo ">>> Done";
+
 help: ## Show help message
 	@IFS=$$'\n' ; \
 	help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \

README.md

@@ -1,11 +1,13 @@
-# BayesNet
+# <img src="logo.png" alt="logo" width="50"/>  BayesNet
 
 ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](<https://opensource.org/licenses/MIT>)
 ![Gitea Release](https://img.shields.io/gitea/v/release/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
+[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
+[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
 ![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es:3000&logo=gitea)
-![Static Badge](https://img.shields.io/badge/Coverage-97,2%25-green)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-97,1%25-green)](html/index.html)
 
 Bayesian Network Classifiers using libtorch from scratch
 
@@ -20,6 +22,12 @@ unzip libtorch-shared-with-deps-latest.zips
 
 ## Setup
 
+### Getting the code
+
+```bash
+git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
+```
+
 ### Release
 
 ```bash
@@ -33,7 +41,13 @@ sudo make install
 ```bash
 make debug
 make test
+```
+
+### Coverage
+
+```bash
 make coverage
+make viewcoverage
 ```
 
 ### Sample app
@@ -47,7 +61,35 @@ make sample fname=tests/data/glass.arff
 
 ## Models
 
-### [BoostAODE](docs/BoostAODE.md)
+#### - TAN
+
+#### - KDB
+
+#### - SPODE
+
+#### - SPnDE
+
+#### - AODE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+#### - BoostA2DE
+
+### With Local Discretization
+
+#### - TANLd
+
+#### - KDBLd
+
+#### - SPODELd
+
+#### - AODELd
+
+## Documentation
+
+### [Manual](https://rmontanana.github.io/bayesnet/)
+
+### [Coverage report](https://rmontanana.github.io/bayesnet/coverage/index.html)
 
 ## Diagrams
 

bayesnet/BaseClassifier.h

@@ -8,16 +8,18 @@
 #include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
+#include "bayesnet/network/Network.h"
+
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     public:
         // X is nxm std::vector, y is nx1 std::vector
-        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
         // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
@@ -39,7 +41,7 @@ namespace bayesnet {
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
     protected:
-        virtual void trainModel(const torch::Tensor& weights) = 0;
+        virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         std::vector<std::string> validHyperparameters;
     };
 }

bayesnet/CMakeLists.txt

@@ -1,6 +1,5 @@
 include_directories(
-    ${BayesNet_SOURCE_DIR}/lib/mdlp
-    ${BayesNet_SOURCE_DIR}/lib/Files
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}

bayesnet/classifiers/Classifier.cc

@@ -9,9 +9,17 @@
 #include "Classifier.h"
 
 namespace bayesnet {
-    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
+    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false), device(torch::kCPU)
+    {
+        if (torch::cuda::is_available()) {
+            device = torch::Device(torch::kCUDA);
+            std::cout << "CUDA is available! Using GPU." << std::endl;
+        } else {
+            std::cout << "CUDA is not available. Using CPU." << std::endl;
+        }
+    }
     const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
-    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->features = features;
         this->className = className;
@@ -23,7 +31,7 @@ namespace bayesnet {
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
         buildModel(weights);
-        trainModel(weights);
+        trainModel(weights, smoothing);
         fitted = true;
         return *this;
     }
@@ -31,7 +39,7 @@ namespace bayesnet {
     {
         try {
             auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
-            dataset = torch::cat({ dataset, yresized }, 0);
+            dataset = torch::cat({ dataset, yresized }, 0).to(device);
         }
         catch (const std::exception& e) {
             std::stringstream oss;
@@ -41,20 +49,20 @@ namespace bayesnet {
             throw std::runtime_error(oss.str());
         }
     }
-    void Classifier::trainModel(const torch::Tensor& weights)
+    void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
     {
-        model.fit(dataset, weights, features, className, states);
+        model.fit(dataset, weights, features, className, states, smoothing);
     }
     // 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 std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         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);
+        const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble).to(device);
+        return build(features, className, states, weights, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
         for (int i = 0; i < X.size(); ++i) {
@@ -63,18 +71,18 @@ namespace bayesnet {
         auto ytmp = torch::tensor(y, torch::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);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         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);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     void Classifier::checkFitParameters()
     {

bayesnet/classifiers/Classifier.h

@@ -8,7 +8,6 @@
 #define CLASSIFIER_H
 #include <torch/torch.h>
 #include "bayesnet/utils/BayesMetrics.h"
-#include "bayesnet/network/Network.h"
 #include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
@@ -16,10 +15,10 @@ namespace bayesnet {
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override;
+        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
@@ -39,6 +38,7 @@ namespace bayesnet {
         std::string dump_cpt() const override;
         void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
     protected:
+        torch::Device device;
         bool fitted;
         unsigned int m, n; // m: number of samples, n: number of features
         Network model;
@@ -51,10 +51,10 @@ namespace bayesnet {
         std::vector<std::string> notes; // Used to store messages occurred during the fit process
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildDataset(torch::Tensor& y);
     private:
-        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/classifiers/KDBLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         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);
+        KDB::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/KDBLd.h

@@ -15,7 +15,7 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };

bayesnet/classifiers/Proposal.cc

@@ -4,7 +4,6 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <ArffFiles.h>
 #include "Proposal.h"
 
 namespace bayesnet {
@@ -54,8 +53,7 @@ namespace bayesnet {
                     yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
                 }
             }
-            auto arff = ArffFiles();
-            auto yxv = arff.factorize(yJoinParents);
+            auto yxv = factorize(yJoinParents);
             auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
             auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
             discretizers[feature]->fit(xvf, yxv);
@@ -72,7 +70,7 @@ namespace bayesnet {
                 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);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
         }
         return states;
     }
@@ -113,4 +111,19 @@ namespace bayesnet {
         }
         return Xtd;
     }
+    std::vector<int> Proposal::factorize(const std::vector<std::string>& labels_t)
+    {
+        std::vector<int> yy;
+        yy.reserve(labels_t.size());
+        std::map<std::string, int> labelMap;
+        int i = 0;
+        for (const std::string& label : labels_t) {
+            if (labelMap.find(label) == labelMap.end()) {
+                labelMap[label] = i++;
+                bool allDigits = std::all_of(label.begin(), label.end(), ::isdigit);
+            }
+            yy.push_back(labelMap[label]);
+        }
+        return yy;
+    }
 }

bayesnet/classifiers/Proposal.h

@@ -27,6 +27,7 @@ namespace bayesnet {
         torch::Tensor y; // y discrete nx1 tensor
         map<std::string, mdlp::CPPFImdlp*> discretizers;
     private:
+        std::vector<int> factorize(const std::vector<std::string>& labels_t);
         torch::Tensor& pDataset; // (n+1)xm tensor
         std::vector<std::string>& pFeatures;
         std::string& pClassName;

bayesnet/classifiers/SPODELd.cc

@@ -8,25 +8,25 @@
 
 namespace bayesnet {
     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         Xf = X_;
         y = y_;
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         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().to(torch::kInt32);
-        return commonFit(features_, className_, states_);
+        return commonFit(features_, className_, states_, smoothing);
     }
 
-    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         features = features_;
         className = className_;
@@ -34,7 +34,7 @@ namespace bayesnet {
         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);
+        SPODE::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/SPODELd.h

@@ -14,10 +14,10 @@ namespace bayesnet {
     public:
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
-        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
-        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
-        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
-        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };
     };

bayesnet/classifiers/SPnDE.cc

@@ -0,0 +1,38 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "SPnDE.h"
+
+namespace bayesnet {
+
+    SPnDE::SPnDE(std::vector<int> parents) : Classifier(Network()), parents(parents) {}
+
+    void SPnDE::buildModel(const torch::Tensor& weights)
+    {
+        // 0. Add all nodes to the model
+        addNodes();
+        std::vector<int> attributes;
+        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
+            if (std::find(parents.begin(), parents.end(), i) == parents.end()) {
+                attributes.push_back(i);
+            }
+        }
+        // 1. Add edges from the class node to all other nodes
+        // 2. Add edges from the parents nodes to all other nodes
+        for (const auto& attribute : attributes) {
+            model.addEdge(className, features[attribute]);
+            for (const auto& root : parents) {
+
+                model.addEdge(features[root], features[attribute]);
+            }
+        }
+    }
+    std::vector<std::string> SPnDE::graph(const std::string& name) const
+    {
+        return model.graph(name);
+    }
+
+}

bayesnet/classifiers/SPnDE.h

@@ -0,0 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SPnDE_H
+#define SPnDE_H
+#include <vector>
+#include "Classifier.h"
+
+namespace bayesnet {
+    class SPnDE : public Classifier {
+    public:
+        explicit SPnDE(std::vector<int> parents);
+        virtual ~SPnDE() = default;
+        std::vector<std::string> graph(const std::string& name = "SPnDE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        std::vector<int> parents;
+
+
+    };
+}
+#endif

bayesnet/classifiers/TANLd.cc

@@ -8,7 +8,7 @@
 
 namespace bayesnet {
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -19,7 +19,7 @@ namespace bayesnet {
         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);
+        TAN::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
 

bayesnet/classifiers/TANLd.h

@@ -15,10 +15,9 @@ namespace bayesnet {
     public:
         TANLd();
         virtual ~TANLd() = default;
-        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
-        std::vector<std::string> graph(const std::string& name = "TAN") const override;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
+        std::vector<std::string> graph(const std::string& name = "TANLd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
-        static inline std::string version() { return "0.0.1"; };
     };
 }
 #endif // !TANLD_H

bayesnet/ensembles/A2DE.cc

@@ -0,0 +1,40 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "A2DE.h"
+
+namespace bayesnet {
+    A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "predict_voting" };
+    }
+    void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void A2DE::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        significanceModels.clear();
+        for (int i = 0; i < features.size() - 1; ++i) {
+            for (int j = i + 1; j < features.size(); ++j) {
+                auto model = std::make_unique<SPnDE>(std::vector<int>({ i, j }));
+                models.push_back(std::move(model));
+            }
+        }
+        n_models = static_cast<unsigned>(models.size());
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    std::vector<std::string> A2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/A2DE.h

@@ -0,0 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef A2DE_H
+#define A2DE_H
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Ensemble.h"
+namespace bayesnet {
+    class A2DE : public Ensemble {
+    public:
+        A2DE(bool predict_voting = false);
+        virtual ~A2DE() {};
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        std::vector<std::string> graph(const std::string& title = "A2DE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    };
+}
+#endif

bayesnet/ensembles/AODELd.cc

@@ -10,7 +10,7 @@ namespace bayesnet {
     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
     {
     }
-    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -21,7 +21,7 @@ namespace bayesnet {
         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);
+        Ensemble::fit(dataset, features, className, states, smoothing);
         return *this;
 
     }
@@ -34,10 +34,10 @@ namespace bayesnet {
         n_models = models.size();
         significanceModels = std::vector<double>(n_models, 1.0);
     }
-    void AODELd::trainModel(const torch::Tensor& weights)
+    void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         for (const auto& model : models) {
-            model->fit(Xf, y, features, className, states);
+            model->fit(Xf, y, features, className, states, smoothing);
         }
     }
     std::vector<std::string> AODELd::graph(const std::string& name) const

bayesnet/ensembles/AODELd.h

@@ -15,10 +15,10 @@ namespace bayesnet {
     public:
         AODELd(bool predict_voting = true);
         virtual ~AODELd() = default;
-        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_) override;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildModel(const torch::Tensor& weights) override;
     };
 }

bayesnet/ensembles/Boost.cc

@@ -0,0 +1,246 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include "Boost.h"
+
+namespace bayesnet {
+    Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
+    {
+        validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
+        "predict_voting", "select_features", "block_update" };
+    }
+    void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("order")) {
+            std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
+            order_algorithm = hyperparameters["order"];
+            if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
+                throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
+            }
+            hyperparameters.erase("order");
+        }
+        if (hyperparameters.contains("convergence")) {
+            convergence = hyperparameters["convergence"];
+            hyperparameters.erase("convergence");
+        }
+        if (hyperparameters.contains("convergence_best")) {
+            convergence_best = hyperparameters["convergence_best"];
+            hyperparameters.erase("convergence_best");
+        }
+        if (hyperparameters.contains("bisection")) {
+            bisection = hyperparameters["bisection"];
+            hyperparameters.erase("bisection");
+        }
+        if (hyperparameters.contains("threshold")) {
+            threshold = hyperparameters["threshold"];
+            hyperparameters.erase("threshold");
+        }
+        if (hyperparameters.contains("maxTolerance")) {
+            maxTolerance = hyperparameters["maxTolerance"];
+            if (maxTolerance < 1 || maxTolerance > 4)
+                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
+            hyperparameters.erase("maxTolerance");
+        }
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        if (hyperparameters.contains("select_features")) {
+            auto selectedAlgorithm = hyperparameters["select_features"];
+            std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
+            selectFeatures = true;
+            select_features_algorithm = selectedAlgorithm;
+            if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
+                throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
+            }
+            hyperparameters.erase("select_features");
+        }
+        if (hyperparameters.contains("block_update")) {
+            block_update = hyperparameters["block_update"];
+            hyperparameters.erase("block_update");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
+    void Boost::buildModel(const torch::Tensor& weights)
+    {
+        // Models shall be built in trainModel
+        models.clear();
+        significanceModels.clear();
+        n_models = 0;
+        // Prepare the validation dataset
+        auto y_ = dataset.index({ -1, "..." });
+        if (convergence) {
+            // Prepare train & validation sets from train data
+            auto fold = folding::StratifiedKFold(5, y_, 271);
+            auto [train, test] = fold.getFold(0);
+            auto train_t = torch::tensor(train);
+            auto test_t = torch::tensor(test);
+            // Get train and validation sets
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+            y_train = dataset.index({ -1, train_t });
+            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+            y_test = dataset.index({ -1, test_t });
+            dataset = X_train;
+            m = X_train.size(1);
+            auto n_classes = states.at(className).size();
+            // Build dataset with train data
+            buildDataset(y_train);
+            metrics = Metrics(dataset, features, className, n_classes);
+        } else {
+            // Use all data to train
+            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
+            y_train = y_;
+        }
+    }
+    std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
+    {
+        int maxFeatures = 0;
+        if (select_features_algorithm == SelectFeatures.CFS) {
+            featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
+        } else if (select_features_algorithm == SelectFeatures.IWSS) {
+            if (threshold < 0 || threshold >0.5) {
+                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
+            }
+            featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+        } else if (select_features_algorithm == SelectFeatures.FCBF) {
+            if (threshold < 1e-7 || threshold > 1) {
+                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
+            }
+            featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
+        }
+        featureSelector->fit();
+        auto featuresUsed = featureSelector->getFeatures();
+        delete featureSelector;
+        return featuresUsed;
+    }
+    std::tuple<torch::Tensor&, double, bool> Boost::update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
+    {
+        bool terminate = false;
+        double alpha_t = 0;
+        auto mask_wrong = ypred != ytrain;
+        auto mask_right = ypred == ytrain;
+        auto masked_weights = weights * mask_wrong.to(weights.dtype());
+        double epsilon_t = masked_weights.sum().item<double>();
+        if (epsilon_t > 0.5) {
+            // Inverse the weights policy (plot ln(wt))
+            // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
+            // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+            terminate = true;
+        } else {
+            double wt = (1 - epsilon_t) / epsilon_t;
+            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
+            // Step 3.2: Update weights for next classifier
+            // Step 3.2.1: Update weights of wrong samples
+            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
+            // Step 3.2.2: Update weights of right samples
+            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
+            // Step 3.3: Normalise the weights
+            double totalWeights = torch::sum(weights).item<double>();
+            weights = weights / totalWeights;
+        }
+        return { weights, alpha_t, terminate };
+    }
+    std::tuple<torch::Tensor&, double, bool> Boost::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
+    {
+        /* Update Block algorithm
+            k = # of models in block
+            n_models = # of models in ensemble to make predictions
+            n_models_bak = # models saved
+            models = vector of models to make predictions
+            models_bak = models not used to make predictions
+            significances_bak = backup of significances vector
+
+            Case list
+            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
+            B) k = 1, n_models = n + 1	=> n_models = n + k
+            C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
+            D) k > 1, n_models = k		=> n = 0, n_models = n + k
+            E) k > 1, n_models = k + n	=> n_models = n + k
+
+            A, D) n=0, k > 0, n_models == k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Don’t move any classifiers out of models
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Don’t restore any classifiers to models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+
+            B, C, E) n > 0, k > 0, n_models == n + k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Move first n classifiers to models_bak
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Insert classifiers in models_bak to be the first n models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+        */
+        //
+        // Make predict with only the last k models
+        //
+        std::unique_ptr<Classifier> model;
+        std::vector<std::unique_ptr<Classifier>> models_bak;
+        // 1. n_models_bak <- n_models 2. significances_bak <- significances
+        auto significance_bak = significanceModels;
+        auto n_models_bak = n_models;
+        // 3. significances = vector(k, 1)
+        significanceModels = std::vector<double>(k, 1.0);
+        // 4. Move first n classifiers to models_bak
+        // backup the first n_models - k models (if n_models == k, don't backup any)
+        for (int i = 0; i < n_models - k; ++i) {
+            model = std::move(models[0]);
+            models.erase(models.begin());
+            models_bak.push_back(std::move(model));
+        }
+        assert(models.size() == k);
+        // 5. n_models <- k
+        n_models = k;
+        // 6. Make prediction, compute alpha, update weights
+        auto ypred = predict(X_train);
+        //
+        // Update weights
+        //
+        double alpha_t;
+        bool terminate;
+        std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
+        //
+        // Restore the models if needed
+        //
+        // 7. Insert classifiers in models_bak to be the first n models
+        // if n_models_bak == k, don't restore any, because none of them were moved
+        if (k != n_models_bak) {
+            // Insert in the same order as they were extracted
+            int bak_size = models_bak.size();
+            for (int i = 0; i < bak_size; ++i) {
+                model = std::move(models_bak[bak_size - 1 - i]);
+                models_bak.erase(models_bak.end() - 1);
+                models.insert(models.begin(), std::move(model));
+            }
+        }
+        // 8. significances <- significances_bak
+        significanceModels = significance_bak;
+        //
+        // Update the significance of the last k models
+        //
+        // 9. Update last k significances
+        for (int i = 0; i < k; ++i) {
+            significanceModels[n_models_bak - k + i] = alpha_t;
+        }
+        // 10. n_models <- n_models_bak
+        n_models = n_models_bak;
+        return { weights, alpha_t, terminate };
+    }
+}

bayesnet/ensembles/Boost.h

@@ -0,0 +1,52 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOST_H
+#define BOOST_H
+#include <string>
+#include <tuple>
+#include <vector>
+#include <nlohmann/json.hpp>
+#include <torch/torch.h>
+#include "Ensemble.h"
+#include "bayesnet/feature_selection/FeatureSelect.h"
+namespace bayesnet {
+    const struct {
+        std::string CFS = "CFS";
+        std::string FCBF = "FCBF";
+        std::string IWSS = "IWSS";
+    }SelectFeatures;
+    const struct {
+        std::string ASC = "asc";
+        std::string DESC = "desc";
+        std::string RAND = "rand";
+    }Orders;
+    class Boost : public Ensemble {
+    public:
+        explicit Boost(bool predict_voting = false);
+        virtual ~Boost() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+    protected:
+        std::vector<int> featureSelection(torch::Tensor& weights_);
+        void buildModel(const torch::Tensor& weights) override;
+        std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
+        std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
+        torch::Tensor X_train, y_train, X_test, y_test;
+        // Hyperparameters
+        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
+        int maxTolerance = 3;
+        std::string order_algorithm; // order to process the KBest features asc, desc, rand
+        bool convergence = true; //if true, stop when the model does not improve
+        bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
+        bool selectFeatures = false; // if true, use feature selection
+        std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
+        FeatureSelect* featureSelector = nullptr;
+        double threshold = -1;
+        bool block_update = false;
+
+    };
+}
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -0,0 +1,167 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <set>
+#include <functional>
+#include <limits.h>
+#include <tuple>
+#include <folding.hpp>
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include "BoostA2DE.h"
+
+namespace bayesnet {
+
+    BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
+    {
+    }
+    std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
+    {
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        if (featuresSelected.size() < 2) {
+            notes.push_back("No features selected in initialization");
+            status = ERROR;
+            return std::vector<int>();
+        }
+        for (int i = 0; i < featuresSelected.size() - 1; i++) {
+            for (int j = i + 1; j < featuresSelected.size(); j++) {
+                auto parents = { featuresSelected[i], featuresSelected[j] };
+                std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                models.push_back(std::move(model));
+                significanceModels.push_back(1.0); // They will be updated later in trainModel
+                n_models++;
+            }
+        }
+        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
+        return featuresSelected;
+    }
+    void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostA2DE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
+        fitted = true;
+        double alpha_t = 0;
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            auto ypred = predict(X_train);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            // Update significance of the models
+            for (int i = 0; i < n_models; ++i) {
+                significanceModels[i] = alpha_t;
+            }
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == Orders.ASC;
+        std::mt19937 g{ 173 };
+        std::vector<std::pair<int, int>> pairSelection;
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
+            if (order_algorithm == Orders.RAND) {
+                std::shuffle(pairSelection.begin(), pairSelection.end(), g);
+            }
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            while (counter++ < k && pairSelection.size() > 0) {
+                auto feature_pair = pairSelection[0];
+                pairSelection.erase(pairSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    auto ypred = model->predict(X_train);
+                    // Step 3.1: Compute the classifier amout of say
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+                }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
+            }
+            if (block_update) {
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+            }
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = 0; i < numItemsPack; ++i) {
+                    significanceModels.pop_back();
+                    models.pop_back();
+                    n_models--;
+                }
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (pairSelection.size() > 0) {
+            notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+            status = WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+    }
+    std::vector<std::string> BoostA2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/BoostA2DE.h

@@ -0,0 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef BOOSTA2DE_H
+#define BOOSTA2DE_H
+#include <string>
+#include <vector>
+#include "bayesnet/classifiers/SPnDE.h"
+#include "Boost.h"
+namespace bayesnet {
+    class BoostA2DE : public Boost {
+    public:
+        explicit BoostA2DE(bool predict_voting = false);
+        virtual ~BoostA2DE() = default;
+        std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+    };
+}
+#endif

bayesnet/ensembles/BoostAODE.cc

@@ -4,264 +4,41 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <random> 
 #include <set>
 #include <functional>
 #include <limits.h>
 #include <tuple>
-#include <folding.hpp>
-#include "bayesnet/feature_selection/CFS.h"
-#include "bayesnet/feature_selection/FCBF.h"
-#include "bayesnet/feature_selection/IWSS.h"
 #include "BoostAODE.h"
 
 namespace bayesnet {
 
-    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
+    BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
-        validHyperparameters = {
-            "maxModels", "bisection", "order", "convergence", "threshold",
-            "select_features", "maxTolerance", "predict_voting", "block_update"
-        };
-
     }
-    void BoostAODE::buildModel(const torch::Tensor& weights)
+    std::vector<int> BoostAODE::initializeModels(const Smoothing_t smoothing)
     {
-        // Models shall be built in trainModel
-        models.clear();
-        significanceModels.clear();
-        n_models = 0;
-        // Prepare the validation dataset
-        auto y_ = dataset.index({ -1, "..." });
-        if (convergence) {
-            // Prepare train & validation sets from train data
-            auto fold = folding::StratifiedKFold(5, y_, 271);
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            // Build dataset with train data
-            buildDataset(y_train);
-            metrics = Metrics(dataset, features, className, n_classes);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
-        }
-    }
-    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
-    {
-        auto hyperparameters = hyperparameters_;
-        if (hyperparameters.contains("order")) {
-            std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
-            order_algorithm = hyperparameters["order"];
-            if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
-            }
-            hyperparameters.erase("order");
-        }
-        if (hyperparameters.contains("convergence")) {
-            convergence = hyperparameters["convergence"];
-            hyperparameters.erase("convergence");
-        }
-        if (hyperparameters.contains("bisection")) {
-            bisection = hyperparameters["bisection"];
-            hyperparameters.erase("bisection");
-        }
-        if (hyperparameters.contains("threshold")) {
-            threshold = hyperparameters["threshold"];
-            hyperparameters.erase("threshold");
-        }
-        if (hyperparameters.contains("maxTolerance")) {
-            maxTolerance = hyperparameters["maxTolerance"];
-            if (maxTolerance < 1 || maxTolerance > 4)
-                throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
-            hyperparameters.erase("maxTolerance");
-        }
-        if (hyperparameters.contains("predict_voting")) {
-            predict_voting = hyperparameters["predict_voting"];
-            hyperparameters.erase("predict_voting");
-        }
-        if (hyperparameters.contains("select_features")) {
-            auto selectedAlgorithm = hyperparameters["select_features"];
-            std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
-            selectFeatures = true;
-            select_features_algorithm = selectedAlgorithm;
-            if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
-                throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
-            }
-            hyperparameters.erase("select_features");
-        }
-        if (hyperparameters.contains("block_update")) {
-            block_update = hyperparameters["block_update"];
-            hyperparameters.erase("block_update");
-        }
-        Classifier::setHyperparameters(hyperparameters);
-    }
-    std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
-    {
-        bool terminate = false;
-        double alpha_t = 0;
-        auto mask_wrong = ypred != ytrain;
-        auto mask_right = ypred == ytrain;
-        auto masked_weights = weights * mask_wrong.to(weights.dtype());
-        double epsilon_t = masked_weights.sum().item<double>();
-        if (epsilon_t > 0.5) {
-            // Inverse the weights policy (plot ln(wt))
-            // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
-            // learner is better than random guess" (Zhi-Hua Zhou, 2012)
-            terminate = true;
-        } else {
-            double wt = (1 - epsilon_t) / epsilon_t;
-            alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
-            // Step 3.2: Update weights for next classifier
-            // Step 3.2.1: Update weights of wrong samples
-            weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
-            // Step 3.2.2: Update weights of right samples
-            weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
-            // Step 3.3: Normalise the weights
-            double totalWeights = torch::sum(weights).item<double>();
-            weights = weights / totalWeights;
-        }
-        return { weights, alpha_t, terminate };
-    }
-    std::tuple<torch::Tensor&, double, bool> BoostAODE::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
-    {
-        /* Update Block algorithm
-            k = # of models in block
-            n_models = # of models in ensemble to make predictions
-            n_models_bak = # models saved
-            models = vector of models to make predictions
-            models_bak = models not used to make predictions
-            significances_bak = backup of significances vector
-
-            Case list
-            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
-            B) k = 1, n_models = n + 1	=> n_models = n + k
-            C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
-            D) k > 1, n_models = k		=> n = 0, n_models = n + k
-            E) k > 1, n_models = k + n	=> n_models = n + k
-
-            A, D) n=0, k > 0, n_models == k
-            1. n_models_bak <- n_models
-            2. significances_bak <- significances
-            3. significances = vector(k, 1)
-            4. Don’t move any classifiers out of models
-            5. n_models <- k
-            6. Make prediction, compute alpha, update weights
-            7. Don’t restore any classifiers to models
-            8. significances <- significances_bak
-            9. Update last k significances
-            10. n_models <- n_models_bak
-
-            B, C, E) n > 0, k > 0, n_models == n + k
-            1. n_models_bak <- n_models
-            2. significances_bak <- significances
-            3. significances = vector(k, 1)
-            4. Move first n classifiers to models_bak
-            5. n_models <- k
-            6. Make prediction, compute alpha, update weights
-            7. Insert classifiers in models_bak to be the first n models
-            8. significances <- significances_bak
-            9. Update last k significances
-            10. n_models <- n_models_bak
-        */
-        //
-        // Make predict with only the last k models
-        //
-        std::unique_ptr<Classifier> model;
-        std::vector<std::unique_ptr<Classifier>> models_bak;
-        // 1. n_models_bak <- n_models 2. significances_bak <- significances
-        auto significance_bak = significanceModels;
-        auto n_models_bak = n_models;
-        // 3. significances = vector(k, 1)
-        significanceModels = std::vector<double>(k, 1.0);
-        // 4. Move first n classifiers to models_bak
-        // backup the first n_models - k models (if n_models == k, don't backup any)
-        for (int i = 0; i < n_models - k; ++i) {
-            model = std::move(models[0]);
-            models.erase(models.begin());
-            models_bak.push_back(std::move(model));
-        }
-        assert(models.size() == k);
-        // 5. n_models <- k
-        n_models = k;
-        // 6. Make prediction, compute alpha, update weights
-        auto ypred = predict(X_train);
-        //
-        // Update weights
-        //
-        double alpha_t;
-        bool terminate;
-        std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
-        //
-        // Restore the models if needed
-        //
-        // 7. Insert classifiers in models_bak to be the first n models
-        // if n_models_bak == k, don't restore any, because none of them were moved
-        if (k != n_models_bak) {
-            // Insert in the same order as they were extracted
-            int bak_size = models_bak.size();
-            for (int i = 0; i < bak_size; ++i) {
-                model = std::move(models_bak[bak_size - 1 - i]);
-                models_bak.erase(models_bak.end() - 1);
-                models.insert(models.begin(), std::move(model));
-            }
-        }
-        // 8. significances <- significances_bak
-        significanceModels = significance_bak;
-        //
-        // Update the significance of the last k models
-        //
-        // 9. Update last k significances
-        for (int i = 0; i < k; ++i) {
-            significanceModels[n_models_bak - k + i] = alpha_t;
-        }
-        // 10. n_models <- n_models_bak
-        n_models = n_models_bak;
-        return { weights, alpha_t, terminate };
-    }
-    std::vector<int> BoostAODE::initializeModels()
-    {
-        std::vector<int> featuresUsed;
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-        int maxFeatures = 0;
-        if (select_features_algorithm == SelectFeatures.CFS) {
-            featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
-        } else if (select_features_algorithm == SelectFeatures.IWSS) {
-            if (threshold < 0 || threshold >0.5) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
-            }
-            featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        } else if (select_features_algorithm == SelectFeatures.FCBF) {
-            if (threshold < 1e-7 || threshold > 1) {
-                throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
-            }
-            featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-        }
-        featureSelector->fit();
-        auto cfsFeatures = featureSelector->getFeatures();
-        auto scores = featureSelector->getScores();
-        for (const int& feature : cfsFeatures) {
-            featuresUsed.push_back(feature);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        for (const int& feature : featuresSelected) {
             std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
-            model->fit(dataset, features, className, states, weights_);
+            model->fit(dataset, features, className, states, weights_, smoothing);
             models.push_back(std::move(model));
             significanceModels.push_back(1.0); // They will be updated later in trainModel
             n_models++;
         }
-        notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
-        delete featureSelector;
-        return featuresUsed;
+        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
+        return featuresSelected;
     }
-    void BoostAODE::trainModel(const torch::Tensor& weights)
+    void BoostAODE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostAODE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
         // Algorithm based on the adaboost algorithm for classification
         // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         fitted = true;
@@ -270,7 +47,7 @@ namespace bayesnet {
         bool finished = false;
         std::vector<int> featuresUsed;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
             auto ypred = predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
@@ -304,14 +81,15 @@ namespace bayesnet {
                 { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
                 end(featureSelection)
             );
-            int k = pow(2, tolerance);
+            int k = bisection ? pow(2, tolerance) : 1;
             int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
             while (counter++ < k && featureSelection.size() > 0) {
                 auto feature = featureSelection[0];
                 featureSelection.erase(featureSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPODE>(feature);
-                model->fit(dataset, features, className, states, weights_);
+                model->fit(dataset, features, className, states, weights_, smoothing);
                 alpha_t = 0.0;
                 if (!block_update) {
                     auto ypred = model->predict(X_train);
@@ -324,6 +102,7 @@ namespace bayesnet {
                 models.push_back(std::move(model));
                 significanceModels.push_back(alpha_t);
                 n_models++;
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
             }
             if (block_update) {
                 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -337,20 +116,28 @@ namespace bayesnet {
                     improvement = accuracy - priorAccuracy;
                 }
                 if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance++;
                 } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance = 0; // Reset the counter if the model performs better
                     numItemsPack = 0;
                 }
-                // Keep the best accuracy until now as the prior accuracy
-                priorAccuracy = std::max(accuracy, priorAccuracy);
-                // priorAccuracy = accuracy;
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
             }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
             finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
         }
         if (tolerance > maxTolerance) {
             if (numItemsPack < n_models) {
                 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
                 for (int i = 0; i < numItemsPack; ++i) {
                     significanceModels.pop_back();
                     models.pop_back();
@@ -358,6 +145,7 @@ namespace bayesnet {
                 }
             } else {
                 notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
             }
         }
         if (featuresUsed.size() != features.size()) {

bayesnet/ensembles/BoostAODE.h

@@ -6,44 +6,21 @@
 
 #ifndef BOOSTAODE_H
 #define BOOSTAODE_H
-#include <map>
+#include <string>
+#include <vector>
 #include "bayesnet/classifiers/SPODE.h"
-#include "bayesnet/feature_selection/FeatureSelect.h"
-#include "Ensemble.h"
+#include "Boost.h"
+
 namespace bayesnet {
-    struct {
-        std::string CFS = "CFS";
-        std::string FCBF = "FCBF";
-        std::string IWSS = "IWSS";
-    }SelectFeatures;
-    struct {
-        std::string ASC = "asc";
-        std::string DESC = "desc";
-        std::string RAND = "rand";
-    }Orders;
-    class BoostAODE : public Ensemble {
+    class BoostAODE : public Boost {
     public:
-        BoostAODE(bool predict_voting = false);
+        explicit BoostAODE(bool predict_voting = false);
         virtual ~BoostAODE() = default;
         std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
-        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
     protected:
-        void buildModel(const torch::Tensor& weights) override;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
     private:
-        std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
-        std::vector<int> initializeModels();
-        torch::Tensor X_train, y_train, X_test, y_test;
-        // Hyperparameters
-        bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
-        int maxTolerance = 3;
-        std::string order_algorithm; // order to process the KBest features asc, desc, rand
-        bool convergence = true; //if true, stop when the model does not improve
-        bool selectFeatures = false; // if true, use feature selection
-        std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
-        FeatureSelect* featureSelector = nullptr;
-        double threshold = -1;
-        bool block_update = false;
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/ensembles/Ensemble.cc

@@ -3,22 +3,21 @@
 // SPDX-FileType: SOURCE
 // SPDX-License-Identifier: MIT
 // ***************************************************************
-
 #include "Ensemble.h"
+#include "bayesnet/utils/CountingSemaphore.h"
 
 namespace bayesnet {
 
     Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
     {
-
     };
     const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
-    void Ensemble::trainModel(const torch::Tensor& weights)
+    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
             // fit with std::vectors
-            models[i]->fit(dataset, features, className, states);
+            models[i]->fit(dataset, features, className, states, smoothing);
         }
     }
     std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
@@ -85,17 +84,9 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
-                auto ypredict = models[i]->predict_proba(X);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred += ypredict * significanceModels[i];
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            auto ypredict = models[i]->predict_proba(X);
+            y_pred += ypredict * significanceModels[i];
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         y_pred /= sum;
@@ -105,23 +96,15 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
-                auto ypredict = models[i]->predict_proba(X);
-                assert(ypredict.size() == y_pred.size());
-                assert(ypredict[0].size() == y_pred[0].size());
-                std::lock_guard<std::mutex> lock(mtx);
-                // Multiply each prediction by the significance of the model and then add it to the final prediction
-                for (auto j = 0; j < ypredict.size(); ++j) {
-                    std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
-                        [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                }
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            auto ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.size());
+            assert(ypredict[0].size() == y_pred[0].size());
+            // Multiply each prediction by the significance of the model and then add it to the final prediction
+            for (auto j = 0; j < ypredict.size(); ++j) {
+                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
+                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
+            }
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         //Divide each element of the prediction by the sum of the significances
@@ -141,17 +124,9 @@ namespace bayesnet {
     {
         // Build a m x n_models tensor with the predictions of each model
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
-                auto ypredict = models[i]->predict(X);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
         }
         return voting(y_pred);
     }

bayesnet/ensembles/Ensemble.h

@@ -46,7 +46,7 @@ namespace bayesnet {
         unsigned n_models;
         std::vector<std::unique_ptr<Classifier>> models;
         std::vector<double> significanceModels;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         bool predict_voting;
     };
 }

bayesnet/network/Network.cc

@@ -5,20 +5,20 @@
 // ***************************************************************
 
 #include <thread>
-#include <mutex>
 #include <sstream>
+#include <numeric>
+#include <algorithm>
 #include "Network.h"
 #include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <pthread.h>
+#include <fstream>
 namespace bayesnet {
-    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
+    Network::Network() : fitted{ false }, classNumStates{ 0 }
     {
     }
-    Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
-    {
-
-    }
-    Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
-        maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
+    Network::Network(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
+        fitted(other.fitted), samples(other.samples)
     {
         if (samples.defined())
             samples = samples.clone();
@@ -35,16 +35,15 @@ namespace bayesnet {
         nodes.clear();
         samples = torch::Tensor();
     }
-    float Network::getMaxThreads() const
-    {
-        return maxThreads;
-    }
     torch::Tensor& Network::getSamples()
     {
         return samples;
     }
     void Network::addNode(const std::string& name)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add node to a fitted network. Initialize first.");
+        }
         if (name == "") {
             throw std::invalid_argument("Node name cannot be empty");
         }
@@ -94,12 +93,21 @@ namespace bayesnet {
     }
     void Network::addEdge(const std::string& parent, const std::string& child)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add edge to a fitted network. Initialize first.");
+        }
         if (nodes.find(parent) == nodes.end()) {
             throw std::invalid_argument("Parent node " + parent + " does not exist");
         }
         if (nodes.find(child) == nodes.end()) {
             throw std::invalid_argument("Child node " + child + " does not exist");
         }
+        // Check if the edge is already in the graph
+        for (auto& node : nodes[parent]->getChildren()) {
+            if (node->getName() == child) {
+                throw std::invalid_argument("Edge " + parent + " -> " + child + " already exists");
+            }
+        }
         // Temporarily add edge to check for cycles
         nodes[parent]->addChild(nodes[child].get());
         nodes[child]->addParent(nodes[parent].get());
@@ -155,7 +163,7 @@ namespace bayesnet {
         classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
@@ -164,17 +172,17 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
         this->className = className;
         this->samples = samples;
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
         checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
@@ -185,21 +193,57 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         setStates(states);
-        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
         std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        const double n_samples = static_cast<double>(samples.size(1));
+        auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
+            std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            double numStates = static_cast<double>(node.second->getNumStates());
+            double smoothing_factor = 0.0;
+            switch (smoothing) {
+                case Smoothing_t::ORIGINAL:
+                    smoothing_factor = 1.0 / n_samples;
+                    break;
+                case Smoothing_t::LAPLACE:
+                    smoothing_factor = 1.0;
+                    break;
+                case Smoothing_t::CESTNIK:
+                    smoothing_factor = 1 / numStates;
+                    break;
+                default:
+                    throw std::invalid_argument("Smoothing method not recognized " + std::to_string(static_cast<int>(smoothing)));
+            }
+            node.second->computeCPT(samples, features, smoothing_factor, weights);
+            semaphore.release();
+            };
+        int i = 0;
         for (auto& node : nodes) {
-            threads.emplace_back([this, &node, &weights]() {
-                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
-                });
+            semaphore.acquire();
+            threads.emplace_back(worker, std::ref(node), i++);
         }
         for (auto& thread : threads) {
             thread.join();
         }
+        // std::fstream file;
+        // file.open("cpt.txt", std::fstream::out | std::fstream::app);
+        // file << std::string(80, '*') << std::endl;
+        // for (const auto& item : graph("Test")) {
+        //     file << item << std::endl;
+        // }
+        // file << std::string(80, '-') << std::endl;
+        // file << dump_cpt() << std::endl;
+        // file << std::string(80, '=') << std::endl;
+        // file.close();
         fitted = true;
     }
     torch::Tensor Network::predict_tensor(const torch::Tensor& samples, const bool proba)
@@ -207,14 +251,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
+        // Ensure the sample size is equal to the number of features
+        if (samples.size(0) != features.size() - 1) {
+            throw std::invalid_argument("(T) Sample size (" + std::to_string(samples.size(0)) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
         torch::Tensor result;
+        std::vector<std::thread> threads;
+        std::mutex mtx;
+        auto& semaphore = CountingSemaphore::getInstance();
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
-        for (int i = 0; i < samples.size(1); ++i) {
-            const torch::Tensor sample = samples.index({ "...", i });
+        auto worker = [&](const torch::Tensor& sample, int i) {
+            std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             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);
+            {
+                std::lock_guard<std::mutex> lock(mtx);
+                result.index_put_({ i, "..." }, temp);
+            }
+            semaphore.release();
+            };
+        for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
+            const torch::Tensor sample = samples.index({ "...", i });
+            threads.emplace_back(worker, sample, i);
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         if (proba)
             return result;
@@ -239,18 +307,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
-        std::vector<int> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<int> predictions(tsamples[0].size(), 0);
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto classProbabilities = predict_sample(sample);
+            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+            int predictedClass = distance(classProbabilities.begin(), maxElem);
+            prediction = predictedClass;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            std::vector<double> classProbabilities = predict_sample(sample);
-            // Find the class with the maximum posterior probability
-            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
-            int predictedClass = distance(classProbabilities.begin(), maxElem);
-            predictions.push_back(predictedClass);
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
@@ -261,14 +349,36 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict_proba()");
         }
-        std::vector<std::vector<double>> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            predictions.push_back(predict_sample(sample));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
@@ -286,11 +396,6 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const std::vector<int>& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size() != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size()) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
@@ -300,44 +405,26 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const torch::Tensor& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size(0) != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size(0)) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::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(std::map<std::string, int>& completeEvidence)
-    {
-        double result = 1.0;
-        for (auto& node : getNodes()) {
-            result *= node.second->getFactorValue(completeEvidence);
-        }
-        return result;
-    }
     std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
     {
         std::vector<double> result(classNumStates, 0.0);
-        std::vector<std::thread> threads;
-        std::mutex mtx;
+        auto completeEvidence = std::map<std::string, int>(evidence);
         for (int i = 0; i < classNumStates; ++i) {
-            threads.emplace_back([this, &result, &evidence, i, &mtx]() {
-                auto completeEvidence = std::map<std::string, int>(evidence);
-                completeEvidence[getClassName()] = i;
-                double factor = computeFactor(completeEvidence);
-                std::lock_guard<std::mutex> lock(mtx);
-                result[i] = factor;
-                });
-        }
-        for (auto& thread : threads) {
-            thread.join();
+            completeEvidence[getClassName()] = i;
+            double partial = 1.0;
+            for (auto& node : getNodes()) {
+                partial *= node.second->getFactorValue(completeEvidence);
+            }
+            result[i] = partial;
         }
         // Normalize result
-        double sum = accumulate(result.begin(), result.end(), 0.0);
+        double sum = std::accumulate(result.begin(), result.end(), 0.0);
         transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }
@@ -410,11 +497,7 @@ namespace bayesnet {
                                 result.insert(it2, fatherName);
                                 ending = false;
                             }
-                        } else {
-                            throw std::logic_error("Error in topological sort because of node " + feature + " is not in result");
                         }
-                    } else {
-                        throw std::logic_error("Error in topological sort because of node father " + fatherName + " is not in result");
                     }
                 }
             }

bayesnet/network/Network.h

@@ -12,14 +12,18 @@
 #include "Node.h"
 
 namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
     class Network {
     public:
         Network();
-        explicit Network(float);
         explicit Network(const Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        float getMaxThreads() const;
         void addNode(const std::string&);
         void addEdge(const std::string&, const std::string&);
         std::map<std::string, std::unique_ptr<Node>>& getNodes();
@@ -32,9 +36,9 @@ namespace bayesnet {
         /*
         Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
         */
-        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
-        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
-        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
         std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
@@ -50,19 +54,16 @@ namespace bayesnet {
     private:
         std::map<std::string, std::unique_ptr<Node>> nodes;
         bool fitted;
-        float maxThreads = 0.95;
         int classNumStates;
         std::vector<std::string> features; // Including classname
         std::string className;
-        double laplaceSmoothing;
         torch::Tensor samples; // n+1xm tensor used to fit the model
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         std::vector<double> predict_sample(const std::vector<int>&);
         std::vector<double> predict_sample(const torch::Tensor&);
         std::vector<double> exactInference(std::map<std::string, int>&);
-        double computeFactor(std::map<std::string, int>&);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
+        void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
         void setStates(const std::map<std::string, std::vector<int>>&);
     };
 }

bayesnet/network/Node.cc

@@ -9,7 +9,7 @@
 namespace bayesnet {
 
     Node::Node(const std::string& name)
-        : name(name), numStates(0), cpTable(torch::Tensor()), parents(std::vector<Node*>()), children(std::vector<Node*>())
+        : name(name)
     {
     }
     void Node::clear()
@@ -90,52 +90,54 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
         transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-
         // Create a tensor of zeros with the dimensions of the CPT
-        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
+        cpTable = torch::zeros(dimensions, torch::kDouble).to(device) + smoothing;
         // Fill table with counts
         auto pos = find(features.begin(), features.end(), name);
         if (pos == features.end()) {
             throw std::logic_error("Feature " + name + " not found in dataset");
         }
         int name_index = pos - features.begin();
+        c10::List<c10::optional<at::Tensor>> coordinates;
         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 }));
+            coordinates.clear();
+            auto sample = dataset.index({ "...", n_sample });
+            coordinates.push_back(sample[name_index]);
             for (auto parent : parents) {
                 pos = find(features.begin(), features.end(), parent->getName());
                 if (pos == features.end()) {
                     throw std::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 }));
+                coordinates.push_back(sample[parent_index]);
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
+            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
         // Normalize the counts
+        // Divide each row by the sum of the row
         cpTable = cpTable / cpTable.sum(0);
     }
-    float Node::getFactorValue(std::map<std::string, int>& evidence)
+    double Node::getFactorValue(std::map<std::string, int>& evidence)
     {
         c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(at::tensor(evidence[name]));
         transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-        return cpTable.index({ coordinates }).item<float>();
+        return cpTable.index({ coordinates }).item<double>();
     }
     std::vector<std::string> Node::graph(const std::string& className)
     {
         auto output = std::vector<std::string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
-        output.push_back(name + " [shape=circle" + suffix + "] \n");
-        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return "\"" + name + "\" -> \"" + child->getName() + "\""; });
         return output;
     }
 }

bayesnet/network/Node.h

@@ -12,14 +12,6 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class Node {
-    private:
-        std::string name;
-        std::vector<Node*> parents;
-        std::vector<Node*> children;
-        int numStates; // number of states of the variable
-        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
-        std::vector<int64_t> dimensions; // dimensions of the cpTable
-        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     public:
         explicit Node(const std::string&);
         void clear();
@@ -31,12 +23,20 @@ namespace bayesnet {
         std::vector<Node*>& getParents();
         std::vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
+        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
         std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
-        float getFactorValue(std::map<std::string, int>&);
+        double getFactorValue(std::map<std::string, int>&);
+    private:
+        std::string name;
+        std::vector<Node*> parents;
+        std::vector<Node*> children;
+        int numStates = 0; // number of states of the variable
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
+        std::vector<int64_t> dimensions; // dimensions of the cpTable
+        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     };
 }
 #endif

bayesnet/utils/BayesMetrics.cc

@@ -4,29 +4,79 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <map>
+#include <unordered_map>
+#include <tuple>
 #include "Mst.h"
 #include "BayesMetrics.h"
 namespace bayesnet {
     //samples is n+1xm tensor used to fit the model
     Metrics::Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
         : samples(samples)
-        , features(features)
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
     {
     }
     //samples is n+1xm std::vector used to fit the model
     Metrics::Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates)
-        : features(features)
+        : samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
         , className(className)
+        , features(features)
         , classNumStates(classNumStates)
-        , samples(torch::zeros({ static_cast<int>(vsamples.size() + 1), static_cast<int>(vsamples[0].size()) }, torch::kInt32))
     {
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
     }
+    std::vector<std::pair<int, int>> Metrics::SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending, unsigned k)
+    {
+        // Return the K Best features 
+        auto n = features.size();
+        // compute scores
+        scoresKPairs.clear();
+        pairsKBest.clear();
+        auto labels = samples.index({ -1, "..." });
+        for (int i = 0; i < n - 1; ++i) {
+            if (std::find(featuresExcluded.begin(), featuresExcluded.end(), i) != featuresExcluded.end()) {
+                continue;
+            }
+            for (int j = i + 1; j < n; ++j) {
+                if (std::find(featuresExcluded.begin(), featuresExcluded.end(), j) != featuresExcluded.end()) {
+                    continue;
+                }
+                auto key = std::make_pair(i, j);
+                auto value = conditionalMutualInformation(samples.index({ i, "..." }), samples.index({ j, "..." }), labels, weights);
+                scoresKPairs.push_back({ key, value });
+            }
+        }
+        // sort scores
+        if (ascending) {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second < b.second; });
+
+        } else {
+            sort(scoresKPairs.begin(), scoresKPairs.end(), [](auto& a, auto& b)
+                { return a.second > b.second; });
+        }
+        for (auto& [pairs, score] : scoresKPairs) {
+            pairsKBest.push_back(pairs);
+        }
+        if (k != 0 && k < pairsKBest.size()) {
+            if (ascending) {
+                int limit = pairsKBest.size() - k;
+                for (int i = 0; i < limit; i++) {
+                    pairsKBest.erase(pairsKBest.begin());
+                    scoresKPairs.erase(scoresKPairs.begin());
+                }
+            } else {
+                pairsKBest.resize(k);
+                scoresKPairs.resize(k);
+            }
+        }
+        return pairsKBest;
+    }
     std::vector<int> Metrics::SelectKBestWeighted(const torch::Tensor& weights, bool ascending, unsigned k)
     {
         // Return the K Best features 
@@ -66,7 +116,10 @@ namespace bayesnet {
     {
         return scoresKBest;
     }
-
+    std::vector<std::pair<std::pair<int, int>, double>> Metrics::getScoresKPairs() const
+    {
+        return scoresKPairs;
+    }
     torch::Tensor Metrics::conditionalEdge(const torch::Tensor& weights)
     {
         auto result = std::vector<double>();
@@ -105,14 +158,8 @@ namespace bayesnet {
         }
         return matrix;
     }
-    // To use in Python
-    std::vector<float> Metrics::conditionalEdgeWeights(std::vector<float>& weights_)
-    {
-        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;
-    }
+    // Measured in nats (natural logarithm (log) base e)
+    // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
     double Metrics::entropy(const torch::Tensor& feature, const torch::Tensor& weights)
     {
         torch::Tensor counts = feature.bincount(weights);
@@ -151,10 +198,54 @@ namespace bayesnet {
         }
         return entropyValue;
     }
-    // I(X;Y) = H(Y) - H(Y|X)
+    // H(X|Y,C) = sum_{y in Y, c in C} p(x,c) H(X|Y=y,C=c)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
+    {
+        // Ensure the tensors are of the same length
+        assert(firstFeature.size(0) == secondFeature.size(0) && firstFeature.size(0) == labels.size(0) && firstFeature.size(0) == weights.size(0));
+        // Convert tensors to vectors for easier processing
+        auto firstFeatureData = firstFeature.accessor<int, 1>();
+        auto secondFeatureData = secondFeature.accessor<int, 1>();
+        auto labelsData = labels.accessor<int, 1>();
+        auto weightsData = weights.accessor<double, 1>();
+        int numSamples = firstFeature.size(0);
+        // Maps for joint and marginal probabilities
+        std::map<std::tuple<int, int, int>, double> jointCount;
+        std::map<std::tuple<int, int>, double> marginalCount;
+        // Compute joint and marginal counts
+        for (int i = 0; i < numSamples; ++i) {
+            auto keyJoint = std::make_tuple(firstFeatureData[i], labelsData[i], secondFeatureData[i]);
+            auto keyMarginal = std::make_tuple(firstFeatureData[i], labelsData[i]);
+
+            jointCount[keyJoint] += weightsData[i];
+            marginalCount[keyMarginal] += weightsData[i];
+        }
+        // Total weight sum
+        double totalWeight = torch::sum(weights).item<double>();
+        if (totalWeight == 0)
+            return 0;
+        // Compute the conditional entropy
+        double conditionalEntropy = 0.0;
+        for (const auto& [keyJoint, jointFreq] : jointCount) {
+            auto [x, c, y] = keyJoint;
+            auto keyMarginal = std::make_tuple(x, c);
+            //double p_xc = marginalCount[keyMarginal] / totalWeight;
+            double p_y_given_xc = jointFreq / marginalCount[keyMarginal];
+            if (p_y_given_xc > 0) {
+                conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
+            }
+        }
+        return conditionalEntropy;
+    }
+    // I(X;Y) = H(Y) - H(Y|X) ; I(X;Y) >= 0
     double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
     {
-        return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
+        return std::max(entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights), 0.0);
+    }
+    // I(X;Y|C) = H(X|C) - H(X|Y,C) >= 0
+    double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
+    {
+        return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);
     }
     /*
     Compute the maximum spanning tree considering the weights as distances

bayesnet/utils/BayesMetrics.h

@@ -16,21 +16,26 @@ namespace bayesnet {
         Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
         Metrics(const std::vector<std::vector<int>>& vsamples, const std::vector<int>& labels, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
         std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
+        std::vector<std::pair<int, int>> SelectKPairs(const torch::Tensor& weights, std::vector<int>& featuresExcluded, bool ascending = false, unsigned k = 0);
         std::vector<double> getScoresKBest() const;
+        std::vector<std::pair<std::pair<int, int>, double>> getScoresKPairs() const;
         double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
-        std::vector<float> conditionalEdgeWeights(std::vector<float>& weights); // To use in Python
+        double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
         torch::Tensor conditionalEdge(const torch::Tensor& weights);
         std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        // Measured in nats (natural logarithm (log) base e)
+        // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
+        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
+        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
     protected:
         torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
         std::string className;
-        double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
         std::vector<std::string> features;
         template <class T>
         std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
         {
             std::vector<std::pair<T, T>> result;
-            for (int i = 0; i < source.size(); ++i) {
+            for (int i = 0; i < source.size() - 1; ++i) {
                 T temp = source[i];
                 for (int j = i + 1; j < source.size(); ++j) {
                     result.push_back({ temp, source[j] });
@@ -49,6 +54,8 @@ namespace bayesnet {
         int classNumStates = 0;
         std::vector<double> scoresKBest;
         std::vector<int> featuresKBest; // sorted indices of the features
+        std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
+        std::vector<std::pair<std::pair<int, int>, double>> scoresKPairs;
         double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
     };
 }

bayesnet/utils/CountingSemaphore.h

@@ -0,0 +1,46 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include <thread>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

docs/BoostAODE.md

@@ -5,6 +5,7 @@
 The hyperparameters defined in the algorithm are:
 
 - ***bisection*** (*boolean*): If set to true allows the algorithm to add *k* models at once (as specified in the algorithm) to the ensemble. Default value: *true*.
+- ***bisection_best*** (*boolean*): If set to *true*, the algorithm will take as *priorAccuracy* the best accuracy computed. If set to *false⁺ it will take the last accuracy as *priorAccuracy*. Default value: *false*.
 
 - ***order*** (*{"asc", "desc", "rand"}*): Sets the order (ascending/descending/random) in which dataset variables will be processed to choose the parents of the *SPODEs*. Default value: *"desc"*.
 
@@ -26,4 +27,4 @@ The hyperparameters defined in the algorithm are:
 
 ## Operation
 
-### [Algorithm](./algorithm.md)
+### [Base Algorithm](./algorithm.md)

docs/algorithm.md

@@ -105,8 +105,7 @@
 
             2. $numItemsPack \leftarrow 0$
 
-    10. If
-        $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
+    10. If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
 
     11. $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
 

gcovr.cfg

@@ -1,5 +0,0 @@
-filter = bayesnet/
-exclude-directories = build_debug/lib/
-exclude = bayesnet/utils/loguru.*
-print-summary = yes
-sort = uncovered-percent

lib/Files/ArffFiles.cc

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

lib/Files/ArffFiles.h

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

lib/Files/CMakeLists.txt

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

sample/CMakeLists.txt

@@ -8,13 +8,12 @@ find_package(Torch REQUIRED)
 find_library(BayesNet NAMES BayesNet.a libBayesNet.a REQUIRED)
 
 include_directories(
-    lib/Files
+    ../tests/lib/Files
     lib/mdlp
     lib/json/include
     /usr/local/include
 )
 
-add_subdirectory(lib/Files)
 add_subdirectory(lib/mdlp)
 add_executable(bayesnet_sample sample.cc) 
-target_link_libraries(bayesnet_sample ArffFiles mdlp "${TORCH_LIBRARIES}" "${BayesNet}")
+target_link_libraries(bayesnet_sample mdlp "${TORCH_LIBRARIES}" "${BayesNet}")

sample/lib/Files/ArffFiles.cc

@@ -1,174 +0,0 @@
-// ***************************************************************
-// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-// SPDX-FileType: SOURCE
-// SPDX-License-Identifier: MIT
-// ***************************************************************
-
-#include "ArffFiles.h"
-#include <fstream>
-#include <sstream>
-#include <map>
-#include <iostream>
-
-ArffFiles::ArffFiles() = default;
-
-std::vector<std::string> ArffFiles::getLines() const
-{
-    return lines;
-}
-
-unsigned long int ArffFiles::getSize() const
-{
-    return lines.size();
-}
-
-std::vector<std::pair<std::string, std::string>> ArffFiles::getAttributes() const
-{
-    return attributes;
-}
-
-std::string ArffFiles::getClassName() const
-{
-    return className;
-}
-
-std::string ArffFiles::getClassType() const
-{
-    return classType;
-}
-
-std::vector<std::vector<float>>& ArffFiles::getX()
-{
-    return X;
-}
-
-std::vector<int>& ArffFiles::getY()
-{
-    return y;
-}
-
-void ArffFiles::loadCommon(std::string fileName)
-{
-    std::ifstream file(fileName);
-    if (!file.is_open()) {
-        throw std::invalid_argument("Unable to open file");
-    }
-    std::string line;
-    std::string keyword;
-    std::string attribute;
-    std::string type;
-    std::string type_w;
-    while (getline(file, line)) {
-        if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
-            continue;
-        }
-        if (line.find("@attribute") != std::string::npos || line.find("@ATTRIBUTE") != std::string::npos) {
-            std::stringstream ss(line);
-            ss >> keyword >> attribute;
-            type = "";
-            while (ss >> type_w)
-                type += type_w + " ";
-            attributes.emplace_back(trim(attribute), trim(type));
-            continue;
-        }
-        if (line[0] == '@') {
-            continue;
-        }
-        lines.push_back(line);
-    }
-    file.close();
-    if (attributes.empty())
-        throw std::invalid_argument("No attributes found");
-}
-
-void ArffFiles::load(const std::string& fileName, bool classLast)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    if (classLast) {
-        className = std::get<0>(attributes.back());
-        classType = std::get<1>(attributes.back());
-        attributes.pop_back();
-        labelIndex = static_cast<int>(attributes.size());
-    } else {
-        className = std::get<0>(attributes.front());
-        classType = std::get<1>(attributes.front());
-        attributes.erase(attributes.begin());
-        labelIndex = 0;
-    }
-    generateDataset(labelIndex);
-}
-void ArffFiles::load(const std::string& fileName, const std::string& name)
-{
-    int labelIndex;
-    loadCommon(fileName);
-    bool found = false;
-    for (int i = 0; i < attributes.size(); ++i) {
-        if (attributes[i].first == name) {
-            className = std::get<0>(attributes[i]);
-            classType = std::get<1>(attributes[i]);
-            attributes.erase(attributes.begin() + i);
-            labelIndex = i;
-            found = true;
-            break;
-        }
-    }
-    if (!found) {
-        throw std::invalid_argument("Class name not found");
-    }
-    generateDataset(labelIndex);
-}
-
-void ArffFiles::generateDataset(int labelIndex)
-{
-    X = std::vector<std::vector<float>>(attributes.size(), std::vector<float>(lines.size()));
-    auto yy = std::vector<std::string>(lines.size(), "");
-    auto removeLines = std::vector<int>(); // Lines with missing values
-    for (size_t i = 0; i < lines.size(); i++) {
-        std::stringstream ss(lines[i]);
-        std::string value;
-        int pos = 0;
-        int xIndex = 0;
-        while (getline(ss, value, ',')) {
-            if (pos++ == labelIndex) {
-                yy[i] = value;
-            } else {
-                if (value == "?") {
-                    X[xIndex++][i] = -1;
-                    removeLines.push_back(i);
-                } else
-                    X[xIndex++][i] = stof(value);
-            }
-        }
-    }
-    for (auto i : removeLines) {
-        yy.erase(yy.begin() + i);
-        for (auto& x : X) {
-            x.erase(x.begin() + i);
-        }
-    }
-    y = factorize(yy);
-}
-
-std::string ArffFiles::trim(const std::string& source)
-{
-    std::string s(source);
-    s.erase(0, s.find_first_not_of(" '\n\r\t"));
-    s.erase(s.find_last_not_of(" '\n\r\t") + 1);
-    return s;
-}
-
-std::vector<int> ArffFiles::factorize(const std::vector<std::string>& labels_t)
-{
-    std::vector<int> yy;
-    yy.reserve(labels_t.size());
-    std::map<std::string, int> labelMap;
-    int i = 0;
-    for (const std::string& label : labels_t) {
-        if (labelMap.find(label) == labelMap.end()) {
-            labelMap[label] = i++;
-        }
-        yy.push_back(labelMap[label]);
-    }
-    return yy;
-}

sample/lib/Files/ArffFiles.h

@@ -1,38 +0,0 @@
-// ***************************************************************
-// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-// SPDX-FileType: SOURCE
-// SPDX-License-Identifier: MIT
-// ***************************************************************
-
-#ifndef ARFFFILES_H
-#define ARFFFILES_H
-
-#include <string>
-#include <vector>
-
-class ArffFiles {
-private:
-    std::vector<std::string> lines;
-    std::vector<std::pair<std::string, std::string>> attributes;
-    std::string className;
-    std::string classType;
-    std::vector<std::vector<float>> X;
-    std::vector<int> y;
-    void generateDataset(int);
-    void loadCommon(std::string);
-public:
-    ArffFiles();
-    void load(const std::string&, bool = true);
-    void load(const std::string&, const std::string&);
-    std::vector<std::string> getLines() const;
-    unsigned long int getSize() const;
-    std::string getClassName() const;
-    std::string getClassType() const;
-    static std::string trim(const std::string&);
-    std::vector<std::vector<float>>& getX();
-    std::vector<int>& getY();
-    std::vector<std::pair<std::string, std::string>> getAttributes() const;
-    static std::vector<int> factorize(const std::vector<std::string>& labels_t);
-};
-
-#endif

sample/lib/Files/CMakeLists.txt

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

sample/sample.cc

@@ -4,9 +4,10 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
-#include <ArffFiles.h>
+#include <ArffFiles.hpp>
 #include <CPPFImdlp.h>
 #include <bayesnet/ensembles/BoostAODE.h>
+#include <torch/torch.h>
 
 std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
 {
@@ -19,7 +20,8 @@ std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, m
     }
     return Xd;
 }
-tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
+
+tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, torch::Device device)
 {
     auto handler = ArffFiles();
     handler.load(name, class_last);
@@ -34,16 +36,16 @@ tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<s
     torch::Tensor Xd;
     auto states = map<std::string, std::vector<int>>();
     auto Xr = discretizeDataset(X, y);
-    Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
+    Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32).to(device);
     for (int i = 0; i < features.size(); ++i) {
         states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
         auto item = states.at(features[i]);
         iota(begin(item), end(item), 0);
-        Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
+        Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32).to(device));
     }
     states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
     iota(begin(states.at(className)), end(states.at(className)), 0);
-    return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
+    return { Xd, torch::tensor(y, torch::kInt32).to(device), features, className, states };
 }
 
 int main(int argc, char* argv[])
@@ -53,16 +55,22 @@ int main(int argc, char* argv[])
         return 1;
     }
     std::string file_name = argv[1];
+    torch::Device device(torch::kCPU);
+    if (torch::cuda::is_available()) {
+        device = torch::Device(torch::kCUDA);
+        std::cout << "CUDA is available! Using GPU." << std::endl;
+    } else {
+        std::cout << "CUDA is not available. Using CPU." << std::endl;
+    }
     torch::Tensor X, y;
     std::vector<std::string> features;
     std::string className;
     map<std::string, std::vector<int>> states;
     auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
     std::cout << "Library version: " << clf.getVersion() << std::endl;
-    tie(X, y, features, className, states) = loadDataset(file_name, true);
-    clf.fit(X, y, features, className, states);
+    tie(X, y, features, className, states) = loadDataset(file_name, true, device);
+    clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
     auto score = clf.score(X, y);
-    std::cout << "File: " << file_name << " score: " << score << std::endl;
+    std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
     return 0;
 }
-

tests/CMakeLists.txt

@@ -1,24 +1,27 @@
 if(ENABLE_TESTING)
     include_directories(
-        ${BayesNet_SOURCE_DIR}/lib/Files
-        ${BayesNet_SOURCE_DIR}/lib/mdlp
+        ${BayesNet_SOURCE_DIR}/tests/lib/Files
         ${BayesNet_SOURCE_DIR}/lib/folding
+        ${BayesNet_SOURCE_DIR}/lib/mdlp/src
         ${BayesNet_SOURCE_DIR}/lib/json/include
         ${BayesNet_SOURCE_DIR}
         ${CMAKE_BINARY_DIR}/configured_files/include
     )
     file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
     add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc 
-        TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc
-        TestUtils.cc TestBayesEnsemble.cc ${BayesNet_SOURCES})
-    target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain )
+        TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestBoostAODE.cc TestA2DE.cc 
+        TestUtils.cc TestBayesEnsemble.cc TestModulesVersions.cc TestBoostA2DE.cc ${BayesNet_SOURCES})
+    target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" mdlp PRIVATE Catch2::Catch2WithMain)
     add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
-    add_test(NAME Network COMMAND TestBayesNet "[Network]")
-    add_test(NAME Node COMMAND TestBayesNet "[Node]")
-    add_test(NAME Metrics COMMAND TestBayesNet "[Metrics]")
-    add_test(NAME FeatureSelection COMMAND TestBayesNet "[FeatureSelection]")
+    add_test(NAME A2DE COMMAND TestBayesNet "[A2DE]")
+    add_test(NAME BoostA2DE COMMAND TestBayesNet "[BoostA2DE]")
+    add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
     add_test(NAME Classifier COMMAND TestBayesNet "[Classifier]")
     add_test(NAME Ensemble COMMAND TestBayesNet "[Ensemble]")
+    add_test(NAME FeatureSelection COMMAND TestBayesNet "[FeatureSelection]")
+    add_test(NAME Metrics COMMAND TestBayesNet "[Metrics]")
     add_test(NAME Models COMMAND TestBayesNet "[Models]")
-    add_test(NAME BoostAODE COMMAND TestBayesNet "[BoostAODE]")
+    add_test(NAME Modules COMMAND TestBayesNet "[Modules]")
+    add_test(NAME Network COMMAND TestBayesNet "[Network]")
+    add_test(NAME Node COMMAND TestBayesNet "[Node]")
 endif(ENABLE_TESTING)

tests/TestA2DE.cc

@@ -0,0 +1,49 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <type_traits>
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include "bayesnet/ensembles/A2DE.h"
+#include "TestUtils.h"
+
+
+TEST_CASE("Fit and Score", "[A2DE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::A2DE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.831776).epsilon(raw.epsilon));
+    REQUIRE(clf.getNumberOfNodes() == 360);
+    REQUIRE(clf.getNumberOfEdges() == 756);
+    REQUIRE(clf.getNotes().size() == 0);
+}
+TEST_CASE("Test score with predict_voting", "[A2DE]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::A2DE(true);
+    auto hyperparameters = nlohmann::json{
+       {"predict_voting", true},
+    };
+    clf.setHyperparameters(hyperparameters);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.82243).epsilon(raw.epsilon));
+    hyperparameters["predict_voting"] = false;
+    clf.setHyperparameters(hyperparameters);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.score(raw.Xv, raw.yv) == Catch::Approx(0.83178).epsilon(raw.epsilon));
+}
+TEST_CASE("Test graph", "[A2DE]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = bayesnet::A2DE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+    auto graph = clf.graph();
+    REQUIRE(graph.size() == 78);
+    REQUIRE(graph[0] == "digraph BayesNet {\nlabel=<BayesNet A2DE_0>\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n");
+    REQUIRE(graph[1] == "class [shape=circle, fontcolor=red, fillcolor=lightblue, style=filled ] \n");
+}

tests/TestBayesClassifier.cc

@@ -18,47 +18,47 @@ TEST_CASE("Test Cannot build dataset with wrong data vector", "[Classifier]")
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
     raw.yv.pop_back();
-    REQUIRE_THROWS_AS(model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), std::runtime_error);
-    REQUIRE_THROWS_WITH(model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
+    REQUIRE_THROWS_AS(model.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
 }
 TEST_CASE("Test Cannot build dataset with wrong data tensor", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
     auto yshort = torch::zeros({ 149 }, torch::kInt32);
-    REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
-    REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.features, raw.className, raw.states, raw.smoothing), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
 }
 TEST_CASE("Invalid data type", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", false);
-    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
-    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "dataset (X, y) must be of type Integer");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing), "dataset (X, y) must be of type Integer");
 }
 TEST_CASE("Invalid number of features", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
     auto Xt = torch::cat({ raw.Xt, torch::zeros({ 1, 150 }, torch::kInt32) }, 0);
-    REQUIRE_THROWS_AS(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
-    REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "Classifier: X 5 and features 4 must have the same number of features");
+    REQUIRE_THROWS_AS(model.fit(Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing), "Classifier: X 5 and features 4 must have the same number of features");
 }
 TEST_CASE("Invalid class name", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
-    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), std::invalid_argument);
-    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), "class name not found in states");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.features, "duck", raw.states, raw.smoothing), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, "duck", raw.states, raw.smoothing), "class name not found in states");
 }
 TEST_CASE("Invalid feature name", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
-    auto statest = raw.statest;
+    auto statest = raw.states;
     statest.erase("petallength");
-    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), std::invalid_argument);
-    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), "feature [petallength] not found in states");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.features, raw.className, statest, raw.smoothing), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, raw.className, statest, raw.smoothing), "feature [petallength] not found in states");
 }
 TEST_CASE("Invalid hyperparameter", "[Classifier]")
 {
@@ -71,7 +71,7 @@ TEST_CASE("Topological order", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
-    model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     auto order = model.topological_order();
     REQUIRE(order.size() == 4);
     REQUIRE(order[0] == "petallength");
@@ -83,7 +83,7 @@ TEST_CASE("Dump_cpt", "[Classifier]")
 {
     auto model = bayesnet::TAN();
     auto raw = RawDatasets("iris", true);
-    model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     auto cpt = model.dump_cpt();
     REQUIRE(cpt.size() == 1713);
 }
@@ -111,7 +111,7 @@ TEST_CASE("KDB Graph", "[Classifier]")
 {
     auto model = bayesnet::KDB(2);
     auto raw = RawDatasets("iris", true);
-    model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    model.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto graph = model.graph();
     REQUIRE(graph.size() == 15);
 }
@@ -119,7 +119,7 @@ TEST_CASE("KDBLd Graph", "[Classifier]")
 {
     auto model = bayesnet::KDBLd(2);
     auto raw = RawDatasets("iris", false);
-    model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     auto graph = model.graph();
     REQUIRE(graph.size() == 15);
 }

tests/TestBayesEnsemble.cc

@@ -18,7 +18,7 @@ TEST_CASE("Topological Order", "[Ensemble]")
 {
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::BoostAODE();
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto order = clf.topological_order();
     REQUIRE(order.size() == 0);
 }
@@ -26,7 +26,7 @@ TEST_CASE("Dump CPT", "[Ensemble]")
 {
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::BoostAODE();
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto dump = clf.dump_cpt();
     REQUIRE(dump == "");
 }
@@ -34,7 +34,7 @@ TEST_CASE("Number of States", "[Ensemble]")
 {
     auto clf = bayesnet::BoostAODE();
     auto raw = RawDatasets("iris", true);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfStates() == 76);
 }
 TEST_CASE("Show", "[Ensemble]")
@@ -46,7 +46,7 @@ TEST_CASE("Show", "[Ensemble]")
             {"maxTolerance", 1},
             {"convergence", false},
         });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     std::vector<std::string> expected = {
         "class -> sepallength, sepalwidth, petallength, petalwidth, ",
         "petallength -> sepallength, sepalwidth, petalwidth, ",
@@ -78,16 +78,16 @@ TEST_CASE("Graph", "[Ensemble]")
 {
     auto clf = bayesnet::BoostAODE();
     auto raw = RawDatasets("iris", true);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto graph = clf.graph();
     REQUIRE(graph.size() == 56);
     auto clf2 = bayesnet::AODE();
-    clf2.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     graph = clf2.graph();
     REQUIRE(graph.size() == 56);
     raw = RawDatasets("glass", false);
     auto clf3 = bayesnet::AODELd();
-    clf3.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    clf3.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     graph = clf3.graph();
     REQUIRE(graph.size() == 261);
 }

tests/TestBayesMetrics.cc

@@ -9,7 +9,7 @@
 #include <catch2/generators/catch_generators.hpp>
 #include "bayesnet/utils/BayesMetrics.h"
 #include "TestUtils.h"
-
+#include "Timer.h"
 
 TEST_CASE("Metrics Test", "[Metrics]")
 {
@@ -27,8 +27,8 @@ TEST_CASE("Metrics Test", "[Metrics]")
         {"diabetes", 0.0345470614}
     };
     map<pair<std::string, int>, std::vector<pair<int, int>>> resultsMST = {
-        { {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {5, 1}, {5, 8}, {5, 4}, {6, 2}, {6, 7} } },
-        { {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {5, 4}, {0, 6}, {0, 3}, {6, 2}, {6, 7} } },
+        { {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {3, 4}, {5, 1}, {5, 8}, {6, 2}, {6, 7} } },
+        { {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {0, 6}, {0, 3}, {3, 4}, {6, 2}, {6, 7} } },
         { {"iris", 0}, { {0, 1}, {0, 2}, {1, 3} } },
         { {"iris", 1}, { {1, 0}, {1, 3}, {0, 2} } },
         { {"ecoli", 0}, { {0, 1}, {0, 2}, {1, 5}, {1, 3}, {5, 6}, {5, 4} } },
@@ -37,8 +37,8 @@ TEST_CASE("Metrics Test", "[Metrics]")
         { {"diabetes", 1}, { {1, 4}, {4, 3}, {3, 2}, {3, 5}, {2, 0}, {0, 7}, {0, 6} } }
     };
     auto raw = RawDatasets(file_name, true);
-    bayesnet::Metrics metrics(raw.dataset, raw.featurest, raw.classNamet, raw.classNumStates);
-    bayesnet::Metrics metricsv(raw.Xv, raw.yv, raw.featurest, raw.classNamet, raw.classNumStates);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    bayesnet::Metrics metricsv(raw.Xv, raw.yv, raw.features, raw.className, raw.classNumStates);
 
     SECTION("Test Constructor")
     {
@@ -69,10 +69,199 @@ TEST_CASE("Metrics Test", "[Metrics]")
         auto weights_matrix = metrics.conditionalEdge(raw.weights);
         auto weights_matrixv = metricsv.conditionalEdge(raw.weights);
         for (int i = 0; i < 2; ++i) {
-            auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, i);
-            auto resultv = metricsv.maximumSpanningTree(raw.featurest, weights_matrixv, i);
+            auto result = metrics.maximumSpanningTree(raw.features, weights_matrix, i);
+            auto resultv = metricsv.maximumSpanningTree(raw.features, weights_matrixv, i);
             REQUIRE(result == resultsMST.at({ file_name, i }));
             REQUIRE(resultv == resultsMST.at({ file_name, i }));
         }
     }
 }
+TEST_CASE("Select all features ordered by Mutual Information", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    auto kBest = metrics.SelectKBestWeighted(raw.weights, true, 0);
+    REQUIRE(kBest.size() == raw.features.size());
+    REQUIRE(kBest == std::vector<int>({ 1, 0, 3, 2 }));
+}
+TEST_CASE("Entropy Test", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    auto result = metrics.entropy(raw.dataset.index({ 0, "..." }), raw.weights);
+    REQUIRE(result == Catch::Approx(0.9848175048828125).epsilon(raw.epsilon));
+    auto data = torch::tensor({ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 }, torch::kInt32);
+    auto weights = torch::tensor({ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }, torch::kFloat32);
+    result = metrics.entropy(data, weights);
+    REQUIRE(result == Catch::Approx(0.61086434125900269).epsilon(raw.epsilon));
+    data = torch::tensor({ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 }, torch::kInt32);
+    result = metrics.entropy(data, weights);
+    REQUIRE(result == Catch::Approx(0.693147180559945).epsilon(raw.epsilon));
+}
+TEST_CASE("Conditional Entropy", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    auto expected = std::map<std::pair<int, int>, double>{
+        { { 0, 1 }, 1.32674 },
+        { { 0, 2 }, 0.236253 },
+        { { 0, 3 }, 0.1202 },
+        { { 1, 2 }, 0.252551 },
+        { { 1, 3 }, 0.10515 },
+        { { 2, 3 }, 0.108323 },
+    };
+    for (int i = 0; i < raw.features.size() - 1; ++i) {
+        for (int j = i + 1; j < raw.features.size(); ++j) {
+            double result = metrics.conditionalEntropy(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
+            REQUIRE(result == Catch::Approx(expected.at({ i, j })).epsilon(raw.epsilon));
+        }
+    }
+}
+TEST_CASE("Conditional Mutual Information", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    auto expected = std::map<std::pair<int, int>, double>{
+        { { 0, 1 }, 0.0 },
+        { { 0, 2 }, 0.287696 },
+        { { 0, 3 }, 0.403749 },
+        { { 1, 2 }, 1.17112 },
+        { { 1, 3 }, 1.31852 },
+        { { 2, 3 }, 0.210068 },
+    };
+    for (int i = 0; i < raw.features.size() - 1; ++i) {
+        for (int j = i + 1; j < raw.features.size(); ++j) {
+            double result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
+            REQUIRE(result == Catch::Approx(expected.at({ i, j })).epsilon(raw.epsilon));
+        }
+    }
+}
+TEST_CASE("Select K Pairs descending", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    std::vector<int> empty;
+    auto results = metrics.SelectKPairs(raw.weights, empty, false);
+    auto expected = std::vector<std::pair<std::pair<int, int>, double>>{
+        { { 1, 3 }, 1.31852 },
+        { { 1, 2 }, 1.17112 },
+        { { 0, 3 }, 0.403749 },
+        { { 0, 2 }, 0.287696 },
+        { { 2, 3 }, 0.210068 },
+        { { 0, 1 }, 0.0 },
+    };
+    auto scores = metrics.getScoresKPairs();
+    for (int i = 0; i < results.size(); ++i) {
+        auto result = results[i];
+        auto expect = expected[i];
+        auto score = scores[i];
+        REQUIRE(result.first == expect.first.first);
+        REQUIRE(result.second == expect.first.second);
+        REQUIRE(score.first.first == expect.first.first);
+        REQUIRE(score.first.second == expect.first.second);
+        REQUIRE(score.second == Catch::Approx(expect.second).epsilon(raw.epsilon));
+    }
+    REQUIRE(results.size() == 6);
+    REQUIRE(scores.size() == 6);
+}
+TEST_CASE("Select K Pairs ascending", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    std::vector<int> empty;
+    auto results = metrics.SelectKPairs(raw.weights, empty, true);
+    auto expected = std::vector<std::pair<std::pair<int, int>, double>>{
+        { { 0, 1 }, 0.0 },
+        { { 2, 3 }, 0.210068 },
+        { { 0, 2 }, 0.287696 },
+        { { 0, 3 }, 0.403749 },
+        { { 1, 2 }, 1.17112 },
+        { { 1, 3 }, 1.31852 },
+    };
+    auto scores = metrics.getScoresKPairs();
+    for (int i = 0; i < results.size(); ++i) {
+        auto result = results[i];
+        auto expect = expected[i];
+        auto score = scores[i];
+        REQUIRE(result.first == expect.first.first);
+        REQUIRE(result.second == expect.first.second);
+        REQUIRE(score.first.first == expect.first.first);
+        REQUIRE(score.first.second == expect.first.second);
+        REQUIRE(score.second == Catch::Approx(expect.second).epsilon(raw.epsilon));
+    }
+    REQUIRE(results.size() == 6);
+    REQUIRE(scores.size() == 6);
+}
+TEST_CASE("Select K Pairs with features excluded", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    std::vector<int> excluded = { 0, 3 };
+    auto results = metrics.SelectKPairs(raw.weights, excluded, true);
+    auto expected = std::vector<std::pair<std::pair<int, int>, double>>{
+        { { 1, 2 }, 1.17112 },
+    };
+    auto scores = metrics.getScoresKPairs();
+    for (int i = 0; i < results.size(); ++i) {
+        auto result = results[i];
+        auto expect = expected[i];
+        auto score = scores[i];
+        REQUIRE(result.first == expect.first.first);
+        REQUIRE(result.second == expect.first.second);
+        REQUIRE(score.first.first == expect.first.first);
+        REQUIRE(score.first.second == expect.first.second);
+        REQUIRE(score.second == Catch::Approx(expect.second).epsilon(raw.epsilon));
+    }
+    REQUIRE(results.size() == 1);
+    REQUIRE(scores.size() == 1);
+}
+TEST_CASE("Select K Pairs with number of pairs descending", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    std::vector<int> empty;
+    auto results = metrics.SelectKPairs(raw.weights, empty, false, 3);
+    auto expected = std::vector<std::pair<std::pair<int, int>, double>>{
+        { { 1, 3 }, 1.31852 },
+        { { 1, 2 }, 1.17112 },
+        { { 0, 3 }, 0.403749 }
+    };
+    auto scores = metrics.getScoresKPairs();
+    REQUIRE(results.size() == 3);
+    REQUIRE(scores.size() == 3);
+    for (int i = 0; i < results.size(); ++i) {
+        auto result = results[i];
+        auto expect = expected[i];
+        auto score = scores[i];
+        REQUIRE(result.first == expect.first.first);
+        REQUIRE(result.second == expect.first.second);
+        REQUIRE(score.first.first == expect.first.first);
+        REQUIRE(score.first.second == expect.first.second);
+        REQUIRE(score.second == Catch::Approx(expect.second).epsilon(raw.epsilon));
+    }
+}
+TEST_CASE("Select K Pairs with number of pairs ascending", "[Metrics]")
+{
+    auto raw = RawDatasets("iris", true);
+    bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
+    std::vector<int> empty;
+    auto results = metrics.SelectKPairs(raw.weights, empty, true, 3);
+    auto expected = std::vector<std::pair<std::pair<int, int>, double>>{
+        { { 0, 3 }, 0.403749 },
+        { { 1, 2 }, 1.17112 },
+        { { 1, 3 }, 1.31852 }
+    };
+    auto scores = metrics.getScoresKPairs();
+    REQUIRE(results.size() == 3);
+    REQUIRE(scores.size() == 3);
+    for (int i = 0; i < results.size(); ++i) {
+        auto result = results[i];
+        auto expect = expected[i];
+        auto score = scores[i];
+        REQUIRE(result.first == expect.first.first);
+        REQUIRE(result.second == expect.first.second);
+        REQUIRE(score.first.first == expect.first.first);
+        REQUIRE(score.first.second == expect.first.second);
+        REQUIRE(score.second == Catch::Approx(expect.second).epsilon(raw.epsilon));
+    }
+}

tests/TestBayesModels.cc

@@ -20,7 +20,7 @@
 #include "bayesnet/ensembles/BoostAODE.h"
 #include "TestUtils.h"
 
-const std::string ACTUAL_VERSION = "1.0.5";
+const std::string ACTUAL_VERSION = "1.0.6";
 
 TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
 {
@@ -54,16 +54,16 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
             auto clf = models[name];
             auto discretize = name.substr(name.length() - 2) != "Ld";
             auto raw = RawDatasets(file_name, discretize);
-            clf->fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+            clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
             auto score = clf->score(raw.Xt, raw.yt);
-            INFO("Classifier: " + name + " File: " + file_name);
+            INFO("Classifier: " << name << " File: " << file_name);
             REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
             REQUIRE(clf->getStatus() == bayesnet::NORMAL);
         }
     }
     SECTION("Library check version")
     {
-        INFO("Checking version of " + name + " classifier");
+        INFO("Checking version of " << name << " classifier");
         REQUIRE(clf->getVersion() == ACTUAL_VERSION);
     }
     delete clf;
@@ -81,7 +81,7 @@ TEST_CASE("Models features & Graph", "[Models]")
     {
         auto raw = RawDatasets("iris", true);
         auto clf = bayesnet::TAN();
-        clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+        clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
         REQUIRE(clf.getNumberOfNodes() == 5);
         REQUIRE(clf.getNumberOfEdges() == 7);
         REQUIRE(clf.getNumberOfStates() == 19);
@@ -93,7 +93,7 @@ TEST_CASE("Models features & Graph", "[Models]")
     {
         auto clf = bayesnet::TANLd();
         auto raw = RawDatasets("iris", false);
-        clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+        clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
         REQUIRE(clf.getNumberOfNodes() == 5);
         REQUIRE(clf.getNumberOfEdges() == 7);
         REQUIRE(clf.getNumberOfStates() == 19);
@@ -106,7 +106,7 @@ TEST_CASE("Get num features & num edges", "[Models]")
 {
     auto raw = RawDatasets("iris", true);
     auto clf = bayesnet::KDB(2);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 5);
     REQUIRE(clf.getNumberOfEdges() == 8);
 }
@@ -166,7 +166,7 @@ TEST_CASE("Model predict_proba", "[Models]")
     SECTION("Test " + model + " predict_proba")
     {
         auto clf = models[model];
-        clf->fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+        clf->fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
         auto y_pred_proba = clf->predict_proba(raw.Xv);
         auto yt_pred_proba = clf->predict_proba(raw.Xt);
         auto y_pred = clf->predict(raw.Xv);
@@ -203,7 +203,7 @@ TEST_CASE("AODE voting-proba", "[Models]")
 {
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::AODE(false);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto score_proba = clf.score(raw.Xv, raw.yv);
     auto pred_proba = clf.predict_proba(raw.Xv);
     clf.setHyperparameters({
@@ -222,9 +222,9 @@ TEST_CASE("SPODELd dataset", "[Models]")
     auto raw = RawDatasets("iris", false);
     auto clf = bayesnet::SPODELd(0);
     // raw.dataset.to(torch::kFloat32);
-    clf.fit(raw.dataset, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing);
     auto score = clf.score(raw.Xt, raw.yt);
-    clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
     auto scoret = clf.score(raw.Xt, raw.yt);
     REQUIRE(score == Catch::Approx(0.97333f).epsilon(raw.epsilon));
     REQUIRE(scoret == Catch::Approx(0.97333f).epsilon(raw.epsilon));
@@ -233,13 +233,13 @@ TEST_CASE("KDB with hyperparameters", "[Models]")
 {
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::KDB(2);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto score = clf.score(raw.Xv, raw.yv);
     clf.setHyperparameters({
         {"k", 3},
         {"theta", 0.7},
         });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto scoret = clf.score(raw.Xv, raw.yv);
     REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
     REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
@@ -248,7 +248,7 @@ TEST_CASE("Incorrect type of data for SPODELd", "[Models]")
 {
     auto raw = RawDatasets("iris", true);
     auto clf = bayesnet::SPODELd(0);
-    REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
+    REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
 }
 TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
 {

tests/TestBayesNetwork.cc

@@ -12,8 +12,10 @@
 #include <string>
 #include "TestUtils.h"
 #include "bayesnet/network/Network.h"
+#include "bayesnet/network/Node.h"
 #include "bayesnet/utils/bayesnetUtils.h"
 
+const double threshold = 1e-4;
 void buildModel(bayesnet::Network& net, const std::vector<std::string>& features, const std::string& className)
 {
     std::vector<pair<int, int>> network = { {0, 1}, {0, 2}, {1, 3} };
@@ -28,13 +30,11 @@ void buildModel(bayesnet::Network& net, const std::vector<std::string>& features
         net.addEdge(className, feature);
     }
 }
-
 TEST_CASE("Test Bayesian Network", "[Network]")
 {
 
     auto raw = RawDatasets("iris", true);
     auto net = bayesnet::Network();
-    double threshold = 1e-4;
 
     SECTION("Test get features")
     {
@@ -73,9 +73,9 @@ TEST_CASE("Test Bayesian Network", "[Network]")
         net3.initialize();
         net2.initialize();
         net.initialize();
-        buildModel(net, raw.featuresv, raw.classNamev);
-        buildModel(net2, raw.featurest, raw.classNamet);
-        buildModel(net3, raw.featurest, raw.classNamet);
+        buildModel(net, raw.features, raw.className);
+        buildModel(net2, raw.features, raw.className);
+        buildModel(net3, raw.features, raw.className);
         std::vector<pair<std::string, std::string>> edges = {
             {"class", "sepallength"}, {"class", "sepalwidth"}, {"class", "petallength"},
             {"class", "petalwidth" }, {"sepallength", "sepalwidth"}, {"sepallength", "petallength"},
@@ -114,9 +114,9 @@ TEST_CASE("Test Bayesian Network", "[Network]")
             REQUIRE(children == children3);
         }
         // Fit networks
-        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
-        net2.fit(raw.dataset, raw.weights, raw.featurest, raw.classNamet, raw.statest);
-        net3.fit(raw.Xt, raw.yt, raw.weights, raw.featurest, raw.classNamet, raw.statest);
+        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
+        net2.fit(raw.dataset, raw.weights, raw.features, raw.className, raw.states, raw.smoothing);
+        net3.fit(raw.Xt, raw.yt, raw.weights, raw.features, raw.className, raw.states, raw.smoothing);
         REQUIRE(net.getStates() == net2.getStates());
         REQUIRE(net.getStates() == net3.getStates());
         REQUIRE(net.getFeatures() == net2.getFeatures());
@@ -149,6 +149,7 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test show")
     {
+        INFO("Test show");
         net.addNode("A");
         net.addNode("B");
         net.addNode("C");
@@ -162,6 +163,7 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test topological_sort")
     {
+        INFO("Test topological sort");
         net.addNode("A");
         net.addNode("B");
         net.addNode("C");
@@ -175,6 +177,7 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test graph")
     {
+        INFO("Test graph");
         net.addNode("A");
         net.addNode("B");
         net.addNode("C");
@@ -192,8 +195,9 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test predict")
     {
-        buildModel(net, raw.featuresv, raw.classNamev);
-        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+        INFO("Test predict");
+        buildModel(net, raw.features, raw.className);
+        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
         std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
         std::vector<int> y_test = { 2, 2, 0, 2, 1 };
         auto y_pred = net.predict(test);
@@ -201,8 +205,9 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test predict_proba")
     {
-        buildModel(net, raw.featuresv, raw.classNamev);
-        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+        INFO("Test predict_proba");
+        buildModel(net, raw.features, raw.className);
+        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
         std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
         std::vector<std::vector<double>> y_test = {
             {0.450237, 0.0866621, 0.463101},
@@ -222,15 +227,17 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test score")
     {
-        buildModel(net, raw.featuresv, raw.classNamev);
-        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+        INFO("Test score");
+        buildModel(net, raw.features, raw.className);
+        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
         auto score = net.score(raw.Xv, raw.yv);
         REQUIRE(score == Catch::Approx(0.97333333).margin(threshold));
     }
     SECTION("Copy constructor")
     {
-        buildModel(net, raw.featuresv, raw.classNamev);
-        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+        INFO("Test copy constructor");
+        buildModel(net, raw.features, raw.className);
+        net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
         auto net2 = bayesnet::Network(net);
         REQUIRE(net.getFeatures() == net2.getFeatures());
         REQUIRE(net.getEdges() == net2.getEdges());
@@ -252,7 +259,8 @@ TEST_CASE("Test Bayesian Network", "[Network]")
     }
     SECTION("Test oddities")
     {
-        buildModel(net, raw.featuresv, raw.classNamev);
+        INFO("Test oddities");
+        buildModel(net, raw.features, raw.className);
         // predict without fitting
         std::vector<std::vector<int>> test = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1}, {2, 2, 2, 2, 1} };
         auto test_tensor = bayesnet::vectorToTensor(test);
@@ -266,52 +274,61 @@ TEST_CASE("Test Bayesian Network", "[Network]")
         REQUIRE_THROWS_WITH(net.score(raw.Xv, raw.yv), "You must call fit() before calling predict()");
         // predict with wrong data
         auto netx = bayesnet::Network();
-        buildModel(netx, raw.featuresv, raw.classNamev);
-        netx.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+        buildModel(netx, raw.features, raw.className);
+        netx.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
         std::vector<std::vector<int>> test2 = { {1, 2, 0, 1, 1}, {0, 1, 2, 0, 1}, {0, 0, 0, 0, 1} };
         auto test_tensor2 = bayesnet::vectorToTensor(test2, false);
-        REQUIRE_THROWS_AS(netx.predict(test2), std::logic_error);
-        REQUIRE_THROWS_WITH(netx.predict(test2), "Sample size (3) does not match the number of features (4)");
-        REQUIRE_THROWS_AS(netx.predict(test_tensor2), std::logic_error);
-        REQUIRE_THROWS_WITH(netx.predict(test_tensor2), "Sample size (3) does not match the number of features (4)");
+        REQUIRE_THROWS_AS(netx.predict(test2), std::invalid_argument);
+        REQUIRE_THROWS_WITH(netx.predict(test2), "(V) Sample size (3) does not match the number of features (4)");
+        REQUIRE_THROWS_AS(netx.predict(test_tensor2), std::invalid_argument);
+        REQUIRE_THROWS_WITH(netx.predict(test_tensor2), "(T) Sample size (3) does not match the number of features (4)");
         // fit with wrong data
         // Weights
         auto net2 = bayesnet::Network();
-        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
+        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
         std::string invalid_weights = "Weights (0) must have the same number of elements as samples (150) in Network::fit";
-        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.featuresv, raw.classNamev, raw.statesv), invalid_weights);
+        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, std::vector<double>(), raw.features, raw.className, raw.states, raw.smoothing), invalid_weights);
         // X & y
         std::string invalid_labels = "X and y must have the same number of samples in Network::fit (150 != 0)";
-        REQUIRE_THROWS_AS(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv), invalid_labels);
+        REQUIRE_THROWS_AS(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, std::vector<int>(), raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing), invalid_labels);
         // Features
         std::string invalid_features = "X and features must have the same number of features in Network::fit (4 != 0)";
-        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.classNamev, raw.statesv), invalid_features);
+        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.className, raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, std::vector<std::string>(), raw.className, raw.states, raw.smoothing), invalid_features);
         // Different number of features
         auto net3 = bayesnet::Network();
         auto test2y = { 1, 2, 3, 4, 5 };
-        buildModel(net3, raw.featuresv, raw.classNamev);
-        auto features3 = raw.featuresv;
+        buildModel(net3, raw.features, raw.className);
+        auto features3 = raw.features;
         features3.pop_back();
         std::string invalid_features2 = "X and local features must have the same number of features in Network::fit (3 != 4)";
-        REQUIRE_THROWS_AS(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.classNamev, raw.statesv), invalid_features2);
+        REQUIRE_THROWS_AS(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.className, raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net3.fit(test2, test2y, std::vector<double>(5, 0), features3, raw.className, raw.states, raw.smoothing), invalid_features2);
         // Uninitialized network
         std::string network_invalid = "The network has not been initialized. You must call addNode() before calling fit()";
-        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), network_invalid);
+        REQUIRE_THROWS_AS(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net2.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), network_invalid);
         // Classname
         std::string invalid_classname = "Class Name not found in Network::features";
-        REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, "duck", raw.statesv), invalid_classname);
+        REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, "duck", raw.states, raw.smoothing), invalid_classname);
         // Invalid feature
-        auto features2 = raw.featuresv;
+        auto features2 = raw.features;
         features2.pop_back();
         features2.push_back("duck");
         std::string invalid_feature = "Feature duck not found in Network::features";
-        REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), std::invalid_argument);
-        REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.classNamev, raw.statesv), invalid_feature);
+        REQUIRE_THROWS_AS(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.className, raw.states, raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net.fit(raw.Xv, raw.yv, raw.weightsv, features2, raw.className, raw.states, raw.smoothing), invalid_feature);
+        // Add twice the same node name to the network => Nothing should happen
+        net.addNode("A");
+        net.addNode("A");
+        // invalid state in checkfit
+        auto net4 = bayesnet::Network();
+        buildModel(net4, raw.features, raw.className);
+        std::string invalid_state = "Feature sepallength not found in states";
+        REQUIRE_THROWS_AS(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), std::invalid_argument);
+        REQUIRE_THROWS_WITH(net4.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, std::map<std::string, std::vector<int>>(), raw.smoothing), invalid_state);
     }
 
 }
@@ -332,15 +349,6 @@ TEST_CASE("Cicle in Network", "[Network]")
     REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
     REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Adding this edge forms a cycle in the graph.");
 }
-TEST_CASE("Test max threads constructor", "[Network]")
-{
-    auto net = bayesnet::Network();
-    REQUIRE(net.getMaxThreads() == 0.95f);
-    auto net2 = bayesnet::Network(4);
-    REQUIRE(net2.getMaxThreads() == 4);
-    auto net3 = bayesnet::Network(1.75);
-    REQUIRE(net3.getMaxThreads() == 1.75);
-}
 TEST_CASE("Edges troubles", "[Network]")
 {
     auto net = bayesnet::Network();
@@ -350,13 +358,16 @@ TEST_CASE("Edges troubles", "[Network]")
     REQUIRE_THROWS_WITH(net.addEdge("A", "C"), "Child node C does not exist");
     REQUIRE_THROWS_AS(net.addEdge("C", "A"), std::invalid_argument);
     REQUIRE_THROWS_WITH(net.addEdge("C", "A"), "Parent node C does not exist");
+    net.addEdge("A", "B");
+    REQUIRE_THROWS_AS(net.addEdge("A", "B"), std::invalid_argument);
+    REQUIRE_THROWS_WITH(net.addEdge("A", "B"), "Edge A -> B already exists");
 }
 TEST_CASE("Dump CPT", "[Network]")
 {
     auto net = bayesnet::Network();
     auto raw = RawDatasets("iris", true);
-    buildModel(net, raw.featuresv, raw.classNamev);
-    net.fit(raw.Xv, raw.yv, raw.weightsv, raw.featuresv, raw.classNamev, raw.statesv);
+    buildModel(net, raw.features, raw.className);
+    net.fit(raw.Xv, raw.yv, raw.weightsv, raw.features, raw.className, raw.states, raw.smoothing);
     auto res = net.dump_cpt();
     std::string expected = R"(* class: (3) : [3]
  0.3333
@@ -449,3 +460,108 @@ TEST_CASE("Dump CPT", "[Network]")
     REQUIRE(res == expected);
 }
 
+TEST_CASE("Test Smoothing A", "[Network]")
+{
+    /*
+    Tomando m = 1 Pa = 0.5
+    Si estoy calculando P(A | C), con C en{ 0,1,2 } y tengo :
+    AC = { 11, 12, 11, 10, 10, 12, 10, 01, 00, 02 }
+    Entonces:
+    P(A = 1 | C = 0) = (3 + 1 / 2 * 1) / (4 + 1) = 3.5 / 5
+    P(A = 0 | C = 0) = (1 + 1 / 2 * 1) / (4 + 1) = 1.5 / 5
+    Donde m aquí es el número de veces de C = 0 que es la que condiciona y la a priori vuelve a ser sobre A que es sobre las que estaríamos calculando esas marginales.
+    P(A = 1 | C = 1) = (2 + 1 / 2 * 1) / (3 + 1) = 2.5 / 4
+    P(A = 0 | C = 1) = (1 + 1 / 2 * 1) / (3 + 1) = 1.5 / 4
+    P(A = 1 | C = 2) = (2 + 1 / 2 * 1) / (3 + 1) = 2.5 / 5
+    P(A = 0 | C = 2) = (1 + 1 / 2 * 1) / (3 + 1) = 1.5 / 5
+    En realidad es parecido a Laplace, que en este caso p.e.con C = 0 sería
+    P(A = 1 | C = 0) = (3 + 1) / (4 + 2) = 4 / 6
+    P(A = 0 | C = 0) = (1 + 1) / (4 + 2) = 2 / 6
+    */
+    auto net = bayesnet::Network();
+    net.addNode("A");
+    net.addNode("C");
+    net.addEdge("C", "A");
+    std::vector<int> C = { 1, 2, 1, 0, 0, 2, 0, 1, 0, 2 };
+    std::vector<std::vector<int>> A = { { 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 } };
+    std::map<std::string, std::vector<int>> states = { { "A", {0, 1} }, { "C", {0, 1, 2} } };
+    auto weights = std::vector<double>(C.size(), 1);
+    //
+    // Laplace
+    //
+    net.fit(A, C, weights, { "A" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
+    auto cpt_c_laplace = net.getNodes().at("C")->getCPT();
+    REQUIRE(cpt_c_laplace.size(0) == 3);
+    auto laplace_c = std::vector<float>({ 0.3846, 0.3077, 0.3077 });
+    for (int i = 0; i < laplace_c.size(); ++i) {
+        REQUIRE(cpt_c_laplace.index({ i }).item<float>() == Catch::Approx(laplace_c[i]).margin(threshold));
+    }
+    auto cpt_a_laplace = net.getNodes().at("A")->getCPT();
+    REQUIRE(cpt_a_laplace.size(0) == 2);
+    REQUIRE(cpt_a_laplace.size(1) == 3);
+    auto laplace_a = std::vector<std::vector<float>>({ {0.3333, 0.4000,0.4000}, {0.6667,  0.6000,  0.6000} });
+    for (int i = 0; i < 2; ++i) {
+        for (int j = 0; j < 3; ++j) {
+            REQUIRE(cpt_a_laplace.index({ i, j }).item<float>() == Catch::Approx(laplace_a[i][j]).margin(threshold));
+        }
+    }
+    //
+    // Cestnik
+    //
+    net.fit(A, C, weights, { "A" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
+    auto cpt_c_cestnik = net.getNodes().at("C")->getCPT();
+    REQUIRE(cpt_c_cestnik.size(0) == 3);
+    auto cestnik_c = std::vector<float>({ 0.3939, 0.3030, 0.3030 });
+    for (int i = 0; i < laplace_c.size(); ++i) {
+        REQUIRE(cpt_c_cestnik.index({ i }).item<float>() == Catch::Approx(cestnik_c[i]).margin(threshold));
+    }
+    auto cpt_a_cestnik = net.getNodes().at("A")->getCPT();
+    REQUIRE(cpt_a_cestnik.size(0) == 2);
+    REQUIRE(cpt_a_cestnik.size(1) == 3);
+    auto cestnik_a = std::vector<std::vector<float>>({ {0.3000, 0.3750, 0.3750}, {0.7000, 0.6250, 0.6250} });
+    for (int i = 0; i < 2; ++i) {
+        for (int j = 0; j < 3; ++j) {
+            REQUIRE(cpt_a_cestnik.index({ i, j }).item<float>() == Catch::Approx(cestnik_a[i][j]).margin(threshold));
+        }
+    }
+}
+TEST_CASE("Test Smoothing B", "[Network]")
+{
+    auto net = bayesnet::Network();
+    net.addNode("X");
+    net.addNode("Y");
+    net.addNode("Z");
+    net.addNode("C");
+    net.addEdge("C", "X");
+    net.addEdge("C", "Y");
+    net.addEdge("C", "Z");
+    net.addEdge("Y", "Z");
+    std::vector<int> C = { 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1 };
+    std::vector<std::vector<int>> Data = {
+        { 0,0,1,1,0,1,0,1,0,1,0,0,0,1,0,1,0,0},
+        { 1,2,0,2,2,2,1,0,0,1,1,1,0,1,2,1,0,2},
+        { 2,1,3,3,2,0,0,1,3,2,1,2,2,3,0,0,1,2}
+    };
+    std::map<std::string, std::vector<int>> states = {
+        { "X", {0, 1} },
+        { "Y", {0, 1, 2} },
+        { "Z", {0, 1, 2, 3} },
+        { "C", {0, 1} }
+    };
+    auto weights = std::vector<double>(C.size(), 1);
+    // Simple
+    std::cout << "LAPLACE\n";
+    net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::LAPLACE);
+    std::cout << net.dump_cpt();
+    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+    std::cout << "ORIGINAL\n";
+    net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::ORIGINAL);
+    std::cout << net.dump_cpt();
+    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+    std::cout << "CESTNIK\n";
+    net.fit(Data, C, weights, { "X", "Y", "Z" }, "C", states, bayesnet::Smoothing_t::CESTNIK);
+    std::cout << net.dump_cpt();
+    std::cout << "Predict proba of {0, 1, 2} y {1, 2, 3} = " << net.predict_proba({ {0, 1}, {1, 2}, {2, 3} }) << std::endl;
+
+
+}

tests/TestBayesNode.cc

@@ -7,7 +7,9 @@
 #include <catch2/catch_test_macros.hpp>
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
+#include <catch2/matchers/catch_matchers.hpp>
 #include <string>
+#include <vector>
 #include "TestUtils.h"
 #include "bayesnet/network/Network.h"
 
@@ -48,6 +50,73 @@ TEST_CASE("Test Node children and parents", "[Node]")
     REQUIRE(parents.size() == 0);
     REQUIRE(children.size() == 0);
 }
+TEST_CASE("Test Node computeCPT", "[Node]")
+{
+    // Generate a test to test the computeCPT method of the Node class
+    // Create a dataset with 3 features and 4 samples
+    // The dataset is a 2D tensor with 4 rows and 4 columns
+    auto dataset = torch::tensor({ {1, 0, 0, 1}, {1, 1, 2, 0}, {0, 1, 2, 1}, {0, 1, 0, 1} });
+    auto states = std::vector<int>({ 2, 3, 3 });
+    // Create a vector with the names of the features
+    auto features = std::vector<std::string>{ "F1", "F2", "F3" };
+    // Create a vector with the names of the classes
+    auto className = std::string("Class");
+    // weights
+    auto weights = torch::tensor({ 1.0, 1.0, 1.0, 1.0 });
+    std::vector<bayesnet::Node> nodes;
+    for (int i = 0; i < features.size(); i++) {
+        auto node = bayesnet::Node(features[i]);
+        node.setNumStates(states[i]);
+        nodes.push_back(node);
+    }
+    nodes.push_back(bayesnet::Node(className));
+    nodes[features.size()].setNumStates(2);
+    for (int i = 0; i < features.size(); i++) {
+        // Add class node as parent of all feature nodes
+        nodes[i].addParent(&nodes[features.size()]);
+        // Node[0] -> Node[1], Node[2]
+        if (i > 0)
+            nodes[i].addParent(&nodes[0]);
+    }
+    features.push_back(className);
+    // Compute the conditional probability table
+    nodes[1].computeCPT(dataset, features, 0.0, weights);
+    // Get the conditional probability table
+    auto cpTable = nodes[1].getCPT();
+    // Get the dimensions of the conditional probability table
+    auto dimensions = cpTable.sizes();
+    // Check the dimensions of the conditional probability table
+    REQUIRE(dimensions.size() == 3);
+    REQUIRE(dimensions[0] == 3);
+    REQUIRE(dimensions[1] == 2);
+    REQUIRE(dimensions[2] == 2);
+    // Check the values of the conditional probability table
+    REQUIRE(cpTable[0][0][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][0][1].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][1][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[0][1][1].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][0][0].item<float>() == Catch::Approx(0));
+    REQUIRE(cpTable[1][0][1].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][1][0].item<float>() == Catch::Approx(1));
+    REQUIRE(cpTable[1][1][1].item<float>() == Catch::Approx(0));
+    // Compute evidence
+    for (auto& node : nodes) {
+        node.computeCPT(dataset, features, 0.0, weights);
+    }
+    auto evidence = std::map<std::string, int>{ { "F1", 0 },  { "F2", 1 }, { "F3", 1 } };
+    REQUIRE(nodes[3].getFactorValue(evidence) == 0.5);
+    // Oddities
+    auto features_back = features;
+    // Remove a parent from features
+    features.pop_back();
+    REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
+    REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature parent Class not found in dataset");
+    // Remove a feature from features
+    features = features_back;
+    features.erase(features.begin());
+    REQUIRE_THROWS_AS(nodes[0].computeCPT(dataset, features, 0.0, weights), std::logic_error);
+    REQUIRE_THROWS_WITH(nodes[0].computeCPT(dataset, features, 0.0, weights), "Feature F1 not found in dataset");
+}
 TEST_CASE("TEST MinFill method", "[Node]")
 {
     // Generate a test to test the minFill method of the Node class

tests/TestBoostA2DE.cc

@@ -0,0 +1,215 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <type_traits>
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/catch_approx.hpp>
+#include <catch2/generators/catch_generators.hpp>
+#include "bayesnet/utils/BayesMetrics.h"
+#include "bayesnet/ensembles/BoostA2DE.h"
+#include "TestUtils.h"
+
+
+TEST_CASE("Build basic model", "[BoostA2DE]")
+{
+    auto raw = RawDatasets("diabetes", true);
+    auto clf = bayesnet::BoostA2DE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.getNumberOfNodes() == 342);
+    REQUIRE(clf.getNumberOfEdges() == 684);
+    REQUIRE(clf.getNotes().size() == 3);
+    REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
+    REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 20");
+    REQUIRE(clf.getNotes()[2] == "Number of models: 38");
+    auto score = clf.score(raw.Xv, raw.yv);
+    REQUIRE(score == Catch::Approx(0.919271).epsilon(raw.epsilon));
+}
+// TEST_CASE("Feature_select IWSS", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("glass", true);
+//     auto clf = bayesnet::BoostAODE();
+//     clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
+//     clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+//     REQUIRE(clf.getNumberOfNodes() == 90);
+//     REQUIRE(clf.getNumberOfEdges() == 153);
+//     REQUIRE(clf.getNotes().size() == 2);
+//     REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
+//     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
+// }
+// TEST_CASE("Feature_select FCBF", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("glass", true);
+//     auto clf = bayesnet::BoostAODE();
+//     clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
+//     clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+//     REQUIRE(clf.getNumberOfNodes() == 90);
+//     REQUIRE(clf.getNumberOfEdges() == 153);
+//     REQUIRE(clf.getNotes().size() == 2);
+//     REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
+//     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
+// }
+// TEST_CASE("Test used features in train note and score", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("diabetes", true);
+//     auto clf = bayesnet::BoostAODE(true);
+//     clf.setHyperparameters({
+//         {"order", "asc"},
+//         {"convergence", true},
+//         {"select_features","CFS"},
+//         });
+//     clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+//     REQUIRE(clf.getNumberOfNodes() == 72);
+//     REQUIRE(clf.getNumberOfEdges() == 120);
+//     REQUIRE(clf.getNotes().size() == 2);
+//     REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
+//     REQUIRE(clf.getNotes()[1] == "Number of models: 8");
+//     auto score = clf.score(raw.Xv, raw.yv);
+//     auto scoret = clf.score(raw.Xt, raw.yt);
+//     REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
+//     REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
+// }
+// TEST_CASE("Voting vs proba", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("iris", true);
+//     auto clf = bayesnet::BoostAODE(false);
+//     clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+//     auto score_proba = clf.score(raw.Xv, raw.yv);
+//     auto pred_proba = clf.predict_proba(raw.Xv);
+//     clf.setHyperparameters({
+//         {"predict_voting",true},
+//         });
+//     auto score_voting = clf.score(raw.Xv, raw.yv);
+//     auto pred_voting = clf.predict_proba(raw.Xv);
+//     REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
+//     REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
+//     REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
+//     REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
+//     REQUIRE(clf.dump_cpt() == "");
+//     REQUIRE(clf.topological_order() == std::vector<std::string>());
+// }
+// TEST_CASE("Order asc, desc & random", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("glass", true);
+//     std::map<std::string, double> scores{
+//         {"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
+//     };
+//     for (const std::string& order : { "asc", "desc", "rand" }) {
+//         auto clf = bayesnet::BoostAODE();
+//         clf.setHyperparameters({
+//             {"order", order},
+//             {"bisection", false},
+//             {"maxTolerance", 1},
+//             {"convergence", false},
+//             });
+//         clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
+//         auto score = clf.score(raw.Xv, raw.yv);
+//         auto scoret = clf.score(raw.Xt, raw.yt);
+//         INFO("BoostAODE order: " + order);
+//         REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
+//         REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
+//     }
+// }
+// TEST_CASE("Oddities", "[BoostAODE]")
+// {
+//     auto clf = bayesnet::BoostAODE();
+//     auto raw = RawDatasets("iris", true);
+//     auto bad_hyper = nlohmann::json{
+//         { { "order", "duck" } },
+//         { { "select_features", "duck" } },
+//         { { "maxTolerance", 0 } },
+//         { { "maxTolerance", 5 } },
+//     };
+//     for (const auto& hyper : bad_hyper.items()) {
+//         INFO("BoostAODE hyper: " + hyper.value().dump());
+//         REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
+//     }
+//     REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
+//     auto bad_hyper_fit = nlohmann::json{
+//         { { "select_features","IWSS" }, { "threshold", -0.01 } },
+//         { { "select_features","IWSS" }, { "threshold", 0.51 } },
+//         { { "select_features","FCBF" }, { "threshold", 1e-8 } },
+//         { { "select_features","FCBF" }, { "threshold", 1.01 } },
+//     };
+//     for (const auto& hyper : bad_hyper_fit.items()) {
+//         INFO("BoostAODE hyper: " + hyper.value().dump());
+//         clf.setHyperparameters(hyper.value());
+//         REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing, std::invalid_argument);
+//     }
+// }
+
+// TEST_CASE("Bisection Best", "[BoostAODE]")
+// {
+//     auto clf = bayesnet::BoostAODE();
+//     auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
+//     clf.setHyperparameters({
+//         {"bisection", true},
+//         {"maxTolerance", 3},
+//         {"convergence", true},
+//         {"block_update", false},
+//         {"convergence_best", false},
+//         });
+//     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+//     REQUIRE(clf.getNumberOfNodes() == 210);
+//     REQUIRE(clf.getNumberOfEdges() == 378);
+//     REQUIRE(clf.getNotes().size() == 1);
+//     REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
+//     auto score = clf.score(raw.X_test, raw.y_test);
+//     auto scoret = clf.score(raw.X_test, raw.y_test);
+//     REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
+//     REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
+// }
+// TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
+// {
+//     auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
+//     auto clf = bayesnet::BoostAODE(true);
+//     auto hyperparameters = nlohmann::json{
+//         {"bisection", true},
+//         {"maxTolerance", 3},
+//         {"convergence", true},
+//         {"convergence_best", true},
+//     };
+//     clf.setHyperparameters(hyperparameters);
+//     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+//     auto score_best = clf.score(raw.X_test, raw.y_test);
+//     REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
+//     // Now we will set the hyperparameter to use the last accuracy
+//     hyperparameters["convergence_best"] = false;
+//     clf.setHyperparameters(hyperparameters);
+//     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+//     auto score_last = clf.score(raw.X_test, raw.y_test);
+//     REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
+// }
+
+// TEST_CASE("Block Update", "[BoostAODE]")
+// {
+//     auto clf = bayesnet::BoostAODE();
+//     auto raw = RawDatasets("mfeat-factors", true, 500);
+//     clf.setHyperparameters({
+//         {"bisection", true},
+//         {"block_update", true},
+//         {"maxTolerance", 3},
+//         {"convergence", true},
+//         });
+//     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+//     REQUIRE(clf.getNumberOfNodes() == 868);
+//     REQUIRE(clf.getNumberOfEdges() == 1724);
+//     REQUIRE(clf.getNotes().size() == 3);
+//     REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
+//     REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
+//     REQUIRE(clf.getNotes()[2] == "Number of models: 4");
+//     auto score = clf.score(raw.X_test, raw.y_test);
+//     auto scoret = clf.score(raw.X_test, raw.y_test);
+//     REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
+//     REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
+//     //
+//     // std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
+//     // std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
+//     // std::cout << "Notes size " << clf.getNotes().size() << std::endl;
+//     // for (auto note : clf.getNotes()) {
+//     //     std::cout << note << std::endl;
+//     // }
+//     // std::cout << "Score " << score << std::endl;
+// }

tests/TestBoostAODE.cc

@@ -8,6 +8,7 @@
 #include <catch2/catch_test_macros.hpp>
 #include <catch2/catch_approx.hpp>
 #include <catch2/generators/catch_generators.hpp>
+#include <catch2/matchers/catch_matchers.hpp>
 #include "bayesnet/ensembles/BoostAODE.h"
 #include "TestUtils.h"
 
@@ -17,7 +18,7 @@ TEST_CASE("Feature_select CFS", "[BoostAODE]")
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::BoostAODE();
     clf.setHyperparameters({ {"select_features", "CFS"} });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 90);
     REQUIRE(clf.getNumberOfEdges() == 153);
     REQUIRE(clf.getNotes().size() == 2);
@@ -29,7 +30,7 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::BoostAODE();
     clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 90);
     REQUIRE(clf.getNumberOfEdges() == 153);
     REQUIRE(clf.getNotes().size() == 2);
@@ -41,11 +42,11 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
     auto raw = RawDatasets("glass", true);
     auto clf = bayesnet::BoostAODE();
     clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 90);
     REQUIRE(clf.getNumberOfEdges() == 153);
     REQUIRE(clf.getNotes().size() == 2);
-    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 5 of 9 with FCBF");
+    REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
 }
 TEST_CASE("Test used features in train note and score", "[BoostAODE]")
@@ -57,7 +58,7 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
         {"convergence", true},
         {"select_features","CFS"},
         });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     REQUIRE(clf.getNumberOfNodes() == 72);
     REQUIRE(clf.getNumberOfEdges() == 120);
     REQUIRE(clf.getNotes().size() == 2);
@@ -65,14 +66,14 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
     REQUIRE(clf.getNotes()[1] == "Number of models: 8");
     auto score = clf.score(raw.Xv, raw.yv);
     auto scoret = clf.score(raw.Xt, raw.yt);
-    REQUIRE(score == Catch::Approx(0.80078).epsilon(raw.epsilon));
-    REQUIRE(scoret == Catch::Approx(0.80078).epsilon(raw.epsilon));
+    REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
 }
 TEST_CASE("Voting vs proba", "[BoostAODE]")
 {
     auto raw = RawDatasets("iris", true);
     auto clf = bayesnet::BoostAODE(false);
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
     auto score_proba = clf.score(raw.Xv, raw.yv);
     auto pred_proba = clf.predict_proba(raw.Xv);
     clf.setHyperparameters({
@@ -101,10 +102,10 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
             {"maxTolerance", 1},
             {"convergence", false},
             });
-        clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
+        clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
         auto score = clf.score(raw.Xv, raw.yv);
         auto scoret = clf.score(raw.Xt, raw.yt);
-        INFO("BoostAODE order: " + order);
+        INFO("BoostAODE order: " << order);
         REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
         REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
     }
@@ -120,7 +121,7 @@ TEST_CASE("Oddities", "[BoostAODE]")
         { { "maxTolerance", 5 } },
     };
     for (const auto& hyper : bad_hyper.items()) {
-        INFO("BoostAODE hyper: " + hyper.value().dump());
+        INFO("BoostAODE hyper: " << hyper.value().dump());
         REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
     }
     REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
@@ -131,54 +132,82 @@ TEST_CASE("Oddities", "[BoostAODE]")
         { { "select_features","FCBF" }, { "threshold", 1.01 } },
     };
     for (const auto& hyper : bad_hyper_fit.items()) {
-        INFO("BoostAODE hyper: " + hyper.value().dump());
+        INFO("BoostAODE hyper: " << hyper.value().dump());
         clf.setHyperparameters(hyper.value());
-        REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
+        REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing), std::invalid_argument);
     }
 }
 
-TEST_CASE("Bisection", "[BoostAODE]")
+TEST_CASE("Bisection Best", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
-    auto raw = RawDatasets("mfeat-factors", true);
+    auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
     clf.setHyperparameters({
         {"bisection", true},
         {"maxTolerance", 3},
         {"convergence", true},
         {"block_update", false},
+        {"convergence_best", false},
         });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
-    REQUIRE(clf.getNumberOfNodes() == 217);
-    REQUIRE(clf.getNumberOfEdges() == 431);
-    REQUIRE(clf.getNotes().size() == 3);
-    REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
-    REQUIRE(clf.getNotes()[1] == "Used features in train: 16 of 216");
-    REQUIRE(clf.getNotes()[2] == "Number of models: 1");
-    auto score = clf.score(raw.Xv, raw.yv);
-    auto scoret = clf.score(raw.Xt, raw.yt);
-    REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
-    REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
+    clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.getNumberOfNodes() == 210);
+    REQUIRE(clf.getNumberOfEdges() == 378);
+    REQUIRE(clf.getNotes().size() == 1);
+    REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
+    auto score = clf.score(raw.X_test, raw.y_test);
+    auto scoret = clf.score(raw.X_test, raw.y_test);
+    REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
+}
+TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
+{
+    auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
+    auto clf = bayesnet::BoostAODE(true);
+    auto hyperparameters = nlohmann::json{
+        {"bisection", true},
+        {"maxTolerance", 3},
+        {"convergence", true},
+        {"convergence_best", true},
+    };
+    clf.setHyperparameters(hyperparameters);
+    clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    auto score_best = clf.score(raw.X_test, raw.y_test);
+    REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
+    // Now we will set the hyperparameter to use the last accuracy
+    hyperparameters["convergence_best"] = false;
+    clf.setHyperparameters(hyperparameters);
+    clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    auto score_last = clf.score(raw.X_test, raw.y_test);
+    REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
 }
 
 TEST_CASE("Block Update", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
-    auto raw = RawDatasets("mfeat-factors", true);
+    auto raw = RawDatasets("mfeat-factors", true, 500);
     clf.setHyperparameters({
         {"bisection", true},
         {"block_update", true},
         {"maxTolerance", 3},
         {"convergence", true},
         });
-    clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
-    REQUIRE(clf.getNumberOfNodes() == 217);
-    REQUIRE(clf.getNumberOfEdges() == 431);
+    clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
+    REQUIRE(clf.getNumberOfNodes() == 868);
+    REQUIRE(clf.getNumberOfEdges() == 1724);
     REQUIRE(clf.getNotes().size() == 3);
     REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
-    REQUIRE(clf.getNotes()[1] == "Used features in train: 16 of 216");
-    REQUIRE(clf.getNotes()[2] == "Number of models: 1");
-    auto score = clf.score(raw.Xv, raw.yv);
-    auto scoret = clf.score(raw.Xt, raw.yt);
-    REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
-    REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
+    REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
+    REQUIRE(clf.getNotes()[2] == "Number of models: 4");
+    auto score = clf.score(raw.X_test, raw.y_test);
+    auto scoret = clf.score(raw.X_test, raw.y_test);
+    REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
+    REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
+    //
+    // std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
+    // std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
+    // std::cout << "Notes size " << clf.getNotes().size() << std::endl;
+    // for (auto note : clf.getNotes()) {
+    //     std::cout << note << std::endl;
+    // }
+    // std::cout << "Score " << score << std::endl;
 }

tests/TestFeatureSelection.cc

@@ -14,14 +14,15 @@
 #include "bayesnet/feature_selection/IWSS.h"
 #include "TestUtils.h"
 
-bayesnet::FeatureSelect* build_selector(RawDatasets& raw, std::string selector, double threshold)
+bayesnet::FeatureSelect* build_selector(RawDatasets& raw, std::string selector, double threshold, int max_features = 0)
 {
+    max_features = max_features == 0 ? raw.features.size() : max_features;
     if (selector == "CFS") {
-        return new bayesnet::CFS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights);
+        return new bayesnet::CFS(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights);
     } else if (selector == "FCBF") {
-        return new bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, threshold);
+        return new bayesnet::FCBF(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights, threshold);
     } else if (selector == "IWSS") {
-        return new bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, threshold);
+        return new bayesnet::IWSS(raw.dataset, raw.features, raw.className, max_features, raw.classNumStates, raw.weights, threshold);
     }
     return nullptr;
 }
@@ -80,10 +81,35 @@ TEST_CASE("Oddities", "[FeatureSelection]")
 {
     auto raw = RawDatasets("iris", true);
     // FCBF Limits
-    REQUIRE_THROWS_AS(bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 1e-8), std::invalid_argument);
-    REQUIRE_THROWS_WITH(bayesnet::FCBF(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 1e-8), "Threshold cannot be less than 1e-7");
-    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, -1e4), std::invalid_argument);
-    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, -1e4), "Threshold has to be in [0, 0.5]");
-    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 0.501), std::invalid_argument);
-    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.featuresv, raw.classNamev, raw.featuresv.size(), raw.classNumStates, raw.weights, 0.501), "Threshold has to be in [0, 0.5]");
+    REQUIRE_THROWS_AS(bayesnet::FCBF(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1e-8), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::FCBF(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 1e-8), "Threshold cannot be less than 1e-7");
+    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -1e4), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, -1e4), "Threshold has to be in [0, 0.5]");
+    REQUIRE_THROWS_AS(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 0.501), std::invalid_argument);
+    REQUIRE_THROWS_WITH(bayesnet::IWSS(raw.dataset, raw.features, raw.className, raw.features.size(), raw.classNumStates, raw.weights, 0.501), "Threshold has to be in [0, 0.5]");
+    // Not fitted error
+    auto selector = build_selector(raw, "CFS", 0);
+    const std::string message = "FeatureSelect not fitted";
+    REQUIRE_THROWS_AS(selector->getFeatures(), std::runtime_error);
+    REQUIRE_THROWS_AS(selector->getScores(), std::runtime_error);
+    REQUIRE_THROWS_WITH(selector->getFeatures(), message);
+    REQUIRE_THROWS_WITH(selector->getScores(), message);
+    delete selector;
+}
+TEST_CASE("Test threshold limits", "[FeatureSelection]")
+{
+    auto raw = RawDatasets("diabetes", true);
+    // FCBF Limits
+    auto selector = build_selector(raw, "FCBF", 0.051);
+    selector->fit();
+    REQUIRE(selector->getFeatures().size() == 2);
+    delete selector;
+    selector = build_selector(raw, "FCBF", 1e-7, 3);
+    selector->fit();
+    REQUIRE(selector->getFeatures().size() == 3);
+    delete selector;
+    selector = build_selector(raw, "IWSS", 0.5, 5);
+    selector->fit();
+    REQUIRE(selector->getFeatures().size() == 5);
+    delete selector;
 }

tests/TestModulesVersions.cc

@@ -0,0 +1,43 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <catch2/catch_test_macros.hpp>
+#include <catch2/matchers/catch_matchers.hpp>
+#include <string>
+#include <CPPFImdlp.h>
+#include <folding.hpp>
+#include <nlohmann/json.hpp>
+#define TO_STR2(x) #x
+#define TO_STR(x) TO_STR2(x)
+#define JSON_VERSION (TO_STR(NLOHMANN_JSON_VERSION_MAJOR)  "."  TO_STR(NLOHMANN_JSON_VERSION_MINOR))
+#include "TestUtils.h"
+
+std::map<std::string, std::string> modules = {
+    { "mdlp", "2.0.0" },
+    { "Folding", "1.1.0" },
+    { "json", "3.11" },
+    { "ArffFiles", "1.1.0" }
+};
+
+TEST_CASE("MDLP", "[Modules]")
+{
+    auto fimdlp = mdlp::CPPFImdlp();
+    REQUIRE(fimdlp.version() == modules["mdlp"]);
+}
+TEST_CASE("Folding", "[Modules]")
+{
+    auto folding = folding::KFold(5, 200);
+    REQUIRE(folding.version() == modules["Folding"]);
+}
+TEST_CASE("NLOHMANN_JSON", "[Modules]")
+{
+    REQUIRE(JSON_VERSION == modules["json"]);
+}
+TEST_CASE("ArffFiles", "[Modules]")
+{
+    auto handler = ArffFiles();
+    REQUIRE(handler.version() == modules["ArffFiles"]);
+}

tests/TestUtils.cc

@@ -4,6 +4,7 @@
 // SPDX-License-Identifier: MIT
 // ***************************************************************
 
+#include <random>
 #include "TestUtils.h"
 #include "bayesnet/config.h"
 
@@ -15,97 +16,110 @@ public:
     }
 };
 
-pair<std::vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<std::string> features)
+class ShuffleArffFiles : public ArffFiles {
+public:
+    ShuffleArffFiles(int num_samples = 0, bool shuffle = false) : ArffFiles(), num_samples(num_samples), shuffle(shuffle) {}
+    void load(const std::string& file_name, bool class_last = true)
+    {
+        ArffFiles::load(file_name, class_last);
+        if (num_samples > 0) {
+            if (num_samples > getY().size()) {
+                throw std::invalid_argument("num_lines must be less than the number of lines in the file");
+            }
+            auto indices = std::vector<int>(num_samples);
+            std::iota(indices.begin(), indices.end(), 0);
+            if (shuffle) {
+                std::mt19937 g{ 173 };
+                std::shuffle(indices.begin(), indices.end(), g);
+            }
+            auto XX = std::vector<std::vector<float>>(attributes.size(), std::vector<float>(num_samples));
+            auto yy = std::vector<int>(num_samples);
+            for (int i = 0; i < num_samples; i++) {
+                yy[i] = getY()[indices[i]];
+                for (int j = 0; j < attributes.size(); j++) {
+                    XX[j][i] = X[j][indices[i]];
+                }
+            }
+            X = XX;
+            y = yy;
+        }
+    }
+private:
+    int num_samples;
+    bool shuffle;
+};
+
+RawDatasets::RawDatasets(const std::string& file_name, bool discretize_, int num_samples_, bool shuffle_, bool class_last, bool debug)
 {
-    std::vector<mdlp::labels_t> Xd;
+    num_samples = num_samples_;
+    shuffle = shuffle_;
+    discretize = discretize_;
+    // Xt can be either discretized or not
+    // Xv is always discretized
+    loadDataset(file_name, class_last);
+    auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
+    dataset = torch::cat({ Xt, yresized }, 0);
+    nSamples = dataset.size(1);
+    weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
+    weightsv = std::vector<double>(nSamples, 1.0 / nSamples);
+    classNumStates = discretize ? states.at(className).size() : 0;
+    auto fold = folding::StratifiedKFold(5, yt, 271);
+    auto [train, test] = fold.getFold(0);
+    auto train_t = torch::tensor(train);
+    auto test_t = torch::tensor(test);
+    // Get train and validation sets
+    X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
+    y_train = dataset.index({ -1, train_t });
+    X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
+    y_test = dataset.index({ -1, test_t });
+    if (debug)
+        std::cout << to_string();
+}
+
+map<std::string, int> RawDatasets::discretizeDataset(std::vector<mdlp::samples_t>& X)
+{
+
     map<std::string, int> maxes;
     auto fimdlp = mdlp::CPPFImdlp();
     for (int i = 0; i < X.size(); i++) {
-        fimdlp.fit(X[i], y);
+        fimdlp.fit(X[i], yv);
         mdlp::labels_t& xd = fimdlp.transform(X[i]);
         maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
-        Xd.push_back(xd);
+        Xv.push_back(xd);
     }
-    return { Xd, maxes };
+    return maxes;
 }
 
-std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
+void RawDatasets::loadDataset(const std::string& name, bool class_last)
 {
-    std::vector<mdlp::labels_t> Xd;
-    auto fimdlp = mdlp::CPPFImdlp();
-    for (int i = 0; i < X.size(); i++) {
-        fimdlp.fit(X[i], y);
-        mdlp::labels_t& xd = fimdlp.transform(X[i]);
-        Xd.push_back(xd);
-    }
-    return Xd;
-}
-
-bool file_exists(const std::string& name)
-{
-    if (FILE* file = fopen(name.c_str(), "r")) {
-        fclose(file);
-        return true;
-    } else {
-        return false;
-    }
-}
-
-tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset)
-{
-    auto handler = ArffFiles();
+    auto handler = ShuffleArffFiles(num_samples, shuffle);
     handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", class_last);
     // Get Dataset X, y
     std::vector<mdlp::samples_t>& X = handler.getX();
-    mdlp::labels_t& y = handler.getY();
+    yv = handler.getY();
     // Get className & Features
-    auto className = handler.getClassName();
-    std::vector<std::string> features;
-    auto attributes = handler.getAttributes();
-    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
-    torch::Tensor Xd;
-    auto states = map<std::string, std::vector<int>>();
-    if (discretize_dataset) {
-        auto Xr = discretizeDataset(X, y);
-        Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
-        for (int i = 0; i < features.size(); ++i) {
-            states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
-            auto item = states.at(features[i]);
-            iota(begin(item), end(item), 0);
-            Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
-        }
-        states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
-        iota(begin(states.at(className)), end(states.at(className)), 0);
-    } else {
-        Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
-        for (int i = 0; i < features.size(); ++i) {
-            Xd.index_put_({ i, "..." }, torch::tensor(X[i]));
-        }
-    }
-    return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
-}
-
-tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name)
-{
-    auto handler = ArffFiles();
-    handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff");
-    // Get Dataset X, y
-    std::vector<mdlp::samples_t>& X = handler.getX();
-    mdlp::labels_t& y = handler.getY();
-    // Get className & Features
-    auto className = handler.getClassName();
-    std::vector<std::string> features;
+    className = handler.getClassName();
     auto attributes = handler.getAttributes();
     transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
     // Discretize Dataset
-    std::vector<mdlp::labels_t> Xd;
-    map<std::string, int> maxes;
-    tie(Xd, maxes) = discretize(X, y, features);
-    maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    map<std::string, std::vector<int>> states;
-    for (auto feature : features) {
-        states[feature] = std::vector<int>(maxes[feature]);
+    auto maxValues = discretizeDataset(X);
+    maxValues[className] = *max_element(yv.begin(), yv.end()) + 1;
+    if (discretize) {
+        // discretize the tensor as well
+        Xt = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, torch::kInt32);
+        for (int i = 0; i < features.size(); ++i) {
+            states[features[i]] = std::vector<int>(maxValues[features[i]]);
+            iota(begin(states.at(features[i])), end(states.at(features[i])), 0);
+            Xt.index_put_({ i, "..." }, torch::tensor(Xv[i], torch::kInt32));
+        }
+        states[className] = std::vector<int>(maxValues[className]);
+        iota(begin(states.at(className)), end(states.at(className)), 0);
+    } else {
+        Xt = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
+        for (int i = 0; i < features.size(); ++i) {
+            Xt.index_put_({ i, "..." }, torch::tensor(X[i]));
+        }
     }
-    states[className] = std::vector<int>(maxes[className]);
-    return { Xd, y, features, className, states };
+    yt = torch::tensor(yv, torch::kInt32);
 }
+

tests/TestUtils.h

@@ -11,39 +11,62 @@
 #include <vector>
 #include <map>
 #include <tuple>
-#include <ArffFiles.h>
+#include <ArffFiles.hpp>
 #include <CPPFImdlp.h>
+#include <folding.hpp>
+#include <bayesnet/network/Network.h>
 
-bool file_exists(const std::string& name);
-std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features);
-std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name);
-std::tuple<torch::Tensor, torch::Tensor, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset);
 
 class RawDatasets {
 public:
-    RawDatasets(const std::string& file_name, bool discretize)
-    {
-        // Xt can be either discretized or not
-        tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
-        // Xv is always discretized
-        tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
-        auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
-        dataset = torch::cat({ Xt, yresized }, 0);
-        nSamples = dataset.size(1);
-        weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
-        weightsv = std::vector<double>(nSamples, 1.0 / nSamples);
-        classNumStates = discretize ? statest.at(classNamet).size() : 0;
-    }
+    RawDatasets(const std::string& file_name, bool discretize_, int num_samples_ = 0, bool shuffle_ = false, bool class_last = true, bool debug = false);
     torch::Tensor Xt, yt, dataset, weights;
+    torch::Tensor X_train, y_train, X_test, y_test;
     std::vector<vector<int>> Xv;
-    std::vector<double> weightsv;
     std::vector<int> yv;
-    std::vector<string> featurest, featuresv;
-    map<std::string, std::vector<int>> statest, statesv;
-    std::string classNamet, classNamev;
+    std::vector<double> weightsv;
+    std::vector<string> features;
+    std::string className;
+    map<std::string, std::vector<int>> states;
     int nSamples, classNumStates;
     double epsilon = 1e-5;
+    bool discretize;
+    int num_samples = 0;
+    bool shuffle = false;
+    bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::ORIGINAL;
+private:
+    std::string to_string()
+    {
+        std::string features_ = "";
+        for (auto& f : features) {
+            features_ += f + " ";
+        }
+        std::string states_ = "";
+        for (auto& s : states) {
+            states_ += s.first + " ";
+            for (auto& v : s.second) {
+                states_ += std::to_string(v) + " ";
+            }
+            states_ += "\n";
+        }
+        return "Xt dimensions: " + std::to_string(Xt.size(0)) + " " + std::to_string(Xt.size(1)) + "\n"
+            "Xv dimensions: " + std::to_string(Xv.size()) + " " + std::to_string(Xv[0].size()) + "\n"
+            + "yt dimensions: " + std::to_string(yt.size(0)) + "\n"
+            + "yv dimensions: " + std::to_string(yv.size()) + "\n"
+            + "X_train dimensions: " + std::to_string(X_train.size(0)) + " " + std::to_string(X_train.size(1)) + "\n"
+            + "X_test dimensions: " + std::to_string(X_test.size(0)) + " " + std::to_string(X_test.size(1)) + "\n"
+            + "y_train dimensions: " + std::to_string(y_train.size(0)) + "\n"
+            + "y_test dimensions: " + std::to_string(y_test.size(0)) + "\n"
+            + "features: " + std::to_string(features.size()) + "\n"
+            + features_ + "\n"
+            + "className: " + className + "\n"
+            + "states: " + std::to_string(states.size()) + "\n"
+            + "nSamples: " + std::to_string(nSamples) + "\n"
+            + "classNumStates: " + std::to_string(classNumStates) + "\n"
+            + "states: " + states_ + "\n";
+    }
+    map<std::string, int> discretizeDataset(std::vector<mdlp::samples_t>& X);
+    void loadDataset(const std::string& name, bool class_last);
 };
 
 #endif //TEST_UTILS_H

tests/Timer.h

@@ -0,0 +1,41 @@
+#pragma once
+#include <chrono>
+#include <string>
+#include <sstream>
+
+namespace platform {
+    class Timer {
+    private:
+        std::chrono::high_resolution_clock::time_point begin;
+        std::chrono::high_resolution_clock::time_point end;
+    public:
+        Timer() = default;
+        ~Timer() = default;
+        void start() { begin = std::chrono::high_resolution_clock::now(); }
+        void stop() { end = std::chrono::high_resolution_clock::now(); }
+        double getDuration()
+        {
+            stop();
+            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (end - begin);
+            return time_span.count();
+        }
+        double getLapse()
+        {
+            std::chrono::duration<double> time_span = std::chrono::duration_cast<std::chrono::duration<double >> (std::chrono::high_resolution_clock::now() - begin);
+            return time_span.count();
+        }
+        std::string getDurationString(bool lapse = false)
+        {
+            double duration = lapse ? getLapse() : getDuration();
+            return translate2String(duration);
+        }
+        std::string translate2String(double duration)
+        {
+            double durationShow = duration > 3600 ? duration / 3600 : duration > 60 ? duration / 60 : duration;
+            std::string durationUnit = duration > 3600 ? "h" : duration > 60 ? "m" : "s";
+            std::stringstream ss;
+            ss << std::setprecision(2) << std::fixed << durationShow << " " << durationUnit;
+            return ss.str();
+        }
+    };
+} /* namespace platform */

update_coverage.py

@@ -11,24 +11,27 @@ readme_file = "README.md"
 print("Updating coverage...")
 # Generate badge line
 output = subprocess.check_output(
-    "lcov --summary " + sys.argv[1] + "/coverage.info|cut -d' ' -f4 |head -2|"
-    "tail -1",
+    "lcov --summary " + sys.argv[1] + "/coverage.info",
     shell=True,
 )
-value = float(output.decode("utf-8").strip().replace("%", ""))
-if value < 90:
+value = output.decode("utf-8").strip()
+percentage = 0
+for line in value.splitlines():
+    if "lines" in line:
+        percentage = float(line.split(":")[1].split("%")[0])
+        break
+print(f"Coverage: {percentage}%")
+if percentage < 90:
     print("⛔Coverage is less than 90%. I won't update the badge.")
     sys.exit(1)
-percentage = output.decode("utf-8").strip().replace(".", ",")
-coverage_line = (
-    f"![Static Badge](https://img.shields.io/badge/Coverage-{percentage}25-green)"
-)
+percentage_label = str(percentage).replace('.', ',')
+coverage_line = f"[![Coverage Badge](https://img.shields.io/badge/Coverage-{percentage_label}%25-green)](html/index.html)"
 # Update README.md
 with open(readme_file, "r") as f:
     lines = f.readlines()
 with open(readme_file, "w") as f:
     for line in lines:
-        if "Coverage" in line:
+        if "img.shields.io/badge/Coverage" in line:
             f.write(coverage_line + "\n")
         else:
             f.write(line)