Fix diagram size in manual

There were cases where a tiny negative number was returned (less than -1e-7)
Fix mst glass test that is affected with this change

.clang-uml

@@ -0,0 +1,39 @@
+compilation_database_dir: build_debug
+output_directory: diagrams
+diagrams:
+  BayesNet:
+    type: class
+    glob:
+      - bayesnet/*.h
+      - bayesnet/classifiers/*.h
+      - bayesnet/classifiers/*.cc
+      - bayesnet/ensembles/*.h
+      - bayesnet/ensembles/*.cc
+      - bayesnet/feature_selection/*.h
+      - bayesnet/feature_selection/*.cc
+      - bayesnet/network/*.h
+      - bayesnet/network/*.cc
+      - bayesnet/utils/*.h
+      - bayesnet/utils/*.cc
+    include:
+      # Only include entities from the following namespaces
+      namespaces:
+        - bayesnet
+    exclude:
+      access:
+        - private
+    plantuml:
+      style:
+        # Apply this style to all classes in the diagram
+        class: "#aliceblue;line:blue;line.dotted;text:blue"
+        # Apply this style to all packages in the diagram
+        package: "#back:grey"
+        # Make all template instantiation relations point upwards and draw them
+        # as green and dotted lines
+        instantiation: "up[#green,dotted]"
+      cmd: "/usr/bin/plantuml -tsvg \"diagrams/{}.puml\""
+      before:
+        - 'title clang-uml class diagram model'
+    mermaid:
+      before:
+        - 'classDiagram'

.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

.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/c_cpp_properties.json

@@ -3,15 +3,47 @@
         {
             "name": "Mac",
             "includePath": [
-                "${workspaceFolder}/**"
+                "/Users/rmontanana/Code/BayesNet/**"
             ],
             "defines": [],
             "macFrameworkPath": [
-                "/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks"
+                "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include"
             ],
             "cStandard": "c17",
             "cppStandard": "c++17",
-            "compileCommands": "${workspaceFolder}/cmake-build-release/compile_commands.json"
+            "compileCommands": "",
+            "intelliSenseMode": "macos-clang-arm64",
+            "mergeConfigurations": false,
+            "browse": {
+                "path": [
+                    "/Users/rmontanana/Code/BayesNet/**",
+                    "${workspaceFolder}"
+                ],
+                "limitSymbolsToIncludedHeaders": true
+            },
+            "configurationProvider": "ms-vscode.cmake-tools"
+        },
+        {
+            "name": "Linux",
+            "includePath": [
+                "/home/rmontanana/Code/BayesNet/**",
+                "/home/rmontanana/Code/libtorch/include/torch/csrc/api/include/",
+                "/home/rmontanana/Code/BayesNet/lib/"
+            ],
+            "defines": [],
+            "cStandard": "c17",
+            "cppStandard": "c++17",
+            "intelliSenseMode": "linux-gcc-x64",
+            "mergeConfigurations": false,
+            "compilerPath": "/usr/bin/g++",
+            "browse": {
+                "path": [
+                    "/home/rmontanana/Code/BayesNet/**",
+                    "${workspaceFolder}"
+                ],
+                "limitSymbolsToIncludedHeaders": true
+            },
+            "configurationProvider": "ms-vscode.cmake-tools"
         }
     ],
     "version": 4

.vscode/launch.json

@@ -8,18 +8,41 @@
             "program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
             "args": [
                 "${workspaceFolder}/tests/data/glass.arff"
-            ],
+            ]
         },
         {
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests_bayesnet",
+            "program": "${workspaceFolder}/build_debug/tests/TestBayesNet",
             "args": [
-                //"-c=\"Metrics Test\"",
-                // "-s",
+                "[Node]"
             ],
-            "cwd": "${workspaceFolder}/build_debug/tests",
+            "cwd": "${workspaceFolder}/build_debug/tests"
+        },
+        {
+            "name": "(gdb) Launch",
+            "type": "cppdbg",
+            "request": "launch",
+            "program": "enter program name, for example ${workspaceFolder}/a.out",
+            "args": [],
+            "stopAtEntry": false,
+            "cwd": "${fileDirname}",
+            "environment": [],
+            "externalConsole": false,
+            "MIMode": "gdb",
+            "setupCommands": [
+                {
+                    "description": "Enable pretty-printing for gdb",
+                    "text": "-enable-pretty-printing",
+                    "ignoreFailures": true
+                },
+                {
+                    "description": "Set Disassembly Flavor to Intel",
+                    "text": "-gdb-set disassembly-flavor intel",
+                    "ignoreFailures": true
+                }
+            ]
         }
     ]
 }

CHANGELOG.md

@@ -5,22 +5,64 @@ 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]
+## [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.
+
+### 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
 
 - Install command and instructions in README.md
+- Prefix to install command to install the package in the any location.
+- The 'block_update' hyperparameter to the BoostAODE class, to control the way weights/significances are updated. Default value is false.
+- Html report of coverage in the coverage folder. It is created with *make viewcoverage*
+- Badges of coverage and code quality (codacy) in README.md. Coverage badge is updated with *make viewcoverage*
+- Tests to reach 97% of coverage.
+- Copyright header to source files.
+- Diagrams to README.md: UML class diagram & dependency diagram
+- Action to create diagrams to Makefile: *make diagrams*
 
 ### Changed
 
 - Sample app now is a separate target in the Makefile and shows how to use the library with a sample dataset
+- 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
 
-- Change _ascending_ hyperparameter to _order_ with these possible values _{"asc", "desc", "rand"}_, Default is _"desc"_.
-- Add the _predict_single_ hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true.
+- Change *ascending* hyperparameter to *order* with these possible values *{"asc", "desc", "rand"}*, Default is *"desc"*.
+- Add the *predict_single* hyperparameter to control if only the last model created is used to predict in boost training or the whole ensemble (all the models built so far). Default is true.
 - sample app to show how to use the library (make sample)
 
 ### Changed

CMakeGraphVizOptions.cmake

@@ -0,0 +1,5 @@
+# Set the default graph title
+set(GRAPHVIZ_GRAPH_NAME "BayesNet dependency graph")
+
+set(GRAPHVIZ_SHARED_LIBS OFF)
+set(GRAPHVIZ_STATIC_LIBS ON)

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.20)
 
 project(BayesNet
-  VERSION 1.0.4
+  VERSION 1.0.5.1
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
@@ -25,6 +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 "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+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)
@@ -48,7 +54,6 @@ if (CODE_COVERAGE)
   enable_testing()
   include(CodeCoverage)
   MESSAGE("Code coverage enabled")
-  set(CMAKE_CXX_FLAGS " ${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -O0 -g")
   SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
@@ -59,20 +64,19 @@ 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")  
+  add_subdirectory(tests/lib/catch2)
   include(CTest)
   add_subdirectory(tests)
 endif (ENABLE_TESTING)
@@ -85,3 +89,14 @@ 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})

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) <year> <copyright holders>
+Copyright (c) 2023 Ricardo Montañana Gómez
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 

Makefile

@@ -1,12 +1,23 @@
 SHELL := /bin/bash
 .DEFAULT_GOAL := help
-.PHONY: coverage setup help buildr buildd test clean debug release sample
+.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 = unit_tests_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 $(test_targets); do \
@@ -29,12 +40,23 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@if [ "$(shell uname)" = "Linux" ]; then \
 		pip install gcovr; \
+		sudo dnf install lcov;\
 	fi
+	@echo "* You should install plantuml & graphviz for the diagrams"
 
-dependency: ## Create a dependency graph diagram of the project (build/dependency.png)
+diagrams: ## Create an UML class diagram & depnendency of the project (diagrams/BayesNet.png)
+	@which $(plantuml) || (echo ">>> Please install plantuml"; exit 1)
+	@which $(dot) || (echo ">>> Please install graphviz"; exit 1)
+	@which $(clang-uml) || (echo ">>> Please install clang-uml"; exit 1)
+	@export PLANTUML_LIMIT_SIZE=16384
+	@echo ">>> Creating UML class diagram of the project...";
+	@$(clang-uml) -p 
+	@cd $(f_diagrams); \
+	$(plantuml) -tsvg BayesNet.puml
 	@echo ">>> Creating dependency graph diagram of the project...";
 	$(MAKE) debug
-	cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
+	cd $(f_debug) && cmake .. --graphviz=dependency.dot 
+	@$(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)
@@ -52,9 +74,10 @@ uninstall: ## Uninstall library
 	xargs rm < $(f_release)/install_manifest.txt
 	@echo ">>> Done";
 
+prefix = "/usr/local"
 install: ## Install library
 	@echo ">>> Installing BayesNet...";
-	@cmake --install $(f_release)
+	@cmake --install $(f_release) --prefix $(prefix)
 	@echo ">>> Done";
 
 debug: ## Build a debug version of the project
@@ -81,23 +104,84 @@ 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)
 	@for t in $(test_targets); do \
+		echo ">>> Running $$t...";\
 		if [ -f $(f_debug)/tests/$$t ]; then \
 			cd $(f_debug)/tests ; \
 			./$$t $(opt) ; \
+			cd ../.. ; \
 		fi ; \
 	done
 	@echo ">>> Done";
 
 coverage: ## Run tests and generate coverage report (build/index.html)
 	@echo ">>> Building tests with coverage..."
-	@$(MAKE) test
-	@gcovr $(f_debug)/tests
+	@which $(lcov) || (echo ">>> Please install lcov"; exit 1)
+	@if [ ! -f $(f_debug)/tests/coverage.info ] ; then $(MAKE) test ; fi
+	@echo ">>> Building report..."
+	@cd $(f_debug)/tests; \
+	$(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
 	@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' ; \

README.md

@@ -1,10 +1,32 @@
-# BayesNet
+# <img src="logo.png" alt="logo" width="50"/>  BayesNet
 
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](<https://opensource.org/licenses/MIT>)
+![Gitea 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)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-97,3%25-green)](html/index.html)
 
 Bayesian Network Classifiers using libtorch from scratch
 
-## Installation
+## Dependencies
+
+The only external dependency is [libtorch](https://pytorch.org/cppdocs/installing.html) which can be installed with the following commands:
+
+```bash
+wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
+unzip libtorch-shared-with-deps-latest.zips
+```
+
+## Setup
+
+### Getting the code
+
+```bash
+git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
+```
 
 ### Release
 
@@ -19,7 +41,13 @@ sudo make install
 ```bash
 make debug
 make test
+```
+
+### Coverage
+
+```bash
 make coverage
+make viewcoverage
 ```
 
 ### Sample app
@@ -33,4 +61,42 @@ 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
+
+### UML Class Diagram
+
+![BayesNet UML Class Diagram](diagrams/BayesNet.svg)
+
+### Dependency Diagram
+
+![BayesNet Dependency Diagram](diagrams/dependency.svg)

bayesnet/BaseClassifier.h

@@ -1,5 +1,10 @@
-#ifndef BASE_H
-#define BASE_H
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#pragma once
 #include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
@@ -30,7 +35,7 @@ namespace bayesnet {
         virtual std::string getVersion() = 0;
         std::vector<std::string> virtual topological_order() = 0;
         std::vector<std::string> virtual getNotes() const = 0;
-        void virtual dump_cpt()const = 0;
+        std::string virtual dump_cpt()const = 0;
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
     protected:
@@ -38,4 +43,3 @@ namespace bayesnet {
         std::vector<std::string> validHyperparameters;
     };
 }
-#endif

bayesnet/CMakeLists.txt

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

bayesnet/classifiers/Classifier.cc

@@ -1,3 +1,10 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
 
@@ -10,7 +17,7 @@ namespace bayesnet {
         this->className = className;
         this->states = states;
         m = dataset.size(1);
-        n = dataset.size(0) - 1;
+        n = features.size();
         checkFitParameters();
         auto n_classes = states.at(className).size();
         metrics = Metrics(dataset, features, className, n_classes);
@@ -27,10 +34,11 @@ namespace bayesnet {
             dataset = torch::cat({ dataset, yresized }, 0);
         }
         catch (const std::exception& e) {
-            std::cerr << e.what() << '\n';
-            std::cout << "X dimensions: " << dataset.sizes() << "\n";
-            std::cout << "y dimensions: " << ytmp.sizes() << "\n";
-            exit(1);
+            std::stringstream oss;
+            oss << "* Error in X and y dimensions *\n";
+            oss << "X dimensions: " << dataset.sizes() << "\n";
+            oss << "y dimensions: " << ytmp.sizes();
+            throw std::runtime_error(oss.str());
         }
     }
     void Classifier::trainModel(const torch::Tensor& weights)
@@ -73,11 +81,11 @@ namespace bayesnet {
         if (torch::is_floating_point(dataset)) {
             throw std::invalid_argument("dataset (X, y) must be of type Integer");
         }
-        if (n != features.size()) {
-            throw std::invalid_argument("Classifier: X " + std::to_string(n) + " and features " + std::to_string(features.size()) + " must have the same number of features");
+        if (dataset.size(0) - 1 != features.size()) {
+            throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
-            throw std::invalid_argument("className not found in states");
+            throw std::invalid_argument("class name not found in states");
         }
         for (auto feature : features) {
             if (states.find(feature) == states.end()) {
@@ -173,12 +181,14 @@ namespace bayesnet {
     {
         return model.topological_sort();
     }
-    void Classifier::dump_cpt() const
+    std::string Classifier::dump_cpt() const
     {
-        model.dump_cpt();
+        return model.dump_cpt();
     }
     void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
     {
-        //For classifiers that don't have hyperparameters
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
     }
 }

bayesnet/classifiers/Classifier.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef CLASSIFIER_H
 #define CLASSIFIER_H
 #include <torch/torch.h>
@@ -30,7 +36,7 @@ namespace bayesnet {
         std::vector<std::string> show() const override;
         std::vector<std::string> topological_order()  override;
         std::vector<std::string> getNotes() const override { return notes; }
-        void dump_cpt() const override;
+        std::string dump_cpt() const override;
         void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
     protected:
         bool fitted;

bayesnet/classifiers/KDB.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDB.h"
 
 namespace bayesnet {
@@ -6,14 +12,18 @@ namespace bayesnet {
         validHyperparameters = { "k", "theta" };
 
     }
-    void KDB::setHyperparameters(const nlohmann::json& hyperparameters)
+    void KDB::setHyperparameters(const nlohmann::json& hyperparameters_)
     {
+        auto hyperparameters = hyperparameters_;
         if (hyperparameters.contains("k")) {
             k = hyperparameters["k"];
+            hyperparameters.erase("k");
         }
         if (hyperparameters.contains("theta")) {
             theta = hyperparameters["theta"];
+            hyperparameters.erase("theta");
         }
+        Classifier::setHyperparameters(hyperparameters);
     }
     void KDB::buildModel(const torch::Tensor& weights)
     {

bayesnet/classifiers/KDB.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef KDB_H
 #define KDB_H
 #include <torch/torch.h>
@@ -14,7 +20,7 @@ namespace bayesnet {
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() = default;
-        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
     };
 }

bayesnet/classifiers/KDBLd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDBLd.h"
 
 namespace bayesnet {

bayesnet/classifiers/KDBLd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef KDBLD_H
 #define KDBLD_H
 #include "Proposal.h"

bayesnet/classifiers/Proposal.cc

@@ -1,4 +1,9 @@
-#include <ArffFiles.h>
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Proposal.h"
 
 namespace bayesnet {
@@ -48,8 +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);
@@ -107,4 +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

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef PROPOSAL_H
 #define PROPOSAL_H
 #include <string>
@@ -21,6 +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/SPODE.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODE.h"
 
 namespace bayesnet {

bayesnet/classifiers/SPODE.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef SPODE_H
 #define SPODE_H
 #include "Classifier.h"

bayesnet/classifiers/SPODELd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODELd.h"
 
 namespace bayesnet {
@@ -5,25 +11,23 @@ namespace bayesnet {
     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_)
     {
         checkInput(X_, y_);
-        features = features_;
-        className = className_;
         Xf = X_;
         y = y_;
-        // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
-        states = fit_local_discretization(y);
-        // We have discretized the input data
-        // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
-        SPODE::fit(dataset, features, className, states);
-        states = localDiscretizationProposal(states, model);
-        return *this;
+        return commonFit(features_, className_, states_);
     }
+
     SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
     {
         if (!torch::is_floating_point(dataset)) {
             throw std::runtime_error("Dataset must be a floating point tensor");
         }
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
-        y = dataset.index({ -1, "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_);
+    }
+
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    {
         features = features_;
         className = className_;
         // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
@@ -34,7 +38,6 @@ namespace bayesnet {
         states = localDiscretizationProposal(states, model);
         return *this;
     }
-
     torch::Tensor SPODELd::predict(torch::Tensor& X)
     {
         auto Xt = prepareX(X);

bayesnet/classifiers/SPODELd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef SPODELD_H
 #define SPODELD_H
 #include "SPODE.h"
@@ -10,6 +16,7 @@ namespace bayesnet {
         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;
         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/TAN.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TAN.h"
 
 namespace bayesnet {

bayesnet/classifiers/TAN.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TAN_H
 #define TAN_H
 #include "Classifier.h"

bayesnet/classifiers/TANLd.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TANLd.h"
 
 namespace bayesnet {

bayesnet/classifiers/TANLd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TANLD_H
 #define TANLD_H
 #include "TAN.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/AODE.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "AODE.h"
 
 namespace bayesnet {
@@ -13,9 +19,7 @@ namespace bayesnet {
             predict_voting = hyperparameters["predict_voting"];
             hyperparameters.erase("predict_voting");
         }
-        if (!hyperparameters.empty()) {
-            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
-        }
+        Classifier::setHyperparameters(hyperparameters);
     }
     void AODE::buildModel(const torch::Tensor& weights)
     {

bayesnet/ensembles/AODE.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef AODE_H
 #define AODE_H
 #include "bayesnet/classifiers/SPODE.h"
@@ -5,7 +11,7 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     public:
-        AODE(bool predict_voting = true);
+        AODE(bool predict_voting = false);
         virtual ~AODE() {};
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
         std::vector<std::string> graph(const std::string& title = "AODE") const override;

bayesnet/ensembles/AODELd.cc

@@ -1,21 +1,14 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "AODELd.h"
 
 namespace bayesnet {
     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
     {
-        validHyperparameters = { "predict_voting" };
-
-    }
-    void AODELd::setHyperparameters(const nlohmann::json& hyperparameters_)
-    {
-        auto hyperparameters = hyperparameters_;
-        if (hyperparameters.contains("predict_voting")) {
-            predict_voting = hyperparameters["predict_voting"];
-            hyperparameters.erase("predict_voting");
-        }
-        if (!hyperparameters.empty()) {
-            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
-        }
     }
     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_)
     {

bayesnet/ensembles/AODELd.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef AODELD_H
 #define AODELD_H
 #include "bayesnet/classifiers/Proposal.h"
@@ -10,7 +16,6 @@ namespace bayesnet {
         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;
-        void setHyperparameters(const nlohmann::json& hyperparameters) override;
         std::vector<std::string> graph(const std::string& name = "AODELd") const override;
     protected:
         void trainModel(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()
+    {
+        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_);
+                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)
+    {
+        //
+        // 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();
+            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_);
+                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) override;
+    private:
+        std::vector<int> initializeModels();
+    };
+}
+#endif

bayesnet/ensembles/BoostAODE.cc

@@ -1,293 +1,158 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// 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 {
-    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;
-    BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
-    {
-        validHyperparameters = {
-            "repeatSparent", "maxModels", "order", "convergence", "threshold",
-            "select_features", "tolerance", "predict_voting", "predict_single"
-        };
 
-    }
-    void BoostAODE::buildModel(const torch::Tensor& weights)
+    BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
     {
-        // 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);
-            dataset_ = torch::clone(dataset);
-            // save input dataset
-            auto [train, test] = fold.getFold(0);
-            auto train_t = torch::tensor(train);
-            auto test_t = torch::tensor(test);
-            // Get train and validation sets
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-            y_train = dataset.index({ -1, train_t });
-            X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-            y_test = dataset.index({ -1, test_t });
-            dataset = X_train;
-            m = X_train.size(1);
-            auto n_classes = states.at(className).size();
-            metrics = Metrics(dataset, features, className, n_classes);
-            // Build dataset with train data
-            buildDataset(y_train);
-        } else {
-            // Use all data to train
-            X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-            y_train = y_;
     }
-    }
-    void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    std::vector<int> BoostAODE::initializeModels()
     {
-        auto hyperparameters = hyperparameters_;
-        if (hyperparameters.contains("repeatSparent")) {
-            repeatSparent = hyperparameters["repeatSparent"];
-            hyperparameters.erase("repeatSparent");
-        }
-        if (hyperparameters.contains("maxModels")) {
-            maxModels = hyperparameters["maxModels"];
-            hyperparameters.erase("maxModels");
-        }
-        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("predict_single")) {
-            predict_single = hyperparameters["predict_single"];
-            hyperparameters.erase("predict_single");
-        }
-        if (hyperparameters.contains("threshold")) {
-            threshold = hyperparameters["threshold"];
-            hyperparameters.erase("threshold");
-        }
-        if (hyperparameters.contains("tolerance")) {
-            tolerance = hyperparameters["tolerance"];
-            hyperparameters.erase("tolerance");
-        }
-        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.empty()) {
-            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
-        }
-    }
-    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::unordered_set<int> BoostAODE::initializeModels()
-    {
-        std::unordered_set<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();
-        for (const int& feature : cfsFeatures) {
-            featuresUsed.insert(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_);
             models.push_back(std::move(model));
-            significanceModels.push_back(1.0);
+            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;
-    }
-    torch::Tensor BoostAODE::ensemble_predict(torch::Tensor& X, SPODE* model)
-    {
-        if (initialize_prob_table) {
-            initialize_prob_table = false;
-            prob_table = model->predict_proba(X) * 1.0;
-        } else {
-            prob_table += model->predict_proba(X) * 1.0;
-        }
-        // prob_table doesn't store probabilities but the sum of them
-        // to have them we need to divide by the sum of the "weights" used to 
-        // consider the results obtanined in the model's predict_proba.
-        return prob_table.argmax(1);
+        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)
     {
+        //
+        // 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)
-        initialize_prob_table = true;
         fitted = true;
         double alpha_t = 0;
         torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-        bool exitCondition = false;
-        std::unordered_set<int> featuresUsed;
+        bool finished = false;
+        std::vector<int> featuresUsed;
         if (selectFeatures) {
             featuresUsed = initializeModels();
             auto ypred = predict(X_train);
-            std::tie(weights_, alpha_t, exitCondition) = update_weights(y_train, ypred, weights_);
+            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 (exitCondition) {
+            if (finished) {
                 return;
             }
         }
-        bool resetMaxModels = false;
-        if (maxModels == 0) {
-            maxModels = .1 * n > 10 ? .1 * n : n;
-            resetMaxModels = true; // Flag to unset maxModels
-        }
+        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 delta = 1.0;
+        double improvement = 1.0;
         double convergence_threshold = 1e-4;
-        int count = 0; // number of times the accuracy is lower than the convergence_threshold
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
         // Step 0: Set the finish condition
-        // if not repeatSparent a finish condition is run out of features
-        // n_models == maxModels
         // 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 };
-        while (!exitCondition) {
+        while (!finished) {
             // Step 1: Build ranking with mutual information
             auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
             if (order_algorithm == Orders.RAND) {
                 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
             }
+            // Remove used features
+            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
+                { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
+                end(featureSelection)
+            );
+            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];
-            if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
-                bool used = true;
-                for (const auto& feat : featureSelection) {
-                    if (std::find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
-                        continue;
-                    }
-                    used = false;
-                    feature = feat;
-                    break;
-                }
-                if (used) {
-                    exitCondition = true;
-                    continue;
-                }
-            }
+                featureSelection.erase(featureSelection.begin());
                 std::unique_ptr<Classifier> model;
                 model = std::make_unique<SPODE>(feature);
                 model->fit(dataset, features, className, states, weights_);
-            torch::Tensor ypred;
-            if (predict_single) {
-                ypred = model->predict(X_train);
-            } else {
-                ypred = ensemble_predict(X_train, dynamic_cast<SPODE*>(model.get()));
-            }
+                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, exitCondition) = update_weights(y_train, ypred, weights_);
-            if (exitCondition) {
-                break;
+                    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
-            featuresUsed.insert(feature);
+                numItemsPack++;
+                featuresUsed.push_back(feature);
                 models.push_back(std::move(model));
                 significanceModels.push_back(alpha_t);
                 n_models++;
-            if (convergence) {
+                // 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 {
-                    delta = accuracy - priorAccuracy;
+                    improvement = accuracy - priorAccuracy;
                 }
-                if (delta < convergence_threshold) {
-                    count++;
+                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;
                 }
-            exitCondition = n_models >= maxModels && repeatSparent || count > tolerance;
+            }
+            // 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();
+                    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 (featuresUsed.size() != features.size()) {
             notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
             status = WARNING;
         }
         notes.push_back("Number of models: " + std::to_string(n_models));
-        if (resetMaxModels) {
-            maxModels = 0;
-        }
     }
     std::vector<std::string> BoostAODE::graph(const std::string& title) const
     {

bayesnet/ensembles/BoostAODE.h

@@ -1,37 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #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 {
-    class BoostAODE : public Ensemble {
+    class BoostAODE : public Boost {
     public:
-        BoostAODE(bool predict_voting = true);
+        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;
     private:
-        std::unordered_set<int> initializeModels();
-        torch::Tensor ensemble_predict(torch::Tensor& X, SPODE* model);
-        torch::Tensor dataset_;
-        torch::Tensor X_train, y_train, X_test, y_test;
-        // Hyperparameters
-        bool repeatSparent = false; // if true, a feature can be selected more than once
-        int maxModels = 0;
-        int tolerance = 0;
-        bool predict_single = true; // wether the last model is used to predict in training or the whole ensemble
-        std::string order_algorithm; // order to process the KBest features asc, desc, rand
-        bool convergence = false; //if true, stop when the model does not improve
-        bool selectFeatures = false; // if true, use feature selection
-        std::string select_features_algorithm = "desc"; // Selected feature selection algorithm
-        bool initialize_prob_table; // if true, initialize the prob_table with the first model (used in train)
-        torch::Tensor prob_table; // Table of probabilities for ensemble predicting if predict_single is false
-        FeatureSelect* featureSelector = nullptr;
-        double threshold = -1;
+        std::vector<int> initializeModels();
     };
 }
 #endif

bayesnet/ensembles/Ensemble.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Ensemble.h"
 
 namespace bayesnet {

bayesnet/ensembles/Ensemble.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef ENSEMBLE_H
 #define ENSEMBLE_H
 #include <torch/torch.h>
@@ -25,8 +31,9 @@ namespace bayesnet {
         {
             return std::vector<std::string>();
         }
-        void dump_cpt() const override
+        std::string dump_cpt() const override
         {
+            return "";
         }
     protected:
         torch::Tensor predict_average_voting(torch::Tensor& X);

bayesnet/feature_selection/CFS.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "CFS.h"
@@ -11,7 +17,7 @@ namespace bayesnet {
         auto feature = featureOrder[0];
         selectedFeatures.push_back(feature);
         selectedScores.push_back(suLabels[feature]);
-        selectedFeatures.erase(selectedFeatures.begin());
+        featureOrder.erase(featureOrder.begin());
         while (continueCondition) {
             double merit = std::numeric_limits<double>::lowest();
             int bestFeature = -1;

bayesnet/feature_selection/CFS.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef CFS_H
 #define CFS_H
 #include <torch/torch.h>

bayesnet/feature_selection/FCBF.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "FCBF.h"
 namespace bayesnet {

bayesnet/feature_selection/FCBF.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef FCBF_H
 #define FCBF_H
 #include <torch/torch.h>

bayesnet/feature_selection/FeatureSelect.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "FeatureSelect.h"

bayesnet/feature_selection/FeatureSelect.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef FEATURE_SELECT_H
 #define FEATURE_SELECT_H
 #include <torch/torch.h>

bayesnet/feature_selection/IWSS.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <limits>
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "IWSS.h"

bayesnet/feature_selection/IWSS.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef IWSS_H
 #define IWSS_H
 #include <vector>

bayesnet/network/Network.cc

@@ -1,27 +1,41 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <thread>
 #include <mutex>
+#include <sstream>
 #include "Network.h"
 #include "bayesnet/utils/bayesnetUtils.h"
 namespace bayesnet {
-    Network::Network() : features(std::vector<std::string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(float maxT) : features(std::vector<std::string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false), laplaceSmoothing(0) {}
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.
-        getmaxThreads()), fitted(other.fitted)
+    Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 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)
+    {
+        if (samples.defined())
+            samples = samples.clone();
         for (const auto& node : other.nodes) {
             nodes[node.first] = std::make_unique<Node>(*node.second);
         }
     }
     void Network::initialize()
     {
-        features = std::vector<std::string>();
+        features.clear();
         className = "";
         classNumStates = 0;
         fitted = false;
         nodes.clear();
         samples = torch::Tensor();
     }
-    float Network::getmaxThreads()
+    float Network::getMaxThreads() const
     {
         return maxThreads;
     }
@@ -114,11 +128,14 @@ namespace bayesnet {
         if (n_features != featureNames.size()) {
             throw std::invalid_argument("X and features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(featureNames.size()) + ")");
         }
+        if (features.size() == 0) {
+            throw std::invalid_argument("The network has not been initialized. You must call addNode() before calling fit()");
+        }
         if (n_features != features.size() - 1) {
             throw std::invalid_argument("X and local features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(features.size() - 1) + ")");
         }
         if (find(features.begin(), features.end(), className) == features.end()) {
-            throw std::invalid_argument("className not found in Network::features");
+            throw std::invalid_argument("Class Name not found in Network::features");
         }
         for (auto& feature : featureNames) {
             if (find(features.begin(), features.end(), feature) == features.end()) {
@@ -393,22 +410,20 @@ 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");
                     }
                 }
             }
         }
         return result;
     }
-    void Network::dump_cpt() const
+    std::string Network::dump_cpt() const
     {
+        std::stringstream oss;
         for (auto& node : nodes) {
-            std::cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
-            std::cout << node.second->getCPT() << std::endl;
-        }
+            oss << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
+            oss << node.second->getCPT() << std::endl;
+        }
+        return oss.str();
     }
 }

bayesnet/network/Network.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef NETWORK_H
 #define NETWORK_H
 #include <map>
@@ -10,10 +16,10 @@ namespace bayesnet {
     public:
         Network();
         explicit Network(float);
-        explicit Network(Network&);
+        explicit Network(const Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        float getmaxThreads();
+        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();
@@ -39,7 +45,7 @@ namespace bayesnet {
         std::vector<std::string> show() const;
         std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format
         void initialize();
-        void dump_cpt() const;
+        std::string dump_cpt() const;
         inline std::string version() { return  { project_version.begin(), project_version.end() }; }
     private:
         std::map<std::string, std::unique_ptr<Node>> nodes;
@@ -49,7 +55,7 @@ namespace bayesnet {
         std::vector<std::string> features; // Including classname
         std::string className;
         double laplaceSmoothing;
-        torch::Tensor samples; // nxm tensor used to fit the model
+        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&);

bayesnet/network/Node.cc

@@ -1,9 +1,15 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "Node.h"
 
 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()
@@ -90,7 +96,6 @@ namespace bayesnet {
         // 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;
         // Fill table with counts

bayesnet/network/Node.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef NODE_H
 #define NODE_H
 #include <unordered_set>
@@ -6,14 +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();
@@ -31,6 +29,14 @@ namespace bayesnet {
         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>&);
+    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

@@ -1,30 +1,86 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// 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 nxm std::vector used to fit the model
+    //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[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
     {
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ i,  "..." }, torch::tensor(vsamples[i], torch::kInt32));
         }
         samples.index_put_({ -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 
-        auto n = samples.size(0) - 1;
+        auto n = features.size();
         if (k == 0) {
             k = n;
         }
@@ -60,7 +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>();
@@ -99,14 +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);
@@ -145,10 +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

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef BAYESNET_METRICS_H
 #define BAYESNET_METRICS_H
 #include <vector>
@@ -5,21 +11,31 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class Metrics {
-    private:
-        int classNumStates = 0;
-        std::vector<double> scoresKBest;
-        std::vector<int> featuresKBest; // sorted indices of the features
-        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+    public:
+        Metrics() = default;
+        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);
+        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] });
@@ -34,16 +50,13 @@ namespace bayesnet {
             v.erase(v.begin());
             return temp;
         }
-    public:
-        Metrics() = default;
-        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<double> getScoresKBest() 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
-        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);
+    private:
+        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);
     };
 }
 #endif

bayesnet/utils/Mst.cc

@@ -1,3 +1,10 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
 #include <vector>
 #include <list>
 #include "Mst.h"
@@ -45,15 +52,6 @@ namespace bayesnet {
             }
         }
     }
-    void Graph::display_mst()
-    {
-        std::cout << "Edge :" << " Weight" << std::endl;
-        for (int i = 0; i < T.size(); i++) {
-            std::cout << T[i].second.first << " - " << T[i].second.second << " : "
-                << T[i].first;
-            std::cout << std::endl;
-        }
-    }
 
     void insertElement(std::list<int>& variables, int variable)
     {

bayesnet/utils/Mst.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef MST_H
 #define MST_H
 #include <vector>
@@ -5,29 +11,28 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class MST {
-    private:
-        torch::Tensor weights;
-        std::vector<std::string> features;
-        int root = 0;
     public:
         MST() = default;
         MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
         std::vector<std::pair<int, int>> maximumSpanningTree();
+    private:
+        torch::Tensor weights;
+        std::vector<std::string> features;
+        int root = 0;
     };
     class Graph {
-    private:
-        int V;      // number of nodes in graph
-        std::vector <std::pair<float, std::pair<int, int>>> G; // std::vector for graph
-        std::vector <std::pair<float, std::pair<int, int>>> T; // std::vector for mst
-        std::vector<int> parent;
     public:
         explicit Graph(int V);
         void addEdge(int u, int v, float wt);
         int find_set(int i);
         void union_set(int u, int v);
         void kruskal_algorithm();
-        void display_mst();
         std::vector <std::pair<float, std::pair<int, int>>> get_mst() { return T; }
+    private:
+        int V;      // number of nodes in graph
+        std::vector <std::pair<float, std::pair<int, int>>> G; // std::vector for graph
+        std::vector <std::pair<float, std::pair<int, int>>> T; // std::vector for mst
+        std::vector<int> parent;
     };
 }
 #endif

bayesnet/utils/bayesnetUtils.cc

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 
 #include "bayesnetUtils.h"
 namespace bayesnet {
@@ -10,18 +16,6 @@ namespace bayesnet {
         sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
         return indices;
     }
-    std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor)
-    {
-        // convert mxn tensor to nxm std::vector
-        std::vector<std::vector<int>> result;
-        // Iterate over cols
-        for (int i = 0; i < dtensor.size(1); ++i) {
-            auto col_tensor = dtensor.index({ "...", i });
-            auto col = std::vector<int>(col_tensor.data_ptr<int>(), col_tensor.data_ptr<int>() + dtensor.size(0));
-            result.push_back(col);
-        }
-        return result;
-    }
     std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor)
     {
         // convert mxn tensor to mxn std::vector

bayesnet/utils/bayesnetUtils.h

@@ -1,10 +1,15 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef BAYESNET_UTILS_H
 #define BAYESNET_UTILS_H
 #include <vector>
 #include <torch/torch.h>
 namespace bayesnet {
     std::vector<int> argsort(std::vector<double>& nums);
-    std::vector<std::vector<int>> tensorToVector(torch::Tensor& dtensor);
     std::vector<std::vector<double>> tensorToVectorDouble(torch::Tensor& dtensor);
     torch::Tensor vectorToTensor(std::vector<std::vector<int>>& vector, bool transpose = true);
 }

diagrams/BayesNet.puml

@@ -0,0 +1,412 @@
+@startuml
+title clang-uml class diagram model
+class "bayesnet::Metrics" as C_0000736965376885623323
+class C_0000736965376885623323 #aliceblue;line:blue;line.dotted;text:blue {
++Metrics() = default : void
++Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
++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) : void
+..
++SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
++conditionalEdge(const torch::Tensor & weights) : torch::Tensor
++conditionalEdgeWeights(std::vector<float> & weights) : std::vector<float>
+#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
+#entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
++getScoresKBest() const : std::vector<double>
++maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
++mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
+#pop_first<T>(std::vector<T> & v) : T
+__
+#className : std::string
+#features : std::vector<std::string>
+#samples : torch::Tensor
+}
+class "bayesnet::Node" as C_0001303524929067080934
+class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
++Node(const std::string &) : void
+..
++addChild(Node *) : void
++addParent(Node *) : void
++clear() : void
++computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double laplaceSmoothing, const torch::Tensor & weights) : void
++getCPT() : torch::Tensor &
++getChildren() : std::vector<Node *> &
++getFactorValue(std::map<std::string,int> &) : float
++getName() const : std::string
++getNumStates() const : int
++getParents() : std::vector<Node *> &
++graph(const std::string & clasName) : std::vector<std::string>
++minFill() : unsigned int
++removeChild(Node *) : void
++removeParent(Node *) : void
++setNumStates(int) : void
+__
+}
+class "bayesnet::Network" as C_0001186707649890429575
+class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
++Network() : void
++Network(float) : void
++Network(const Network &) : void
++~Network() = default : void
+..
++addEdge(const std::string &, const std::string &) : void
++addNode(const std::string &) : void
++dump_cpt() const : std::string
++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 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 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
++getClassName() const : std::string
++getClassNumStates() const : int
++getEdges() const : std::vector<std::pair<std::string,std::string>>
++getFeatures() const : std::vector<std::string>
++getMaxThreads() const : float
++getNodes() : std::map<std::string,std::unique_ptr<Node>> &
++getNumEdges() const : int
++getSamples() : torch::Tensor &
++getStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++initialize() : void
++predict(const std::vector<std::vector<int>> &) : std::vector<int>
++predict(const torch::Tensor &) : torch::Tensor
++predict_proba(const std::vector<std::vector<int>> &) : std::vector<std::vector<double>>
++predict_proba(const torch::Tensor &) : torch::Tensor
++predict_tensor(const torch::Tensor & samples, const bool proba) : torch::Tensor
++score(const std::vector<std::vector<int>> &, const std::vector<int> &) : double
++show() const : std::vector<std::string>
++topological_sort() : std::vector<std::string>
++version() : std::string
+__
+}
+enum "bayesnet::status_t" as C_0000738420730783851375
+enum C_0000738420730783851375 {
+NORMAL
+WARNING
+ERROR
+}
+abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
+abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
++~BaseClassifier() = default : void
+..
+{abstract} +dump_cpt() const = 0 : std::string
+{abstract} +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 : BaseClassifier &
+{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
+{abstract} +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 : BaseClassifier &
+{abstract} +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 : BaseClassifier &
+{abstract} +getClassNumStates() const = 0 : int
+{abstract} +getNotes() const = 0 : std::vector<std::string>
+{abstract} +getNumberOfEdges() const = 0 : int
+{abstract} +getNumberOfNodes() const = 0 : int
+{abstract} +getNumberOfStates() const = 0 : int
+{abstract} +getStatus() const = 0 : status_t
++getValidHyperparameters() : std::vector<std::string> &
+{abstract} +getVersion() = 0 : std::string
+{abstract} +graph(const std::string & title = "") const = 0 : std::vector<std::string>
+{abstract} +predict(std::vector<std::vector<int>> & X) = 0 : std::vector<int>
+{abstract} +predict(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +predict_proba(std::vector<std::vector<int>> & X) = 0 : std::vector<std::vector<double>>
+{abstract} +predict_proba(torch::Tensor & X) = 0 : torch::Tensor
+{abstract} +score(std::vector<std::vector<int>> & X, std::vector<int> & y) = 0 : float
+{abstract} +score(torch::Tensor & X, torch::Tensor & y) = 0 : float
+{abstract} +setHyperparameters(const nlohmann::json & hyperparameters) = 0 : void
+{abstract} +show() const = 0 : std::vector<std::string>
+{abstract} +topological_order() = 0 : std::vector<std::string>
+{abstract} #trainModel(const torch::Tensor & weights) = 0 : void
+__
+#validHyperparameters : std::vector<std::string>
+}
+abstract "bayesnet::Classifier" as C_0002043996622900301644
+abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
++Classifier(Network model) : void
++~Classifier() = default : void
+..
++addNodes() : void
+#buildDataset(torch::Tensor & y) : void
+{abstract} #buildModel(const torch::Tensor & weights) = 0 : void
+#checkFitParameters() : void
++dump_cpt() const : std::string
++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 &
++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 &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : 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 &
++getClassNumStates() const : int
++getNotes() const : std::vector<std::string>
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++getStatus() const : status_t
++getVersion() : std::string
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(torch::Tensor & X, torch::Tensor & y) : float
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++setHyperparameters(const nlohmann::json & hyperparameters) : void
++show() const : std::vector<std::string>
++topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+__
+#className : std::string
+#dataset : torch::Tensor
+#features : std::vector<std::string>
+#fitted : bool
+#m : unsigned int
+#metrics : Metrics
+#model : Network
+#n : unsigned int
+#notes : std::vector<std::string>
+#states : std::map<std::string,std::vector<int>>
+#status : status_t
+}
+class "bayesnet::KDB" as C_0001112865019015250005
+class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
++KDB(int k, float theta = 0.03) : void
++~KDB() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::TAN" as C_0001760994424884323017
+class C_0001760994424884323017 #aliceblue;line:blue;line.dotted;text:blue {
++TAN() : void
++~TAN() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "TAN") const : std::vector<std::string>
+__
+}
+class "bayesnet::Proposal" as C_0002219995589162262979
+class C_0002219995589162262979 #aliceblue;line:blue;line.dotted;text:blue {
++Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
++~Proposal() : void
+..
+#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
+#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
+#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
+#prepareX(torch::Tensor & X) : torch::Tensor
+__
+#Xf : torch::Tensor
+#discretizers : map<std::string,mdlp::CPPFImdlp *>
+#y : torch::Tensor
+}
+class "bayesnet::TANLd" as C_0001668829096702037834
+class C_0001668829096702037834 #aliceblue;line:blue;line.dotted;text:blue {
++TANLd() : void
++~TANLd() = default : void
+..
++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) : TANLd &
++graph(const std::string & name = "TAN") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
+abstract C_0001695326193250580823 #aliceblue;line:blue;line.dotted;text:blue {
++FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~FeatureSelect() : void
+..
+#computeMeritCFS() : double
+#computeSuFeatures(const int a, const int b) : double
+#computeSuLabels() : void
+{abstract} +fit() = 0 : void
++getFeatures() const : std::vector<int>
++getScores() const : std::vector<double>
+#initialize() : void
+#symmetricalUncertainty(int a, int b) : double
+__
+#fitted : bool
+#maxFeatures : int
+#selectedFeatures : std::vector<int>
+#selectedScores : std::vector<double>
+#suFeatures : std::map<std::pair<int,int>,double>
+#suLabels : std::vector<double>
+#weights : const torch::Tensor &
+}
+class "bayesnet::CFS" as C_0000011627355691342494
+class C_0000011627355691342494 #aliceblue;line:blue;line.dotted;text:blue {
++CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
++~CFS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::FCBF" as C_0000144682015341746929
+class C_0000144682015341746929 #aliceblue;line:blue;line.dotted;text:blue {
++FCBF(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
++~FCBF() : void
+..
++fit() : void
+__
+}
+class "bayesnet::IWSS" as C_0000008268514674428553
+class C_0000008268514674428553 #aliceblue;line:blue;line.dotted;text:blue {
++IWSS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
++~IWSS() : void
+..
++fit() : void
+__
+}
+class "bayesnet::SPODE" as C_0000512022813807538451
+class C_0000512022813807538451 #aliceblue;line:blue;line.dotted;text:blue {
++SPODE(int root) : void
++~SPODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "SPODE") const : std::vector<std::string>
+__
+}
+class "bayesnet::Ensemble" as C_0001985241386355360576
+class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
++Ensemble(bool predict_voting = true) : void
++~Ensemble() = default : void
+..
+#compute_arg_max(std::vector<std::vector<double>> & X) : std::vector<int>
+#compute_arg_max(torch::Tensor & X) : torch::Tensor
++dump_cpt() const : std::string
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++predict(std::vector<std::vector<int>> & X) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(torch::Tensor & X) : torch::Tensor
+#predict_average_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+#predict_average_voting(torch::Tensor & X) : torch::Tensor
+#predict_average_voting(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
++predict_proba(torch::Tensor & X) : torch::Tensor
++score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
++score(torch::Tensor & X, torch::Tensor & y) : float
++show() const : std::vector<std::string>
++topological_order() : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+#voting(torch::Tensor & votes) : torch::Tensor
+__
+#models : std::vector<std::unique_ptr<Classifier>>
+#n_models : unsigned int
+#predict_voting : bool
+#significanceModels : std::vector<double>
+}
+class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
+class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
+class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0000358471592399852382
+class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
++BoostAODE(bool predict_voting = false) : void
++~BoostAODE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+class "bayesnet::MST" as C_0000131858426172291700
+class C_0000131858426172291700 #aliceblue;line:blue;line.dotted;text:blue {
++MST() = default : void
++MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
+..
++maximumSpanningTree() : std::vector<std::pair<int,int>>
+__
+}
+class "bayesnet::Graph" as C_0001197041682001898467
+class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
++Graph(int V) : void
+..
++addEdge(int u, int v, float wt) : void
++find_set(int i) : int
++get_mst() : std::vector<std::pair<float,std::pair<int,int>>>
++kruskal_algorithm() : void
++union_set(int u, int v) : void
+__
+}
+class "bayesnet::KDBLd" as C_0000344502277874806837
+class C_0000344502277874806837 #aliceblue;line:blue;line.dotted;text:blue {
++KDBLd(int k) : void
++~KDBLd() = default : void
+..
++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) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODE" as C_0000786111576121788282
+class C_0000786111576121788282 #aliceblue;line:blue;line.dotted;text:blue {
++AODE(bool predict_voting = false) : void
++~AODE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "AODE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::SPODELd" as C_0001369655639257755354
+class C_0001369655639257755354 #aliceblue;line:blue;line.dotted;text:blue {
++SPODELd(int root) : void
++~SPODELd() = default : void
+..
++commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
++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) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
++graph(const std::string & name = "SPODE") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::AODELd" as C_0000487273479333793647
+class C_0000487273479333793647 #aliceblue;line:blue;line.dotted;text:blue {
++AODELd(bool predict_voting = true) : void
++~AODELd() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++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_) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights) : void
+__
+}
+C_0001303524929067080934 --> C_0001303524929067080934 : -parents
+C_0001303524929067080934 --> C_0001303524929067080934 : -children
+C_0001186707649890429575 o-- C_0001303524929067080934 : -nodes
+C_0000327135989451974539 ..> C_0000738420730783851375
+C_0002043996622900301644 o-- C_0001186707649890429575 : #model
+C_0002043996622900301644 o-- C_0000736965376885623323 : #metrics
+C_0002043996622900301644 o-- C_0000738420730783851375 : #status
+C_0000327135989451974539 <|-- C_0002043996622900301644
+C_0002043996622900301644 <|-- C_0001112865019015250005
+C_0002043996622900301644 <|-- C_0001760994424884323017
+C_0002219995589162262979 ..> C_0001186707649890429575
+C_0001760994424884323017 <|-- C_0001668829096702037834
+C_0002219995589162262979 <|-- C_0001668829096702037834
+C_0000736965376885623323 <|-- C_0001695326193250580823
+C_0001695326193250580823 <|-- C_0000011627355691342494
+C_0001695326193250580823 <|-- C_0000144682015341746929
+C_0001695326193250580823 <|-- C_0000008268514674428553
+C_0002043996622900301644 <|-- C_0000512022813807538451
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docs/BoostAODE.md

@@ -1,20 +1,19 @@
 # BoostAODE Algorithm Operation
 
-The algorithm is based on the AdaBoost algorithm with some new proposals that can be activated using the following hyperparameters.
-
 ## Hyperparameters
 
 The hyperparameters defined in the algorithm are:
 
-- ***repeatSparent*** (*boolean*): Allows dataset variables to be repeated as parents of an *SPODE*. Default value: *false*.
-
-- ***maxModels*** (*int*): Maximum number of models (*SPODEs*) to build. This hyperparameter is only taken into account if ***repeatSparent*** is set to *true*. Default value: *0*.
+- ***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"*.
 
-- ***convergence*** (*boolean*): Sets whether the convergence of the result will be used as a termination condition. If this hyperparameter is set to true, the training dataset passed to the model is divided into two sets, one serving as training data and the other as a test set (so the original test partition will become a validation partition in this case). The partition is made by taking the first partition generated by a process of generating a 5 fold partition with stratification using a predetermined seed. The exit condition used in this *convergence* is that the difference between the accuracy obtained by the current model and that obtained by the previous model is greater than *1e-4*; otherwise, one will be added to the number of models that worsen the result (see next hyperparameter). Default value: *false*.
+- ***block_update*** (*boolean*): Sets whether the algorithm will update the weights of the models in blocks. If set to false, the algorithm will update the weights of the models one by one. Default value: *false*.
 
-- ***tolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. Default value: *0*.
+- ***convergence*** (*boolean*): Sets whether the convergence of the result will be used as a termination condition. If this hyperparameter is set to true, the training dataset passed to the model is divided into two sets, one serving as training data and the other as a test set (so the original test partition will become a validation partition in this case). The partition is made by taking the first partition generated by a process of generating a 5 fold partition with stratification using a predetermined seed. The exit condition used in this *convergence* is that the difference between the accuracy obtained by the current model and that obtained by the previous model is greater than *1e-4*; otherwise, one will be added to the number of models that worsen the result (see next hyperparameter). Default value: *true*.
+
+- ***maxTolerance*** (*int*): Sets the maximum number of models that can worsen the result without constituting a termination condition. if ***bisection*** is set to *true*, the value of this hyperparameter will be exponent of base 2 to compute the number of models to insert at once. Default value: *3*
 
 - ***select_features*** (*{"IWSS", "FCBF", "CFS", ""}*): Selects the variable selection method to be used to build initial models for the ensemble that will be included without considering any of the other exit conditions. Once the models of the selected variables are built, the algorithm will update the weights using the ensemble and set the significance of all the models built with the same &alpha;<sub>t</sub>. Default value: *""*.
 
@@ -24,48 +23,8 @@ The hyperparameters defined in the algorithm are:
 
   Default value is *-1* so every time any of those algorithms are called, the threshold has to be set to the desired value.
 
-- ***predict_voting*** (*boolean*): Sets whether the algorithm will use *model voting* to predict the result. If set to false, the weighted average of the probabilities of each model's prediction will be used. Default value: *true*.
-
-- ***predict_single*** (*boolean*): Sets whether the algorithm will use single-model prediction in the learning process. If set to *false*, all models trained up to that point will be used to calculate the prediction necessary to update the weights in the learning process. Default value: *true*.
+- ***predict_voting*** (*boolean*): Sets whether the algorithm will use *model voting* to predict the result. If set to false, the weighted average of the probabilities of each model's prediction will be used. Default value: *false*.
 
 ## Operation
 
-The algorithm performs the following steps:
-
-1. **Initialization**
-
-    - If ***select_features*** is set, as many *SPODEs* are created as variables selected by the corresponding feature selection algorithm, and these variables are marked as used.
-
-    - Initial weights of the examples are set to *1/m*.
-
-1. **Main Training Loop:**
-
-   - Variables are sorted by mutual information order with the class variable and processed in ascending, descending or random order, according to the value of the *order* hyperparameter. If it is random, the variables are shuffled.
-
-   - If the parent repetition is not established, the variable is marked as used.
-
-   - A *SPODE* is created using the selected variable as the parent.
-
-   - The model is trained, and the class variable corresponding to the training dataset is calculated. The calculation can be done using the last trained model or the set of models trained up to that point, according to the value of the *predict_single* hyperparameter.
-
-   - The weights associated with the examples are updated using this expression:
-
-     - w<sub>i</sub> · e<sup>&alpha;<sub>t</sub></sup> (if the example has been misclassified)
-
-     - w<sub>i</sub> · e<sup>-&alpha;<sub>t</sub></sup> (if the example has been correctly classified)
-
-   - The model significance is set to &alpha;<sub>t</sub>.
-
-   - If the ***convergence*** hyperparameter is set, the accuracy value on the test dataset that we separated in an initial step is calculated.
-
-1. **Exit Conditions:**
-
-   - &epsilon;<sub>t</sub> > 0.5 => misclassified examples are penalized.
-
-   - Number of models with worse accuracy greater than ***tolerance*** and ***convergence*** established.
-
-   - There are no more variables to create models, and ***repeatSparent*** is not set.
-
-   - Number of models > ***maxModels*** if ***repeatSparent*** is set.
-
-### [Proposal for *predict_single = false*](./BoostAODE_train_predict.pdf)
+### [Base Algorithm](./algorithm.md)

docs/algorithm.md

@@ -0,0 +1,117 @@
+# Algorithm
+
+- // notation
+
+- $n$ features ${\cal{X}} = \{X_1, \dots, X_n\}$ and the class $Y$
+
+- $m$ instances.
+
+- $D = \{ (x_1^i, \dots, x_n^i, y^i) \}_{i=1}^{m}$
+
+- $W$ a weights vector. $W_0$ are the initial weights.
+
+- $D[W]$ dataset with weights $W$ for the instances.
+
+1. // initialization
+
+2. $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
+
+3. $W \leftarrow W_0$
+
+4. $Vars \leftarrow {\cal{X}}$
+
+5. $\delta \leftarrow 10^{-4}$
+
+6. $convergence \leftarrow True$ // hyperparameter
+
+7. $maxTolerancia \leftarrow 3$ // hyperparameter
+
+8. $bisection \leftarrow False$ // hyperparameter
+
+9. $finished \leftarrow False$
+
+10. $AODE \leftarrow \emptyset$ // the ensemble
+
+11. $tolerance \leftarrow 0$
+
+12. $numModelsInPack \leftarrow 0$
+
+13. $maxAccuracy \leftarrow -1$
+
+14.
+
+15. // main loop
+
+16. While $(\lnot finished)$
+
+    1. $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
+
+    2. $k \leftarrow 2^{tolerance}$
+
+    3. if ($tolerance == 0$) $numItemsPack \leftarrow0$
+
+    4. $P \leftarrow Head(\pi,k)$ // first k features in order
+
+    5. $spodes \leftarrow \emptyset$
+
+    6. $i \leftarrow 0$
+
+    7. While ($i < size(P)$)
+
+        1. $X \leftarrow P[i]$
+
+        2. $i \leftarrow i + 1$
+
+        3. $numItemsPack \leftarrow numItemsPack + 1$
+
+        4. $Vars.remove(X)$
+
+        5. $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
+
+        6. $\hat{y}[] \leftarrow spode.Predict(D)$
+
+        7. $\epsilon \leftarrow error(\hat{y}[], y[])$
+
+        8. $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$
+
+        9. if ($\epsilon > 0.5$)
+
+            1. $finished \leftarrow True$
+
+            2. break
+
+        10. $spodes.add( (spode,\alpha_t) )$
+
+        11. $W \leftarrow UpdateWeights(W,\alpha,y[],\hat{y}[])$
+
+    8. $AODE.add( spodes )$
+
+    9. if ($convergence \land \lnot finished$)
+
+        1. $\hat{y}[] \leftarrow AODE.Predict(D)$
+
+        2. $actualAccuracy \leftarrow accuracy(\hat{y}[], y[])$
+
+        3. $if (maxAccuracy == -1)\; maxAccuracy \leftarrow actualAccuracy$
+
+        4. if $((accuracy - maxAccuracy) < \delta)$ // result doesn't
+            improve enough
+
+            1. $tolerance \leftarrow tolerance + 1$
+
+        5. else
+
+            1. $tolerance \leftarrow 0$
+
+            2. $numItemsPack \leftarrow 0$
+
+    10. If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
+
+    11. $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
+
+17. if ($tolerance > maxTolerance$) // algorithm finished because of
+    lack of convergence
+
+    1. $removeModels(AODE, numItemsPack)$
+
+18. Return $AODE$

docs/algorithm.tex

@@ -0,0 +1,80 @@
+\section{Algorithm}
+\begin{itemize}
+\item[] // notation
+\item $n$ features ${\cal{X}} = \{X_1, \dots, X_n\}$ and the class $Y$
+\item $m$ instances. 
+\item $D = \{ (x_1^i, \dots, x_n^i, y^i) \}_{i=1}^{m}$
+\item $W$ a weights vector. $W_0$ are the initial weights.
+\item $D[W]$ dataset with weights $W$ for the instances.
+\end{itemize}
+\bigskip
+
+
+\begin{enumerate}
+\item[] // initialization
+\item $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
+\item $W \leftarrow W_0$
+\item $Vars \leftarrow {\cal{X}}$
+\item $\delta \leftarrow 10^{-4}$
+\item $convergence \leftarrow True$ // hyperparameter
+\item $maxTolerancia \leftarrow 3$ // hyperparameter
+\item $bisection \leftarrow False$ // hyperparameter
+\item $finished \leftarrow False$
+\item $AODE \leftarrow \emptyset$ \hspace*{2cm} // the ensemble
+\item $tolerance \leftarrow 0$
+\item $numModelsInPack \leftarrow 0$
+\item $maxAccuracy \leftarrow -1$
+\item[] 
+\newpage
+\item[] // main loop
+\item While $(\lnot finished)$
+\begin{enumerate}
+    \item $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
+    \item $k \leftarrow 2^{tolerance}$
+    \item if ($tolerance == 0$) $numItemsPack \leftarrow0$
+    \item $P \leftarrow Head(\pi,k)$ \hspace*{2cm} //  first k features in order
+    \item $spodes \leftarrow \emptyset$
+    \item $i \leftarrow 0$
+    \item While ($ i < size(P)$)
+    \begin{enumerate}
+        \item $X \leftarrow P[i]$
+        \item $i \leftarrow i + 1$
+        \item $numItemsPack \leftarrow numItemsPack + 1$
+        \item $Vars.remove(X)$
+        \item $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
+        \item $\hat{y}[] \leftarrow spode.Predict(D)$
+        \item $\epsilon \leftarrow error(\hat{y}[], y[])$
+        \item $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )$
+        \item if ($\epsilon > 0.5$)
+        \begin{enumerate}
+            \item $finished \leftarrow True$
+            \item break
+        \end{enumerate}
+        \item $spodes.add( (spode,\alpha_t) )$
+        \item $W \leftarrow UpdateWeights(W,\alpha,y[],\hat{y}[])$
+    \end{enumerate}       
+    \item $AODE.add( spodes )$
+    \item if ($convergence \land \lnot finished$) 
+    \begin{enumerate}       
+        \item $\hat{y}[] \leftarrow AODE.Predict(D)$
+        \item $actualAccuracy \leftarrow accuracy(\hat{y}[], y[])$
+        \item $if (maxAccuracy == -1)\; maxAccuracy \leftarrow actualAccuracy$
+        \item if $((accuracy - maxAccuracy) < \delta)$\hspace*{2cm} // result doesn't improve enough
+        \begin{enumerate}
+            \item $tolerance \leftarrow tolerance + 1$
+        \end{enumerate}
+        \item else
+        \begin{enumerate}
+            \item $tolerance \leftarrow 0$
+            \item $numItemsPack \leftarrow 0$
+         \end{enumerate}
+    \end{enumerate}
+    \item If $(Vars == \emptyset \lor tolerance>maxTolerance) \; finished \leftarrow True$
+    \item $lastAccuracy \leftarrow max(lastAccuracy, actualAccuracy)$
+\end{enumerate}
+\item if ($tolerance > maxTolerance$) \hspace*{1cm} // algorithm finished because of lack of convergence
+\begin{enumerate}
+    \item $removeModels(AODE, numItemsPack)$
+\end{enumerate}
+\item Return $AODE$
+\end{enumerate}

gcovr.cfg

@@ -1,4 +0,0 @@
-filter = bayesnet/
-exclude-directories = build_debug/lib/
-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

@@ -14,7 +14,6 @@ include_directories(
     /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

@@ -1,168 +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;
-}

sample/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

sample/lib/Files/CMakeLists.txt

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

sample/lib/mdlp/CPPFImdlp.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef CPPFIMDLP_H
 #define CPPFIMDLP_H
 

sample/lib/mdlp/Metrics.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef CCMETRICS_H
 #define CCMETRICS_H
 

sample/lib/mdlp/typesFImdlp.h

@@ -1,3 +1,9 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TYPES_H
 #define TYPES_H
 

sample/sample.cc

@@ -1,4 +1,10 @@
-#include <ArffFiles.h>
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <ArffFiles.hpp>
 #include <CPPFImdlp.h>
 #include <bayesnet/ensembles/BoostAODE.h>
 

tests/CMakeLists.txt

@@ -1,16 +1,27 @@
 if(ENABLE_TESTING)
-    set(TEST_BAYESNET "unit_tests_bayesnet")
     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
         ${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")
-    set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCES})
-    add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET})
-    target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain )
-    add_test(NAME ${TEST_BAYESNET} COMMAND ${TEST_BAYESNET})
+    add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc 
+        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 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 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);
+    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);
+    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);
+    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);
+    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

@@ -0,0 +1,125 @@
+// ***************************************************************
+// 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 "TestUtils.h"
+#include "bayesnet/classifiers/TAN.h"
+#include "bayesnet/classifiers/KDB.h"
+#include "bayesnet/classifiers/KDBLd.h"
+
+
+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.features, raw.className, raw.states), std::runtime_error);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states), "* 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.features, raw.className, raw.states), std::runtime_error);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.features, raw.className, raw.states), "* 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.features, raw.className, raw.states), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states), "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.features, raw.className, raw.states), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.features, raw.className, raw.states), "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.features, "duck", raw.states), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, "duck", raw.states), "class name not found in states");
+}
+TEST_CASE("Invalid feature name", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    auto statest = raw.states;
+    statest.erase("petallength");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.features, raw.className, statest), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.features, raw.className, statest), "feature [petallength] not found in states");
+}
+TEST_CASE("Invalid hyperparameter", "[Classifier]")
+{
+    auto model = bayesnet::KDB(2);
+    auto raw = RawDatasets("iris", true);
+    REQUIRE_THROWS_AS(model.setHyperparameters({ { "alpha", "0.0" } }), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.setHyperparameters({ { "alpha", "0.0" } }), "Invalid hyperparameters{\"alpha\":\"0.0\"}");
+}
+TEST_CASE("Topological order", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
+    auto order = model.topological_order();
+    REQUIRE(order.size() == 4);
+    REQUIRE(order[0] == "petallength");
+    REQUIRE(order[1] == "sepallength");
+    REQUIRE(order[2] == "sepalwidth");
+    REQUIRE(order[3] == "petalwidth");
+}
+TEST_CASE("Dump_cpt", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
+    auto cpt = model.dump_cpt();
+    REQUIRE(cpt.size() == 1713);
+}
+TEST_CASE("Not fitted model", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    auto message = "Classifier has not been fitted";
+    // tensors
+    REQUIRE_THROWS_AS(model.predict(raw.Xt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict(raw.Xt), message);
+    REQUIRE_THROWS_AS(model.predict_proba(raw.Xt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict_proba(raw.Xt), message);
+    REQUIRE_THROWS_AS(model.score(raw.Xt, raw.yt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.score(raw.Xt, raw.yt), message);
+    // vectors
+    REQUIRE_THROWS_AS(model.predict(raw.Xv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict(raw.Xv), message);
+    REQUIRE_THROWS_AS(model.predict_proba(raw.Xv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict_proba(raw.Xv), message);
+    REQUIRE_THROWS_AS(model.score(raw.Xv, raw.yv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.score(raw.Xv, raw.yv), message);
+}
+TEST_CASE("KDB Graph", "[Classifier]")
+{
+    auto model = bayesnet::KDB(2);
+    auto raw = RawDatasets("iris", true);
+    model.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    auto graph = model.graph();
+    REQUIRE(graph.size() == 15);
+}
+TEST_CASE("KDBLd Graph", "[Classifier]")
+{
+    auto model = bayesnet::KDBLd(2);
+    auto raw = RawDatasets("iris", false);
+    model.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
+    auto graph = model.graph();
+    REQUIRE(graph.size() == 15);
+}

tests/TestBayesEnsemble.cc

@@ -0,0 +1,126 @@
+// ***************************************************************
+// 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/BoostAODE.h"
+#include "bayesnet/ensembles/AODE.h"
+#include "bayesnet/ensembles/AODELd.h"
+#include "TestUtils.h"
+
+
+TEST_CASE("Topological Order", "[Ensemble]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    auto order = clf.topological_order();
+    REQUIRE(order.size() == 0);
+}
+TEST_CASE("Dump CPT", "[Ensemble]")
+{
+    auto raw = RawDatasets("glass", true);
+    auto clf = bayesnet::BoostAODE();
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    auto dump = clf.dump_cpt();
+    REQUIRE(dump == "");
+}
+TEST_CASE("Number of States", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    REQUIRE(clf.getNumberOfStates() == 76);
+}
+TEST_CASE("Show", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.setHyperparameters({
+            {"bisection", false},
+            {"maxTolerance", 1},
+            {"convergence", false},
+        });
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    std::vector<std::string> expected = {
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> sepallength, sepalwidth, petalwidth, ",
+        "petalwidth -> ",
+        "sepallength -> ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> sepallength, sepalwidth, petallength, ",
+        "sepallength -> ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> ",
+        "sepallength -> sepalwidth, petallength, petalwidth, ",
+        "sepalwidth -> ",
+        "class -> sepallength, sepalwidth, petallength, petalwidth, ",
+        "petallength -> ",
+        "petalwidth -> ",
+        "sepallength -> ",
+        "sepalwidth -> sepallength, petallength, petalwidth, ",
+    };
+    auto show = clf.show();
+    REQUIRE(show.size() == expected.size());
+    for (size_t i = 0; i < show.size(); i++)
+        REQUIRE(show[i] == expected[i]);
+}
+TEST_CASE("Graph", "[Ensemble]")
+{
+    auto clf = bayesnet::BoostAODE();
+    auto raw = RawDatasets("iris", true);
+    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    auto graph = clf.graph();
+    REQUIRE(graph.size() == 56);
+    auto clf2 = bayesnet::AODE();
+    clf2.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
+    graph = clf2.graph();
+    REQUIRE(graph.size() == 56);
+    raw = RawDatasets("glass", false);
+    auto clf3 = bayesnet::AODELd();
+    clf3.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
+    graph = clf3.graph();
+    REQUIRE(graph.size() == 261);
+}
+TEST_CASE("Compute ArgMax", "[Ensemble]")
+{
+    class TestEnsemble : public bayesnet::BoostAODE {
+    public:
+        TestEnsemble() : bayesnet::BoostAODE() {}
+        torch::Tensor compute_arg_max(torch::Tensor& X) { return Ensemble::compute_arg_max(X); }
+        std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X) { return Ensemble::compute_arg_max(X); }
+    };
+    TestEnsemble clf;
+    std::vector<std::vector<double>> X = {
+        {0.1f, 0.2f, 0.3f},
+        {0.4f, 0.9f, 0.6f},
+        {0.7f, 0.8f, 0.9f},
+        {0.5f, 0.2f, 0.1f},
+        {0.3f, 0.7f, 0.2f},
+        {0.5f, 0.5f, 0.2f}
+    };
+    std::vector<int> expected = { 2, 1, 2, 0, 1, 0 };
+    auto argmax = clf.compute_arg_max(X);
+    REQUIRE(argmax.size() == expected.size());
+    REQUIRE(argmax == expected);
+    auto Xt = torch::zeros({ 6, 3 }, torch::kFloat32);
+    Xt[0][0] = 0.1f; Xt[0][1] = 0.2f; Xt[0][2] = 0.3f;
+    Xt[1][0] = 0.4f; Xt[1][1] = 0.9f; Xt[1][2] = 0.6f;
+    Xt[2][0] = 0.7f; Xt[2][1] = 0.8f; Xt[2][2] = 0.9f;
+    Xt[3][0] = 0.5f; Xt[3][1] = 0.2f; Xt[3][2] = 0.1f;
+    Xt[4][0] = 0.3f; Xt[4][1] = 0.7f; Xt[4][2] = 0.2f;
+    Xt[5][0] = 0.5f; Xt[5][1] = 0.5f; Xt[5][2] = 0.2f;
+    auto argmaxt = clf.compute_arg_max(Xt);
+    REQUIRE(argmaxt.size(0) == expected.size());
+    for (int i = 0; i < argmaxt.size(0); i++)
+        REQUIRE(argmaxt[i].item<int>() == expected[i]);
+}