Reorder CMakeLists

Reviewed-on: #33

.clang-format

@@ -0,0 +1,10 @@
+# .clang-format
+---
+BasedOnStyle: LLVM
+AccessModifierOffset: -4
+BreakBeforeBraces: Linux
+ColumnLimit: 0
+FixNamespaceComments: false
+IndentWidth: 4
+NamespaceIndentation: All
+TabWidth: 4

.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 +1,58 @@
+FROM mcr.microsoft.com/devcontainers/cpp:ubuntu22.04
+
+ARG REINSTALL_CMAKE_VERSION_FROM_SOURCE="3.29.3"
+
+# 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 doxygen
+
+# 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 && \
+RUN wget --quiet "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.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

.gitea/workflows/action.yaml

@@ -0,0 +1,19 @@
+name: Gitea Actions Demo
+run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v4
+      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
+      - run: echo "🖥️ The workflow is now ready to test your code on the runner."
+      - name: List files in the repository
+        run: |
+          ls ${{ github.workspace }}
+      - run: echo "🍏 This job's status is ${{ job.status }}."
+

.gitea/workflows/testing.yaml

@@ -0,0 +1,19 @@
+name: Gitea Actions Demo
+run-name: ${{ github.actor }} is testing out Gitea Actions 🚀
+on: [push]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v4
+      - run: echo "💡 The ${{ github.repository }} repository has been cloned to the runner."
+      - run: echo "🖥️ The workflow is now ready to test your code on the runner."
+      - name: List files in the repository
+        run: |
+          ls ${{ github.workspace }}
+      - run: echo "🍏 This job's status is ${{ job.status }}."
+

.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 +39,10 @@ cmake-build*/**
 puml/**
 .vscode/settings.json
 sample/build
+**/.DS_Store
+docs/manual
+docs/man3
+docs/man
+docs/Doxyfile
+.cache
 

.gitmodules

@@ -1,13 +1,3 @@
-[submodule "lib/mdlp"]
-	path = lib/mdlp
-	url = https://github.com/rmontanana/mdlp
-	main = main
-	update = merge
-[submodule "lib/catch2"]
-	path = lib/catch2
-    main  = v2.x
-	update = merge
-	url = https://github.com/catchorg/Catch2.git
 [submodule "lib/json"]
 	path = lib/json
 	url = https://github.com/nlohmann/json.git
@@ -18,3 +8,14 @@
 	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
+[submodule "lib/mdlp"]
+	path = lib/mdlp
+	url = https://github.com/rmontanana/mdlp

.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

@@ -5,21 +5,44 @@
             "type": "lldb",
             "request": "launch",
             "name": "sample",
-            "program": "${workspaceFolder}/build_release/sample/bayesnet_sample",
+            "program": "${workspaceFolder}/sample/build/bayesnet_sample",
             "args": [
                 "${workspaceFolder}/tests/data/glass.arff"
-            ],
+            ]
         },
         {
             "type": "lldb",
             "request": "launch",
             "name": "test",
-            "program": "${workspaceFolder}/build_debug/tests/unit_tests_bayesnet",
+            "program": "${workspaceFolder}/build_Debug/tests/TestBayesNet",
             "args": [
-                //"-c=\"Metrics Test\"",
+                "[XBAODE]"
-                // "-s",
             ],
-            "cwd": "${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 +5,112 @@ 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]
+
+### Internal
+
+- Add changes to .clang-format to ajust to vscode format style thanks to https://clang-format-configurator.site/
+
+## [1.0.7] 2025-03-16
+
+### Added
+
+- A new hyperparameter to the BoostAODE class, *alphablock*, to control the way α is computed, with the last model or with the ensmble built so far. Default value is *false*.
+- A new hyperparameter to the SPODE class, *parent*, to set the root node of the model. If no value is set the root parameter of the constructor is used.
+- A new hyperparameter to the TAN class, *parent*, to set the root node of the model. If not set the first feature is used as root.
+- A new model named XSPODE, an optimized for speed averaged one dependence estimator.
+- A new model named XSP2DE, an optimized for speed averaged two dependence estimator.
+- A new model named XBAODE, an optimized for speed BoostAODE model.
+- A new model named XBA2DE, an optimized for speed BoostA2DE model.
+
+### Internal
+
+- Optimize ComputeCPT method in the Node class.
+- Add methods getCount and getMaxCount to the CountingSemaphore class, returning the current count and the maximum count of threads respectively.
+
+### Changed
+
+- Hyperparameter *maxTolerance* in the BoostAODE class is now in [1, 6] range (it was in [1, 4] range before).
+
+## [1.0.6] 2024-11-23
+
+### Fixed
+
+- Prevent existing edges to be added to the network in the `add_edge` method.
+- Don't allow to add nodes or edges on already fiited networks.
+- Number of threads spawned
+- Network class tests
+
+### 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.
+- BoostA2DE model.
+- A2DE & SPnDE tests.
+- Add tests to reach 99% of coverage.
+- Add tests to check the correct version of the mdlp, folding and json libraries.
+- Library documentation generated with Doxygen.
+- Link to documentation in the README.md.
+- Three types of smoothing the Bayesian Network ORIGINAL, LAPLACE and CESTNIK.
+
+### Internal
+
+- Fixed doxygen optional dependency
+- Add env parallel variable to Makefile
+- Add CountingSemaphore class to manage the number of threads spawned.
+- Ignore CUDA language in CMake CodeCoverage module.
+- Update mdlp library as a git submodule.
+- 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.
+
+### Libraries versions
+
+- mdlp: 2.0.1
+- Folding: 1.1.0
+- json: 3.11
+- ArffFiles: 1.1.0
+
+## [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"_.
+- 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.
+- 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

@@ -1,22 +1,18 @@
-cmake_minimum_required(VERSION 3.20)
+cmake_minimum_required(VERSION 3.24)
 
 project(BayesNet
-  VERSION 1.0.4
+  VERSION 1.0.7
   DESCRIPTION "Bayesian Network and basic classifiers Library."
   HOMEPAGE_URL "https://github.com/rmontanana/bayesnet"
   LANGUAGES CXX
 )
 
 if (CODE_COVERAGE AND NOT ENABLE_TESTING)
-  MESSAGE(FATAL_ERROR "Code coverage requires testing enabled")
+  message(FATAL_ERROR "Code coverage requires testing enabled")
 endif (CODE_COVERAGE AND NOT ENABLE_TESTING)
 
 find_package(Torch REQUIRED)
 
-if (POLICY CMP0135)
-  cmake_policy(SET CMP0135 NEW)
-endif ()
-
 # Global CMake variables
 # ----------------------
 set(CMAKE_CXX_STANDARD 17)
@@ -24,7 +20,13 @@ set(CMAKE_CXX_STANDARD_REQUIRED            ON)
 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 "${CMAKE_CXX_FLAGS} -pthread")
+set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fprofile-arcs -ftest-coverage -fno-elide-constructors")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -Ofast")
+if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+    set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fno-default-inline")
+endif()
+
 # Options
 # -------
 option(ENABLE_CLANG_TIDY "Enable to add clang tidy."              OFF)
@@ -38,44 +40,46 @@ set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules ${CMAKE_MODULE_P
 include(AddGitSubmodule)
 
 if (CMAKE_BUILD_TYPE STREQUAL "Debug")
-  MESSAGE("Debug mode")
+  message(STATUS "* Debug mode")
+  enable_testing()
   set(ENABLE_TESTING ON)
   set(CODE_COVERAGE ON)
 endif (CMAKE_BUILD_TYPE STREQUAL "Debug")
 
-
+# Testing
+# -------
+if (ENABLE_TESTING)
+  message(STATUS "Testing enabled")
+  add_subdirectory(tests/lib/catch2)
+  include(CTest)
+  add_subdirectory(tests)
+endif (ENABLE_TESTING)
 if (CODE_COVERAGE)
-  enable_testing()
   include(CodeCoverage)
-  MESSAGE("Code coverage enabled")
+  message(STATUS "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")
-  SET(GCC_COVERAGE_LINK_FLAGS " ${GCC_COVERAGE_LINK_FLAGS} -lgcov --coverage")
 endif (CODE_COVERAGE)
 
+get_property(LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
+message(STATUS "Languages=${LANGUAGES}")
+
 if (ENABLE_CLANG_TIDY)
   include(StaticAnalyzers) # clang-tidy
 endif (ENABLE_CLANG_TIDY)
 
 # External libraries - dependencies of BayesNet
 # ---------------------------------------------
+
 # include(FetchContent)
-add_git_submodule("lib/mdlp")
 add_git_submodule("lib/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")  
-  include(CTest)
-  add_subdirectory(tests)
-endif (ENABLE_TESTING)
 
 # Installation
 # ------------
@@ -85,3 +89,16 @@ 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)
+if (Doxygen_FOUND)
+  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})
+else (Doxygen_FOUND) 
+  message(WARNING "* Doxygen not found")
+endif (Doxygen_FOUND)

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 +1,22 @@
 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_release = build_Release
-f_debug = build_debug
+f_debug = build_Debug
+f_diagrams = diagrams
 app_targets = BayesNet
-test_targets = unit_tests_bayesnet
+test_targets = TestBayesNet
-n_procs = -j 16
+clang-uml = clang-uml
+plantuml = plantuml
+lcov = lcov
+genhtml = genhtml
+dot = dot
+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 $(test_targets); do \
@@ -29,18 +39,29 @@ setup: ## Install dependencies for tests and coverage
 	fi
 	@if [ "$(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 & dependency 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)
+	cmake --build $(f_debug) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug BayesNet tests...";
@@ -52,9 +73,10 @@ 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
@@ -75,29 +97,98 @@ fname = "tests/data/iris.arff"
 sample: ## Build sample
 	@echo ">>> Building Sample...";
 	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
-	@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
+	@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Debug && cmake --build build -t bayesnet_sample
 	sample/build/bayesnet_sample $(fname)
 	@echo ">>> Done";
 
+fname = "tests/data/iris.arff"
+sample2: ## Build sample2
+	@echo ">>> Building Sample...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
+	@cd sample && cmake -B build -S . -D CMAKE_BUILD_TYPE=Debug && cmake --build build -t bayesnet_sample_xspode
+	sample/build/bayesnet_sample_xspode $(fname)
+	@echo ">>> Done";		
+
 opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
-	@echo ">>> Running BayesNet & Platform tests...";
+	@echo ">>> Running BayesNet tests...";
 	@$(MAKE) clean
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel $(CMAKE_BUILD_PARALLEL_LEVEL)
 	@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
+	@which $(lcov) || (echo ">>ease install lcov"; exit 1)
-	@gcovr $(f_debug)/tests
+	@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, i.e. docdir=../bayesnet_help"; \
+		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 +1,33 @@
-# 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)
+[![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&logo=gitea)
+[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](https://gitea.rmontanana.es/rmontanana/BayesNet)
+[![DOI](https://zenodo.org/badge/667782806.svg)](https://doi.org/10.5281/zenodo.14210344)
 
-Bayesian Network Classifiers using libtorch from scratch
+Bayesian Network Classifiers library
 
-## 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.zip
+```
+
+## Setup
+
+### Getting the code
+
+```bash
+git clone --recurse-submodules https://github.com/doctorado-ml/bayesnet
+```
 
 ### Release
 
@@ -19,7 +42,13 @@ sudo make install
 ```bash
 make debug
 make test
+```
+
+### Coverage
+
+```bash
 make coverage
+make viewcoverage
 ```
 
 ### Sample app
@@ -33,4 +62,48 @@ make sample fname=tests/data/glass.arff
 
 ## Models
 
-### [BoostAODE](docs/BoostAODE.md)
+#### - TAN
+
+#### - KDB
+
+#### - SPODE
+
+#### - SPnDE
+
+#### - AODE
+
+#### - A2DE
+
+#### - [BoostAODE](docs/BoostAODE.md)
+
+#### - XBAODE
+
+#### - BoostA2DE
+
+#### - XBA2DE
+
+### 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

@@ -1,19 +1,26 @@
-#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>
+#include "bayesnet/network/Network.h"
+
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     public:
-        // X is nxm std::vector, y is nx1 std::vector
-        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        // X is nxm tensor, y is nx1 tensor
-        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
-        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;
         virtual ~BaseClassifier() = default;
+        // X is nxm std::vector, y is nx1 std::vector
+        virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        // X is nxm tensor, y is nx1 tensor
+        virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
         torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
@@ -21,8 +28,8 @@ namespace bayesnet {
         status_t virtual getStatus() const = 0;
         float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
         float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
-        int virtual getNumberOfNodes()const = 0;
+        int virtual getNumberOfNodes() const = 0;
-        int virtual getNumberOfEdges()const = 0;
+        int virtual getNumberOfEdges() const = 0;
         int virtual getNumberOfStates() const = 0;
         int virtual getClassNumStates() const = 0;
         std::vector<std::string> virtual show() const = 0;
@@ -30,12 +37,13 @@ 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:
-        virtual void trainModel(const torch::Tensor& weights) = 0;
+        virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
         std::vector<std::string> validHyperparameters;
+        std::vector<std::string> notes; // Used to store messages occurred during the fit process
+        status_t status = NORMAL;
     };
 }
-#endif

bayesnet/CMakeLists.txt

@@ -1,6 +1,6 @@
 include_directories(
-    ${BayesNet_SOURCE_DIR}/lib/mdlp
+    ${BayesNet_SOURCE_DIR}/lib/log
-    ${BayesNet_SOURCE_DIR}/lib/Files
+    ${BayesNet_SOURCE_DIR}/lib/mdlp/src
     ${BayesNet_SOURCE_DIR}/lib/folding
     ${BayesNet_SOURCE_DIR}/lib/json/include
     ${BayesNet_SOURCE_DIR}
@@ -10,4 +10,4 @@ include_directories(
 file(GLOB_RECURSE Sources "*.cc")
 
 add_library(BayesNet ${Sources})
-target_link_libraries(BayesNet mdlp "${TORCH_LIBRARIES}")
+target_link_libraries(BayesNet fimdlp "${TORCH_LIBRARIES}")

bayesnet/classifiers/Classifier.cc

@@ -1,22 +1,28 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <sstream>
 #include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
+    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
-    Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
     {
         this->features = features;
         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);
         model.initialize();
         buildModel(weights);
-        trainModel(weights);
+        trainModel(weights, smoothing);
         fitted = true;
         return *this;
     }
@@ -27,26 +33,27 @@ namespace bayesnet {
             dataset = torch::cat({ dataset, yresized }, 0);
         }
         catch (const std::exception& e) {
-            std::cerr << e.what() << '\n';
+            std::stringstream oss;
-            std::cout << "X dimensions: " << dataset.sizes() << "\n";
+            oss << "* Error in X and y dimensions *\n";
-            std::cout << "y dimensions: " << ytmp.sizes() << "\n";
+            oss << "X dimensions: " << dataset.sizes() << "\n";
-            exit(1);
+            oss << "y dimensions: " << ytmp.sizes();
+            throw std::runtime_error(oss.str());
         }
     }
-    void Classifier::trainModel(const torch::Tensor& weights)
+    void Classifier::trainModel(const torch::Tensor& weights, Smoothing_t smoothing)
     {
-        model.fit(dataset, weights, features, className, states);
+        model.fit(dataset, weights, features, className, states, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = X;
         buildDataset(y);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     // X is nxm where n is the number of features and m the number of samples
-    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
         for (int i = 0; i < X.size(); ++i) {
@@ -55,29 +62,29 @@ namespace bayesnet {
         auto ytmp = torch::tensor(y, torch::kInt32);
         buildDataset(ytmp);
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
         const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
-    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->dataset = dataset;
-        return build(features, className, states, weights);
+        return build(features, className, states, weights, smoothing);
     }
     void Classifier::checkFitParameters()
     {
         if (torch::is_floating_point(dataset)) {
             throw std::invalid_argument("dataset (X, y) must be of type Integer");
         }
-        if (n != features.size()) {
+        if (dataset.size(0) - 1 != 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");
+            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 +180,14 @@ 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

@@ -1,8 +1,13 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef CLASSIFIER_H
 #define CLASSIFIER_H
 #include <torch/torch.h>
 #include "bayesnet/utils/BayesMetrics.h"
-#include "bayesnet/network/Network.h"
 #include "bayesnet/BaseClassifier.h"
 
 namespace bayesnet {
@@ -10,10 +15,10 @@ namespace bayesnet {
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
-        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override;
+        Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void addNodes();
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
@@ -30,7 +35,7 @@ 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;
@@ -41,14 +46,13 @@ namespace bayesnet {
         std::string className;
         std::map<std::string, std::vector<int>> states;
         torch::Tensor dataset; // (n+1)xm tensor
-        status_t status = NORMAL;
-        std::vector<std::string> notes; // Used to store messages occurred during the fit process
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
-        void trainModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildDataset(torch::Tensor& y);
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
     private:
-        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
     };
 }
 #endif

bayesnet/classifiers/KDB.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 "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

@@ -1,20 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef KDB_H
 #define KDB_H
 #include <torch/torch.h>
-#include "bayesnet/utils/bayesnetUtils.h"
 #include "Classifier.h"
 namespace bayesnet {
     class KDB : public Classifier {
     private:
         int k;
         float theta;
-        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
     protected:
+        void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
         void buildModel(const torch::Tensor& weights) override;
     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

@@ -1,8 +1,14 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "KDBLd.h"
 
 namespace bayesnet {
     KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
-    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -13,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
-        KDB::fit(dataset, features, className, states);
+        KDB::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
     }

bayesnet/classifiers/KDBLd.h

@@ -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"
@@ -9,7 +15,7 @@ namespace bayesnet {
     public:
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
-        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
         std::vector<std::string> graph(const std::string& name = "KDB") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };

bayesnet/classifiers/Proposal.cc

@@ -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 = factorize(yJoinParents);
-            auto yxv = arff.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);
@@ -66,7 +70,7 @@ namespace bayesnet {
                 states[pFeatures[index]] = xStates;
             }
             const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
-            model.fit(pDataset, weights, pFeatures, pClassName, states);
+            model.fit(pDataset, weights, pFeatures, pClassName, states, Smoothing_t::ORIGINAL);
         }
         return states;
     }
@@ -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

@@ -1,15 +1,36 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODE.h"
 
 namespace bayesnet {
 
-    SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
+    SPODE::SPODE(int root) : Classifier(Network()), root(root)
+    {
+        validHyperparameters = { "parent" };
+    }
 
+    void SPODE::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            root = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
     void SPODE::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
         addNodes();
         // 1. Add edges from the class node to all other nodes
         // 2. Add edges from the root node to all other nodes
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
             model.addEdge(className, features[i]);
             if (i != root) {

bayesnet/classifiers/SPODE.h

@@ -1,17 +1,24 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef SPODE_H
 #define SPODE_H
 #include "Classifier.h"
 
 namespace bayesnet {
     class SPODE : public Classifier {
-    private:
-        int root;
-    protected:
-        void buildModel(const torch::Tensor& weights) override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int root;
     };
 }
 #endif

bayesnet/classifiers/SPODELd.cc

@@ -1,40 +1,43 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "SPODELd.h"
 
 namespace bayesnet {
     SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
-    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
-        features = features_;
-        className = className_;
         Xf = X_;
         y = y_;
-        // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
+        return commonFit(features_, className_, states_, smoothing);
-        states = fit_local_discretization(y);
-        // We have discretized the input data
-        // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
-        SPODE::fit(dataset, features, className, states);
-        states = localDiscretizationProposal(states, model);
-        return *this;
     }
-    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         if (!torch::is_floating_point(dataset)) {
             throw std::runtime_error("Dataset must be a floating point tensor");
         }
         Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
-        y = dataset.index({ -1, "..." }).clone();
+        y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
+        return commonFit(features_, className_, states_, smoothing);
+    }
+
+    SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
+    {
         features = features_;
         className = className_;
         // 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);
+        SPODE::fit(dataset, features, className, states, smoothing);
         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"
@@ -8,9 +14,10 @@ namespace bayesnet {
     public:
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
-        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
-        std::vector<std::string> graph(const std::string& name = "SPODE") const override;
+        SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
+        std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
         torch::Tensor predict(torch::Tensor& X) override;
         static inline std::string version() { return "0.0.1"; };
     };

bayesnet/classifiers/SPnDE.cc

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

bayesnet/classifiers/SPnDE.h

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

bayesnet/classifiers/TAN.cc

@@ -1,8 +1,26 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TAN.h"
 
 namespace bayesnet {
-    TAN::TAN() : Classifier(Network()) {}
+    TAN::TAN() : Classifier(Network())
+    {
+        validHyperparameters = { "parent" };
+    }
 
+    void TAN::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("parent")) {
+            parent = hyperparameters["parent"];
+            hyperparameters.erase("parent");
+        }
+        Classifier::setHyperparameters(hyperparameters);
+    }
     void TAN::buildModel(const torch::Tensor& weights)
     {
         // 0. Add all nodes to the model
@@ -17,7 +35,10 @@ namespace bayesnet {
             mi.push_back({ i, mi_value });
         }
         sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
-        auto root = mi[mi.size() - 1].first;
+        auto root = parent == -1 ? mi[mi.size() - 1].first : parent;
+        if (root >= static_cast<int>(features.size())) {
+            throw std::invalid_argument("The parent node is not in the dataset");
+        }
         // 2. Compute mutual information between each feature and the class
         auto weights_matrix = metrics.conditionalEdge(weights);
         // 3. Compute the maximum spanning tree

bayesnet/classifiers/TAN.h

@@ -1,15 +1,23 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef TAN_H
 #define TAN_H
 #include "Classifier.h"
 namespace bayesnet {
     class TAN : public Classifier {
-    private:
-    protected:
-        void buildModel(const torch::Tensor& weights) override;
     public:
         TAN();
         virtual ~TAN() = default;
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
         std::vector<std::string> graph(const std::string& name = "TAN") const override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+    private:
+        int parent = -1;
     };
 }
 #endif

bayesnet/classifiers/TANLd.cc

@@ -1,8 +1,14 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include "TANLd.h"
 
 namespace bayesnet {
     TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
-    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
         checkInput(X_, y_);
         features = features_;
@@ -13,7 +19,7 @@ namespace bayesnet {
         states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        TAN::fit(dataset, features, className, states);
+        TAN::fit(dataset, features, className, states, smoothing);
         states = localDiscretizationProposal(states, model);
         return *this;
 

bayesnet/classifiers/TANLd.h

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

bayesnet/classifiers/XSP2DE.cc

@@ -0,0 +1,575 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "XSP2DE.h"
+#include <pthread.h>   // for pthread_setname_np on linux
+#include <cassert>
+#include <cmath>
+#include <limits>
+#include <stdexcept>
+#include <iostream>
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+// --------------------------------------
+// Constructor
+// --------------------------------------
+XSp2de::XSp2de(int spIndex1, int spIndex2)
+  : superParent1_{ spIndex1 }
+  , superParent2_{ spIndex2 }
+  , nFeatures_{0}
+  , statesClass_{0}
+  , alpha_{1.0}
+  , initializer_{1.0}
+  , semaphore_{ CountingSemaphore::getInstance() }
+  , Classifier(Network())
+{
+  validHyperparameters = { "parent1", "parent2" };
+}
+
+// --------------------------------------
+// setHyperparameters
+// --------------------------------------
+void XSp2de::setHyperparameters(const nlohmann::json &hyperparameters_)
+{
+  auto hyperparameters = hyperparameters_;
+  if (hyperparameters.contains("parent1")) {
+    superParent1_ = hyperparameters["parent1"];
+    hyperparameters.erase("parent1");
+  }
+  if (hyperparameters.contains("parent2")) {
+    superParent2_ = hyperparameters["parent2"];
+    hyperparameters.erase("parent2");
+  }
+  // Hand off anything else to base Classifier
+  Classifier::setHyperparameters(hyperparameters);
+}
+
+// --------------------------------------
+// fitx
+// --------------------------------------
+void XSp2de::fitx(torch::Tensor & X, torch::Tensor & y, 
+                  torch::Tensor & weights_, const Smoothing_t smoothing)
+{
+  m = X.size(1);  // number of samples
+  n = X.size(0);  // number of features
+  dataset = X;
+
+  // Build the dataset in your environment if needed:
+  buildDataset(y);
+
+  // Construct the data structures needed for counting
+  buildModel(weights_);
+
+  // Accumulate counts & convert to probabilities
+  trainModel(weights_, smoothing);
+  fitted = true;
+}
+
+// --------------------------------------
+// buildModel
+// --------------------------------------
+void XSp2de::buildModel(const torch::Tensor &weights)
+{
+  nFeatures_ = n;
+
+  // Derive the number of states for each feature from the dataset
+  // states_[f] = max value in dataset[f] + 1.
+  states_.resize(nFeatures_);
+  for (int f = 0; f < nFeatures_; f++) {
+    // This is naive: we take max in feature f. You might adapt for real data.
+    states_[f] = dataset[f].max().item<int>() + 1;
+  }
+  // Class states:
+  statesClass_ = dataset[-1].max().item<int>() + 1;
+
+  // Initialize the class counts
+  classCounts_.resize(statesClass_, 0.0);
+
+  // For sp1 -> p(sp1Val| c)
+  sp1FeatureCounts_.resize(states_[superParent1_] * statesClass_, 0.0);
+
+  // For sp2 -> p(sp2Val| c)
+  sp2FeatureCounts_.resize(states_[superParent2_] * statesClass_, 0.0);
+
+  // For child features, we store p(childVal | c, sp1Val, sp2Val).
+  // childCounts_ will hold raw counts. We’ll gather them in one big vector.
+  // We need an offset for each feature.
+  childOffsets_.resize(nFeatures_, -1);
+
+  int totalSize = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) {
+      // skip the superparents
+      childOffsets_[f] = -1;
+      continue;
+    }
+    childOffsets_[f] = totalSize;
+    // block size for a single child f: states_[f] * statesClass_ 
+    //                               * states_[superParent1_] 
+    //                               * states_[superParent2_].
+    totalSize += (states_[f] * statesClass_ 
+                  * states_[superParent1_] 
+                  * states_[superParent2_]);
+  }
+  childCounts_.resize(totalSize, 0.0);
+}
+
+// --------------------------------------
+// trainModel
+// --------------------------------------
+void XSp2de::trainModel(const torch::Tensor &weights, 
+                        const bayesnet::Smoothing_t smoothing)
+{
+  // Accumulate raw counts
+  for (int i = 0; i < m; i++) {
+    std::vector<int> instance(nFeatures_ + 1);
+    for (int f = 0; f < nFeatures_; f++) {
+      instance[f] = dataset[f][i].item<int>();
+    }
+    instance[nFeatures_] = dataset[-1][i].item<int>();  // class
+    double w = weights[i].item<double>();
+    addSample(instance, w);
+  }
+
+  // Choose alpha based on smoothing:
+  switch (smoothing) {
+    case bayesnet::Smoothing_t::ORIGINAL:
+      alpha_ = 1.0 / m;
+      break;
+    case bayesnet::Smoothing_t::LAPLACE:
+      alpha_ = 1.0;
+      break;
+    default:
+      alpha_ = 0.0; // no smoothing
+  }
+
+  // Large initializer factor for numerical stability
+  initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
+
+  // Convert raw counts to probabilities
+  computeProbabilities();
+}
+
+// --------------------------------------
+// addSample
+// --------------------------------------
+void XSp2de::addSample(const std::vector<int> &instance, double weight)
+{
+  if (weight <= 0.0)
+    return;
+
+  int c = instance.back();
+  // increment classCounts
+  classCounts_[c] += weight;
+
+  int sp1Val = instance[superParent1_];
+  int sp2Val = instance[superParent2_];
+
+  // p(sp1|c)
+  sp1FeatureCounts_[sp1Val * statesClass_ + c] += weight;
+
+  // p(sp2|c)
+  sp2FeatureCounts_[sp2Val * statesClass_ + c] += weight;
+
+  // p(childVal| c, sp1Val, sp2Val)
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_)
+      continue;
+
+    int childVal = instance[f];
+    int offset = childOffsets_[f];
+    // block layout: 
+    //    offset + (sp1Val*(states_[sp2_]* states_[f]* statesClass_)) 
+    //            + (sp2Val*(states_[f]* statesClass_)) 
+    //            + childVal*(statesClass_) 
+    //            + c
+    int blockSizeSp2 = states_[superParent2_] 
+                       * states_[f] 
+                       * statesClass_;
+    int blockSizeChild = states_[f] * statesClass_;
+
+    int idx = offset 
+            + sp1Val*blockSizeSp2 
+            + sp2Val*blockSizeChild 
+            + childVal*statesClass_ 
+            + c;
+    childCounts_[idx] += weight;
+  }
+}
+
+// --------------------------------------
+// computeProbabilities
+// --------------------------------------
+void XSp2de::computeProbabilities()
+{
+  double totalCount = std::accumulate(classCounts_.begin(), 
+                                      classCounts_.end(), 0.0);
+
+  // classPriors_
+  classPriors_.resize(statesClass_, 0.0);
+  if (totalCount <= 0.0) {
+    // fallback => uniform
+    double unif = 1.0 / static_cast<double>(statesClass_);
+    for (int c = 0; c < statesClass_; c++) {
+      classPriors_[c] = unif;
+    }
+  } else {
+    for (int c = 0; c < statesClass_; c++) {
+      classPriors_[c] = 
+        (classCounts_[c] + alpha_) 
+        / (totalCount + alpha_ * statesClass_);
+    }
+  }
+
+  // p(sp1Val| c)
+  sp1FeatureProbs_.resize(sp1FeatureCounts_.size());
+  int sp1Card = states_[superParent1_];
+  for (int spVal = 0; spVal < sp1Card; spVal++) {
+    for (int c = 0; c < statesClass_; c++) {
+      double denom = classCounts_[c] + alpha_ * sp1Card;
+      double num = sp1FeatureCounts_[spVal * statesClass_ + c] + alpha_;
+      sp1FeatureProbs_[spVal * statesClass_ + c] = 
+         (denom <= 0.0 ? 0.0 : num / denom);
+    }
+  }
+
+  // p(sp2Val| c)
+  sp2FeatureProbs_.resize(sp2FeatureCounts_.size());
+  int sp2Card = states_[superParent2_];
+  for (int spVal = 0; spVal < sp2Card; spVal++) {
+    for (int c = 0; c < statesClass_; c++) {
+      double denom = classCounts_[c] + alpha_ * sp2Card;
+      double num = sp2FeatureCounts_[spVal * statesClass_ + c] + alpha_;
+      sp2FeatureProbs_[spVal * statesClass_ + c] = 
+         (denom <= 0.0 ? 0.0 : num / denom);
+    }
+  }
+
+  // p(childVal| c, sp1Val, sp2Val)
+  childProbs_.resize(childCounts_.size());
+  int offset = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) 
+      continue;
+
+    int fCard = states_[f];
+    int sp1Card_ = states_[superParent1_];
+    int sp2Card_ = states_[superParent2_];
+    int childBlockSizeSp2 = sp2Card_ * fCard * statesClass_;
+    int childBlockSizeF   = fCard * statesClass_;
+
+    int blockSize = fCard * sp1Card_ * sp2Card_ * statesClass_;
+    for (int sp1Val = 0; sp1Val < sp1Card_; sp1Val++) {
+      for (int sp2Val = 0; sp2Val < sp2Card_; sp2Val++) {
+        for (int childVal = 0; childVal < fCard; childVal++) {
+          for (int c = 0; c < statesClass_; c++) {
+            // index in childCounts_ 
+            int idx = offset 
+                    + sp1Val*childBlockSizeSp2 
+                    + sp2Val*childBlockSizeF 
+                    + childVal*statesClass_ 
+                    + c;
+            double num = childCounts_[idx] + alpha_;
+            // denominator is the count of (sp1Val,sp2Val,c) plus alpha * fCard
+            // We can find that by summing childVal dimension, but we already
+            // have it in childCounts_[...] or we can re-check the superparent 
+            // counts if your approach is purely hierarchical. 
+            // Here we'll do it like the XSpode approach: sp1&sp2 are 
+            // conditionally independent given c, so denominators come from 
+            // summing the relevant block or we treat sp1,sp2 as "parents."
+            // A simpler approach: 
+            double sumSp1Sp2C = 0.0;
+            // sum over all childVal:
+            for (int cv = 0; cv < fCard; cv++) {
+              int idx2 = offset
+                       + sp1Val*childBlockSizeSp2
+                       + sp2Val*childBlockSizeF
+                       + cv*statesClass_ + c;
+              sumSp1Sp2C += childCounts_[idx2];
+            }
+            double denom = sumSp1Sp2C + alpha_ * fCard;
+            childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
+          }
+        }
+      }
+    }
+    offset += blockSize;
+  }
+}
+
+// --------------------------------------
+// predict_proba (single instance)
+// --------------------------------------
+std::vector<double> XSp2de::predict_proba(const std::vector<int> &instance) const
+{
+  if (!fitted) {
+    throw std::logic_error(CLASSIFIER_NOT_FITTED);
+  }
+  std::vector<double> probs(statesClass_, 0.0);
+
+  int sp1Val = instance[superParent1_];
+  int sp2Val = instance[superParent2_];
+
+  // Start with p(c) * p(sp1Val| c) * p(sp2Val| c)
+  for (int c = 0; c < statesClass_; c++) {
+    double pC = classPriors_[c];
+    double pSp1C = sp1FeatureProbs_[sp1Val * statesClass_ + c];
+    double pSp2C = sp2FeatureProbs_[sp2Val * statesClass_ + c];
+    probs[c] = pC * pSp1C * pSp2C * initializer_;
+  }
+
+  // Multiply by each child feature f
+  int offset = 0;
+  for (int f = 0; f < nFeatures_; f++) {
+    if (f == superParent1_ || f == superParent2_) 
+      continue;
+
+    int valF = instance[f];
+    int fCard = states_[f];
+    int sp1Card = states_[superParent1_];
+    int sp2Card = states_[superParent2_];
+    int blockSizeSp2 = sp2Card * fCard * statesClass_;
+    int blockSizeF   = fCard * statesClass_;
+
+    // base index for childProbs_ for this child and sp1Val, sp2Val
+    int base = offset 
+             + sp1Val*blockSizeSp2 
+             + sp2Val*blockSizeF 
+             + valF*statesClass_;
+    for (int c = 0; c < statesClass_; c++) {
+      probs[c] *= childProbs_[base + c];
+    }
+    offset += (fCard * sp1Card * sp2Card * statesClass_);
+  }
+
+  // Normalize
+  normalize(probs);
+  return probs;
+}
+
+// --------------------------------------
+// predict_proba (batch)
+// --------------------------------------
+std::vector<std::vector<double>> XSp2de::predict_proba(std::vector<std::vector<int>> &test_data)
+{
+  int test_size = test_data[0].size();  // each feature is test_data[f], size = #samples
+  int sample_size = test_data.size();   // = nFeatures_
+  std::vector<std::vector<double>> probabilities(
+      test_size, std::vector<double>(statesClass_, 0.0));
+
+  // same concurrency approach
+  int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+  std::vector<std::thread> threads;
+
+  auto worker = [&](const std::vector<std::vector<int>> &samples, 
+                    int begin, 
+                    int chunk, 
+                    int sample_size, 
+                    std::vector<std::vector<double>> &predictions) {
+    std::string threadName =
+      "XSp2de-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+    pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+    pthread_setname_np(threadName.c_str());
+#endif
+
+    std::vector<int> instance(sample_size);
+    for (int sample = begin; sample < begin + chunk; ++sample) {
+      for (int feature = 0; feature < sample_size; ++feature) {
+        instance[feature] = samples[feature][sample];
+      }
+      predictions[sample] = predict_proba(instance);
+    }
+    semaphore_.release();
+  };
+
+  for (int begin = 0; begin < test_size; begin += chunk_size) {
+    int chunk = std::min(chunk_size, test_size - begin);
+    semaphore_.acquire();
+    threads.emplace_back(worker, test_data, begin, chunk, sample_size, 
+                         std::ref(probabilities));
+  }
+  for (auto &th : threads) {
+    th.join();
+  }
+  return probabilities;
+}
+
+// --------------------------------------
+// predict (single instance)
+// --------------------------------------
+int XSp2de::predict(const std::vector<int> &instance) const
+{
+  auto p = predict_proba(instance);
+  return static_cast<int>(
+    std::distance(p.begin(), std::max_element(p.begin(), p.end()))
+  );
+}
+
+// --------------------------------------
+// predict (batch of data)
+// --------------------------------------
+std::vector<int> XSp2de::predict(std::vector<std::vector<int>> &test_data)
+{
+  auto probabilities = predict_proba(test_data);
+  std::vector<int> predictions(probabilities.size(), 0);
+
+  for (size_t i = 0; i < probabilities.size(); i++) {
+    predictions[i] = static_cast<int>(
+      std::distance(probabilities[i].begin(), 
+                    std::max_element(probabilities[i].begin(), 
+                                     probabilities[i].end()))
+    );
+  }
+  return predictions;
+}
+
+// --------------------------------------
+// predict (torch::Tensor version)
+// --------------------------------------
+torch::Tensor XSp2de::predict(torch::Tensor &X)
+{
+  auto X_ = TensorUtils::to_matrix(X);
+  auto result_v = predict(X_);
+  return torch::tensor(result_v, torch::kInt32);
+}
+
+// --------------------------------------
+// predict_proba (torch::Tensor version)
+// --------------------------------------
+torch::Tensor XSp2de::predict_proba(torch::Tensor &X)
+{
+  auto X_ = TensorUtils::to_matrix(X);
+  auto result_v = predict_proba(X_);
+  int n_samples = X.size(1);
+  torch::Tensor result =
+    torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
+  for (int i = 0; i < (int)result_v.size(); ++i) {
+    result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
+  }
+  return result;
+}
+
+// --------------------------------------
+// score (torch::Tensor version)
+// --------------------------------------
+float XSp2de::score(torch::Tensor &X, torch::Tensor &y)
+{
+  torch::Tensor y_pred = predict(X);
+  return (y_pred == y).sum().item<float>() / y.size(0);
+}
+
+// --------------------------------------
+// score (vector version)
+// --------------------------------------
+float XSp2de::score(std::vector<std::vector<int>> &X, std::vector<int> &y)
+{
+  auto y_pred = predict(X);
+  int correct = 0;
+  for (size_t i = 0; i < y_pred.size(); ++i) {
+    if (y_pred[i] == y[i]) {
+      correct++;
+    }
+  }
+  return static_cast<float>(correct) / static_cast<float>(y_pred.size());
+}
+
+// --------------------------------------
+// Utility: normalize
+// --------------------------------------
+void XSp2de::normalize(std::vector<double> &v) const
+{
+  double sum = 0.0;
+  for (auto &val : v) {
+    sum += val;
+  }
+  if (sum > 0.0) {
+    for (auto &val : v) {
+      val /= sum;
+    }
+  }
+}
+
+// --------------------------------------
+// to_string
+// --------------------------------------
+std::string XSp2de::to_string() const
+{
+  std::ostringstream oss;
+  oss << "----- XSp2de Model -----\n"
+      << "nFeatures_    = " << nFeatures_    << "\n"
+      << "superParent1_ = " << superParent1_ << "\n"
+      << "superParent2_ = " << superParent2_ << "\n"
+      << "statesClass_  = " << statesClass_  << "\n\n";
+
+  oss << "States: [";
+  for (auto s : states_) oss << s << " ";
+  oss << "]\n";
+
+  oss << "classCounts_:\n";
+  for (auto v : classCounts_) oss << v << " ";
+  oss << "\nclassPriors_:\n";
+  for (auto v : classPriors_) oss << v << " ";
+  oss << "\nsp1FeatureCounts_ (size=" << sp1FeatureCounts_.size() << ")\n";
+  for (auto v : sp1FeatureCounts_) oss << v << " ";
+  oss << "\nsp2FeatureCounts_ (size=" << sp2FeatureCounts_.size() << ")\n";
+  for (auto v : sp2FeatureCounts_) oss << v << " ";
+  oss << "\nchildCounts_ (size=" << childCounts_.size() << ")\n";
+  for (auto v : childCounts_) oss << v << " ";
+
+  oss << "\nchildOffsets_:\n";
+  for (auto c : childOffsets_) oss << c << " ";
+
+  oss << "\n----------------------------------------\n";
+  return oss.str();
+}
+
+// --------------------------------------
+// Some introspection about the graph
+// --------------------------------------
+int XSp2de::getNumberOfNodes() const 
+{
+  // nFeatures + 1 class node
+  return nFeatures_ + 1;
+}
+
+int XSp2de::getClassNumStates() const 
+{ 
+  return statesClass_; 
+}
+
+int XSp2de::getNFeatures() const 
+{ 
+  return nFeatures_; 
+}
+
+int XSp2de::getNumberOfStates() const
+{
+  // purely an example. Possibly you want to sum up actual 
+  // cardinalities or something else. 
+  return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+}
+
+int XSp2de::getNumberOfEdges() const
+{
+  // In an SPNDE with n=2, for each feature we have edges from class, sp1, sp2. 
+  // So that’s 3*(nFeatures_) edges, minus the ones for the superparents themselves, 
+  // plus the edges from class->superparent1, class->superparent2. 
+  // For a quick approximation:
+  //   - class->sp1, class->sp2 => 2 edges
+  //   - class->child => (nFeatures -2) edges
+  //   - sp1->child, sp2->child => 2*(nFeatures -2) edges
+  // total = 2 + (nFeatures-2) + 2*(nFeatures-2) = 2 + 3*(nFeatures-2) 
+  //         = 3nFeatures - 4 (just an example).
+  // You can adapt to your liking:
+  return 3 * nFeatures_ - 4; 
+}
+
+} // namespace bayesnet
+

bayesnet/classifiers/XSP2DE.h

@@ -0,0 +1,75 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XSP2DE_H
+#define XSP2DE_H
+
+#include "Classifier.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <torch/torch.h>
+#include <vector>
+
+namespace bayesnet {
+
+class XSp2de : public Classifier {
+  public:
+    XSp2de(int spIndex1, int spIndex2);
+    void setHyperparameters(const nlohmann::json &hyperparameters_) override;
+    void fitx(torch::Tensor &X, torch::Tensor &y, torch::Tensor &weights_, const Smoothing_t smoothing);
+    std::vector<double> predict_proba(const std::vector<int> &instance) const;
+    std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>> &test_data) override;
+    int predict(const std::vector<int> &instance) const;
+    std::vector<int> predict(std::vector<std::vector<int>> &test_data) override;
+    torch::Tensor predict(torch::Tensor &X) override;
+    torch::Tensor predict_proba(torch::Tensor &X) override;
+
+    float score(torch::Tensor &X, torch::Tensor &y) override;
+    float score(std::vector<std::vector<int>> &X, std::vector<int> &y) override;
+    std::string to_string() const;
+    std::vector<std::string> graph(const std::string &title) const override {
+        return std::vector<std::string>({title});
+    }
+
+    int getNumberOfNodes() const override;
+    int getNumberOfEdges() const override;
+    int getNFeatures() const;
+    int getClassNumStates() const override;
+    int getNumberOfStates() const override;
+
+  protected:
+    void buildModel(const torch::Tensor &weights) override;
+    void trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing) override;
+
+  private:
+    void addSample(const std::vector<int> &instance, double weight);
+    void normalize(std::vector<double> &v) const;
+    void computeProbabilities();
+
+    int superParent1_;
+    int superParent2_;
+    int nFeatures_;
+    int statesClass_;
+    double alpha_;
+    double initializer_;
+
+    std::vector<int> states_;
+    std::vector<double> classCounts_;
+    std::vector<double> classPriors_;
+    std::vector<double> sp1FeatureCounts_, sp1FeatureProbs_;
+    std::vector<double> sp2FeatureCounts_, sp2FeatureProbs_;
+    // childOffsets_[f] will be the offset into childCounts_ for feature f.
+    // If f is either superParent1 or superParent2, childOffsets_[f] = -1
+    std::vector<int> childOffsets_;
+    // For each child f, we store p(x_f | c, sp1Val, sp2Val).  We'll store the raw
+    // counts in childCounts_, and the probabilities in childProbs_, with a
+    // dimension block of size: states_[f]* statesClass_* states_[sp1]* states_[sp2].
+    std::vector<double> childCounts_;
+    std::vector<double> childProbs_;
+    CountingSemaphore &semaphore_;
+};
+
+} // namespace bayesnet
+#endif // XSP2DE_H

bayesnet/classifiers/XSPODE.cc

@@ -0,0 +1,450 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <numeric>
+#include <sstream>
+#include <stdexcept>
+#include "XSPODE.h"
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+  // --------------------------------------
+  // Constructor
+  // --------------------------------------
+  XSpode::XSpode(int spIndex)
+    : superParent_{ spIndex }, nFeatures_{ 0 }, statesClass_{ 0 }, alpha_{ 1.0 },
+    initializer_{ 1.0 }, semaphore_{ CountingSemaphore::getInstance() },
+    Classifier(Network())
+  {
+    validHyperparameters = { "parent" };
+  }
+
+  void XSpode::setHyperparameters(const nlohmann::json& hyperparameters_)
+  {
+    auto hyperparameters = hyperparameters_;
+    if (hyperparameters.contains("parent")) {
+      superParent_ = hyperparameters["parent"];
+      hyperparameters.erase("parent");
+    }
+    Classifier::setHyperparameters(hyperparameters);
+  }
+
+  void XSpode::fitx(torch::Tensor & X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing)
+  {
+    m = X.size(1);
+    n = X.size(0);
+    dataset = X;
+    buildDataset(y);
+    buildModel(weights_);
+    trainModel(weights_, smoothing);
+    fitted = true;
+  }
+
+  // --------------------------------------
+  // trainModel
+  // --------------------------------------
+  // Initialize storage needed for the super-parent and child features counts and
+  // probs.
+  // --------------------------------------
+  void XSpode::buildModel(const torch::Tensor& weights)
+  {
+    int numInstances = m;
+    nFeatures_ = n;
+
+    // Derive the number of states for each feature and for the class.
+    // (This is just one approach; adapt to match your environment.)
+    // Here, we assume the user also gave us the total #states per feature in e.g.
+    // statesMap. We'll simply reconstruct the integer states_ array. The last
+    // entry is statesClass_.
+    states_.resize(nFeatures_);
+    for (int f = 0; f < nFeatures_; f++) {
+      // Suppose you look up in “statesMap” by the feature name, or read directly
+      // from X. We'll assume states_[f] = max value in X[f] + 1.
+      states_[f] = dataset[f].max().item<int>() + 1;
+    }
+    // For the class: states_.back() = max(y)+1
+    statesClass_ = dataset[-1].max().item<int>() + 1;
+
+    // Initialize counts
+    classCounts_.resize(statesClass_, 0.0);
+    // p(x_sp = spVal | c)
+    // We'll store these counts in spFeatureCounts_[spVal * statesClass_ + c].
+    spFeatureCounts_.resize(states_[superParent_] * statesClass_, 0.0);
+
+    // For each child ≠ sp, we store p(childVal| c, spVal) in a separate block of
+    // childCounts_. childCounts_ will be sized as sum_{child≠sp} (states_[child]
+    // * statesClass_ * states_[sp]). We also need an offset for each child to
+    // index into childCounts_.
+    childOffsets_.resize(nFeatures_, -1);
+    int totalSize = 0;
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue; // skip sp
+      childOffsets_[f] = totalSize;
+      // block size for this child's counts: states_[f] * statesClass_ *
+      // states_[superParent_]
+      totalSize += (states_[f] * statesClass_ * states_[superParent_]);
+    }
+    childCounts_.resize(totalSize, 0.0);
+  }
+  // --------------------------------------
+  // buildModel
+  // --------------------------------------
+  //
+  // We only store conditional probabilities for:
+  //   p(x_sp| c)   (the super-parent feature)
+  //   p(x_child| c, x_sp)  for all child ≠ sp
+  //
+  // --------------------------------------
+  void XSpode::trainModel(const torch::Tensor& weights,
+    const bayesnet::Smoothing_t smoothing)
+  {
+    // Accumulate raw counts
+    for (int i = 0; i < m; i++) {
+      std::vector<int> instance(nFeatures_ + 1);
+      for (int f = 0; f < nFeatures_; f++) {
+        instance[f] = dataset[f][i].item<int>();
+      }
+      instance[nFeatures_] = dataset[-1][i].item<int>();
+      addSample(instance, weights[i].item<double>());
+    }
+    switch (smoothing) {
+      case bayesnet::Smoothing_t::ORIGINAL:
+        alpha_ = 1.0 / m;
+        break;
+      case bayesnet::Smoothing_t::LAPLACE:
+        alpha_ = 1.0;
+        break;
+      default:
+        alpha_ = 0.0; // No smoothing
+    }
+    initializer_ = std::numeric_limits<double>::max() /
+      (nFeatures_ * nFeatures_); // for numerical stability
+    // Convert raw counts to probabilities
+    computeProbabilities();
+  }
+
+  // --------------------------------------
+  // addSample
+  // --------------------------------------
+  //
+  // instance has size nFeatures_ + 1, with the class at the end.
+  // We add 1 to the appropriate counters for each (c, superParentVal, childVal).
+  //
+  void XSpode::addSample(const std::vector<int>& instance, double weight)
+  {
+    if (weight <= 0.0)
+      return;
+
+    int c = instance.back();
+    // (A) increment classCounts
+    classCounts_[c] += weight;
+
+    // (B) increment super-parent counts => p(x_sp | c)
+    int spVal = instance[superParent_];
+    spFeatureCounts_[spVal * statesClass_ + c] += weight;
+
+    // (C) increment child counts => p(childVal | c, x_sp)
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue;
+      int childVal = instance[f];
+      int offset = childOffsets_[f];
+      // Compute index in childCounts_.
+      // Layout: [ offset + (spVal * states_[f] + childVal) * statesClass_ + c ]
+      int blockSize = states_[f] * statesClass_;
+      int idx = offset + spVal * blockSize + childVal * statesClass_ + c;
+      childCounts_[idx] += weight;
+    }
+  }
+
+  // --------------------------------------
+  // computeProbabilities
+  // --------------------------------------
+  //
+  // Once all samples are added in COUNTS mode, call this to:
+  //    p(c)
+  //    p(x_sp = spVal | c)
+  //    p(x_child = v | c, x_sp = s_sp)
+  //
+  // --------------------------------------
+  void XSpode::computeProbabilities()
+  {
+    double totalCount =
+      std::accumulate(classCounts_.begin(), classCounts_.end(), 0.0);
+
+    // p(c) => classPriors_
+    classPriors_.resize(statesClass_, 0.0);
+    if (totalCount <= 0.0) {
+      // fallback => uniform
+      double unif = 1.0 / static_cast<double>(statesClass_);
+      for (int c = 0; c < statesClass_; c++) {
+        classPriors_[c] = unif;
+      }
+    } else {
+      for (int c = 0; c < statesClass_; c++) {
+        classPriors_[c] =
+          (classCounts_[c] + alpha_) / (totalCount + alpha_ * statesClass_);
+      }
+    }
+
+    // p(x_sp | c)
+    spFeatureProbs_.resize(spFeatureCounts_.size());
+    // denominator for spVal * statesClass_ + c is just classCounts_[c] + alpha_ *
+    // (#states of sp)
+    int spCard = states_[superParent_];
+    for (int spVal = 0; spVal < spCard; spVal++) {
+      for (int c = 0; c < statesClass_; c++) {
+        double denom = classCounts_[c] + alpha_ * spCard;
+        double num = spFeatureCounts_[spVal * statesClass_ + c] + alpha_;
+        spFeatureProbs_[spVal * statesClass_ + c] = (denom <= 0.0 ? 0.0 : num / denom);
+      }
+    }
+
+    // p(x_child | c, x_sp)
+    childProbs_.resize(childCounts_.size());
+    for (int f = 0; f < nFeatures_; f++) {
+      if (f == superParent_)
+        continue;
+      int offset = childOffsets_[f];
+      int childCard = states_[f];
+
+      // For each spVal, c, childVal in childCounts_:
+      for (int spVal = 0; spVal < spCard; spVal++) {
+        for (int childVal = 0; childVal < childCard; childVal++) {
+          for (int c = 0; c < statesClass_; c++) {
+            int idx = offset + spVal * (childCard * statesClass_) +
+              childVal * statesClass_ + c;
+
+            double num = childCounts_[idx] + alpha_;
+            // denominator = spFeatureCounts_[spVal * statesClass_ + c] + alpha_ *
+            // (#states of child)
+            double denom =
+              spFeatureCounts_[spVal * statesClass_ + c] + alpha_ * childCard;
+            childProbs_[idx] = (denom <= 0.0 ? 0.0 : num / denom);
+          }
+        }
+      }
+    }
+  }
+
+  // --------------------------------------
+  // predict_proba
+  // --------------------------------------
+  //
+  // For a single instance x of dimension nFeatures_:
+  //  P(c | x) ∝ p(c) × p(x_sp | c) × ∏(child ≠ sp) p(x_child | c, x_sp).
+  //
+  // --------------------------------------
+  std::vector<double> XSpode::predict_proba(const std::vector<int>& instance) const
+  {
+    if (!fitted) {
+      throw std::logic_error(CLASSIFIER_NOT_FITTED);
+    }
+    std::vector<double> probs(statesClass_, 0.0);
+    // Multiply p(c) × p(x_sp | c)
+    int spVal = instance[superParent_];
+    for (int c = 0; c < statesClass_; c++) {
+      double pc = classPriors_[c];
+      double pSpC = spFeatureProbs_[spVal * statesClass_ + c];
+      probs[c] = pc * pSpC * initializer_;
+    }
+
+    // Multiply by each child’s probability p(x_child | c, x_sp)
+    for (int feature = 0; feature < nFeatures_; feature++) {
+      if (feature == superParent_)
+        continue; // skip sp
+      int sf = instance[feature];
+      int offset = childOffsets_[feature];
+      int childCard = states_[feature]; // not used directly, but for clarity
+      // Index into childProbs_ = offset + spVal*(childCard*statesClass_) +
+      // childVal*statesClass_ + c
+      int base = offset + spVal * (childCard * statesClass_) + sf * statesClass_;
+      for (int c = 0; c < statesClass_; c++) {
+        probs[c] *= childProbs_[base + c];
+      }
+    }
+
+    // Normalize
+    normalize(probs);
+    return probs;
+  }
+  std::vector<std::vector<double>> XSpode::predict_proba(std::vector<std::vector<int>>& test_data)
+  {
+    int test_size = test_data[0].size();
+    int sample_size = test_data.size();
+    auto probabilities = std::vector<std::vector<double>>(
+      test_size, std::vector<double>(statesClass_));
+
+    int chunk_size = std::min(150, int(test_size / semaphore_.getMaxCount()) + 1);
+    std::vector<std::thread> threads;
+    auto worker = [&](const std::vector<std::vector<int>>& samples, int begin,
+      int chunk, int sample_size,
+      std::vector<std::vector<double>>& predictions) {
+        std::string threadName =
+          "(V)PWorker-" + std::to_string(begin) + "-" + std::to_string(chunk);
+#if defined(__linux__)
+        pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+        pthread_setname_np(threadName.c_str());
+#endif
+
+        std::vector<int> instance(sample_size);
+        for (int sample = begin; sample < begin + chunk; ++sample) {
+          for (int feature = 0; feature < sample_size; ++feature) {
+            instance[feature] = samples[feature][sample];
+          }
+          predictions[sample] = predict_proba(instance);
+        }
+        semaphore_.release();
+      };
+    for (int begin = 0; begin < test_size; begin += chunk_size) {
+      int chunk = std::min(chunk_size, test_size - begin);
+      semaphore_.acquire();
+      threads.emplace_back(worker, test_data, begin, chunk, sample_size, std::ref(probabilities));
+    }
+    for (auto& thread : threads) {
+      thread.join();
+    }
+    return probabilities;
+  }
+
+  // --------------------------------------
+  // Utility: normalize
+  // --------------------------------------
+  void XSpode::normalize(std::vector<double>& v) const
+  {
+    double sum = 0.0;
+    for (auto val : v) {
+      sum += val;
+    }
+    if (sum <= 0.0) {
+      return;
+    }
+    for (auto& val : v) {
+      val /= sum;
+    }
+  }
+
+  // --------------------------------------
+  // representation of the model
+  // --------------------------------------
+  std::string XSpode::to_string() const
+  {
+    std::ostringstream oss;
+    oss << "----- XSpode Model -----" << std::endl
+      << "nFeatures_  = " << nFeatures_ << std::endl
+      << "superParent_ = " << superParent_ << std::endl
+      << "statesClass_ = " << statesClass_ << std::endl
+      << std::endl;
+
+    oss << "States: [";
+    for (int s : states_)
+      oss << s << " ";
+    oss << "]" << std::endl;
+    oss << "classCounts_: [";
+    for (double c : classCounts_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "classPriors_: [";
+    for (double c : classPriors_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "spFeatureCounts_: size = " << spFeatureCounts_.size() << std::endl
+      << "[";
+    for (double c : spFeatureCounts_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "spFeatureProbs_: size = " << spFeatureProbs_.size() << std::endl
+      << "[";
+    for (double c : spFeatureProbs_)
+      oss << c << " ";
+    oss << "]" << std::endl;
+    oss << "childCounts_: size = " << childCounts_.size() << std::endl << "[";
+    for (double cc : childCounts_)
+      oss << cc << " ";
+    oss << "]" << std::endl;
+
+    for (double cp : childProbs_)
+      oss << cp << " ";
+    oss << "]" << std::endl;
+    oss << "childOffsets_: [";
+    for (int co : childOffsets_)
+      oss << co << " ";
+    oss << "]" << std::endl;
+    oss << std::string(40,'-') << std::endl;
+    return oss.str();
+  }
+  int XSpode::getNumberOfNodes() const { return nFeatures_ + 1; }
+  int XSpode::getClassNumStates() const { return statesClass_; }
+  int XSpode::getNFeatures() const { return nFeatures_; }
+  int XSpode::getNumberOfStates() const
+  {
+    return std::accumulate(states_.begin(), states_.end(), 0) * nFeatures_;
+  }
+  int XSpode::getNumberOfEdges() const
+  {
+    return 2 * nFeatures_ + 1;
+  }
+
+  // ------------------------------------------------------
+  // Predict overrides (classifier interface)
+  // ------------------------------------------------------
+  int XSpode::predict(const std::vector<int>& instance) const
+  {
+    auto p = predict_proba(instance);
+    return static_cast<int>(std::distance(p.begin(), std::max_element(p.begin(), p.end())));
+  }
+  std::vector<int> XSpode::predict(std::vector<std::vector<int>>& test_data)
+  {
+    auto probabilities = predict_proba(test_data);
+    std::vector<int> predictions(probabilities.size(), 0);
+
+    for (size_t i = 0; i < probabilities.size(); i++) {
+      predictions[i] = std::distance(
+        probabilities[i].begin(),
+        std::max_element(probabilities[i].begin(), probabilities[i].end()));
+    }
+    return predictions;
+  }
+  torch::Tensor XSpode::predict(torch::Tensor& X)
+  {
+    auto X_ = TensorUtils::to_matrix(X);
+    auto result_v = predict(X_);
+    return torch::tensor(result_v, torch::kInt32);
+  }
+  torch::Tensor XSpode::predict_proba(torch::Tensor& X)
+  {
+    auto X_ = TensorUtils::to_matrix(X);
+    auto result_v = predict_proba(X_);
+    int n_samples = X.size(1);
+    torch::Tensor result =
+      torch::zeros({ n_samples, statesClass_ }, torch::kDouble);
+    for (int i = 0; i < result_v.size(); ++i) {
+      result.index_put_({ i, "..." }, torch::tensor(result_v[i]));
+    }
+    return result;
+  }
+  float XSpode::score(torch::Tensor& X, torch::Tensor& y)
+  {
+    torch::Tensor y_pred = predict(X);
+    return (y_pred == y).sum().item<float>() / y.size(0);
+  }
+  float XSpode::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+  {
+    auto y_pred = this->predict(X);
+    int correct = 0;
+    for (int i = 0; i < y_pred.size(); ++i) {
+      if (y_pred[i] == y[i]) {
+        correct++;
+      }
+    }
+    return (double)correct / y_pred.size();
+  }
+} // namespace bayesnet

bayesnet/classifiers/XSPODE.h

@@ -0,0 +1,76 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XSPODE_H
+#define XSPODE_H
+
+#include <vector>
+#include <torch/torch.h>
+#include "Classifier.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+
+namespace bayesnet {
+
+    class XSpode : public Classifier {
+    public:
+        explicit XSpode(int spIndex);
+        std::vector<double> predict_proba(const std::vector<int>& instance) const;
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        int predict(const std::vector<int>& instance) const;
+        void normalize(std::vector<double>& v) const;
+        std::string to_string() const;
+        int getNFeatures() const;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        int getClassNumStates() const override;
+        std::vector<int>& getStates();
+        std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({ title }); }
+        void fitx(torch::Tensor& X, torch::Tensor& y, torch::Tensor& weights_, const Smoothing_t smoothing);
+        void setHyperparameters(const nlohmann::json& hyperparameters_) override;
+
+        //
+        // Classifier interface
+        //
+        torch::Tensor predict(torch::Tensor& X) override;
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+    protected:
+        void buildModel(const torch::Tensor& weights) override;
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        void addSample(const std::vector<int>& instance, double weight);
+        void computeProbabilities();
+        int superParent_;
+        int nFeatures_;
+        int statesClass_;
+        std::vector<int> states_;          // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
+
+        // Class counts
+        std::vector<double> classCounts_;  // [c], accumulative
+        std::vector<double> classPriors_;  // [c], after normalization
+
+        // For p(x_sp = spVal | c)
+        std::vector<double> spFeatureCounts_; // [spVal * statesClass_ + c]
+        std::vector<double> spFeatureProbs_;  // same shape, after normalization
+
+        // For p(x_child = childVal | x_sp = spVal, c)
+        // childCounts_ is big enough to hold all child features except sp:
+        //   For each child f, we store childOffsets_[f] as the start index, then
+        //   childVal, spVal, c => the data.
+        std::vector<double> childCounts_;
+        std::vector<double> childProbs_;
+        std::vector<int>    childOffsets_;
+
+        double alpha_ = 1.0;
+        double initializer_; // for numerical stability
+        CountingSemaphore& semaphore_;
+    };
+}
+
+#endif // XSPODE_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()) {
+        Classifier::setHyperparameters(hyperparameters);
-            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
-        }
     }
     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

@@ -1,23 +1,16 @@
+// ***************************************************************
+// 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_)
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
-    {
-        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_)
     {
         checkInput(X_, y_);
         features = features_;
@@ -27,8 +20,9 @@ namespace bayesnet {
         // 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 TAN structure, TAN::fit initializes the base Bayesian network
+        // 1st we need to fit the model to build the normal AODE structure, Ensemble::fit  
-        Ensemble::fit(dataset, features, className, states);
+        // calls buildModel to initialize the base models
+        Ensemble::fit(dataset, features, className, states, smoothing);
         return *this;
 
     }
@@ -41,10 +35,10 @@ namespace bayesnet {
         n_models = models.size();
         significanceModels = std::vector<double>(n_models, 1.0);
     }
-    void AODELd::trainModel(const torch::Tensor& weights)
+    void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         for (const auto& model : models) {
-            model->fit(Xf, y, features, className, states);
+            model->fit(Xf, y, features, className, states, smoothing);
         }
     }
     std::vector<std::string> AODELd::graph(const std::string& name) const

bayesnet/ensembles/AODELd.h

@@ -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"
@@ -9,11 +15,10 @@ namespace bayesnet {
     public:
         AODELd(bool predict_voting = true);
         virtual ~AODELd() = default;
-        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_) override;
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing) override;
-        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;
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildModel(const torch::Tensor& weights) override;
     };
 }

bayesnet/ensembles/Boost.cc

@@ -0,0 +1,268 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "Boost.h"
+#include "bayesnet/feature_selection/CFS.h"
+#include "bayesnet/feature_selection/FCBF.h"
+#include "bayesnet/feature_selection/IWSS.h"
+#include <folding.hpp>
+
+namespace bayesnet {
+Boost::Boost(bool predict_voting) : Ensemble(predict_voting) {
+    validHyperparameters = {"alpha_block", "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("alpha_block")) {
+        alpha_block = hyperparameters["alpha_block"];
+        hyperparameters.erase("alpha_block");
+    }
+    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 > 6)
+            throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 6]");
+        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");
+    }
+    if (block_update && alpha_block) {
+        throw std::invalid_argument("alpha_block and block_update cannot be true at the same time");
+    }
+    if (block_update && !bisection) {
+        throw std::invalid_argument("block_update needs bisection to be true");
+    }
+    Classifier::setHyperparameters(hyperparameters);
+}
+void Boost::add_model(std::unique_ptr<Classifier> model, double significance) {
+    models.push_back(std::move(model));
+    n_models++;
+    significanceModels.push_back(significance);
+}
+void Boost::remove_last_model() {
+    models.pop_back();
+    significanceModels.pop_back();
+    n_models--;
+}
+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>();
+    // std::cout << "epsilon_t: " << epsilon_t << " count wrong: " << mask_wrong.sum().item<int>() << " count right: "
+    // << mask_right.sum().item<int>() << std::endl;
+    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};
+}
+} // namespace bayesnet

bayesnet/ensembles/Boost.h

@@ -0,0 +1,57 @@
+// ***************************************************************
+// 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() override = 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);
+        void add_model(std::unique_ptr<Classifier> model, double significance);
+        void remove_last_model();
+        //
+        // Attributes
+        //
+        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 = Orders.DESC; // 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; // Selected feature selection algorithm
+        FeatureSelect* featureSelector = nullptr;
+        double threshold = -1;
+        bool block_update = false; // if true, use block update algorithm, only meaningful if bisection is true
+        bool alpha_block = false; // if true, the alpha is computed with the ensemble built so far and the new model
+    };
+}
+#endif

bayesnet/ensembles/BoostA2DE.cc

@@ -0,0 +1,165 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <limits.h>
+#include <tuple>
+#include <folding.hpp>
+#include "BoostA2DE.h"
+
+namespace bayesnet {
+
+    BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
+    {
+    }
+    std::vector<int> BoostA2DE::initializeModels(const Smoothing_t smoothing)
+    {
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        if (featuresSelected.size() < 2) {
+            notes.push_back("No features selected in initialization");
+            status = ERROR;
+            return std::vector<int>();
+        }
+        for (int i = 0; i < featuresSelected.size() - 1; i++) {
+            for (int j = i + 1; j < featuresSelected.size(); j++) {
+                auto parents = { featuresSelected[i], featuresSelected[j] };
+                std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                models.push_back(std::move(model));
+                significanceModels.push_back(1.0); // They will be updated later in trainModel
+                n_models++;
+            }
+        }
+        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
+        return featuresSelected;
+    }
+    void BoostA2DE::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
+    {
+        //
+        // Logging setup
+        //
+        // loguru::set_thread_name("BoostA2DE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
+
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
+        fitted = true;
+        double alpha_t = 0;
+        torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            if (featuresUsed.size() == 0) {
+                return;
+            }
+            auto ypred = predict(X_train);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            // Update significance of the models
+            for (int i = 0; i < n_models; ++i) {
+                significanceModels[i] = alpha_t;
+            }
+            if (finished) {
+                return;
+            }
+        }
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
+        // Variables to control the accuracy finish condition
+        double priorAccuracy = 0.0;
+        double improvement = 1.0;
+        double convergence_threshold = 1e-4;
+        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
+        // Step 0: Set the finish condition
+        // epsilon sub t > 0.5 => inverse the weights policy
+        // validation error is not decreasing
+        // run out of features
+        bool ascending = order_algorithm == Orders.ASC;
+        std::mt19937 g{ 173 };
+        std::vector<std::pair<int, int>> pairSelection;
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
+            if (order_algorithm == Orders.RAND) {
+                std::shuffle(pairSelection.begin(), pairSelection.end(), g);
+            }
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            while (counter++ < k && pairSelection.size() > 0) {
+                auto feature_pair = pairSelection[0];
+                pairSelection.erase(pairSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    auto ypred = model->predict(X_train);
+                    // Step 3.1: Compute the classifier amout of say
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+                }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
+            }
+            if (block_update) {
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+            }
+            if (convergence && !finished) {
+                auto y_val_predict = predict(X_test);
+                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+                if (priorAccuracy == 0) {
+                    priorAccuracy = accuracy;
+                } else {
+                    improvement = accuracy - priorAccuracy;
+                }
+                if (improvement < convergence_threshold) {
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance++;
+                } else {
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    tolerance = 0; // Reset the counter if the model performs better
+                    numItemsPack = 0;
+                }
+                if (convergence_best) {
+                    // Keep the best accuracy until now as the prior accuracy
+                    priorAccuracy = std::max(accuracy, priorAccuracy);
+                } else {
+                    // Keep the last accuray obtained as the prior accuracy
+                    priorAccuracy = accuracy;
+                }
+            }
+            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
+            finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+        }
+        if (tolerance > maxTolerance) {
+            if (numItemsPack < n_models) {
+                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                for (int i = 0; i < numItemsPack; ++i) {
+                    significanceModels.pop_back();
+                    models.pop_back();
+                    n_models--;
+                }
+            } else {
+                notes.push_back("Convergence threshold reached & 0 models eliminated");
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+            }
+        }
+        if (pairSelection.size() > 0) {
+            notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+            status = WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+    }
+    std::vector<std::string> BoostA2DE::graph(const std::string& title) const
+    {
+        return Ensemble::graph(title);
+    }
+}

bayesnet/ensembles/BoostA2DE.h

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

bayesnet/ensembles/BoostAODE.cc

@@ -1,295 +1,181 @@
-#include <set>
+// ***************************************************************
-#include <functional>
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-#include <limits.h>
+// SPDX-FileType: SOURCE
-#include <tuple>
+// 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 "BoostAODE.h"
+#include "bayesnet/classifiers/SPODE.h"
+#include <limits.h>
+#include <loguru.cpp>
+#include <loguru.hpp>
+#include <random>
+#include <set>
+#include <tuple>
 
-namespace bayesnet {
+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"
-        };
 
-    }
+    BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
-    void BoostAODE::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);
-            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(const Smoothing_t smoothing)
     {
-        auto hyperparameters = hyperparameters_;
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
-        if (hyperparameters.contains("repeatSparent")) {
+        std::vector<int> featuresSelected = featureSelection(weights_);
-            repeatSparent = hyperparameters["repeatSparent"];
+        for (const int &feature : featuresSelected) {
-            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::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
-            model->fit(dataset, features, className, states, weights_);
+            model->fit(dataset, features, className, states, weights_, smoothing);
             models.push_back(std::move(model));
-            significanceModels.push_back(1.0);
+            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);
+        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
-        delete featureSelector;
+        return featuresSelected;
-        return featuresUsed;
     }
-    torch::Tensor BoostAODE::ensemble_predict(torch::Tensor& X, SPODE* model)
+    void BoostAODE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing)
-    {
-        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);
-    }
-    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);
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
-        bool exitCondition = false;
+        bool finished = false;
-        std::unordered_set<int> featuresUsed;
+        std::vector<int> featuresUsed;
+        n_models = 0;
         if (selectFeatures) {
-            featuresUsed = initializeModels();
+            featuresUsed = initializeModels(smoothing);
             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;
+                significanceModels.push_back(alpha_t);
             }
-            if (exitCondition) {
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
+            if (finished) {
                 return;
             }
         }
-        bool resetMaxModels = false;
+        int numItemsPack = 0; // The counter of the models inserted in the current pack
-        if (maxModels == 0) {
-            maxModels = .1 * n > 10 ? .1 * n : n;
-            resetMaxModels = true; // Flag to unset maxModels
-        }
         // 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 };
+        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);
             }
-            auto feature = featureSelection[0];
+            // Remove used features
-            if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
+            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x) { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed); }),
-                bool used = true;
+                                   end(featureSelection));
-                for (const auto& feat : featureSelection) {
+            int k = bisection ? pow(2, tolerance) : 1;
-                    if (std::find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
+            int counter = 0; // The model counter of the current pack
-                        continue;
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<SPODE>(feature);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                alpha_t = 0.0;
+                if (!block_update) {
+                    torch::Tensor ypred;
+                    if (alpha_block) {
+                        //
+                        // Compute the prediction with the current ensemble + model
+                        //
+                        // Add the model to the ensemble
+                        n_models++;
+                        models.push_back(std::move(model));
+                        significanceModels.push_back(1);
+                        // Compute the prediction
+                        ypred = predict(X_train);
+                        // Remove the model from the ensemble
+                        model = std::move(models.back());
+                        models.pop_back();
+                        significanceModels.pop_back();
+                        n_models--;
+                    } else {
+                        ypred = model->predict(X_train);
                     }
-                    used = false;
+                    // Step 3.1: Compute the classifier amout of say
-                    feature = feat;
+                    std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-                    break;
-                }
-                if (used) {
-                    exitCondition = true;
-                    continue;
                 }
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                models.push_back(std::move(model));
+                significanceModels.push_back(alpha_t);
+                n_models++;
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
             }
-            std::unique_ptr<Classifier> model;
+            if (block_update) {
-            model = std::make_unique<SPODE>(feature);
+                std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
-            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()));
             }
-            // Step 3.1: Compute the classifier amout of say
+            if (convergence && !finished) {
-            std::tie(weights_, alpha_t, exitCondition) = update_weights(y_train, ypred, weights_);
-            if (exitCondition) {
-                break;
-            }
-            // Step 3.4: Store classifier and its accuracy to weigh its future vote
-            featuresUsed.insert(feature);
-            models.push_back(std::move(model));
-            significanceModels.push_back(alpha_t);
-            n_models++;
-            if (convergence) {
                 auto y_val_predict = predict(X_test);
                 double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
                 if (priorAccuracy == 0) {
                     priorAccuracy = accuracy;
                 } else {
-                    delta = accuracy - priorAccuracy;
+                    improvement = accuracy - priorAccuracy;
                 }
-                if (delta < convergence_threshold) {
+                if (improvement < convergence_threshold) {
-                    count++;
+                    // 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;
                 }
-                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");
+                // VLG_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
+    std::vector<std::string> BoostAODE::graph(const std::string &title) const
     {
         return Ensemble::graph(title);
     }

bayesnet/ensembles/BoostAODE.h

@@ -1,37 +1,25 @@
+// ***************************************************************
+// 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 "bayesnet/classifiers/SPODE.h"
+#include <vector>
-#include "bayesnet/feature_selection/FeatureSelect.h"
+#include "Boost.h"
-#include "Ensemble.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, const Smoothing_t smoothing) override;
-        void trainModel(const torch::Tensor& weights) override;
     private:
-        std::unordered_set<int> initializeModels();
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
-        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;
     };
 }
 #endif

bayesnet/ensembles/Ensemble.cc

@@ -1,18 +1,22 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
 #include "Ensemble.h"
 
 namespace bayesnet {
 
     Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
     {
-
     };
     const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
-    void Ensemble::trainModel(const torch::Tensor& weights)
+    void Ensemble::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
             // fit with std::vectors
-            models[i]->fit(dataset, features, className, states);
+            models[i]->fit(dataset, features, className, states, smoothing);
         }
     }
     std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
@@ -79,17 +83,10 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict_proba(X);
-                auto ypredict = models[i]->predict_proba(X);
+            /*std::cout << "model " << i << " prediction: " << ypredict << " significance " << significanceModels[i] << std::endl;*/
-                std::lock_guard<std::mutex> lock(mtx);
+            y_pred += ypredict * significanceModels[i];
-                y_pred += ypredict * significanceModels[i];
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         y_pred /= sum;
@@ -99,23 +96,15 @@ namespace bayesnet {
     {
         auto n_states = models[0]->getClassNumStates();
         std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict_proba(X);
-                auto ypredict = models[i]->predict_proba(X);
+            assert(ypredict.size() == y_pred.size());
-                assert(ypredict.size() == y_pred.size());
+            assert(ypredict[0].size() == y_pred[0].size());
-                assert(ypredict[0].size() == y_pred[0].size());
+            // Multiply each prediction by the significance of the model and then add it to the final prediction
-                std::lock_guard<std::mutex> lock(mtx);
+            for (auto j = 0; j < ypredict.size(); ++j) {
-                // Multiply each prediction by the significance of the model and then add it to the final prediction
+                std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
-                for (auto j = 0; j < ypredict.size(); ++j) {
+                    [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                    std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
+            }
-                        [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-                }
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
         //Divide each element of the prediction by the sum of the significances
@@ -135,17 +124,9 @@ namespace bayesnet {
     {
         // Build a m x n_models tensor with the predictions of each model
         torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
-        auto threads{ std::vector<std::thread>() };
-        std::mutex mtx;
         for (auto i = 0; i < n_models; ++i) {
-            threads.push_back(std::thread([&, i]() {
+            auto ypredict = models[i]->predict(X);
-                auto ypredict = models[i]->predict(X);
+            y_pred.index_put_({ "...", i }, ypredict);
-                std::lock_guard<std::mutex> lock(mtx);
-                y_pred.index_put_({ "...", i }, ypredict);
-                }));
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         return voting(y_pred);
     }

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>
@@ -25,10 +31,17 @@ namespace bayesnet {
         {
             return std::vector<std::string>();
         }
-        void dump_cpt() const override
+        std::string dump_cpt() const override
         {
+            std::string output;
+            for (auto& model : models) {
+                output += model->dump_cpt();
+                output += std::string(80, '-') + "\n";
+            }
+            return output;
         }
     protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         torch::Tensor predict_average_voting(torch::Tensor& X);
         std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
         torch::Tensor predict_average_proba(torch::Tensor& X);
@@ -36,10 +49,10 @@ namespace bayesnet {
         torch::Tensor compute_arg_max(torch::Tensor& X);
         std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
         torch::Tensor voting(torch::Tensor& votes);
+        // Attributes
         unsigned n_models;
         std::vector<std::unique_ptr<Classifier>> models;
         std::vector<double> significanceModels;
-        void trainModel(const torch::Tensor& weights) override;
         bool predict_voting;
     };
 }

bayesnet/ensembles/XBA2DE.cc

@@ -0,0 +1,168 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <folding.hpp>
+#include <limits.h>
+#include "XBA2DE.h"
+#include "bayesnet/classifiers/XSP2DE.h"
+#include "bayesnet/utils/TensorUtils.h"
+
+namespace bayesnet {
+
+XBA2DE::XBA2DE(bool predict_voting) : Boost(predict_voting) {}
+std::vector<int> XBA2DE::initializeModels(const Smoothing_t smoothing) {
+    torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+    std::vector<int> featuresSelected = featureSelection(weights_);
+    if (featuresSelected.size() < 2) {
+        notes.push_back("No features selected in initialization");
+        status = ERROR;
+        return std::vector<int>();
+    }
+    for (int i = 0; i < featuresSelected.size() - 1; i++) {
+        for (int j = i + 1; j < featuresSelected.size(); j++) {
+            std::unique_ptr<Classifier> model = std::make_unique<XSp2de>(featuresSelected[i], featuresSelected[j]);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            add_model(std::move(model), 1.0);
+        }
+    }
+    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 XBA2DE::trainModel(const torch::Tensor &weights, const Smoothing_t smoothing) {
+    //
+    // Logging setup
+    //
+    // loguru::set_thread_name("XBA2DE");
+    // 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)
+    X_train_ = TensorUtils::to_matrix(X_train);
+    y_train_ = TensorUtils::to_vector<int>(y_train);
+    if (convergence) {
+        X_test_ = TensorUtils::to_matrix(X_test);
+        y_test_ = TensorUtils::to_vector<int>(y_test);
+    }
+    fitted = true;
+    double alpha_t = 0;
+    torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+    bool finished = false;
+    std::vector<int> featuresUsed;
+    if (selectFeatures) {
+        featuresUsed = initializeModels(smoothing);
+        if (featuresUsed.size() == 0) {
+            return;
+        }
+        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<XSp2de>(feature_pair.first, feature_pair.second);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            alpha_t = 0.0;
+            if (!block_update) {
+                auto ypred = model->predict(X_train);
+                // Step 3.1: Compute the classifier amout of say
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
+            }
+            // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            numItemsPack++;
+            models.push_back(std::move(model));
+            significanceModels.push_back(alpha_t);
+            n_models++;
+            // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models,
+            // featuresUsed.size());
+        }
+        if (block_update) {
+            std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
+        }
+        if (convergence && !finished) {
+            auto y_val_predict = predict(X_test);
+            double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
+            if (priorAccuracy == 0) {
+                priorAccuracy = accuracy;
+            } else {
+                improvement = accuracy - priorAccuracy;
+            }
+            if (improvement < convergence_threshold) {
+                // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f
+                // current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                tolerance++;
+            } else {
+                // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f
+                // prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                tolerance = 0; // Reset the counter if the model performs better
+                numItemsPack = 0;
+            }
+            if (convergence_best) {
+                // Keep the best accuracy until now as the prior accuracy
+                priorAccuracy = std::max(accuracy, priorAccuracy);
+            } else {
+                // Keep the last accuray obtained as the prior accuracy
+                priorAccuracy = accuracy;
+            }
+        }
+        // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(),
+        // features.size());
+        finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
+    }
+    if (tolerance > maxTolerance) {
+        if (numItemsPack < n_models) {
+            notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
+            // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+            for (int i = 0; i < numItemsPack; ++i) {
+                significanceModels.pop_back();
+                models.pop_back();
+                n_models--;
+            }
+        } else {
+            notes.push_back("Convergence threshold reached & 0 models eliminated");
+            // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d",
+            // n_models, numItemsPack);
+        }
+    }
+    if (pairSelection.size() > 0) {
+        notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
+        status = WARNING;
+    }
+    notes.push_back("Number of models: " + std::to_string(n_models));
+}
+std::vector<std::string> XBA2DE::graph(const std::string &title) const { return Ensemble::graph(title); }
+} // namespace bayesnet

bayesnet/ensembles/XBA2DE.h

@@ -0,0 +1,28 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XBA2DE_H
+#define XBA2DE_H
+#include <string>
+#include <vector>
+#include "Boost.h"
+namespace bayesnet {
+    class XBA2DE : public Boost {
+    public:
+        explicit XBA2DE(bool predict_voting = false);
+        virtual ~XBA2DE() = default;
+        std::vector<std::string> graph(const std::string& title = "XBA2DE") const override;
+        std::string getVersion() override { return version; };
+    protected:
+        void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+        std::vector<std::vector<int>> X_train_, X_test_;
+        std::vector<int> y_train_, y_test_;
+        std::string version = "0.9.7";
+    };
+}
+#endif

bayesnet/ensembles/XBAODE.cc

@@ -0,0 +1,184 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#include "XBAODE.h"
+#include "bayesnet/classifiers/XSPODE.h"
+#include "bayesnet/utils/TensorUtils.h"
+#include <limits.h>
+#include <random>
+#include <tuple>
+
+namespace bayesnet
+{
+    XBAODE::XBAODE() : Boost(false) {}
+    std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
+    {
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+        std::vector<int> featuresSelected = featureSelection(weights_);
+        for (const int &feature : featuresSelected) {
+            std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
+            model->fit(dataset, features, className, states, weights_, smoothing);
+            add_model(std::move(model), 1.0);
+        }
+        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 XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
+    {
+        X_train_ = TensorUtils::to_matrix(X_train);
+        y_train_ = TensorUtils::to_vector<int>(y_train);
+        if (convergence) {
+            X_test_ = TensorUtils::to_matrix(X_test);
+            y_test_ = TensorUtils::to_vector<int>(y_test);
+        }
+        fitted = true;
+        double alpha_t;
+        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
+        bool finished = false;
+        std::vector<int> featuresUsed;
+        n_models = 0;
+        if (selectFeatures) {
+            featuresUsed = initializeModels(smoothing);
+            auto ypred = predict(X_train_);
+            auto ypred_t = torch::tensor(ypred);
+            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+            // Update significance of the models
+            for (const int &feature : featuresUsed) {
+                significanceModels.pop_back();
+            }
+            for (const int &feature : featuresUsed) {
+                significanceModels.push_back(alpha_t);
+            }
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
+            // n_models);
+            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 == bayesnet::Orders.ASC;
+        std::mt19937 g{173};
+        while (!finished) {
+            // Step 1: Build ranking with mutual information
+            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
+            if (order_algorithm == bayesnet::Orders.RAND) {
+                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
+            }
+            // Remove used features
+            featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
+                                             [&](auto x) {
+                                                 return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
+                                                        featuresUsed.end();
+                                             }),
+                                   featureSelection.end());
+            int k = bisection ? pow(2, tolerance) : 1;
+            int counter = 0; // The model counter of the current pack
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
+            // featureSelection.size());
+            while (counter++ < k && featureSelection.size() > 0) {
+                auto feature = featureSelection[0];
+                featureSelection.erase(featureSelection.begin());
+                std::unique_ptr<Classifier> model;
+                model = std::make_unique<XSpode>(feature);
+                model->fit(dataset, features, className, states, weights_, smoothing);
+                /*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
+                 * smoothing); // using exclusive XSpode fit method*/
+                // DEBUG
+                /*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
+                 * std::endl;*/
+                // DEBUG
+                std::vector<int> ypred;
+                if (alpha_block) {
+                    //
+                    // Compute the prediction with the current ensemble + model
+                    //
+                    // Add the model to the ensemble
+                    add_model(std::move(model), 1.0);
+                    // Compute the prediction
+                    ypred = predict(X_train_);
+                    model = std::move(models.back());
+                    // Remove the model from the ensemble
+                    remove_last_model();
+                } else {
+                    ypred = model->predict(X_train_);
+                }
+                // Step 3.1: Compute the classifier amout of say
+                auto ypred_t = torch::tensor(ypred);
+                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
+                // Step 3.4: Store classifier and its accuracy to weigh its future vote
+                numItemsPack++;
+                featuresUsed.push_back(feature);
+                add_model(std::move(model), alpha_t);
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
+                // featuresUsed: %zu", finished, numItemsPack, n_models,
+                // featuresUsed.size());
+            } // End of the pack
+            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 || 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 = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
+                    remove_last_model();
+                }
+                // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
+                // used.", n_models, featuresUsed.size());
+            } 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 = bayesnet::WARNING;
+        }
+        notes.push_back("Number of models: " + std::to_string(n_models));
+        return;
+    }
+} // namespace bayesnet

bayesnet/ensembles/XBAODE.h

@@ -0,0 +1,27 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XBAODE_H
+#define XBAODE_H
+#include <vector>
+#include <cmath>
+#include "Boost.h"
+
+namespace bayesnet {
+    class XBAODE : public Boost {
+    public:
+        XBAODE();
+        std::string getVersion() override { return version; };
+    protected:
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+    private:
+        std::vector<int> initializeModels(const Smoothing_t smoothing);
+        std::vector<std::vector<int>> X_train_, X_test_;
+        std::vector<int> y_train_, y_test_;
+        std::string version = "0.9.7";
+    };
+}
+#endif // XBAODE_H

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

@@ -1,36 +1,49 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #include <thread>
-#include <mutex>
+#include <sstream>
+#include <numeric>
+#include <algorithm>
 #include "Network.h"
 #include "bayesnet/utils/bayesnetUtils.h"
+#include "bayesnet/utils/CountingSemaphore.h"
+#include <pthread.h>
+#include <fstream>
 namespace bayesnet {
-    Network::Network() : features(std::vector<std::string>()), className(""), classNumStates(0), fitted(false), laplaceSmoothing(0) {}
+    Network::Network() : fitted{ false }, classNumStates{ 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(const Network& other) : features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
+        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()
-    {
-        return maxThreads;
-    }
     torch::Tensor& Network::getSamples()
     {
         return samples;
     }
     void Network::addNode(const std::string& name)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add node to a fitted network. Initialize first.");
+        }
         if (name == "") {
             throw std::invalid_argument("Node name cannot be empty");
         }
@@ -80,12 +93,21 @@ namespace bayesnet {
     }
     void Network::addEdge(const std::string& parent, const std::string& child)
     {
+        if (fitted) {
+            throw std::invalid_argument("Cannot add edge to a fitted network. Initialize first.");
+        }
         if (nodes.find(parent) == nodes.end()) {
             throw std::invalid_argument("Parent node " + parent + " does not exist");
         }
         if (nodes.find(child) == nodes.end()) {
             throw std::invalid_argument("Child node " + child + " does not exist");
         }
+        // Check if the edge is already in the graph
+        for (auto& node : nodes[parent]->getChildren()) {
+            if (node->getName() == child) {
+                throw std::invalid_argument("Edge " + parent + " -> " + child + " already exists");
+            }
+        }
         // Temporarily add edge to check for cycles
         nodes[parent]->addChild(nodes[child].get());
         nodes[child]->addParent(nodes[parent].get());
@@ -114,11 +136,14 @@ 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()) {
@@ -138,7 +163,7 @@ namespace bayesnet {
         classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
         this->className = className;
@@ -147,17 +172,17 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
         }
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
         this->className = className;
         this->samples = samples;
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
+    void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing)
     {
         const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
         checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
@@ -168,17 +193,43 @@ namespace bayesnet {
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit(states, weights);
+        completeFit(states, weights, smoothing);
     }
-    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
+    void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         setStates(states);
-        laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
         std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        const double n_samples = static_cast<double>(samples.size(1));
+        auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
+            std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            double numStates = static_cast<double>(node.second->getNumStates());
+            double smoothing_factor;
+            switch (smoothing) {
+                case Smoothing_t::ORIGINAL:
+                    smoothing_factor = 1.0 / n_samples;
+                    break;
+                case Smoothing_t::LAPLACE:
+                    smoothing_factor = 1.0;
+                    break;
+                case Smoothing_t::CESTNIK:
+                    smoothing_factor = 1 / numStates;
+                    break;
+                default:
+                    smoothing_factor = 0.0; // No smoothing 
+            }
+            node.second->computeCPT(samples, features, smoothing_factor, weights);
+            semaphore.release();
+            };
+        int i = 0;
         for (auto& node : nodes) {
-            threads.emplace_back([this, &node, &weights]() {
+            semaphore.acquire();
-                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
+            threads.emplace_back(worker, std::ref(node), i++);
-                });
         }
         for (auto& thread : threads) {
             thread.join();
@@ -190,14 +241,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
+        // Ensure the sample size is equal to the number of features
+        if (samples.size(0) != features.size() - 1) {
+            throw std::invalid_argument("(T) Sample size (" + std::to_string(samples.size(0)) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
         torch::Tensor result;
+        std::vector<std::thread> threads;
+        std::mutex mtx;
+        auto& semaphore = CountingSemaphore::getInstance();
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
-        for (int i = 0; i < samples.size(1); ++i) {
+        auto worker = [&](const torch::Tensor& sample, int i) {
-            const torch::Tensor sample = samples.index({ "...", i });
+            std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
-            //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
+            {
-            result.index_put_({ i, "..." }, temp);
+                std::lock_guard<std::mutex> lock(mtx);
+                result.index_put_({ i, "..." }, temp);
+            }
+            semaphore.release();
+            };
+        for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
+            const torch::Tensor sample = samples.index({ "...", i });
+            threads.emplace_back(worker, sample, i);
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         if (proba)
             return result;
@@ -222,18 +297,38 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict()");
         }
-        std::vector<int> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<int> predictions(tsamples[0].size(), 0);
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            auto classProbabilities = predict_sample(sample);
+            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+            int predictedClass = distance(classProbabilities.begin(), maxElem);
+            prediction = predictedClass;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            std::vector<double> classProbabilities = predict_sample(sample);
+            semaphore.acquire();
-            // Find the class with the maximum posterior probability
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
-            auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
+        }
-            int predictedClass = distance(classProbabilities.begin(), maxElem);
+        for (auto& thread : threads) {
-            predictions.push_back(predictedClass);
+            thread.join();
         }
         return predictions;
     }
@@ -244,14 +339,36 @@ namespace bayesnet {
         if (!fitted) {
             throw std::logic_error("You must call fit() before calling predict_proba()");
         }
-        std::vector<std::vector<double>> predictions;
+        // Ensure the sample size is equal to the number of features
+        if (tsamples.size() != features.size() - 1) {
+            throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
+                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
+        }
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            predictions.push_back(predict_sample(sample));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
@@ -269,11 +386,6 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const std::vector<int>& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size() != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size()) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(); ++i) {
             evidence[features[i]] = sample[i];
@@ -283,44 +395,26 @@ namespace bayesnet {
     // Return 1xn std::vector of probabilities
     std::vector<double> Network::predict_sample(const torch::Tensor& sample)
     {
-        // Ensure the sample size is equal to the number of features
-        if (sample.size(0) != features.size() - 1) {
-            throw std::invalid_argument("Sample size (" + std::to_string(sample.size(0)) +
-                ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
-        }
         std::map<std::string, int> evidence;
         for (int i = 0; i < sample.size(0); ++i) {
             evidence[features[i]] = sample[i].item<int>();
         }
         return exactInference(evidence);
     }
-    double Network::computeFactor(std::map<std::string, int>& completeEvidence)
-    {
-        double result = 1.0;
-        for (auto& node : getNodes()) {
-            result *= node.second->getFactorValue(completeEvidence);
-        }
-        return result;
-    }
     std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
     {
         std::vector<double> result(classNumStates, 0.0);
-        std::vector<std::thread> threads;
+        auto completeEvidence = std::map<std::string, int>(evidence);
-        std::mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
-            threads.emplace_back([this, &result, &evidence, i, &mtx]() {
+            completeEvidence[getClassName()] = i;
-                auto completeEvidence = std::map<std::string, int>(evidence);
+            double partial = 1.0;
-                completeEvidence[getClassName()] = i;
+            for (auto& node : getNodes()) {
-                double factor = computeFactor(completeEvidence);
+                partial *= node.second->getFactorValue(completeEvidence);
-                std::lock_guard<std::mutex> lock(mtx);
+            }
-                result[i] = factor;
+            result[i] = partial;
-                });
-        }
-        for (auto& thread : threads) {
-            thread.join();
         }
         // Normalize result
-        double sum = accumulate(result.begin(), result.end(), 0.0);
+        double sum = std::accumulate(result.begin(), result.end(), 0.0);
         transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
         return result;
     }
@@ -393,22 +487,20 @@ 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;
+            oss << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
-            std::cout << node.second->getCPT() << std::endl;
+            oss << node.second->getCPT() << std::endl;
         }
+        return oss.str();
     }
 }

bayesnet/network/Network.h

@@ -1,19 +1,25 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
 #ifndef NETWORK_H
 #define NETWORK_H
 #include <map>
 #include <vector>
 #include "bayesnet/config.h"
 #include "Node.h"
+#include "Smoothing.h"
 
 namespace bayesnet {
+
     class Network {
     public:
         Network();
-        explicit Network(float);
+        explicit Network(const Network&);
-        explicit Network(Network&);
         ~Network() = default;
         torch::Tensor& getSamples();
-        float getmaxThreads();
         void addNode(const std::string&);
         void addEdge(const std::string&, const std::string&);
         std::map<std::string, std::unique_ptr<Node>>& getNodes();
@@ -26,9 +32,9 @@ namespace bayesnet {
         /*
         Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
         */
-        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
-        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
+        void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
         std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
@@ -39,24 +45,21 @@ 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;
         bool fitted;
-        float maxThreads = 0.95;
         int classNumStates;
         std::vector<std::string> features; // Including classname
         std::string className;
-        double laplaceSmoothing;
+        torch::Tensor samples; // n+1xm tensor used to fit the model
-        torch::Tensor samples; // nxm tensor used to fit the model
         bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
         std::vector<double> predict_sample(const std::vector<int>&);
         std::vector<double> predict_sample(const torch::Tensor&);
         std::vector<double> exactInference(std::map<std::string, int>&);
-        double computeFactor(std::map<std::string, int>&);
+        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
-        void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
+        void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
         void setStates(const std::map<std::string, std::vector<int>>&);
     };
 }

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()
@@ -84,52 +90,60 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
+    void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights)
     {
         dimensions.clear();
+        dimensions.reserve(parents.size() + 1);
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+        for (const auto& parent : parents) {
-
+            dimensions.push_back(parent->getNumStates());
-        // Create a tensor of zeros with the dimensions of the CPT
-        cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
-        // Fill table with counts
-        auto pos = find(features.begin(), features.end(), name);
-        if (pos == features.end()) {
-            throw std::logic_error("Feature " + name + " not found in dataset");
         }
-        int name_index = pos - features.begin();
+        //transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
+        // Create a tensor initialized with smoothing
+        cpTable = torch::full(dimensions, smoothing, torch::kDouble);
+        // Create a map for quick feature index lookup
+        std::unordered_map<std::string, int> featureIndexMap;
+        for (size_t i = 0; i < features.size(); ++i) {
+            featureIndexMap[features[i]] = i;
+        }
+        // Fill table with counts
+        // Get the index of this node's feature
+        int name_index = featureIndexMap[name];
+        // Get parent indices in dataset
+        std::vector<int> parent_indices;
+        parent_indices.reserve(parents.size());
+        for (const auto& parent : parents) {
+            parent_indices.push_back(featureIndexMap[parent->getName()]);
+        }
+        c10::List<c10::optional<at::Tensor>> coordinates;
         for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-            c10::List<c10::optional<at::Tensor>> coordinates;
+            coordinates.clear();
-            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            auto sample = dataset.index({ "...", n_sample });
-            for (auto parent : parents) {
+            coordinates.push_back(sample[name_index]);
-                pos = find(features.begin(), features.end(), parent->getName());
+            for (size_t i = 0; i < parent_indices.size(); ++i) {
-                if (pos == features.end()) {
+                coordinates.push_back(sample[parent_indices[i]]);
-                    throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
-                }
-                int parent_index = pos - features.begin();
-                coordinates.push_back(dataset.index({ parent_index, n_sample }));
             }
             // Increment the count of the corresponding coordinate
-            cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
+            cpTable.index_put_({ coordinates }, weights.index({ n_sample }), true);
         }
-        // Normalize the counts
+        // Normalize the counts (dividing each row by the sum of the row)
-        cpTable = cpTable / cpTable.sum(0);
+        cpTable /= cpTable.sum(0, true);
     }
-    float Node::getFactorValue(std::map<std::string, int>& evidence)
+    double Node::getFactorValue(std::map<std::string, int>& evidence)
     {
         c10::List<c10::optional<at::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(at::tensor(evidence[name]));
         transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
-        return cpTable.index({ coordinates }).item<float>();
+        return cpTable.index({ coordinates }).item<double>();
     }
     std::vector<std::string> Node::graph(const std::string& className)
     {
         auto output = std::vector<std::string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
-        output.push_back(name + " [shape=circle" + suffix + "] \n");
+        output.push_back("\"" + name + "\" [shape=circle" + suffix + "] \n");
-        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
+        transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return "\"" + name + "\" -> \"" + child->getName() + "\""; });
         return output;
     }
 }

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();
@@ -25,12 +23,20 @@ namespace bayesnet {
         std::vector<Node*>& getParents();
         std::vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
+        void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double smoothing, const torch::Tensor& weights);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
         std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
-        float getFactorValue(std::map<std::string, int>&);
+        double getFactorValue(std::map<std::string, int>&);
+    private:
+        std::string name;
+        std::vector<Node*> parents;
+        std::vector<Node*> children;
+        int numStates = 0; // number of states of the variable
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
+        std::vector<int64_t> dimensions; // dimensions of the cpTable
+        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     };
 }
 #endif

bayesnet/network/Smoothing.h

@@ -0,0 +1,17 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef SMOOTHING_H
+#define SMOOTHING_H
+namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
+}
+#endif // SMOOTHING_H

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
+    // Measured in nats (natural logarithm (log) base e)
-    std::vector<float> Metrics::conditionalEdgeWeights(std::vector<float>& weights_)
+    // Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
-    {
-        const torch::Tensor weights = torch::tensor(weights_);
-        auto matrix = conditionalEdge(weights);
-        std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
-        return v;
-    }
     double Metrics::entropy(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:
+    public:
-        int classNumStates = 0;
+        Metrics() = default;
-        std::vector<double> scoresKBest;
+        Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
-        std::vector<int> featuresKBest; // sorted indices of the features
+        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);
-        double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+        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:
+    private:
-        Metrics() = default;
+        int classNumStates = 0;
-        Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
+        std::vector<double> scoresKBest;
-        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> featuresKBest; // sorted indices of the features
-        std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
+        std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
-        std::vector<double> getScoresKBest() const;
+        std::vector<std::pair<std::pair<int, int>, double>> scoresKPairs;
-        double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
+        double conditionalEntropy(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);
     };
 }
 #endif

bayesnet/utils/CountingSemaphore.h

@@ -0,0 +1,54 @@
+#ifndef COUNTING_SEMAPHORE_H
+#define COUNTING_SEMAPHORE_H
+#include <mutex>
+#include <condition_variable>
+#include <algorithm>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include <thread>
+
+class CountingSemaphore {
+public:
+    static CountingSemaphore& getInstance()
+    {
+        static CountingSemaphore instance;
+        return instance;
+    }
+    // Delete copy constructor and assignment operator
+    CountingSemaphore(const CountingSemaphore&) = delete;
+    CountingSemaphore& operator=(const CountingSemaphore&) = delete;
+    void acquire()
+    {
+        std::unique_lock<std::mutex> lock(mtx_);
+        cv_.wait(lock, [this]() { return count_ > 0; });
+        --count_;
+    }
+    void release()
+    {
+        std::lock_guard<std::mutex> lock(mtx_);
+        ++count_;
+        if (count_ <= max_count_) {
+            cv_.notify_one();
+        }
+    }
+    uint getCount() const
+    {
+        return count_;
+    }
+    uint getMaxCount() const
+    {
+        return max_count_;
+    }
+private:
+    CountingSemaphore()
+        : max_count_(std::max(1u, static_cast<uint>(0.95 * std::thread::hardware_concurrency()))),
+        count_(max_count_)
+    {
+    }
+    std::mutex mtx_;
+    std::condition_variable cv_;
+    const uint max_count_;
+    uint count_;
+};
+#endif

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"
@@ -45,24 +52,15 @@ 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)
+    void MST::insertElement(std::list<int>& variables, int variable)
     {
         if (std::find(variables.begin(), variables.end(), variable) == variables.end()) {
             variables.push_front(variable);
         }
     }
 
-    std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
+    std::vector<std::pair<int, int>> MST::reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original)
     {
         // Create the edges of a DAG from the MST
         // replacing unordered_set with list because unordered_set cannot guarantee the order of the elements inserted

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 +11,30 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class MST {
+    public:
+        MST() = default;
+        MST(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
+        void insertElement(std::list<int>& variables, int variable);
+        std::vector<std::pair<int, int>> reorder(std::vector<std::pair<float, std::pair<int, int>>> T, int root_original);
+        std::vector<std::pair<int, int>> maximumSpanningTree();
     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();
     };
     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/TensorUtils.h

@@ -0,0 +1,51 @@
+#ifndef TENSORUTILS_H
+#define TENSORUTILS_H
+#include <torch/torch.h>
+#include <vector>
+namespace bayesnet {
+    class TensorUtils {
+    public:
+        static std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
+        {
+            // Ensure tensor is contiguous in memory
+            auto X_contig = X.contiguous();
+
+            // Access tensor data pointer directly
+            auto data_ptr = X_contig.data_ptr<int>();
+
+            // IF you are using int64_t as the data type, use the following line
+            //auto data_ptr = X_contig.data_ptr<int64_t>();
+            //std::vector<std::vector<int64_t>> data(X.size(0), std::vector<int64_t>(X.size(1)));
+
+            // Prepare output container
+            std::vector<std::vector<int>> data(X.size(0), std::vector<int>(X.size(1)));
+
+            // Fill the 2D vector in a single loop using pointer arithmetic
+            int rows = X.size(0);
+            int cols = X.size(1);
+            for (int i = 0; i < rows; ++i) {
+                std::copy(data_ptr + i * cols, data_ptr + (i + 1) * cols, data[i].begin());
+            }
+            return data;
+        }
+        template <typename T>
+        static std::vector<T> to_vector(const torch::Tensor& y)
+        {
+            // Ensure the tensor is contiguous in memory
+            auto y_contig = y.contiguous();
+
+            // Access data pointer
+            auto data_ptr = y_contig.data_ptr<T>();
+
+            // Prepare output container
+            std::vector<T> data(y.size(0));
+
+            // Copy data efficiently
+            std::copy(data_ptr, data_ptr + y.size(0), data.begin());
+
+            return data;
+        }
+    };
+}
+
+#endif // TENSORUTILS_H

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);
 }

cmake/modules/CodeCoverage.cmake

@@ -137,7 +137,7 @@
 
 include(CMakeParseArguments)
 
-option(CODE_COVERAGE_VERBOSE "Verbose information" FALSE)
+option(CODE_COVERAGE_VERBOSE "Verbose information" TRUE)
 
 # Check prereqs
 find_program( GCOV_PATH gcov )
@@ -160,7 +160,11 @@ foreach(LANG ${LANGUAGES})
     endif()
   elseif(NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "GNU"
          AND NOT "${CMAKE_${LANG}_COMPILER_ID}" MATCHES "(LLVM)?[Ff]lang")
-    message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+         if ("${LANG}" MATCHES "CUDA")
+             message(STATUS "Ignoring CUDA")
+        else()
+            message(FATAL_ERROR "Compiler is not GNU or Flang! Aborting...")
+        endif()
   endif()
 endforeach()
 

diagrams/BayesNet.puml

@@ -0,0 +1,676 @@
+@startuml
+title clang-uml class diagram model
+class "bayesnet::Node" as C_0010428199432536647474
+class C_0010428199432536647474 #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 smoothing, const torch::Tensor & weights) : void
++getCPT() : torch::Tensor &
++getChildren() : std::vector<Node *> &
++getFactorValue(std::map<std::string,int> &) : double
++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
+__
+}
+enum "bayesnet::Smoothing_t" as C_0013393078277439680282
+enum C_0013393078277439680282 {
+NONE
+ORIGINAL
+LAPLACE
+CESTNIK
+}
+class "bayesnet::Network" as C_0009493661199123436603
+class C_0009493661199123436603 #aliceblue;line:blue;line.dotted;text:blue {
++Network() : 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, const Smoothing_t smoothing) : void
++fit(const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : void
++fit(const std::vector<std::vector<int>> & input_data, const std::vector<int> & labels, const std::vector<double> & weights, const std::vector<std::string> & featureNames, const std::string & className, const std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : void
++getClassName() const : std::string
++getClassNumStates() const : int
++getEdges() const : std::vector<std::pair<std::string,std::string>>
++getFeatures() const : std::vector<std::string>
++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_0005907365846270811004
+enum C_0005907365846270811004 {
+NORMAL
+WARNING
+ERROR
+}
+abstract "bayesnet::BaseClassifier" as C_0002617087915615796317
+abstract C_0002617087915615796317 #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, const Smoothing_t smoothing) = 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 Smoothing_t smoothing) = 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, const Smoothing_t smoothing) = 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, const Smoothing_t smoothing) = 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, const Smoothing_t smoothing) = 0 : void
+__
+#notes : std::vector<std::string>
+#status : status_t
+#validHyperparameters : std::vector<std::string>
+}
+class "bayesnet::Metrics" as C_0005895723015084986588
+class C_0005895723015084986588 #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>
++SelectKPairs(const torch::Tensor & weights, std::vector<int> & featuresExcluded, bool ascending = false, unsigned int k = 0) : std::vector<std::pair<int,int>>
++conditionalEdge(const torch::Tensor & weights) : torch::Tensor
++conditionalEntropy(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
++conditionalMutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
+#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>
++getScoresKPairs() const : std::vector<std::pair<std::pair<int,int>,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
+}
+abstract "bayesnet::Classifier" as C_0016351972983202413152
+abstract C_0016351972983202413152 #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, const Smoothing_t smoothing) : Classifier &
++fit(std::vector<std::vector<int>> & X, std::vector<int> & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const torch::Tensor & weights, const Smoothing_t smoothing) : 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, const Smoothing_t smoothing) : void
+__
+#CLASSIFIER_NOT_FITTED : const std::string
+#className : std::string
+#dataset : torch::Tensor
+#features : std::vector<std::string>
+#fitted : bool
+#m : unsigned int
+#metrics : Metrics
+#model : Network
+#n : unsigned int
+#states : std::map<std::string,std::vector<int>>
+}
+class "bayesnet::Proposal" as C_0017759964713298103839
+class C_0017759964713298103839 #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::KDB" as C_0008902920152122000044
+class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
++KDB(int k, float theta = 0.03) : void
++~KDB() = default : void
+..
+#add_m_edges(int idx, std::vector<int> & S, torch::Tensor & weights) : 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::KDBLd" as C_0002756018222998454702
+class C_0002756018222998454702 #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, const Smoothing_t smoothing) : KDBLd &
++graph(const std::string & name = "KDB") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::SPODE" as C_0004096182510460307610
+class C_0004096182510460307610 #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>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::SPODELd" as C_0010957245114062042836
+class C_0010957245114062042836 #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, const Smoothing_t smoothing) : 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, const Smoothing_t smoothing) : SPODELd &
++fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
++graph(const std::string & name = "SPODELd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+{static} +version() : std::string
+__
+}
+class "bayesnet::SPnDE" as C_0016268916386101512883
+class C_0016268916386101512883 #aliceblue;line:blue;line.dotted;text:blue {
++SPnDE(std::vector<int> parents) : void
++~SPnDE() = default : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & name = "SPnDE") const : std::vector<std::string>
+__
+}
+class "bayesnet::TAN" as C_0014087955399074584137
+class C_0014087955399074584137 #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>
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+__
+}
+class "bayesnet::TANLd" as C_0013350632773616302678
+class C_0013350632773616302678 #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, const Smoothing_t smoothing) : TANLd &
++graph(const std::string & name = "TANLd") const : std::vector<std::string>
++predict(torch::Tensor & X) : torch::Tensor
+__
+}
+class "bayesnet::XSp2de" as C_0007640742442325463418
+class C_0007640742442325463418 #aliceblue;line:blue;line.dotted;text:blue {
++XSp2de(int spIndex1, int spIndex2) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++graph(const std::string & title) const : std::vector<std::string>
++predict(const std::vector<int> & instance) const : int
++predict(std::vector<std::vector<int>> & test_data) : std::vector<int>
++predict(torch::Tensor & X) : torch::Tensor
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++predict_proba(std::vector<std::vector<int>> & test_data) : 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
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::XSpode" as C_0015654113248178830206
+class C_0015654113248178830206 #aliceblue;line:blue;line.dotted;text:blue {
++XSpode(int spIndex) : void
+..
+#buildModel(const torch::Tensor & weights) : void
++fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
++getClassNumStates() const : int
++getNFeatures() const : int
++getNumberOfEdges() const : int
++getNumberOfNodes() const : int
++getNumberOfStates() const : int
++getStates() : std::vector<int> &
++graph(const std::string & title) const : std::vector<std::string>
++normalize(std::vector<double> & v) const : void
++predict(const std::vector<int> & instance) const : int
++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
++predict_proba(const std::vector<int> & instance) const : std::vector<double>
++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
++to_string() const : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::TensorUtils" as C_0010304804115474100819
+class C_0010304804115474100819 #aliceblue;line:blue;line.dotted;text:blue {
+{static} +to_matrix(const torch::Tensor & X) : std::vector<std::vector<int>>
+{static} +to_vector<T>(const torch::Tensor & y) : std::vector<T>
+__
+}
+class "bayesnet::Ensemble" as C_0015881931090842884611
+class C_0015881931090842884611 #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, const Smoothing_t smoothing) : 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::A2DE" as C_0001410789567057647859
+class C_0001410789567057647859 #aliceblue;line:blue;line.dotted;text:blue {
++A2DE(bool predict_voting = false) : void
++~A2DE() : void
+..
+#buildModel(const torch::Tensor & weights) : void
++graph(const std::string & title = "A2DE") const : std::vector<std::string>
++setHyperparameters(const nlohmann::json & hyperparameters) : void
+__
+}
+class "bayesnet::AODE" as C_0006288892608974306258
+class C_0006288892608974306258 #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::AODELd" as C_0003898187834670349177
+class C_0003898187834670349177 #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_, const Smoothing_t smoothing) : AODELd &
++graph(const std::string & name = "AODELd") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
+abstract C_0013562609546004646591 #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::(anonymous_60357672)" as C_0006397015156479549697
+class C_0006397015156479549697 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60358326)" as C_0013066254331852347304
+class C_0013066254331852347304 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::Boost" as C_0009819322948617116148
+class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
++Boost(bool predict_voting = false) : void
++~Boost() = default : void
+..
+#add_model(std::unique_ptr<Classifier> model, double significance) : void
+#buildModel(const torch::Tensor & weights) : void
+#featureSelection(torch::Tensor & weights_) : std::vector<int>
+#remove_last_model() : void
++setHyperparameters(const nlohmann::json & hyperparameters_) : void
+#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) : std::tuple<torch::Tensor &,double,bool>
+__
+#X_test : torch::Tensor
+#X_train : torch::Tensor
+#alpha_block : bool
+#bisection : bool
+#block_update : bool
+#convergence : bool
+#convergence_best : bool
+#featureSelector : FeatureSelect *
+#maxTolerance : int
+#order_algorithm : std::string
+#selectFeatures : bool
+#select_features_algorithm : std::string
+#threshold : double
+#y_test : torch::Tensor
+#y_train : torch::Tensor
+}
+class "bayesnet::BoostA2DE" as C_0000272055465257861326
+class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
++BoostA2DE(bool predict_voting = false) : void
++~BoostA2DE() = default : void
+..
++graph(const std::string & title = "BoostA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60425028)" as C_0000461144706913711531
+class C_0000461144706913711531 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60425682)" as C_0014849589915262463453
+class C_0014849589915262463453 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::BoostAODE" as C_0002867772739198819061
+class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
++BoostAODE(bool predict_voting = false) : void
++~BoostAODE() = default : void
+..
++graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::XBA2DE" as C_0008480973840710001141
+class C_0008480973840710001141 #aliceblue;line:blue;line.dotted;text:blue {
++XBA2DE(bool predict_voting = false) : void
++~XBA2DE() = default : void
+..
++getVersion() : std::string
++graph(const std::string & title = "XBA2DE") const : std::vector<std::string>
+#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::(anonymous_60414016)" as C_0008746994658440620779
+class C_0008746994658440620779 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60414670)" as C_0008030559132212449356
+class C_0008030559132212449356 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::XBAODE" as C_0005198482342493966768
+class C_0005198482342493966768 #aliceblue;line:blue;line.dotted;text:blue {
++XBAODE() : void
+..
++getVersion() : std::string
+#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
+__
+}
+class "bayesnet::CFS" as C_0000093018845530739957
+class C_0000093018845530739957 #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_0001157456122733975432
+class C_0001157456122733975432 #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_0000066148117395428429
+class C_0000066148117395428429 #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::(anonymous_60810808)" as C_0012002108046995621535
+class C_0012002108046995621535 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60811462)" as C_0004735044229422764240
+class C_0004735044229422764240 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60804220)" as C_0007082100550474633839
+class C_0007082100550474633839 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60804874)" as C_0003669430095936529648
+class C_0003669430095936529648 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::(anonymous_60809706)" as C_0012336951062058157227
+class C_0012336951062058157227 #aliceblue;line:blue;line.dotted;text:blue {
+__
++CFS : std::string
++FCBF : std::string
++IWSS : std::string
+}
+class "bayesnet::(anonymous_60810360)" as C_0002435892998884329673
+class C_0002435892998884329673 #aliceblue;line:blue;line.dotted;text:blue {
+__
++ASC : std::string
++DESC : std::string
++RAND : std::string
+}
+class "bayesnet::MST" as C_0001054867409378333602
+class C_0001054867409378333602 #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
+..
++insertElement(std::list<int> & variables, int variable) : void
++maximumSpanningTree() : std::vector<std::pair<int,int>>
++reorder(std::vector<std::pair<float,std::pair<int,int>>> T, int root_original) : std::vector<std::pair<int,int>>
+__
+}
+class "bayesnet::Graph" as C_0009576333456015187741
+class C_0009576333456015187741 #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
+__
+}
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+'Generated with clang-uml, version 0.5.5
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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*.
+- ***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*.
-- ***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*.
 
 - ***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_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*.
-
-- ***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*.
 
 ## Operation
 
-The algorithm performs the following steps:
+### [Base Algorithm](./algorithm.md)
-
-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)

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)