BayesNet/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

BayesNet is a C++ library implementing Bayesian Network Classifiers. It provides various algorithms for machine learning classification including TAN, KDB, SPODE, SPnDE, AODE, A2DE, and their ensemble variants (Boost, XB). The library also includes local discretization variants (Ld) and feature selection algorithms.

Build System & Dependencies

Dependency Management

The project supports two package managers:

vcpkg (Default)

• Uses vcpkg with private registry at https://github.com/rmontanana/vcpkg-stash
• Core dependencies: libtorch, nlohmann-json, folding, fimdlp, arff-files, catch2
• All dependencies defined in vcpkg.json with version overrides

Conan (Alternative)

• Modern C++ package manager with better dependency resolution
• Configured via conanfile.py for packaging and distribution
• Supports subset of dependencies (libtorch, nlohmann-json, catch2)
• Custom dependencies (folding, fimdlp, arff-files) need custom Conan recipes

Build Commands

Using vcpkg (Default)

# Initialize dependencies
make init

# Build debug version (with tests and coverage)
make debug
make buildd

# Build release version  
make release
make buildr

# Run tests
make test

# Generate coverage report
make coverage
make viewcoverage

# Clean project
make clean

Using Conan

# Install Conan first: pip install conan

# Initialize dependencies
make conan-init

# Build debug version (with tests and coverage)
make conan-debug
make buildd

# Build release version
make conan-release
make buildr

# Create and test Conan package
make conan-create

# Upload to Conan remote
make conan-upload remote=myremote

# Clean Conan cache and builds
make conan-clean

CMake Configuration

• Uses CMake 3.27+ with C++17 standard
• Debug builds automatically enable testing and coverage
• Release builds optimize with -Ofast
• Automatic package manager detection: CMake detects whether Conan or vcpkg is being used
• Supports both static library and package manager installation
• Conditional dependency linking based on availability

Testing Framework

• Catch2 testing framework (version 3.8.1)
• Test executable: TestBayesNet in build_Debug/tests/
• Individual test categories can be run: ./TestBayesNet "[CategoryName]"
• Coverage reporting with lcov/genhtml

Test Categories

• A2DE, BoostA2DE, BoostAODE, XSPODE, XSPnDE, XBAODE, XBA2DE
• Classifier, Ensemble, FeatureSelection, Metrics, Models
• Network, Node, MST, Modules

Code Architecture

Core Structure

bayesnet/
├── BaseClassifier.h          # Abstract base for all classifiers
├── classifiers/             # Basic Bayesian classifiers (TAN, KDB, SPODE, etc.)
├── ensembles/              # Ensemble methods (AODE, A2DE, Boost variants)
├── feature_selection/      # Feature selection algorithms (CFS, FCBF, IWSS, L1FS)
├── network/               # Bayesian network structure (Network, Node)
└── utils/                 # Utilities (metrics, MST, tensor operations)

Key Design Patterns

• BaseClassifier abstract interface for all algorithms
• Template-based design with both std::vector and torch::Tensor support
• Network/Node abstraction for Bayesian network representation
• Feature selection as separate, composable modules

Data Handling

• Supports both discrete integer data and continuous data with discretization
• ARFF file format support through arff-files library
• Tensor operations via PyTorch C++ (libtorch)
• Local discretization variants use fimdlp library

Documentation & Tools

• Doxygen for API documentation: make doc
• lcov for coverage reports: make coverage
• plantuml + clang-uml for UML diagrams: make diagrams
• Man pages available in docs/man3/

Sample Applications

Sample code in sample/ directory demonstrates library usage:

make sample fname=tests/data/iris.arff model=TANLd

Package Distribution

Creating Conan Packages

# Create package locally
make conan-create

# Test package installation
cd test_package
conan create ..

# Upload to remote repository
make conan-upload remote=myremote profile=myprofile

Using the Library

With Conan:

# conanfile.txt or conanfile.py
[requires]
bayesnet/1.1.2@user/channel

[generators]
cmake

With vcpkg:

{
  "dependencies": ["bayesnet"]
}

Common Development Tasks

• Add new classifier: Extend BaseClassifier, implement in appropriate subdirectory
• Add new test: Update tests/CMakeLists.txt and create test in tests/
• Modify build: Edit main CMakeLists.txt or use Makefile targets
• Update dependencies:
  • vcpkg: Modify vcpkg.json and run make init
  • Conan: Modify conanfile.py and run make conan-init
• Package for distribution: Use make conan-create for Conan packaging