101 lines
3.5 KiB
Markdown
101 lines
3.5 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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PyClassifiers is a C++ library that provides wrappers for Python machine learning classifiers. It enables C++ applications to use Python-based ML algorithms (scikit-learn, XGBoost, custom implementations) through a unified interface.
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## Essential Commands
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### Build System
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```bash
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# Setup build configurations
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make debug # Configure debug build with testing and coverage
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make release # Configure release build
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# Build targets
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make buildd # Build debug version
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make buildr # Build release version
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# Testing
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make test # Run all unit tests
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make test opt="-s" # Run tests with verbose output
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make test opt="-c='Test Name'" # Run specific test section
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# Coverage
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make coverage # Run tests and generate coverage report
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# Installation
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sudo make install # Install library to system (requires release build)
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# Utilities
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make clean # Clean test artifacts
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make help # Show all available targets
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```
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### Dependencies
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- Requires `VCPKG_ROOT` environment variable set
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- Miniconda installation required for Python classifiers
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- Boost library (preferably system package: `sudo dnf install boost-devel`)
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## Architecture
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### Core Components
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**PyWrap** (`pyclfs/PyWrap.h`): Singleton managing Python interpreter lifecycle and thread-safe Python/C++ communication.
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**PyClassifier** (`pyclfs/PyClassifier.h`): Abstract base class inheriting from `bayesnet::BaseClassifier`. All Python classifier wrappers extend this class.
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**Individual Classifiers**: Each classifier (STree, ODTE, SVC, RandomForest, XGBoost, AdaBoostPy) wraps specific Python modules with consistent C++ interface.
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### Data Flow
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- Uses PyTorch tensors for efficient C++/Python data exchange
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- JSON-based hyperparameter configuration
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- Automatic memory management for Python objects
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## Key Directories
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- `pyclfs/` - Core library source code
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- `tests/` - Catch2 unit tests with ARFF test datasets
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- `build_debug/` - Debug build artifacts
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- `build_release/` - Release build artifacts
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- `cmake/modules/` - Custom CMake modules
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## Development Patterns
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### Adding New Classifiers
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1. Inherit from `PyClassifier` base class
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2. Implement required virtual methods: `fit()`, `predict()`, `predict_proba()`
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3. Use `PyWrap::getInstance()` for Python interpreter access
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4. Handle hyperparameters via JSON configuration
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5. Add corresponding unit tests in `tests/TestPythonClassifiers.cc`
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### Python Integration
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- All Python interactions go through PyWrap singleton
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- Use RAII pattern for Python object management
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- Convert data using PyTorch tensors (discrete/continuous data support)
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- Handle Python exceptions and convert to C++ exceptions
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### Testing
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- Catch2 framework with parameterized tests using GENERATE()
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- Test data in ARFF format located in `tests/data/`
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- Performance benchmarks validate expected accuracy scores
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- Coverage reports generated with gcovr
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## Important Files
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- `pyclfs/PyWrap.h` - Python interpreter management
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- `pyclfs/PyClassifier.h` - Base classifier interface
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- `CMakeLists.txt` - Main build configuration
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- `Makefile` - Build automation and common tasks
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- `vcpkg.json` - Package dependencies
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- `tests/TestPythonClassifiers.cc` - Main test suite
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## Technical Requirements
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- C++17 standard compliance
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- Python 3.11+ required
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- Boost library with Python and NumPy support
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- PyTorch for tensor operations
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- Thread-safe design for concurrent usage |