SVMClassifier/CHANGELOG.md

Changelog

All notable changes to the SVM Classifier C++ project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Planned

• Feature importance extraction for linear kernels
• Model serialization and persistence
• CUDA GPU acceleration support
• Python bindings via pybind11
• Sparse matrix support optimization
• Online learning capabilities

[1.0.0] - 2024-12-XX

Added

• Initial release of SVM Classifier C++

• Core Features

  • Support Vector Machine classifier with scikit-learn compatible API
  • Multiple kernel support: Linear, RBF, Polynomial, Sigmoid
  • Automatic library selection: liblinear for linear, libsvm for non-linear
  • Multiclass classification: One-vs-Rest (OvR) and One-vs-One (OvO) strategies
  • Native PyTorch tensor integration
  • JSON-based parameter configuration using nlohmann::json

• API Methods

  • fit(): Train the classifier on labeled data
  • predict(): Predict class labels for new samples
  • predict_proba(): Predict class probabilities (when supported)
  • score(): Calculate accuracy on test data
  • decision_function(): Get decision function values
  • cross_validate(): K-fold cross-validation
  • grid_search(): Hyperparameter optimization
  • evaluate(): Comprehensive evaluation metrics

• Data Handling

  • Efficient tensor to SVM format conversion
  • Automatic CPU/GPU tensor handling
  • Sparse feature support with configurable threshold
  • Memory-efficient data structures
  • Support for various tensor data types

• Kernel Support

  • Linear: Fast, optimized for high-dimensional data
  • RBF: Radial Basis Function with auto/manual gamma
  • Polynomial: Configurable degree and coefficients
  • Sigmoid: Neural network-like kernel

• Multiclass Strategies

  • One-vs-Rest: Faster training, good for many classes
  • One-vs-One: Better accuracy, voting-based prediction

• Testing & Quality

  • Comprehensive test suite with Catch2
  • Unit tests for all components
  • Integration tests for end-to-end workflows
  • Performance benchmarks and profiling
  • Memory leak detection with Valgrind
  • Code coverage analysis with lcov
  • Cross-platform compatibility (Linux, macOS, Windows)

• Build System

  • Modern CMake build system (3.15+)
  • Automatic dependency management
  • Multiple build configurations (Debug, Release, RelWithDebInfo)
  • Package generation with CPack
  • Docker support for containerized builds
  • Automated installation script

• Documentation

  • Comprehensive README with usage examples
  • Quick start guide for immediate productivity
  • Development guide for contributors
  • API documentation with Doxygen
  • Performance benchmarking results
  • Troubleshooting and FAQ sections

• Examples & Demos

  • Basic usage example with simple dataset
  • Advanced usage with hyperparameter tuning
  • Performance comparison between kernels
  • Cross-validation and model evaluation
  • Feature preprocessing demonstrations
  • Imbalanced dataset handling

• CI/CD Pipeline

  • GitHub Actions workflow
  • Multi-platform testing (Ubuntu, macOS)
  • Multiple compiler support (GCC, Clang)
  • Automated testing and validation
  • Code quality checks (formatting, static analysis)
  • Documentation generation and deployment
  • Release automation

• Development Tools

  • clang-format configuration for consistent code style
  • clang-tidy setup for static analysis
  • Doxygen configuration for documentation
  • Docker development environment
  • Comprehensive validation script
  • Performance profiling tools

Technical Details

• Language: C++17 with modern C++ practices
• Dependencies:
  • libtorch (PyTorch C++) for tensor operations
  • libsvm for non-linear SVM algorithms
  • liblinear for efficient linear classification
  • nlohmann::json for configuration management
  • Catch2 for testing framework
• Architecture: Modular design with clear separation of concerns
• Memory Management: RAII principles, automatic resource cleanup
• Error Handling: Exception-based with meaningful error messages
• Performance: Optimized data conversion, efficient memory usage

Supported Platforms

• Linux: Ubuntu 18.04+, CentOS 7+, Debian 9+
• macOS: 10.14+ (Mojave and later)
• Windows: Windows 10 with Visual Studio 2019+

Performance Characteristics

• Linear Kernel: Handles datasets up to 100K+ samples efficiently
• RBF Kernel: Optimized for datasets up to 10K samples
• Memory Usage: Scales linearly with dataset size
• Training Speed: Competitive with scikit-learn for equivalent operations
• Prediction Speed: Sub-millisecond prediction for individual samples

Compatibility

• Compiler Support: GCC 7+, Clang 5+, MSVC 2019+
• CMake: Version 3.15 or higher required
• PyTorch: Compatible with libtorch 1.9+ and 2.x series
• Standards: Follows C++17 standard, forward compatible with C++20

[0.9.0] - 2024-11-XX (Beta Release)

Added

• Core SVM classifier implementation
• Basic kernel support (Linear, RBF)
• Initial multiclass support
• Proof-of-concept examples
• Basic test suite

Known Issues

• Limited documentation
• Performance not optimized
• Missing advanced features

[0.5.0] - 2024-10-XX (Alpha Release)

Added

• Project structure and build system
• Initial CMake configuration
• Basic tensor conversion utilities
• Preliminary API design

Development Notes

• Focus on architecture and design
• Establishing coding standards
• Setting up CI/CD pipeline

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Contributing

See DEVELOPMENT.md for information about contributing to this project.

Migration Guide

From scikit-learn

If you're migrating from scikit-learn, here are the key differences:

# scikit-learn (Python)
from sklearn.svm import SVC
svm = SVC(kernel='rbf', C=1.0, gamma='auto')
svm.fit(X, y)
predictions = svm.predict(X_test)
probabilities = svm.predict_proba(X_test)
accuracy = svm.score(X_test, y_test)

// SVM Classifier C++
#include <svm_classifier/svm_classifier.hpp>
using namespace svm_classifier;

json config = {{"kernel", "rbf"}, {"C", 1.0}, {"gamma", "auto"}};
SVMClassifier svm(config);
auto metrics = svm.fit(X, y);
auto predictions = svm.predict(X_test);
auto probabilities = svm.predict_proba(X_test);
double accuracy = svm.score(X_test, y_test);

API Mapping

scikit-learn	SVM Classifier C++	Notes
SVC()	SVMClassifier()	Constructor with similar parameters
fit(X, y)	fit(X, y)	Returns training metrics
predict(X)	predict(X)	Returns torch::Tensor
predict_proba(X)	predict_proba(X)	Returns torch::Tensor
score(X, y)	score(X, y)	Returns double accuracy
decision_function(X)	decision_function(X)	Returns torch::Tensor

Acknowledgments

This project builds upon the excellent work of:

• libsvm by Chih-Chung Chang and Chih-Jen Lin
• liblinear by the LIBLINEAR Project team
• PyTorch by Facebook AI Research
• nlohmann::json by Niels Lohmann
• Catch2 by the Catch2 team
• scikit-learn for API inspiration

Special thanks to the open-source community for their invaluable tools and libraries.