# Changelog

All notable changes to AutoML Lite will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [1.0.0] - 2025-07-15

### 🎉 Production Release

This is the first production-ready release of AutoML Lite, featuring a complete automated machine learning pipeline with advanced features.

### ✨ Added

#### Core Features
- **AutoMLite Class**: Main class for automated machine learning
- **Automated Model Selection**: Tests multiple algorithms and selects the best performer
- **Hyperparameter Optimization**: Uses Optuna for efficient parameter tuning
- **Cross-Validation**: Robust model evaluation with customizable folds
- **Model Persistence**: Save and load trained models easily
- **Progress Tracking**: Clear progress indicators and verbose output options

#### Advanced Features
- **Ensemble Methods**: Automatic ensemble creation with voting classifiers
  - Soft voting for compatible models
  - Hard voting fallback for incompatible models
  - Top-K model selection
- **Feature Selection**: Intelligent feature importance and selection
  - Mutual information-based selection
  - Configurable thresholds and limits
- **Model Interpretability**: SHAP values and feature effects analysis
  - SHAP value calculations
  - Feature importance analysis
  - Model complexity metrics
- **Early Stopping**: Optimized training with patience and early stopping
  - Configurable patience and minimum delta
  - Automatic stopping when no improvement

#### CLI Interface
- **Complete Command-Line Interface**: Full CLI for all operations
  - `train`: Train models with various options
  - `predict`: Make predictions on new data
  - `report`: Generate comprehensive HTML reports
  - `interactive`: Interactive mode for guided training
- **Advanced CLI Options**:
  - Time budget control
  - Model count limits
  - Feature selection flags
  - Ensemble and interpretability options
  - Verbose output control

#### Reporting System
- **Comprehensive HTML Reports**: Interactive reports with visualizations
  - Executive summary with key metrics
  - Model performance leaderboard
  - Feature importance analysis
  - Training history and learning curves
  - Ensemble information
  - Model interpretability results
- **Advanced Visualizations**:
  - Interactive charts with Plotly
  - Confusion matrices for classification
  - ROC curves and precision-recall plots
  - Residual analysis for regression
  - Feature correlation matrices
  - Learning curves and training history

#### Supported Algorithms
- **Classification**:
  - Random Forest
  - XGBoost
  - LightGBM
  - Support Vector Machine (SVM)
  - Logistic Regression
  - Naive Bayes
  - Neural Network (MLP)
  - Extra Trees
  - Linear Discriminant Analysis
- **Regression**:
  - Random Forest
  - XGBoost
  - LightGBM
  - Support Vector Regression (SVR)
  - Linear Regression
  - Ridge Regression
  - Lasso Regression
  - Neural Network (MLP)
  - Extra Trees

#### Data Handling
- **Flexible Input Formats**: Support for various data formats
  - Pandas DataFrames
  - NumPy arrays
  - CSV files
  - Mixed data types
- **Automatic Preprocessing**: Handles common data issues
  - Missing value imputation
  - Categorical encoding
  - Feature scaling
  - Data type conversion

#### Error Handling
- **Robust Error Handling**: Comprehensive error management
  - Graceful handling of model compatibility issues
  - Automatic fallback mechanisms
  - Clear error messages and warnings
  - Debug mode for troubleshooting

### 🔧 Changed

#### Architecture Improvements
- **Modular Design**: Clean separation of concerns
  - Core AutoML engine
  - Optimization module
  - Preprocessing pipeline
  - Visualization system
  - CLI interface
- **Type Hints**: Full type annotations for better development experience
- **Logging System**: Comprehensive logging for debugging and monitoring

#### Performance Optimizations
- **Parallel Processing**: Efficient use of multiple CPU cores
- **Memory Management**: Optimized memory usage for large datasets
- **Early Stopping**: Reduced unnecessary training time
- **Caching**: Intelligent caching of intermediate results

### 🐛 Fixed

#### Model Compatibility
- **predict_proba Support**: Fixed issues with models lacking predict_proba
  - Automatic detection of predict_proba support
  - Fallback to decision_function when available
  - Clear error messages for unsupported operations
- **Ensemble Voting**: Fixed ensemble voting compatibility issues
  - Automatic soft/hard voting detection
  - Proper handling of mixed model types
  - Robust error handling for incompatible models

#### Data Processing
- **Training History**: Fixed training history data structure issues
  - Consistent field naming (time vs training_time)
  - Proper handling of both field names
  - Accurate total training time calculation
- **Feature Selection**: Fixed feature selection edge cases
  - Proper handling of single-feature datasets
  - Robust threshold-based selection
  - Memory-efficient processing

#### Reporting System
- **Template Rendering**: Fixed template data type issues
  - Proper string type conversion
  - Robust error handling for template rendering
  - Consistent data formatting
- **Visualization Generation**: Fixed chart generation issues
  - Proper base64 encoding for images
  - Robust error handling for failed visualizations
  - Fallback mechanisms for missing data

#### CLI Interface
- **Command Parsing**: Fixed CLI argument parsing issues
  - Proper handling of optional arguments
  - Clear help messages and usage examples
  - Robust error handling for invalid inputs
- **File Handling**: Fixed file path and permission issues
  - Proper path resolution
  - Error handling for missing files
  - Clear error messages for file operations

### 📚 Documentation

#### Comprehensive Documentation
- **README.md**: Complete project overview and quick start guide
- **API Reference**: Detailed API documentation with examples
- **User Guide**: Step-by-step usage instructions
- **Installation Guide**: Complete setup and troubleshooting guide
- **Examples**: Comprehensive examples for various use cases
- **Changelog**: Detailed change history

#### Code Documentation
- **Docstrings**: Complete docstrings for all classes and methods
- **Type Hints**: Full type annotations throughout the codebase
- **Comments**: Inline comments for complex logic
- **Examples**: Code examples in docstrings

### 🧪 Testing

#### Test Coverage
- **Unit Tests**: Comprehensive unit test coverage
  - Core functionality tests
  - Edge case handling
  - Error condition testing
- **Integration Tests**: End-to-end workflow testing
  - Complete training and prediction workflows
  - CLI command testing
  - Report generation testing
- **Performance Tests**: Performance benchmarking
  - Memory usage testing
  - Training time optimization
  - Scalability testing

### 🔒 Security

#### Security Improvements
- **Input Validation**: Robust input validation and sanitization
- **Error Handling**: Secure error handling without information leakage
- **File Operations**: Safe file handling and path validation

### 🚀 Performance

#### Performance Improvements
- **Training Speed**: Optimized training algorithms and parallel processing
- **Memory Usage**: Efficient memory management for large datasets
- **Scalability**: Improved performance with dataset size
- **Resource Management**: Better CPU and memory utilization

### 📦 Packaging

#### Distribution
- **Setup Configuration**: Complete setup.py configuration
- **Requirements**: Comprehensive requirements.txt
- **Development Setup**: Development dependencies and tools
- **Installation Scripts**: Automated installation and setup

### 🌟 Highlights

This release represents a complete, production-ready automated machine learning solution with:

- **Complete Feature Set**: All core AutoML features implemented
- **Production Ready**: Robust error handling and performance optimization
- **User Friendly**: Intuitive CLI and comprehensive documentation
- **Extensible**: Modular design for easy extension and customization
- **Well Tested**: Comprehensive test coverage and validation

### 🔗 Links

- **Repository**: https://github.com/Sherin-SEF-AI/AutoML-Lite
- **Documentation**: https://github.com/Sherin-SEF-AI/AutoML-Lite/wiki
- **Issues**: https://github.com/Sherin-SEF-AI/AutoML-Lite/issues
- **Support**: sherin.joseph2217@gmail.com

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## [0.9.0] - 2025-07-14

### ✨ Added
- Initial implementation of AutoMLite class
- Basic model training and prediction functionality
- Simple CLI interface
- Basic reporting capabilities

### 🔧 Changed
- Core architecture design
- Optimization algorithms
- Data preprocessing pipeline

### 🐛 Fixed
- Various bugs and issues during development

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## [0.8.0] - 2025-07-13

### ✨ Added
- Project structure and basic setup
- Core dependencies and requirements
- Initial documentation structure

### 🔧 Changed
- Project organization and file structure

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## [0.7.0] - 2025-07-12

### ✨ Added
- Repository initialization
- Basic project setup
- License and contribution guidelines

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## [Unreleased]

### Planned Features
- **Advanced Interpretability**: More sophisticated SHAP analysis
- **Model Deployment**: Easy deployment to cloud platforms
- **AutoML Pipelines**: Pre-built pipelines for common use cases
- **Performance Monitoring**: Real-time performance tracking
- **Model Versioning**: Version control for trained models
- **API Server**: RESTful API for model serving
- **Web Interface**: Web-based UI for model management
- **Distributed Training**: Support for distributed computing
- **GPU Acceleration**: GPU support for faster training
- **Custom Algorithms**: Framework for adding custom algorithms

### Planned Improvements
- **Enhanced CLI**: More interactive CLI with progress bars
- **Better Error Messages**: More descriptive error messages
- **Performance Optimization**: Further performance improvements
- **Memory Optimization**: Better memory management
- **Documentation**: More examples and tutorials
- **Testing**: Expanded test coverage
- **CI/CD**: Automated testing and deployment

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## Version History

- **1.0.0**: Production release with complete feature set
- **0.9.0**: Beta release with core functionality
- **0.8.0**: Alpha release with basic structure
- **0.7.0**: Initial project setup

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## Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.

## Support

- **Issues**: [GitHub Issues](https://github.com/Sherin-SEF-AI/AutoML-Lite/issues)
- **Email**: sherin.joseph2217@gmail.com
- **Documentation**: [GitHub Wiki](https://github.com/Sherin-SEF-AI/AutoML-Lite/wiki)

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**Made with ❤️ by Sherin Joseph Roy** 