Refactor: Replace if-elif chain with optimizer config dict

[aries043]

Pull Request Description

Summary

This PR refactors the benchmarking script by replacing the 80+ line if-elif chain for optimizer selection with a centralized, maintainable configuration system. The change improves code readability, maintainability, and makes it easier to add new optimizers in the future.

Problem

The current benchmarking script (benchmarking_lsbbo_2.py) contains a very long if-elif chain (80+ lines) for optimizer selection:

if params['optimizer'] == 'PRS':
    from pypop7.optimizers.rs.prs import PRS as Optimizer
elif params['optimizer'] == 'SRS':
    from pypop7.optimizers.rs.srs import SRS as Optimizer
elif params['optimizer'] == 'GS':
    from pypop7.optimizers.rs.gs import GS as Optimizer
# ... 80+ more lines

This approach has several issues:

• Hard to maintain: Adding new optimizers requires modifying the long chain
• Error-prone: Easy to make typos or miss entries
• Poor readability: The logic is scattered across many lines
• Code duplication: Similar import patterns repeated many times

Solution

1. Centralized Configuration

Replaced the if-elif chain with a dictionary-based configuration system:

OPTIMIZER_CONFIGS: Dict[str, OptimizerConfig] = {
    'PRS': OptimizerConfig('pypop7.optimizers.rs.prs', 'PRS', True),
    'SRS': OptimizerConfig('pypop7.optimizers.rs.srs', 'SRS', True),
    # ... all optimizers in one place
}

2. Dynamic Import Function

Created a clean function to handle optimizer loading:

def get_optimizer_class(optimizer_name: str) -> Type[Any]:
    """Dynamically import and return optimizer class based on name."""

3. Improved Error Handling

Added proper validation and helpful error messages:

• Clear error when optimizer is not found
• List of available optimizers in error message
• Proper exception handling for import failures

4. Type Safety

Added comprehensive type hints throughout the code for better IDE support and catch potential issues early.

Key Benefits

• ✅ Maintainability: Adding new optimizers now requires only one line in the config dictionary
• ✅ Readability: All optimizer configurations are in one clear location
• ✅ Error Prevention: Type hints and validation catch issues early
• ✅ Consistency: Uniform handling of all optimizers
• ✅ Extensibility: Easy to add metadata (like sigma requirements) for optimizers

Backward Compatibility

• ✅ Command-line interface unchanged: All existing scripts and commands work exactly the same
• ✅ All optimizers supported: Every optimizer from the original code is included
• ✅ Same functionality: No behavioral changes, only structural improvements
• ✅ Same output format: Results are saved in identical format

Testing

Added comprehensive GitHub Actions workflow that tests:

• ✅ Syntax validation across Python 3.8-3.11
• ✅ Optimizer class loading for all configured optimizers
• ✅ Argument validation and error handling
• ✅ Code quality (flake8, black, mypy)
• ✅ Quick integration test with CMAES optimizer

Code Quality Improvements

• Type hints: Full type annotation for better IDE support
• Documentation: Added docstrings for all new functions
• Error messages: More helpful and informative error reporting
• Code organization: Logical separation of concerns

Files Changed

• tutorials/benchmarking_lsbbo_2.py - New improved version
• .github/workflows/test-refactoring.yml - Comprehensive test suite

Migration Path

The improved script is provided as a new file (benchmarking_lsbbo_2_improved.py) to:

1. Allow side-by-side comparison
2. Enable gradual migration if desired
3. Maintain the original as a reference

If this approach is accepted, the original file can be replaced in a follow-up commit.

Future Enhancements

This refactoring lays the groundwork for future improvements:

• Parallel execution support
• Configuration file-based experiments
• Enhanced logging and monitoring
• Memory usage optimization

However, this PR focuses solely on the structural improvement to keep changes focused and reviewable.

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Testing Instructions:

# Test the new script with any optimizer
python tutorials/benchmarking_lsbbo_2.py --start 0 --end 0 --optimizer CMAES --ndim_problem 10

# Verify all optimizers are loadable
python -c "from tutorials.benchmarking_lsbbo_2 import OPTIMIZER_CONFIGS; print(f'Configured optimizers: {len(OPTIMIZER_CONFIGS)}')"