Advanced Usage
====================

Callbacks
----------

LLMize supports custom callbacks for monitoring and controlling the optimization process:

Available Callbacks
~~~~~~~~~~~~~~~~~~~~

1. **EarlyStopping**: Stop optimization when no improvement is seen
2. **AdaptTempOnPlateau**: Reduce temperature when optimization plateaus
3. **OptimalScoreStopping**: Stop when reaching a target score

.. code-block:: python

    from llmize.callbacks import EarlyStopping, AdaptTempOnPlateau, OptimalScoreStopping

    callbacks = [
        EarlyStopping(patience=10, monitor='best_score'),
        AdaptTempOnPlateau(factor=0.5, patience=5),
        OptimalScoreStopping(optimal_score=0.99, tolerance=0.01)
    ]

    results = optimizer.maximize(
        callbacks=callbacks,
        # ... other parameters
    )

Parallel Processing
---------------------

Enable parallel evaluation of solutions:

.. code-block:: python

    results = optimizer.maximize(
        parallel_n_jobs=4,  # Number of parallel processes
        # ... other parameters
    )

Result Analysis
------------------

The ``OptimizationResult`` class provides comprehensive optimization results:

.. code-block:: python

    # Access optimization results
    print(f"Best solution: {results.best_solution}")
    print(f"Best score: {results.best_score}")
    print(f"Score history: {results.best_score_history}")
    print(f"Per-step best scores: {results.best_score_per_step}")
    print(f"Per-step average scores: {results.avg_score_per_step}")
    
    # Convert to dictionary for serialization
    results_dict = results.to_dict()