Advanced Usage

Callbacks

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

from llmize.callbacks import EarlyStopping, AdaptTempOnPlateau

callbacks = [
    EarlyStopping(patience=5),
    AdaptTempOnPlateau(factor=0.5)
]

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

Parallel Processing

Enable parallel evaluation of solutions:

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

Result Analysis

The new OptimizationResult class provides comprehensive optimization results:

# 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 scores: {results.best_score_per_step}")
print(f"Average scores: {results.avg_score_per_step}")
print(f"Number of steps: {results.num_steps}")
print(f"Total time: {results.total_time} seconds")