Advanced Usage

Can I run optimizations in parallel?

Currently, Hyperactive v5 runs single optimizer instances. For parallel evaluation of candidates, consider using Optuna backend optimizers which support parallel trials:

from hyperactive.opt.optuna import TPEOptimizer

optimizer = TPEOptimizer(
    search_space=search_space,
    n_iter=100,
    experiment=objective,
    # Optuna handles parallelization
)

Can I save and resume optimization?

This feature is planned but not yet available in v5. As a workaround, you can log results during optimization and use them as initial points for a new run.

Are callbacks supported?

User-defined callbacks during optimization are not currently supported in v5. The Optuna backend has internal early-stopping callbacks, but there’s no general callback interface for tracking progress or modifying behavior during optimization.

For progress monitoring, you can add logging inside your objective function:

iteration = 0

def objective(params):
    global iteration
    iteration += 1
    score = evaluate_model(params)
    print(f"Iteration {iteration}: score={score:.4f}")
    return score

How do I add constraints between parameters?

Handle constraints in your objective function by returning a poor score for invalid combinations:

def objective(params):
    # Constraint: min_samples_split must be >= min_samples_leaf
    if params["min_samples_split"] < params["min_samples_leaf"]:
        return -np.inf  # Invalid configuration

    return evaluate_model(params)