Optuna Backend#

Hyperactive provides wrappers for Optuna’s powerful samplers, giving you access to Optuna’s algorithms with Hyperactive’s unified interface.

Available Optimizers#

from hyperactive.opt.optuna import (
    TPEOptimizer,       # Tree-Parzen Estimators
    CmaEsOptimizer,     # CMA-ES evolution strategy
    GPOptimizer,        # Gaussian Process
    NSGAIIOptimizer,    # Multi-objective (NSGA-II)
    NSGAIIIOptimizer,   # Multi-objective (NSGA-III)
    QMCOptimizer,       # Quasi-Monte Carlo
    RandomOptimizer,    # Random sampling
    GridOptimizer,      # Grid search
)

Example: TPE with Optuna#

Example with Optuna TPE:

from hyperactive.opt.optuna import TPEOptimizer

optimizer = TPEOptimizer(
    search_space=search_space,
    n_iter=50,
    experiment=objective,
)

When to Use Optuna Backend#

The Optuna backend is useful when:

  • You want access to Optuna’s well-tested sampler implementations

  • You’re familiar with Optuna and want similar behavior

  • You need specific Optuna features like CMA-ES or NSGA-II/III

For most use cases, the native GFO optimizers provide equivalent functionality with the same interface.