Package Overview

SMAC supports you in determining well-performing hyperparameter configurations for your algorithms. By being a robust and flexible framework for Bayesian Optimization, SMAC can improve performance within a few function evaluations. It offers several entry points and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances.

Features

SMAC has the following characteristics and capabilities:

Global Optimizer

Bayesian Optimization is used for sample-efficient optimization.

Optimize Black-Box Functions

Optimization is only aware of input and output. It is agnostic to internals of the function.

Flexible Hyperparameters

Use categorical, continuous, hierarchical and/or conditional hyperparameters with the well-integrated ConfigurationSpace. SMAC can optimize up to 100 hyperparameters efficiently.

Any Objectives

Optimization with any objective (e.g., accuracy, runtime, cross-validation, ...) is possible.

Multi-Objective Optimization

Optimize arbitrary number of objectives using scalarized multi-objective algorithms. Both ParEGO [Know06] and mean aggregation strategies are supported.

Multi-Fidelity Optimization

Judge configurations on multiple budgets to discard unsuitable configurations early on. This will result in a massive speed-up, depending on the budgets.

Instances

Find well-performing hyperparameter configurations not only for one instance (e.g. dataset) of an algorithm, but for many.

Command-Line Interface

SMAC can not only be executed within a python file but also from the command line. Consequently, not only algorithms in python can be optimized, but implementations in other languages as well.

Note

Command-line interface has been temporarily disabled in v2.0. Please fall back to v1.4 if you need it.

Comparison

The following table provides an overview of SMAC's capabilities in comparison with other optimization tools.

Package	Complex Hyperparameter Space	Multi-Objective	Multi-Fidelity	Instances	Command-Line Interface	Parallelism
HyperMapper	✅	✅	❌	❌	❌	❌
Optuna	✅	✅	✅	❌	✅	✅
Hyperopt	✅	❌	❌	❌	✅	✅
BoTorch	❌	✅	✅	❌	❌	✅
OpenBox	✅	✅	❌	❌	❌	✅
HpBandSter	✅	❌	✅	❌	❌	✅
SMAC	✅	✅	✅	✅	✅	✅