HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO

Abstract

Hyperparameter optimization benchmark introduced in the paper HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. The original benchmark is available here. Please note that this benchmark provides the results only at the last epoch of each configuration.

APIs

class Problem(dataset_id: int, seed: int | None = None, metric_names: list[str] | None = None)

• dataset_id: ID of the dataset to use. It must be in the range of [0, 7]. Please use Problem.available_dataset_names to see the available dataset names.
• seed: The seed for the random number generator of the dataset.
• metric_names: The metrics to use in optimization. Defaults to None, leading to single-objective optimization of the main metric defined in here. Please use Problem.available_metric_names to see the available metric names.

Methods and Properties

• search_space: Return the search space.
  • Returns: dict[str, optuna.distributions.BaseDistribution]
• directions: Return the optimization directions.
  • Returns: list[optuna.study.StudyDirection]
• metric_names: The names of the metrics to be used in the optimization.
  • Returns: list[str]
• available_metric_names: list[str]
  • Returns: The names of the available metrics.
• available_dataset_names: list[str]
  • Returns: The names of the available datasets.
• __call__(trial: optuna.Trial): Evaluate the objective functions and return the objective values.
  • Args:
    • trial: Optuna trial object.
  • Returns: list[float]
• evaluate(params: dict[str, int | float | str]): Evaluate the objective function given a dictionary of parameters.
  • Args:
    • params: The parameters defined in search_space.
  • Returns: list[float]
• reseed(seed: int | None = None): Recreate the random number generator with the given seed.
  • Args:
    • seed: The seed to be used.

Installation

To use this benchmark, you need to install simple-hpo-bench.

$ pip install simple-hpo-bench

Example

from __future__ import annotations

import optuna
import optunahub


hpobench = optunahub.load_module("benchmarks/hpobench_nn")
problem = hpobench.Problem(dataset_id=0)
study = optuna.create_study(directions=problem.directions)
study.optimize(problem, n_trials=30)
print(study.best_trial)

Others

Reference

This benchmark was originally introduced by AutoML.org, but our backend relies on simple-hpo-bench.

Bibtex

@inproceedings{
  eggensperger2021hpobench,
  title={{HPOB}ench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for {HPO}},
  author={Katharina Eggensperger and Philipp M{\"u}ller and Neeratyoy Mallik and Matthias Feurer and Rene Sass and Aaron Klein and Noor Awad and Marius Lindauer and Frank Hutter},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021},
  url={https://openreview.net/forum?id=1k4rJYEwda-}
}