HPOLib: Tabular Benchmarks for Hyperparameter Optimization and Neural Architecture Search

Abstract

Hyperparameter optimization benchmark introduced in the paper Tabular Benchmarks for Hyperparameter Optimization and Neural Architecture Search. 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, 3]. 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/hpolib")
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

@article{klein2019tabular,
  title={Tabular benchmarks for joint architecture and hyperparameter optimization},
  author={Klein, Aaron and Hutter, Frank},
  journal={arXiv preprint arXiv:1905.04970},
  year={2019}
}