build(deps): bump optuna from 4.1.0 to 4.2.0

[dependabot]

Bumps optuna from 4.1.0 to 4.2.0.

Release notes

Sourced from optuna's releases.

v4.2.0

This is the release note of v4.2.0. In conjunction with the Optuna release, OptunaHub 0.2.0 is released. Please refer to the release note of OptunaHub 0.2.0 for more details.

Highlights of this release include:

• 🚀gRPC Storage Proxy for Scalable Hyperparameter Optimization
• 🤖 SMAC3: Support for New State-of-the-art Optimization Algorithm by AutoML.org (@​automl)
• 📁 OptunaHub Now Supports Benchmark Functions
• 🧑‍💻 Gaussian Process-Based Bayesian Optimization with Inequality Constraints
• 🧑‍💻 c-TPE: Support Constrained TPESampler

Highlights

gRPC Storage Proxy for Scalable Hyperparameter Optimization

The gRPC storage proxy is a feature designed to support large-scale distributed optimization. As shown in the diagram below, gRPC storage proxy sits between the optimization workers and the database server, proxying the calls of Optuna’s storage APIs.

In large-scale distributed optimization settings where hundreds to thousands of workers are operating, placing a gRPC storage proxy for every few tens can significantly reduce the load on the RDB server which would otherwise be a single point of failure. The gRPC storage proxy enables sharing the cache about Optuna studies and trials, which can further mitigate load. Please refer to the official documentation for further details on how to utilize gRPC storage proxy.

SMAC3: Random Forest-Based Bayesian Optimization Developed by AutoML.org

SMAC3 is a hyperparameter optimization framework developed by AutoML.org, one of the most influential AutoML research groups. The Optuna-compatible SMAC3 sampler is now available thanks to the contribution to OptunaHub by Difan Deng (@​dengdifan), one of the core members of AutoML.org. We can now use the method widely used in AutoML research and real-world applications from Optuna.

# pip install optunahub smac
import optuna
import optunahub
from optuna.distributions import FloatDistribution
def objective(trial: optuna.Trial) -> float:
x = trial.suggest_float("x", -10, 10)
y = trial.suggest_float("y", -10, 10)
return x2 + y2
smac_mod = optunahub.load_module("samplers/smac_sampler")
n_trials = 100
sampler = smac_mod.SMACSampler(
{"x": FloatDistribution(-10, 10), "y": FloatDistribution(-10, 10)},
n_trials=n_trials,
)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=n_trials)

Please refer to https://hub.optuna.org/samplers/smac_sampler/ for more details.

OptunaHub Now Supports Benchmark Functions

... (truncated)

Commits

• 13ebffa Merge pull request #5926 from c-bata/release-v4.2.0
• 4d2e6c9 Bump the version up to v4.2.0
• 8be82d0 Merge pull request #5913 from HideakiImamura/avoid-port-number-conflict-for-c...
• 92df0b2 changed the name to the camel case
• 1272d7d Simplify annotations
• f3af33d Fix
• f2f7f0d Add RuntimeError for try-catch
• a853c73 Merge pull request #5924 from c-bata/fix-load-study-sampler-multi-objective
• 2f78d5a Fix the default sampler of load_study func
• 514b174 Introduce sphinx-notfound-page for better 404 page
• Additional commits viewable in compare view

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Summary by Sourcery

Update Optuna to v4.2.0.

New Features:

• Introduce gRPC storage proxy for scalable hyperparameter optimization.
• Add SMAC3: Random Forest-Based Bayesian Optimization developed by AutoML.org.
• Support benchmark functions in OptunaHub.
• Enable Gaussian Process-Based Bayesian Optimization with inequality constraints.
• Support constrained TPESampler with c-TPE.

Tests:

• Avoid port number conflict for CI.

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