[tune](deps): Bump optuna from 2.6.0 to 2.8.0 in /python/requirements/tune

[dependabot]

Bumps optuna from 2.6.0 to 2.8.0.

Release notes

Sourced from optuna's releases.

v2.8.0

This is the release note of v2.8.0.

New Examples Repository

The number of Optuna examples has grown as the number of integrations have increased, and we’ve moved them to their own repository: optuna/optuna-examples.

Highlights

TPE Sampler Improvements

Constant Liar for Distributed Optimization

In distributed environments, the TPE sampler may sample many points in a small neighborhood, because it does not have knowledge that other trials running in parallel are sampling nearby. To avoid this issue, we’ve implemented the Constant Liar (CL) heuristic to return a poor value for trials which have started but are not yet complete, to reduce search effort.

study = optuna.create_study(sampler=optuna.samplers.TPESampler(constant_liar=True))

The following history plots demonstrate how optimization can be improved using this feature. Ten parallel workers are simultaneously trying to optimize the same function which takes about one second to compute. The first plot has constant_liar=False, and the second with constant_liar=True, uses the Constant Liar feature. We can see that with Constant Liar, the sampler does a better job of assigning different parameter configurations to different trials and converging faster.

See #2664 for details.

Tree-structured Search Space Support

The TPE sampler with multivariate=True now supports tree-structured search spaces. Previously, if the user split the search space with an if-else statement, as shown below, the TPE sampler with multivariate=True would fall back to random sampling. Now, if you set multivariate=True and group=True, the TPE sampler algorithm will be applied to each partitioned search space to perform efficient sampling.

See #2526 for more details.

def objective(trial):
    classifier_name = trial.suggest_categorical("classifier", ["SVC", "RandomForest"])
if classifier_name == "SVC":
    # If `multivariate=True` and `group=True`, the following 2 parameters are sampled jointly by TPE.
    svc_c = trial.suggest_float("svc_c", 1e-10, 1e10, log=True)
    svc_kernel = trial.suggest_categorical("kernel", ["linear", "rbf", "sigmoid"])
classifier_obj = sklearn.svm.SVC(C=svc_c, kernel=svc_kernel)

else:
# If multivariate=True and group=True, the following 3 parameters are sampled jointly by TPE.
rf_n_estimators = trial.suggest_int("rf_n_estimators", 1, 20)
rf_criterion = trial.suggest_categorical("rf_criterion", ["gini", "entropy"])
rf_max_depth = trial.suggest_int("rf_max_depth", 2, 32, log=True)
classifier_obj = sklearn.ensemble.RandomForestClassifier(n_estimators=rf_n_estimators, criterion=rf_criterion, max_depth=rf_max_depth)

...

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Commits

• cbfefe3 Merge pull request #2728 from hvy/gp-2717
• b4a84e3 Merge pull request #2717 from MagiaSN/fix_allennlp_transformer_cache
• 72557c7 Merge pull request #2724 from Crissman/release-v2.8.0
• ee5fd45 Remove dev label
• f2192f2 Merge pull request #2719 from Crissman/contrib
• 0a20e4e Some edits for CONTRIBUTING.md
• d4a286b Merge pull request #2709 from Muktan/optuna-2605
• 5ccaa4d Merge pull request #2711 from 01-vyom/matplotlib-colour-scale-2595
• 32a40f2 Merge pull request #2681 from himkt/bugfix/dump-best-params
• 78c326c Add newline between tests and use os.environ.pop
• Additional commits viewable in compare view

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[dependabot]

Superseded by #17.