OptunaTuner

class lightautoml.ml_algo.tuning.optuna.OptunaTuner(timeout=1000, n_trials=100, direction='maximize', fit_on_holdout=True, random_state=42)[source]

Bases: lightautoml.ml_algo.tuning.base.ParamsTuner

Wrapper for optuna tuner.

__init__(timeout=1000, n_trials=100, direction='maximize', fit_on_holdout=True, random_state=42)[source]
Parameters
  • timeout (Optional[int]) – Maximum learning time.

  • n_trials (Optional[int]) – Maximum number of trials.

  • direction (Optional[str]) – Direction of optimization. Set minimize for minimization and maximize for maximization.

  • fit_on_holdout (bool) – Will be used holdout cv-iterator.

  • random_state (int) – Seed for optuna sampler.

fit(ml_algo, train_valid_iterator=None)[source]

Tune model.

Parameters
Return type

Tuple[Optional[~TunableAlgo], Optional[LAMLDataset]]

Returns

Tuple (None, None) if an optuna exception raised or fit_on_holdout=True and train_valid_iterator is not HoldoutIterator. Tuple (MlALgo, preds_ds) otherwise.

plot()[source]

Plot optimization history of all trials in a study.