[英]Best parameters of an Optuna multi-objective optimization
When performing a single-objective optimization with Optuna , the best parameters of the study are accessible using:使用Optuna执行单目标优化时,可以使用以下方法访问研究的最佳参数:
import optuna
def objective(trial):
x = trial.suggest_uniform('x', -10, 10)
return (x - 2) ** 2
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=100)
study.best_params # E.g. {'x': 2.002108042}
If I want to perform a multi-objective optimization, this would be become for example:如果我想执行多目标优化,这将成为例如:
import optuna
def multi_objective(trial):
x = trial.suggest_uniform('x', -10, 10)
f1 = (x - 2) ** 2
f2 = -f1
return f1, f2
study = optuna.create_study(directions=['minimize', 'maximize'])
study.optimize(multi_objective, n_trials=100)
This works, but the command study.best_params
fails with RuntimeError: The best trial of a 'study' is only supported for single-objective optimization.
这可行,但命令
study.best_params
失败并出现RuntimeError: The best trial of a 'study' is only supported for single-objective optimization.
How can I get the best parameters for a multi-objective optimization?如何获得多目标优化的最佳参数?
In multi-objective optimization, you often end up with more than one best trial, but rather a set of trials.在多目标优化中,您通常会得到不止一个最佳试验,而是一组试验。 This set if often referred to as the Pareto front.
这个集合如果经常被称为帕累托前沿。 You can get this Pareto front, or the list of trials, via
study.best_trials
, then look at the parameters from each individual trial ie study.best_trials[some_index].params
.您可以通过
study.best_trials
获取此 Pareto 前沿或试验列表,然后查看每个单独试验的参数,即study.best_trials[some_index].params
。
For instance, given your directions of minimizing f1
and maximizing f2
, you might end up with a trial that has a small value for f1
(good) but at the same time small value for f2
(bad) while another trial might have a large value for both f1
(bad) and f2
(good).例如,给定最小化
f1
和最大化f2
的方向,您最终可能会得到一个f1
值较小(好)但同时f2
值较小(坏)的试验,而另一个试验可能具有较大的值对于f1
(坏)和f2
(好)。 Both of these trials could be returned from study.best_trials
.这两个试验都可以从
study.best_trials
中返回。
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