在 Optuna 试用对象已提供建议值后，我可以覆盖其超参数吗？
有时，Optuna会建议我真正不想评估的样本 - 通常是因为它与先前评估的解决方案相同或太近。在这种情况下，我想评估一个随机样本。
是否有一种方法可以从agger.suggest_float（）（例如）中覆盖建议的值并将其传递回优化器？
给定两种方法：eval_check（），该方法列出了变量列表，并确定是否应评估样本，请返回true如果要为评估和false（如果不是）；并且，estairate（）获取变量列表并评估它们，返回实际数字 - 下面是我试图实现的目标的草图。
import numpy as np
import random
class Objective(object):
def __call__(self, trial):
# suggest values
x1 = trial.suggest_float('x1', 0.0, 1.0)
x2 = trial.suggest_int('x2', 10, 20)
x3 = trial.suggest_categorical('x3', ['cat1', 'cat2', 'cat3'])
# check if we should evaluate, if not get random value
while not eval_check([x1, x2, x3]):
x1 = np.random.uniform(0.0, 1.0)
x2 = np.random.randint(10, 20)
x3 = random.choice(['cat1', 'cat2', 'cat3'])
return evaluate([x1, x2, x3])
sampler = optuna.samplers.NSGAIISampler(population_size=100)
study = optuna.create_study(sampler=sampler)
study.optimize(Objective(),n_trials=1000)
现在，我知道这不会像我想要的那样起作用，因为，例如Optuna 试用对象建议样本[0.5，15，'cat2']但是代码> eval_check 不喜欢该示例，因此建议随机示例[0.2，18，'cat1']，如果我返回的输出，则18，'cat1']）），然后Optuna会认为这是评估的输出（[0.5，15，'cat2']），并将该分数与该样本相关联。
有什么方法可以覆盖试用对象中建议的超参数，以便当我返回分数时，Optuna会将该分数与其模型中的新覆盖超参数相关联？
Occasionally Optuna will suggest a sample that I don't really want to evaluate - usually either because it is the same as, or too close to, a previously evaluated solution. In this case I would like to evaluate a random sample instead.
Is there a way to overwrite the suggested values from trial.suggest_float() (for example) for a single trial and pass that back to the optimiser?
Given two methods: eval_check(), which takes a list of variables and makes a determination as to whether the sample should be evaluated or not, returning True if it is to be evaluated and False if not; and,evaluate() which takes a list of variables and evaluates them, returning a real number - below is a sketch of what I am trying to achieve.
import numpy as np
import random
class Objective(object):
def __call__(self, trial):
# suggest values
x1 = trial.suggest_float('x1', 0.0, 1.0)
x2 = trial.suggest_int('x2', 10, 20)
x3 = trial.suggest_categorical('x3', ['cat1', 'cat2', 'cat3'])
# check if we should evaluate, if not get random value
while not eval_check([x1, x2, x3]):
x1 = np.random.uniform(0.0, 1.0)
x2 = np.random.randint(10, 20)
x3 = random.choice(['cat1', 'cat2', 'cat3'])
return evaluate([x1, x2, x3])
sampler = optuna.samplers.NSGAIISampler(population_size=100)
study = optuna.create_study(sampler=sampler)
study.optimize(Objective(),n_trials=1000)
Now, I know this won't work as I want it to, because, say the Optuna trial object suggests the sample [0.5, 15, 'cat2'] but eval_check doesn't like that sample and so suggests the random sample [0.2, 18, 'cat1'], if I return the output of evaluate([0.2, 18, 'cat1']), then Optuna will think this is the output of evaluate([0.5, 15, 'cat2']) and associate that score with that sample in its model.
Is there a way that I can overwrite the suggested hyperparameters in the trial object such that when I return the score, Optuna will associate that score with the new overwritten hyperparameters in its model?
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评论（1）
Optuna 不假设用户覆盖试用参数。相反，向搜索空间添加约束怎么样？
或者，当
not eval_check([x1, x2, x3])变为True。Optuna doesn't assume users overwrite a parameters of trial. Instead, how about adding a constraint to the search space?
Or, raising
optuna.TrialPrunedfor an undesired combination of parameters also skips a computing objective function whennot eval_check([x1, x2, x3])becomesTrue.