如何使用optuna或hyperopt调整条件目标函数

Question

I tried to use optuna to tune hyperparameters. But my objective function is conditional which creates issues in getting optimal parameters.

i want to get cwc only if the condtion is met otherwise continue trial for next hyperparameters. But i guess since the condition is not met and objective func reurns cwc it gives error

UnboundLocalError: local variable 'cwc_train' referenced before assignment

define objective (trial):
    k_dis = trial.suggest_uniform('k_dis', 0.0, 5.0)
    l_dis = trial.suggest_uniform('l_dis', 0.0, 5.0)
    k_bound = trial.suggest_uniform('k_bound', 0.0, 5.0)
    l_bound = trial.suggest_uniform('l_bound', 0.0, 5.0) 

    picp = .....
    pinrw = .....


    if picp_train >= 0.8 and pinrw_train < 0.18: 
        cwc_train = fc.CWC_proposed(predict_bound_train, Y_train)
    else:
        print("error = ")
    return  cwc_train
study = optuna.create_study()
study.optimize(objective, n_trials=100)

UnboundLocalError: local variable 'cwc_train' referenced before assignment

Answer 1

i want to get cwc only if the condtion is met otherwise continue trial for next hyperparameters.

In this case, please raise optuna.structs.TrialPruned instead of returning cwc_train. Note that the default sampler ( TPESampler ) is aware of pruned solutions so that it can lower the probability of re-sampling.

if picp_train >= 0.8 and pinrw_train < 0.18: 
    cwc_train = fc.CWC_proposed(predict_bound_train, Y_train)
    return cwc_train
raise optuna.structs.TrialPruned()