问题描述
我有以下 Optuna 代码来对 Xgboost 分类器进行超参数调整。
import optuna
from optuna import Trial,visualization
from optuna.samplers import TPESampler
from xgboost import XGBClassifier
def objective(trial: Trial,X_train,y_train,X_test,y_test):
param = {
"n_estimators" : Trial.suggest_int("n_estimators",1000),'max_depth':Trial.suggest_int('max_depth',2,25),'reg_alpha':Trial.suggest_int('reg_alpha',5),'reg_lambda':Trial.suggest_int('reg_lambda','min_child_weight':Trial.suggest_int('min_child_weight','gamma':Trial.suggest_int('gamma','learning_rate':Trial.suggest_loguniform('learning_rate',0.005,0.5),'colsample_bytree':Trial.suggest_discrete_uniform('colsample_bytree',0.1,1,0.01),'nthread' : -1
}
model = XGBClassifier(**param)
model.fit(X_train,y_train)
return cross_val_score(model,y_test).mean()
study = optuna.create_study(direction='maximize',sampler=TPESampler())
study.optimize(lambda trial : objective(trial,y_test),n_trials= 50)
它不断给我以下错误:
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\envs\Janestreet\lib\site-packages\optuna\_optimize.py",line 217,in _run_trial
value_or_values = func(trial)
File "<ipython-input-74-c1454daaa53e>",line 2,in <lambda>
study.optimize(lambda trial : objective(trial,n_trials= 50)
File "<ipython-input-73-4438e1db47ef>",line 4,in objective
"n_estimators" : Trial.suggest_int("n_estimators",TypeError: suggest_int() missing 1 required positional argument: 'high'
非常感谢
解决方法
问题是您在类 suggest_int
上调用 Trial
,就好像它是类/静态方法一样。 suggest_int
是常规方法,应在对象上调用,在本例中为 trial
。将 Trial.suggest_int
更改为 trial.suggest_int
应该可以消除错误。