BayesSearchCV ValueError：所有整数值均应大于0.000000

如何解决BayesSearchCV ValueError：所有整数值均应大于0.000000

我正在尝试使用BayesSearchCV调整xgboost模型以进行多类分类。这是我的代码。

n_iterations = 50

estimator = xgb.XGBClassifier(
    n_jobs=-1,objective="multi:softmax",eval_metric="merror",verbosity=0,num_class=3)

search_space = {
    "learning_rate": (0.01,1.0,"log-uniform"),"min_child_weight": (0,10),"max_depth": (1,50),"max_delta_step": (0,"subsample": (0.01,"uniform"),"colsample_bytree": (0.01,"colsample_bylevel": (0.01,"reg_lambda": (1e-9,1000,"reg_alpha": (1e-9,"gamma": (1e-9,0.5,5),"n_estimators": (5,5000),"scale_pos_weight": (1e-6,500,}

cv = GroupKFold(n_splits=10)
#cv = StratifiedKFold(n_splits=3,shuffle=True)

bayes_cv_tuner = BayesSearchCV(
    estimator=estimator,search_spaces=search_space,scoring="accuracy",cv=cv,n_jobs=-1,n_iter=n_iterations,verbose=0,refit=True,)

import pandas as pd
import numpy as np

def print_status(optimal_result):
    """Shows the best parameters found and accuracy attained of the search so far."""
    models_tested = pd.DataFrame(bayes_cv_tuner.cv_results_)
    best_parameters_so_far = pd.Series(bayes_cv_tuner.best_params_)
    print(
        "Model #{}\nBest accuracy so far: {}\nBest parameters so far: {}\n".format(
            len(models_tested),np.round(bayes_cv_tuner.best_score_,3),bayes_cv_tuner.best_params_,)
    )

    clf_type = bayes_cv_tuner.estimator.__class__.__name__
    models_tested.to_csv(clf_type + "_cv_results_summary.csv")


result = bayes_cv_tuner.fit(X,y,callback=print_status,groups=data.groups)

运行此命令时，一切正常，直到到达模型10，在这里返回此错误：

Traceback (most recent call last):

  File "<ipython-input-189-dc299c53649b>",line 1,in <module>
    result = bayes_cv_tuner.fit(X,groups=data_nobands2.AGREEMENT_NUMBER2)

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\searchcv.py",line 694,in fit
    groups=groups,n_points=n_points_adjusted

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\searchcv.py",line 565,in _step
    params = optimizer.ask(n_points=n_points)

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\optimizer\optimizer.py",line 417,in ask
    opt._tell(x,y_lie)

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\optimizer\optimizer.py",line 553,in _tell
    n_samples=self.n_points,random_state=self.rng))

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\space\space.py",line 963,in transform
    columns[j] = self.dimensions[j].transform(columns[j])

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\space\space.py",line 162,in transform
    return self.transformer.transform(X)

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\space\transformers.py",line 304,in transform
    X = transformer.transform(X)

  File "C:\Users\CatKa\Anaconda3\lib\site-packages\skopt\space\transformers.py",line 251,in transform
    "be greater than %f" % self.low)

ValueError: All integer values shouldbe greater than 0.000000

我显然已经在Google上搜索过，但没有发现任何有用的信息。有什么想法吗？

顺便说一句，以防万一，我的数据集中根本没有负值。

解决方法

对于二进制分类任务，我遇到了完全相同的问题。 经过连续测试后，我确定该错误与scale_pos_weight有关。

from XGBoost documentation:

scale_pos_weight [默认值= 1]

控制正负权重的平衡，这对于不平衡类很有用。需要考虑的典型值：sum（负 实例）/ sum（正实例）。有关更多信息，请参见参数调整。 讨论。另外，请参见Higgs Kaggle竞争演示示例：R， py1，py2，py3。

知道数据后，我手动设置了此参数。

对于多类分类，我建议您在weight中实例化数据时使用xgboost.DMatrix参数，如this answer所示