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

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)

当我运行它时，一切都很好，直到它到达模型 10，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, y, callback=print_status, 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

我显然已经用谷歌搜索了，但没有发现任何有用的东西。有任何想法吗？

顺便说一句，以防万一，我的数据集中根本没有负值。

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1 回答 1

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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，我发现错误与scale_pos_weight.

来自 XGBoost 文档：

scale_pos_weight [默认=1]

控制正负权重的平衡，对不平衡的类很有用。需要考虑的典型值：sum(negative instances) / sum(positive instances)。有关更多讨论，请参阅参数调整。此外，请参阅 Higgs Kaggle 竞赛演示以获取示例：R、py1、py2、py3。

知道我的数据后，我手动设置了这个参数。

在您的多类分类的情况下，我建议您weight在实例化数据时使用该参数，xgboost.DMatrix如本答案所示

于 2020-11-15T10:17:53.930 回答