我是Sagemaker的新手,正在尝试在Sagemaker中为xgboost算法设置超参数调优工作。我有非常不平衡的数据(98%的多数类,2%的少数类),我想使用“scale\u pos\u weight”参数,但出现以下错误
ClientError: An error occurred (ValidationException) when calling the CreateHyperParameterTuningJob operation: The hyperparameter tuning job that you requested has the following untunable hyperparameters: [scale_pos_weight]. For the algorithm, ---------------.us-east-1.amazonaws.com/xgboost:1, you can tune only [colsample_bytree, lambda, eta, max_depth, alpha, num_round, colsample_bylevel, subsample, min_child_weight, max_delta_step, gamma]. Delete untunable hyperparameters.
我已经升级了sagemaker软件包,重新启动了内核(我正在使用juptyer笔记本),并安装了实例,但问题仍然存在
有没有人知道为什么会发生这种错误,以及我如何修复它?我感谢你的帮助
下面是我在AWS中的一个示例中遵循的代码
sess = sagemaker.Session()
container = get_image_uri(region, 'xgboost', '1.0-1')
xgb = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.m4.4xlarge',
output_path='s3://{}/{}/output'.format(bucket, prefix),
sagemaker_session=sess)
xgb.set_hyperparameters(eval_metric='auc',
objective='binary:logistic',
num_round=100,
rate_drop=0.3,
tweedie_variance_power=1.4)
hyperparameter_ranges = {'eta': ContinuousParameter(0, 1),
'min_child_weight': ContinuousParameter(1, 10),
'scale_pos_weight' : ContinuousParameter(700, 800),
'alpha': ContinuousParameter(0, 2),
'max_depth': IntegerParameter(1, 10),
'colsample_bytree' : ContinuousParameter(0.1, 0.9)
}
objective_metric_name = 'validation:auc'
tuner = HyperparameterTuner(xgb,
objective_metric_name,
hyperparameter_ranges,
max_jobs=10,
max_parallel_jobs=2)
s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train'.format(bucket, prefix), content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data='s3://{}/{}/validation/'.format(bucket, prefix), content_type='csv')
tuner.fit({'train': s3_input_train, 'validation': s3_input_validation}, include_cls_metadata=False)
根据Sagemaker开发人员文档https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html,超参数
scale_pos_weight
是不可调的。链接中给出了您可以调整的唯一参数相关问题 更多 >
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