我正在尝试在python中的Amazon Sagemaker中构建超参数优化作业,但某些方法不起作用。这是我所拥有的:
sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(containers[boto3.Session().region_name],
role,
train_instance_count=1,
train_instance_type='ml.m4.4xlarge',
output_path=output_path_1,
base_job_name='HPO-xgb',
sagemaker_session=sess)
from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter
hyperparameter_ranges = {'eta': ContinuousParameter(0.01, 0.2),
'num_rounds': ContinuousParameter(100, 500),
'num_class': 4,
'max_depth': IntegerParameter(3, 9),
'gamma': IntegerParameter(0, 5),
'min_child_weight': IntegerParameter(2, 6),
'subsample': ContinuousParameter(0.5, 0.9),
'colsample_bytree': ContinuousParameter(0.5, 0.9)}
objective_metric_name = 'validation:mlogloss'
objective_type='minimize'
metric_definitions = [{'Name': 'validation-mlogloss',
'Regex': 'validation-mlogloss=([0-9\\.]+)'}]
tuner = HyperparameterTuner(xgb,
objective_metric_name,
objective_type,
hyperparameter_ranges,
metric_definitions,
max_jobs=9,
max_parallel_jobs=3)
tuner.fit({'train': s3_input_train, 'validation': s3_input_validation})
我得到的错误是:
AttributeError: 'str' object has no attribute 'keys'
错误似乎来自tuner.py
文件:
----> 1 tuner.fit({'train': s3_input_train, 'validation': s3_input_validation})
~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/tuner.py in fit(self, inputs, job_name, **kwargs)
144 self.estimator._prepare_for_training(job_name)
145
--> 146 self._prepare_for_training(job_name=job_name)
147 self.latest_tuning_job = _TuningJob.start_new(self, inputs)
148
~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/tuner.py in _prepare_for_training(self, job_name)
120
121 self.static_hyperparameters = {to_str(k): to_str(v) for (k, v) in self.estimator.hyperparameters().items()}
--> 122 for hyperparameter_name in self._hyperparameter_ranges.keys():
123 self.static_hyperparameters.pop(hyperparameter_name, None)
124
AttributeError: 'list' object has no attribute 'keys'
答案 0 :(得分:3)
初始化HyperparameterTuner对象时,您的参数顺序错误。构造函数具有以下签名:
HyperparameterTuner(estimator,
objective_metric_name,
hyperparameter_ranges,
metric_definitions=None,
strategy='Bayesian',
objective_type='Maximize',
max_jobs=1,
max_parallel_jobs=1,
tags=None,
base_tuning_job_name=None)
因此,在这种情况下,您的objective_type
位置错误。有关更多详细信息,请参见the docs。