Base definition of a Profet task¶
Base class for Tasks that are generated using the Profet algorithm.
For more information on Profet, see original paper at https://arxiv.org/abs/1905.12982.
Klein, Aaron, Zhenwen Dai, Frank Hutter, Neil Lawrence, and Javier Gonzalez. “Meta-surrogate benchmarking for hyperparameter optimization.” Advances in Neural Information Processing Systems 32 (2019): 6270-6280.
- class orion.benchmark.task.profet.profet_task.ProfetTask(max_trials: int = 100, input_dir: Union[Path, str] = 'profet_data', checkpoint_dir: Optional[Union[Path, str]] = None, model_config: Optional[MetaModelConfig] = None, device: Optional[Union[str, Any]] = None, with_grad: bool = False)[source]¶
Base class for Tasks that are generated using the Profet algorithm.
For more information on Profet, see original paper at https://arxiv.org/abs/1905.12982.
Klein, Aaron, Zhenwen Dai, Frank Hutter, Neil Lawrence, and Javier Gonzalez. “Meta-surrogate benchmarking for hyperparameter optimization.” Advances in Neural Information Processing Systems 32 (2019): 6270-6280.
- Parameters
- max_trialsint, optional
Max number of trials to run, by default 100
- input_dirUnion[Path, str], optional
Input directory containing the data used to train the meta-model, by default None.
- checkpoint_dirUnion[Path, str], optional
Directory used to save/load trained meta-models, by default None.
- model_configMetaModelConfig, optional
Configuration options for the training of the meta-model, by default None
- devicestr, optional
The device to use for training, by default None.
- with_gradbool, optional
Whether the task should also return the gradients of the objective function with respect to the inputs. Defaults to False.
- Attributes
configuration
Return the configuration of the task.
- space
Methods
alias of
MetaModelConfig
call
(**kwargs)Get the value of the sampled objective function at the given point (hyper-parameters).
- ModelConfig¶
alias of
MetaModelConfig
- call(**kwargs) List[Dict] [source]¶
Get the value of the sampled objective function at the given point (hyper-parameters).
If self.with_grad is set, also returns the gradient of the objective function with respect to the inputs.
- Parameters
- **kwargs
Dictionary of hyper-parameters.
- Returns
- List[Dict]
Result dictionaries: objective and optionally gradient.
- Raises
- ValueError
If the input isn’t of a supported type.
- property configuration¶
Return the configuration of the task.