Source code for orion.benchmark.task.base

#!/usr/bin/env python
Base definition of Task

from abc import ABC, abstractmethod
from typing import Any, Dict, List

from orion.core.utils import GenericFactory

[docs]class BenchmarkTask(ABC): """Base class describing what a task can do. A task will define the objective function and search space of it. Parameters ---------- max_trials : int Max number of trials the experiment will run against this task. kwargs : dict Configurable parameters of the task, a particular task implementation can have its own parameters. """ def __init__(self, max_trials: int, **kwargs): self._max_trials = max_trials self._param_names = kwargs self._param_names["max_trials"] = max_trials
[docs] @abstractmethod def call(self) -> List[Dict]: """ Define the black box function to optimize, the function will expect hyper-parameters to search and return objective values of trial with the hyper-parameters. This method should be overridden by subclasses. It should receive the hyper-parameters as keyword arguments, with argument names matching the keys of the dictionary returned by `get_search_space`. """
[docs] def __call__(self, *args, **kwargs): """ All tasks will be callable by default, and method `call()` will be executed when a task is called directly. """ return*args, **kwargs)
@property def max_trials(self) -> int: """Return the max number of trials to run for the task.""" return self._max_trials
[docs] @abstractmethod def get_search_space(self) -> Dict[str, str]: """Return the search space for the task objective function"""
@property def configuration(self) -> Dict[str, Any]: """Return the configuration of the task.""" return {self.__class__.__qualname__: self._param_names}
bench_task_factory = GenericFactory(BenchmarkTask)