hp.process函数定义

`hp.process()` 函数是 Hyperopt 库中的一个函数，用于执行一个优化过程。它的定义如下：

def process(fn, space, algo, max_evals, trials, rstate, verbose=0,
            catch_eval_exceptions=False, return_argmin=True,
            pass_expr_memo_ctrl=None):
    """
    Minimize a function over a hyperparameter space.

    Parameters
    ----------
    fn : callable
        A Python callable that is the objective function. This function should
        return a real-valued loss that hyperopt will seek to minimize. It should
        have signature ``fn(params) -> loss`` where ``params`` is a dictionary
        containing the hyperparameters to be optimized and ``loss`` is a float
        value representing the objective function's value at those hyperparameters.
    space : hyperopt.pyll.Apply node
        A Pyll expression representing a tree of (potentially conditional)
        hyperparameters to optimize.
    algo : search algorithm
        A search algorithm. An algorithm is an object that defines the search
        strategy. It should have a ``suggest`` method that takes a number of
        trials and returns a set of hyperparameters to evaluate next.
    max_evals : int
        The maximum number of evaluations of ``fn``.
    trials : ``Trials`` object
        A container for storing the details of each trial. This object defines
        the interface for communicating back to hyperopt.
    rstate : numpy.random.RandomState instance
        A numpy RandomState instance used for random sampling.
    verbose : int
        Controls verbosity of output:
            * 0: no output
            * 1: print only status updates
            * 2: print status and results
    catch_eval_exceptions : bool
        If True, catch exceptions thrown by the objective function and store the
        traceback in the corresponding trial document.
    return_argmin : bool
        If True, return the hyperparameters that gave the best observed value.
        Otherwise, return the full dictionary of hyperparameters and their
        values for each evaluated point.

    Returns
    -------
    results : either the best point found (if return_argmin=True) or the full
              dictionary of evaluated points (if return_argmin=False).
    """

该函数的主要作用是最小化一个给定的目标函数 `fn`，其参数由 `space` 指定，使用一个指定的优化算法 `algo` 进行搜索，并在给定的最大评估次数 `max_evals` 之后返回结果。它还接受一个 `Trials` 对象来存储每个评估的详细信息，以及一个随机数生成器 `rstate` 来执行随机采样。函数的输出结果可以是最优点的超参数（如果 `return_argmin` 为 `True`），或者每个评估点的超参数和对应的值（如果 `return_argmin` 为 `False`）。函数还支持捕获评估函数 `fn` 抛出的异常，并将其存储在相应的评估记录中。