Function fmin

hyperopt/fmin.py:369–598

Minimize a function over a hyperparameter space. More realistically: *explore* a function over a hyperparameter space according to a given algorithm, allowing up to a certain number of function evaluations. As points are explored, they are accumulated in `trials` Parameters

(
    fn,
    space,
    algo=None,
    max_evals=None,
    timeout=None,
    loss_threshold=None,
    trials=None,
    rstate=None,
    allow_trials_fmin=True,
    pass_expr_memo_ctrl=None,
    catch_eval_exceptions=False,
    verbose=True,
    return_argmin=True,
    points_to_evaluate=None,
    max_queue_len=1,
    show_progressbar=True,
    early_stop_fn=None,
    trials_save_file="",
)

def fmin(
    fn,
    space,
    algo=None,
    max_evals=None,
    timeout=None,
    loss_threshold=None,
    trials=None,
    rstate=None,
    allow_trials_fmin=True,
    pass_expr_memo_ctrl=None,
    catch_eval_exceptions=False,
    verbose=True,
    return_argmin=True,
    points_to_evaluate=None,
    max_queue_len=1,
    show_progressbar=True,
    early_stop_fn=None,
    trials_save_file="",
):
    """Minimize a function over a hyperparameter space.

    More realistically: *explore* a function over a hyperparameter space
    according to a given algorithm, allowing up to a certain number of
    function evaluations.  As points are explored, they are accumulated in
    `trials`


    Parameters
    ----------

    fn : callable (trial point -> loss)
        This function will be called with a value generated from `space`
        as the first and possibly only argument.  It can return either
        a scalar-valued loss, or a dictionary.  A returned dictionary must
        contain a 'status' key with a value from `STATUS_STRINGS`, must
        contain a 'loss' key if the status is `STATUS_OK`. Particular
        optimization algorithms may look for other keys as well.  An
        optional sub-dictionary associated with an 'attachments' key will
        be removed by fmin its contents will be available via
        `trials.trial_attachments`. The rest (usually all) of the returned
        dictionary will be stored and available later as some 'result'
        sub-dictionary within `trials.trials`.

    space : hyperopt.pyll.Apply node or "annotated"
        The set of possible arguments to `fn` is the set of objects
        that could be created with non-zero probability by drawing randomly
        from this stochastic program involving involving hp_<xxx> nodes
        (see `hyperopt.hp` and `hyperopt.pyll_utils`).
        If set to "annotated", will read space using type hint in fn. Ex:
        (`def fn(x: hp.uniform("x", -1, 1)): return x`)

    algo : search algorithm
        This object, such as `hyperopt.rand.suggest` and
        `hyperopt.tpe.suggest` provides logic for sequential search of the
        hyperparameter space.

    max_evals : int