cross_validation_param¶

`cross_validation_param` ¶

Classes¶

`CrossValidationParam (BaseParam)` ¶

Define cross validation params

Parameters¶

int, default: 5

Specify how many splits used in KFold

str, default: 'Hetero'

Indicate what mode is current task

{'Guest', 'Host', 'Arbiter'}, default: 'Guest'

Indicate what role is current party

bool, default: True

Define whether do shuffle before KFold or not.

int, default: 1

Specify the random seed for numpy shuffle

bool, default False

Indicate if this module needed to be run

bool, default True

Indicate whether to output table of ids used by each fold, else return original input data returned ids are formatted as: {original_id}#fold{fold_num}#{train/validate}

{'score', 'instance'}, default score

Indicate whether to include original instance or predict score in the output fold history, only effective when output_fold_history set to True

Source code in federatedml/param/cross_validation_param.py

class CrossValidationParam(BaseParam):
    """
    Define cross validation params

    Parameters
    ----------
    n_splits: int, default: 5
        Specify how many splits used in KFold

    mode: str, default: 'Hetero'
        Indicate what mode is current task

    role: {'Guest', 'Host', 'Arbiter'}, default: 'Guest'
        Indicate what role is current party

    shuffle: bool, default: True
        Define whether do shuffle before KFold or not.

    random_seed: int, default: 1
        Specify the random seed for numpy shuffle

    need_cv: bool, default False
        Indicate if this module needed to be run

    output_fold_history: bool, default True
        Indicate whether to output table of ids used by each fold, else return original input data
        returned ids are formatted as: {original_id}#fold{fold_num}#{train/validate}

    history_value_type: {'score', 'instance'}, default score
        Indicate whether to include original instance or predict score in the output fold history,
        only effective when output_fold_history set to True

    """

    def __init__(self, n_splits=5, mode=consts.HETERO, role=consts.GUEST, shuffle=True, random_seed=1,
                 need_cv=False, output_fold_history=True, history_value_type="score"):
        super(CrossValidationParam, self).__init__()
        self.n_splits = n_splits
        self.mode = mode
        self.role = role
        self.shuffle = shuffle
        self.random_seed = random_seed
        # self.evaluate_param = copy.deepcopy(evaluate_param)
        self.need_cv = need_cv
        self.output_fold_history = output_fold_history
        self.history_value_type = history_value_type

    def check(self):
        model_param_descr = "cross validation param's "
        self.check_positive_integer(self.n_splits, model_param_descr)
        self.check_valid_value(self.mode, model_param_descr, valid_values=[consts.HOMO, consts.HETERO])
        self.check_valid_value(self.role, model_param_descr, valid_values=[consts.HOST, consts.GUEST, consts.ARBITER])
        self.check_boolean(self.shuffle, model_param_descr)
        self.check_boolean(self.output_fold_history, model_param_descr)
        self.history_value_type = self.check_and_change_lower(
            self.history_value_type, ["instance", "score"], model_param_descr)
        if self.random_seed is not None:
            self.check_positive_integer(self.random_seed, model_param_descr)

`init(self, n_splits=5, mode='hetero', role='guest', shuffle=True, random_seed=1, need_cv=False, output_fold_history=True, history_value_type='score')` `special` ¶

Source code in federatedml/param/cross_validation_param.py

def __init__(self, n_splits=5, mode=consts.HETERO, role=consts.GUEST, shuffle=True, random_seed=1,
             need_cv=False, output_fold_history=True, history_value_type="score"):
    super(CrossValidationParam, self).__init__()
    self.n_splits = n_splits
    self.mode = mode
    self.role = role
    self.shuffle = shuffle
    self.random_seed = random_seed
    # self.evaluate_param = copy.deepcopy(evaluate_param)
    self.need_cv = need_cv
    self.output_fold_history = output_fold_history
    self.history_value_type = history_value_type

`check(self)` ¶

Source code in federatedml/param/cross_validation_param.py

def check(self):
    model_param_descr = "cross validation param's "
    self.check_positive_integer(self.n_splits, model_param_descr)
    self.check_valid_value(self.mode, model_param_descr, valid_values=[consts.HOMO, consts.HETERO])
    self.check_valid_value(self.role, model_param_descr, valid_values=[consts.HOST, consts.GUEST, consts.ARBITER])
    self.check_boolean(self.shuffle, model_param_descr)
    self.check_boolean(self.output_fold_history, model_param_descr)
    self.history_value_type = self.check_and_change_lower(
        self.history_value_type, ["instance", "score"], model_param_descr)
    if self.random_seed is not None:
        self.check_positive_integer(self.random_seed, model_param_descr)

Last update: 2022-07-07

cross_validation_param¶