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cross_validation_param

cross_validation_param

Classes

CrossValidationParam (BaseParam)

Define cross validation params

Parameters:

Name Type Description Default
n_splits int, default: 5

Specify how many splits used in KFold

 5
mode str, default: 'Hetero'

Indicate what mode is current task

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

Indicate what role is current party

 'guest'
shuffle bool, default: True

Define whether do shuffle before KFold or not.

 True
random_seed int, default: 1

Specify the random seed for numpy shuffle

 1
need_cv bool, default False

Indicate if this module needed to be run

 False
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}

 True
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

 'score'
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: 2022-01-27
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