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stepwise_param

stepwise_param

Classes

StepwiseParam (BaseParam)

Define stepwise params

Parameters:

Name Type Description Default
score_name {"AIC", "BIC"}, default: 'AIC'

Specify which model selection criterion to be used

'AIC'
mode {"Hetero", "Homo"}, default: 'Hetero'

Indicate what mode is current task

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

Indicate what role is current party

'guest'
direction {"both", "forward", "backward"}, default: 'both'

Indicate which direction to go for stepwise. 'forward' means forward selection; 'backward' means elimination; 'both' means possible models of both directions are examined at each step.

'both'
max_step int, default: '10'

Specify total number of steps to run before forced stop.

10
nvmin int, default: '2'

Specify the min subset size of final model, cannot be lower than 2. When nvmin > 2, the final model size may be smaller than nvmin due to max_step limit.

2
nvmax int, default: None

Specify the max subset size of final model, 2 <= nvmin <= nvmax. The final model size may be larger than nvmax due to max_step limit.

None
need_stepwise bool, default False

Indicate if this module needed to be run

False
Source code in federatedml/param/stepwise_param.py
class StepwiseParam(BaseParam):
    """
    Define stepwise params

    Parameters
    ----------
    score_name: {"AIC", "BIC"}, default: 'AIC'
        Specify which model selection criterion to be used

    mode: {"Hetero", "Homo"}, default: 'Hetero'
        Indicate what mode is current task

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

    direction: {"both", "forward", "backward"}, default: 'both'
        Indicate which direction to go for stepwise.
        'forward' means forward selection; 'backward' means elimination; 'both' means possible models of both directions are examined at each step.

    max_step: int, default: '10'
        Specify total number of steps to run before forced stop.

    nvmin: int, default: '2'
        Specify the min subset size of final model, cannot be lower than 2. When nvmin > 2, the final model size may be smaller than nvmin due to max_step limit.

    nvmax: int, default: None
        Specify the max subset size of final model, 2 <= nvmin <= nvmax. The final model size may be larger than nvmax due to max_step limit.

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

    """

    def __init__(self, score_name="AIC", mode=consts.HETERO, role=consts.GUEST, direction="both",
                 max_step=10, nvmin=2, nvmax=None, need_stepwise=False):
        super(StepwiseParam, self).__init__()
        self.score_name = score_name
        self.mode = mode
        self.role = role
        self.direction = direction
        self.max_step = max_step
        self.nvmin = nvmin
        self.nvmax = nvmax
        self.need_stepwise = need_stepwise

    def check(self):
        model_param_descr = "stepwise param's"
        self.score_name = self.check_and_change_lower(self.score_name, ["aic", "bic"], 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.direction = self.check_and_change_lower(self.direction, ["forward", "backward", "both"], model_param_descr)
        self.check_positive_integer(self.max_step, model_param_descr)
        self.check_positive_integer(self.nvmin, model_param_descr)
        if self.nvmin < 2:
            raise ValueError(model_param_descr + " nvmin must be no less than 2.")
        if self.nvmax is not None:
            self.check_positive_integer(self.nvmax, model_param_descr)
            if self.nvmin > self.nvmax:
                raise ValueError(model_param_descr + " nvmax must be greater than nvmin.")
        self.check_boolean(self.need_stepwise, model_param_descr)
__init__(self, score_name='AIC', mode='hetero', role='guest', direction='both', max_step=10, nvmin=2, nvmax=None, need_stepwise=False) special
Source code in federatedml/param/stepwise_param.py
def __init__(self, score_name="AIC", mode=consts.HETERO, role=consts.GUEST, direction="both",
             max_step=10, nvmin=2, nvmax=None, need_stepwise=False):
    super(StepwiseParam, self).__init__()
    self.score_name = score_name
    self.mode = mode
    self.role = role
    self.direction = direction
    self.max_step = max_step
    self.nvmin = nvmin
    self.nvmax = nvmax
    self.need_stepwise = need_stepwise
check(self)
Source code in federatedml/param/stepwise_param.py
def check(self):
    model_param_descr = "stepwise param's"
    self.score_name = self.check_and_change_lower(self.score_name, ["aic", "bic"], 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.direction = self.check_and_change_lower(self.direction, ["forward", "backward", "both"], model_param_descr)
    self.check_positive_integer(self.max_step, model_param_descr)
    self.check_positive_integer(self.nvmin, model_param_descr)
    if self.nvmin < 2:
        raise ValueError(model_param_descr + " nvmin must be no less than 2.")
    if self.nvmax is not None:
        self.check_positive_integer(self.nvmax, model_param_descr)
        if self.nvmin > self.nvmax:
            raise ValueError(model_param_descr + " nvmax must be greater than nvmin.")
    self.check_boolean(self.need_stepwise, model_param_descr)

Last update: 2021-12-03
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