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local_baseline_param

local_baseline_param

Classes

LocalBaselineParam (BaseParam)

Define the local baseline model param

Parameters:

Name Type Description Default
model_name str

sklearn model used to train on baseline model

'LogisticRegression'
model_opts dict or none, default None

Param to be used as input into baseline model

None
predict_param PredictParam object, default: default PredictParam object

predict param

<federatedml.param.predict_param.PredictParam object at 0x7f27551f7090>
need_run bool, default True

Indicate if this module needed to be run

True
Source code in federatedml/param/local_baseline_param.py
class LocalBaselineParam(BaseParam):
    """
    Define the local baseline model param

    Parameters
    ----------
    model_name : str
        sklearn model used to train on baseline model

    model_opts : dict or none, default None
        Param to be used as input into baseline model

    predict_param : PredictParam object, default: default PredictParam object
        predict param

    need_run: bool, default True
        Indicate if this module needed to be run
    """

    def __init__(self, model_name="LogisticRegression", model_opts=None, predict_param=PredictParam(), need_run=True):
        super(LocalBaselineParam, self).__init__()
        self.model_name = model_name
        self.model_opts = model_opts
        self.predict_param = copy.deepcopy(predict_param)
        self.need_run = need_run

    def check(self):
        descr = "local baseline param"

        self.model_name = self.check_and_change_lower(self.model_name,
                                                      ["logisticregression"],
                                                      descr)
        self.check_boolean(self.need_run, descr)
        if self.model_opts is not None:
            if not isinstance(self.model_opts, dict):
                raise ValueError(descr + " model_opts must be None or dict.")
        if self.model_opts is None:
            self.model_opts = {}
        self.predict_param.check()

        return True
__init__(self, model_name='LogisticRegression', model_opts=None, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f27551f7090>, need_run=True) special
Source code in federatedml/param/local_baseline_param.py
def __init__(self, model_name="LogisticRegression", model_opts=None, predict_param=PredictParam(), need_run=True):
    super(LocalBaselineParam, self).__init__()
    self.model_name = model_name
    self.model_opts = model_opts
    self.predict_param = copy.deepcopy(predict_param)
    self.need_run = need_run
check(self)
Source code in federatedml/param/local_baseline_param.py
def check(self):
    descr = "local baseline param"

    self.model_name = self.check_and_change_lower(self.model_name,
                                                  ["logisticregression"],
                                                  descr)
    self.check_boolean(self.need_run, descr)
    if self.model_opts is not None:
        if not isinstance(self.model_opts, dict):
            raise ValueError(descr + " model_opts must be None or dict.")
    if self.model_opts is None:
        self.model_opts = {}
    self.predict_param.check()

    return True

Last update: 2021-12-03
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