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sample_weight_param

sample_weight_param

Classes

SampleWeightParam (BaseParam)

Define sample weight parameters

Parameters:

Name Type Description Default
class_weight str or dict, or None, default None

class weight dictionary or class weight computation mode, string value only accepts 'balanced'; If dict provided, key should be class(label), and weight will not be normalize, e.g.: {'0': 1, '1': 2} If both class_weight and sample_weight_name are None, return original input data.

None
sample_weight_name str

name of column which specifies sample weight. feature name of sample weight; if both class_weight and sample_weight_name are None, return original input data

None
normalize bool, default False

whether to normalize sample weight extracted from sample_weight_name column

False
need_run bool, default True

whether to run this module or not

True
Source code in federatedml/param/sample_weight_param.py
class SampleWeightParam(BaseParam):
    """
    Define sample weight parameters

    Parameters
    ----------

    class_weight : str or dict, or None, default None
        class weight dictionary or class weight computation mode, string value only accepts 'balanced';
        If dict provided, key should be class(label), and weight will not be normalize, e.g.: {'0': 1, '1': 2}
        If both class_weight and sample_weight_name are None, return original input data.

    sample_weight_name : str
        name of column which specifies sample weight.
        feature name of sample weight; if both class_weight and sample_weight_name are None, return original input data

    normalize : bool, default False
        whether to normalize sample weight extracted from `sample_weight_name` column

    need_run : bool, default True
        whether to run this module or not

    """

    def __init__(self, class_weight=None, sample_weight_name=None, normalize=False, need_run=True):
        self.class_weight = class_weight
        self.sample_weight_name = sample_weight_name
        self.normalize = normalize
        self.need_run = need_run

    def check(self):

        descr = "sample weight param's"

        if self.class_weight:
            if not isinstance(self.class_weight, str) and not isinstance(self.class_weight, dict):
                raise ValueError(f"{descr} class_weight must be str, dict, or None.")
            if isinstance(self.class_weight, str):
                self.class_weight = self.check_and_change_lower(self.class_weight,
                                                                [consts.BALANCED],
                                                                f"{descr} class_weight")
            if isinstance(self.class_weight, dict):
                for k, v in self.class_weight.items():
                    if v < 0:
                        LOGGER.warning(f"Negative value {v} provided for class {k} as class_weight.")

        if self.sample_weight_name:
            self.check_string(self.sample_weight_name, f"{descr} sample_weight_name")

        self.check_boolean(self.need_run, f"{descr} need_run")

        self.check_boolean(self.normalize, f"{descr} normalize")

        return True
__init__(self, class_weight=None, sample_weight_name=None, normalize=False, need_run=True) special
Source code in federatedml/param/sample_weight_param.py
def __init__(self, class_weight=None, sample_weight_name=None, normalize=False, need_run=True):
    self.class_weight = class_weight
    self.sample_weight_name = sample_weight_name
    self.normalize = normalize
    self.need_run = need_run
check(self)
Source code in federatedml/param/sample_weight_param.py
def check(self):

    descr = "sample weight param's"

    if self.class_weight:
        if not isinstance(self.class_weight, str) and not isinstance(self.class_weight, dict):
            raise ValueError(f"{descr} class_weight must be str, dict, or None.")
        if isinstance(self.class_weight, str):
            self.class_weight = self.check_and_change_lower(self.class_weight,
                                                            [consts.BALANCED],
                                                            f"{descr} class_weight")
        if isinstance(self.class_weight, dict):
            for k, v in self.class_weight.items():
                if v < 0:
                    LOGGER.warning(f"Negative value {v} provided for class {k} as class_weight.")

    if self.sample_weight_name:
        self.check_string(self.sample_weight_name, f"{descr} sample_weight_name")

    self.check_boolean(self.need_run, f"{descr} need_run")

    self.check_boolean(self.normalize, f"{descr} normalize")

    return True

Last update: 2021-12-03
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