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scale_param

scale_param

Classes

ScaleParam (BaseParam)

Define the feature scale parameters.

Parameters:

Name Type Description Default
method {"standard_scale", "min_max_scale"}

like scale in sklearn, now it support "min_max_scale" and "standard_scale", and will support other scale method soon. Default standard_scale, which will do nothing for scale

'standard_scale'
mode {"normal", "cap"}

for mode is "normal", the feat_upper and feat_lower is the normal value like "10" or "3.1" and for "cap", feat_upper and feature_lower will between 0 and 1, which means the percentile of the column. Default "normal"

'normal'
feat_upper int or float or list of int or float

the upper limit in the column. If use list, mode must be "normal", and list length should equal to the number of features to scale. If the scaled value is larger than feat_upper, it will be set to feat_upper

None
feat_lower int or float or list of int or float

the lower limit in the column. If use list, mode must be "normal", and list length should equal to the number of features to scale. If the scaled value is less than feat_lower, it will be set to feat_lower

None
scale_col_indexes list

the idx of column in scale_column_idx will be scaled, while the idx of column is not in, it will not be scaled.

-1
scale_names list of string

Specify which columns need to scaled. Each element in the list represent for a column name in header. default: []

None
with_mean bool

used for "standard_scale". Default True.

True
with_std bool

used for "standard_scale". Default True. The standard scale of column x is calculated as : z = (x - u) / s , where u is the mean of the column and s is the standard deviation of the column. if with_mean is False, u will be 0, and if with_std is False, s will be 1.

True
need_run bool

Indicate if this module needed to be run, default True

True
Source code in federatedml/param/scale_param.py
class ScaleParam(BaseParam):
    """
    Define the feature scale parameters.

    Parameters
    ----------
    method : {"standard_scale", "min_max_scale"}
        like scale in sklearn, now it support "min_max_scale" and "standard_scale", and will support other scale method soon.
        Default standard_scale, which will do nothing for scale

    mode : {"normal", "cap"}
        for mode is "normal", the feat_upper and feat_lower is the normal value like "10" or "3.1"
        and for "cap", feat_upper and feature_lower will between 0 and 1, which means the percentile of the column. Default "normal"

    feat_upper : int or float or list of int or float
        the upper limit in the column.
        If use list, mode must be "normal", and list length should equal to the number of features to scale.
        If the scaled value is larger than feat_upper, it will be set to feat_upper

    feat_lower: int or float or list of int or float
        the lower limit in the column.
        If use list, mode must be "normal", and list length should equal to the number of features to scale.
        If the scaled value is less than feat_lower, it will be set to feat_lower

    scale_col_indexes: list
        the idx of column in scale_column_idx will be scaled, while the idx of column is not in, it will not be scaled.

    scale_names : list of string
        Specify which columns need to scaled. Each element in the list represent for a column name in header. default: []

    with_mean : bool
        used for "standard_scale". Default True.

    with_std : bool
        used for "standard_scale". Default True.
        The standard scale of column x is calculated as : $z = (x - u) / s$ , where $u$ is the mean of the column and $s$ is the standard deviation of the column.
        if with_mean is False, $u$ will be 0, and if with_std is False, $s$ will be 1.

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

    """

    def __init__(self, method="standard_scale", mode="normal", scale_col_indexes=-1, scale_names=None, feat_upper=None, feat_lower=None,
                 with_mean=True, with_std=True, need_run=True):
        super().__init__()
        self.scale_names = [] if scale_names is None else scale_names

        self.method = method
        self.mode = mode
        self.feat_upper = feat_upper
        # LOGGER.debug("self.feat_upper:{}, type:{}".format(self.feat_upper, type(self.feat_upper)))
        self.feat_lower = feat_lower
        self.scale_col_indexes = scale_col_indexes

        self.with_mean = with_mean
        self.with_std = with_std

        self.need_run = need_run

    def check(self):
        if self.method is not None:
            descr = "scale param's method"
            self.method = self.check_and_change_lower(self.method,
                                                      [consts.MINMAXSCALE, consts.STANDARDSCALE],
                                                      descr)

        descr = "scale param's mode"
        self.mode = self.check_and_change_lower(self.mode,
                                                [consts.NORMAL, consts.CAP],
                                                descr)
        # LOGGER.debug("self.feat_upper:{}, type:{}".format(self.feat_upper, type(self.feat_upper)))
        # if type(self.feat_upper).__name__ not in ["float", "int"]:
        #     raise ValueError("scale param's feat_upper {} not supported, should be float or int".format(
        #         self.feat_upper))


        if self.scale_col_indexes != -1  and not isinstance(self.scale_col_indexes, list):
            raise ValueError("scale_col_indexes is should be -1 or a list")

        if self.scale_names is None:
            self.scale_names = []
        if not isinstance(self.scale_names, list):
            raise ValueError("scale_names is should be a list of string")
        else:
            for e in self.scale_names:
                if not isinstance(e, str):
                    raise ValueError("scale_names is should be a list of string")

        self.check_boolean(self.with_mean, "scale_param with_mean")
        self.check_boolean(self.with_std, "scale_param with_std")
        self.check_boolean(self.need_run, "scale_param need_run")

        LOGGER.debug("Finish scale parameter check!")
        return True
__init__(self, method='standard_scale', mode='normal', scale_col_indexes=-1, scale_names=None, feat_upper=None, feat_lower=None, with_mean=True, with_std=True, need_run=True) special
Source code in federatedml/param/scale_param.py
def __init__(self, method="standard_scale", mode="normal", scale_col_indexes=-1, scale_names=None, feat_upper=None, feat_lower=None,
             with_mean=True, with_std=True, need_run=True):
    super().__init__()
    self.scale_names = [] if scale_names is None else scale_names

    self.method = method
    self.mode = mode
    self.feat_upper = feat_upper
    # LOGGER.debug("self.feat_upper:{}, type:{}".format(self.feat_upper, type(self.feat_upper)))
    self.feat_lower = feat_lower
    self.scale_col_indexes = scale_col_indexes

    self.with_mean = with_mean
    self.with_std = with_std

    self.need_run = need_run
check(self)
Source code in federatedml/param/scale_param.py
def check(self):
    if self.method is not None:
        descr = "scale param's method"
        self.method = self.check_and_change_lower(self.method,
                                                  [consts.MINMAXSCALE, consts.STANDARDSCALE],
                                                  descr)

    descr = "scale param's mode"
    self.mode = self.check_and_change_lower(self.mode,
                                            [consts.NORMAL, consts.CAP],
                                            descr)
    # LOGGER.debug("self.feat_upper:{}, type:{}".format(self.feat_upper, type(self.feat_upper)))
    # if type(self.feat_upper).__name__ not in ["float", "int"]:
    #     raise ValueError("scale param's feat_upper {} not supported, should be float or int".format(
    #         self.feat_upper))


    if self.scale_col_indexes != -1  and not isinstance(self.scale_col_indexes, list):
        raise ValueError("scale_col_indexes is should be -1 or a list")

    if self.scale_names is None:
        self.scale_names = []
    if not isinstance(self.scale_names, list):
        raise ValueError("scale_names is should be a list of string")
    else:
        for e in self.scale_names:
            if not isinstance(e, str):
                raise ValueError("scale_names is should be a list of string")

    self.check_boolean(self.with_mean, "scale_param with_mean")
    self.check_boolean(self.with_std, "scale_param with_std")
    self.check_boolean(self.need_run, "scale_param need_run")

    LOGGER.debug("Finish scale parameter check!")
    return True

Last update: 2021-12-03
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