label_transform_param¶

`label_transform_param` ¶

Classes¶

`LabelTransformParam (BaseParam)` ¶

Define label transform param that used in label transform.

Parameters¶

label_encoder : None or dict, default : None Specify (label, encoded label) key-value pairs for transforming labels to new values. e.g. {"Yes": 1, "No": 0}; **new in ver 1.9: during training, input labels not found in label_encoder will retain its original value

label_list : None or list, default : None List all input labels, used for matching types of original keys in label_encoder dict, length should match key count in label_encoder, e.g. ["Yes", "No"]; **new in ver 1.9: given non-emtpy label_encoder, when label_list not provided, module will inference label types from input data

bool, default: True

Specify whether to run label transform

Source code in federatedml/param/label_transform_param.py

class LabelTransformParam(BaseParam):
    """
    Define label transform param that used in label transform.

    Parameters
    ----------

    label_encoder : None or dict, default : None
        Specify (label, encoded label) key-value pairs for transforming labels to new values.
        e.g. {"Yes": 1, "No": 0};
        **new in ver 1.9: during training, input labels not found in `label_encoder` will retain its original value

    label_list : None or list, default : None
        List all input labels, used for matching types of original keys in label_encoder dict,
        length should match key count in label_encoder, e.g. ["Yes", "No"];
        **new in ver 1.9: given non-emtpy `label_encoder`, when `label_list` not provided,
        module will inference label types from input data

    need_run: bool, default: True
        Specify whether to run label transform

    """

    def __init__(self, label_encoder=None, label_list=None, need_run=True):
        super(LabelTransformParam, self).__init__()
        self.label_encoder = label_encoder
        self.label_list = label_list
        self.need_run = need_run

    def check(self):
        model_param_descr = "label transform param's "

        BaseParam.check_boolean(self.need_run, f"{model_param_descr} need run ")

        if self.label_encoder is not None:
            if not isinstance(self.label_encoder, dict):
                raise ValueError(f"{model_param_descr} label_encoder should be dict type")
            if len(self.label_encoder) == 0:
                self.label_encoder = None

        if self.label_list is not None:
            if not isinstance(self.label_list, list):
                raise ValueError(f"{model_param_descr} label_list should be list type")
            if self.label_encoder and self.label_list and len(self.label_list) != len(self.label_encoder.keys()):
                raise ValueError(f"label_list's length not matching label_encoder key count.")
            if len(self.label_list) == 0:
                self.label_list = None

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

`init(self, label_encoder=None, label_list=None, need_run=True)` `special` ¶

Source code in federatedml/param/label_transform_param.py

def __init__(self, label_encoder=None, label_list=None, need_run=True):
    super(LabelTransformParam, self).__init__()
    self.label_encoder = label_encoder
    self.label_list = label_list
    self.need_run = need_run

`check(self)` ¶

Source code in federatedml/param/label_transform_param.py

def check(self):
    model_param_descr = "label transform param's "

    BaseParam.check_boolean(self.need_run, f"{model_param_descr} need run ")

    if self.label_encoder is not None:
        if not isinstance(self.label_encoder, dict):
            raise ValueError(f"{model_param_descr} label_encoder should be dict type")
        if len(self.label_encoder) == 0:
            self.label_encoder = None

    if self.label_list is not None:
        if not isinstance(self.label_list, list):
            raise ValueError(f"{model_param_descr} label_list should be list type")
        if self.label_encoder and self.label_list and len(self.label_list) != len(self.label_encoder.keys()):
            raise ValueError(f"label_list's length not matching label_encoder key count.")
        if len(self.label_list) == 0:
            self.label_list = None

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

Last update: 2022-08-30

label_transform_param¶