Label Transform

Label Transform replaces label with designated values.

User may specify encoder to transform labels into designated values. If no specification is provided, module will automatically encodes label into categorical label values(integers) starting at 0.

Param

label_transform_param

Attributes

Classes

LabelTransformParam(label_encoder=None, label_list=None, need_run=True)

Bases: BaseParam

Define label transform param that used in label transform.

Parameters:

Name	Type	Description	Default
label_encoder	None or dict, default	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	None
label_list	None or list, default	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	None
need_run		Specify whether to run label transform	True

Source code in python/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

Attributes

label_encoder = label_encoder instance-attribute

label_list = label_list instance-attribute

need_run = need_run instance-attribute

Functions

check()

Source code in python/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

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Last update: 2021-11-15