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
¶
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