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
最后更新:
2022-08-31