local_baseline_param¶
local_baseline_param
¶
Classes¶
LocalBaselineParam (BaseParam)
¶
Define the local baseline model param
Parameters¶
model_name : str sklearn model used to train on baseline model
model_opts : dict or none, default None Param to be used as input into baseline model
predict_param : PredictParam object, default: default PredictParam object predict param
bool, default True
Indicate if this module needed to be run
Source code in federatedml/param/local_baseline_param.py
class LocalBaselineParam(BaseParam):
"""
Define the local baseline model param
Parameters
----------
model_name : str
sklearn model used to train on baseline model
model_opts : dict or none, default None
Param to be used as input into baseline model
predict_param : PredictParam object, default: default PredictParam object
predict param
need_run: bool, default True
Indicate if this module needed to be run
"""
def __init__(self, model_name="LogisticRegression", model_opts=None, predict_param=PredictParam(), need_run=True):
super(LocalBaselineParam, self).__init__()
self.model_name = model_name
self.model_opts = model_opts
self.predict_param = copy.deepcopy(predict_param)
self.need_run = need_run
def check(self):
descr = "local baseline param"
self.model_name = self.check_and_change_lower(self.model_name,
["logisticregression"],
descr)
self.check_boolean(self.need_run, descr)
if self.model_opts is not None:
if not isinstance(self.model_opts, dict):
raise ValueError(descr + " model_opts must be None or dict.")
if self.model_opts is None:
self.model_opts = {}
self.predict_param.check()
return True
__init__(self, model_name='LogisticRegression', model_opts=None, predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f94559df9d0>, need_run=True)
special
¶
Source code in federatedml/param/local_baseline_param.py
def __init__(self, model_name="LogisticRegression", model_opts=None, predict_param=PredictParam(), need_run=True):
super(LocalBaselineParam, self).__init__()
self.model_name = model_name
self.model_opts = model_opts
self.predict_param = copy.deepcopy(predict_param)
self.need_run = need_run
check(self)
¶
Source code in federatedml/param/local_baseline_param.py
def check(self):
descr = "local baseline param"
self.model_name = self.check_and_change_lower(self.model_name,
["logisticregression"],
descr)
self.check_boolean(self.need_run, descr)
if self.model_opts is not None:
if not isinstance(self.model_opts, dict):
raise ValueError(descr + " model_opts must be None or dict.")
if self.model_opts is None:
self.model_opts = {}
self.predict_param.check()
return True
最后更新:
2022-08-31