feature_imputation_param¶
feature_imputation_param
¶
Classes¶
FeatureImputationParam (BaseParam)
¶
Define feature imputation parameters
Parameters¶
default_value : None or single object type or list the value to replace missing value. if None, it will use default value defined in federatedml/feature/imputer.py, if single object, will fill missing value with this object, if list, it's length should be the same as input data' feature dimension, means that if some column happens to have missing values, it will replace it the value by element in the identical position of this list.
missing_fill_method : [None, 'min', 'max', 'mean', 'designated'] the method to replace missing value
None or dict of (column name, missing_fill_method) pairs
specifies method to replace missing value for each column; any column not specified will take missing_fill_method, if missing_fill_method is None, unspecified column will not be imputed;
missing_impute : None or list element of list can be any type, or auto generated if value is None, define which values to be consider as missing, default: None
bool, default True
need run or not
Source code in federatedml/param/feature_imputation_param.py
class FeatureImputationParam(BaseParam):
"""
Define feature imputation parameters
Parameters
----------
default_value : None or single object type or list
the value to replace missing value.
if None, it will use default value defined in federatedml/feature/imputer.py,
if single object, will fill missing value with this object,
if list, it's length should be the same as input data' feature dimension,
means that if some column happens to have missing values, it will replace it
the value by element in the identical position of this list.
missing_fill_method : [None, 'min', 'max', 'mean', 'designated']
the method to replace missing value
col_missing_fill_method: None or dict of (column name, missing_fill_method) pairs
specifies method to replace missing value for each column;
any column not specified will take missing_fill_method,
if missing_fill_method is None, unspecified column will not be imputed;
missing_impute : None or list
element of list can be any type, or auto generated if value is None, define which values to be consider as missing, default: None
need_run: bool, default True
need run or not
"""
def __init__(self, default_value=0, missing_fill_method=None, col_missing_fill_method=None,
missing_impute=None, need_run=True):
self.default_value = default_value
self.missing_fill_method = missing_fill_method
self.col_missing_fill_method = col_missing_fill_method
self.missing_impute = missing_impute
self.need_run = need_run
def check(self):
descr = "feature imputation param's "
self.check_boolean(self.need_run, descr + "need_run")
if self.missing_fill_method is not None:
self.missing_fill_method = self.check_and_change_lower(self.missing_fill_method,
['min', 'max', 'mean', 'designated'],
f"{descr}missing_fill_method ")
if self.col_missing_fill_method:
if not isinstance(self.col_missing_fill_method, dict):
raise ValueError(f"{descr}col_missing_fill_method should be a dict")
for k, v in self.col_missing_fill_method.items():
if not isinstance(k, str):
raise ValueError(f"{descr}col_missing_fill_method should contain str key(s) only")
v = self.check_and_change_lower(v,
['min', 'max', 'mean', 'designated'],
f"per column method specified in {descr} col_missing_fill_method dict")
self.col_missing_fill_method[k] = v
if self.missing_impute:
if not isinstance(self.missing_impute, list):
raise ValueError(f"{descr}missing_impute must be None or list.")
return True
__init__(self, default_value=0, missing_fill_method=None, col_missing_fill_method=None, missing_impute=None, need_run=True)
special
¶
Source code in federatedml/param/feature_imputation_param.py
def __init__(self, default_value=0, missing_fill_method=None, col_missing_fill_method=None,
missing_impute=None, need_run=True):
self.default_value = default_value
self.missing_fill_method = missing_fill_method
self.col_missing_fill_method = col_missing_fill_method
self.missing_impute = missing_impute
self.need_run = need_run
check(self)
¶
Source code in federatedml/param/feature_imputation_param.py
def check(self):
descr = "feature imputation param's "
self.check_boolean(self.need_run, descr + "need_run")
if self.missing_fill_method is not None:
self.missing_fill_method = self.check_and_change_lower(self.missing_fill_method,
['min', 'max', 'mean', 'designated'],
f"{descr}missing_fill_method ")
if self.col_missing_fill_method:
if not isinstance(self.col_missing_fill_method, dict):
raise ValueError(f"{descr}col_missing_fill_method should be a dict")
for k, v in self.col_missing_fill_method.items():
if not isinstance(k, str):
raise ValueError(f"{descr}col_missing_fill_method should contain str key(s) only")
v = self.check_and_change_lower(v,
['min', 'max', 'mean', 'designated'],
f"per column method specified in {descr} col_missing_fill_method dict")
self.col_missing_fill_method[k] = v
if self.missing_impute:
if not isinstance(self.missing_impute, list):
raise ValueError(f"{descr}missing_impute must be None or list.")
return True