def fill_missing_value(self, input_data_features, mode="fit"):
if self.missing_fill:
from federatedml.feature.imputer import Imputer
imputer_processor = Imputer(self.missing_impute)
if mode == "fit":
input_data_features, self.default_value = imputer_processor.fit(input_data_features,
replace_method=self.missing_fill_method,
replace_value=self.default_value)
if self.missing_impute is None:
self.missing_impute = imputer_processor.get_imputer_value_list()
else:
input_data_features = imputer_processor.transform(input_data_features,
replace_method=self.missing_fill_method,
transform_value=self.default_value)
if self.missing_impute is None:
self.missing_impute = imputer_processor.get_imputer_value_list()
return input_data_features
def replace_outlier_value(self, input_data_features, mode="fit"):
if self.outlier_replace:
from federatedml.feature.imputer import Imputer
imputer_processor = Imputer(self.outlier_impute)
if mode == "fit":
input_data_features, self.outlier_replace_value = \
imputer_processor.fit(input_data_features,
replace_method=self.outlier_replace_method,
replace_value=self.outlier_replace_value)
if self.outlier_impute is None:
self.outlier_impute = imputer_processor.get_imputer_value_list()
else:
input_data_features = imputer_processor.transform(input_data_features,
replace_method=self.outlier_replace_method,
transform_value=self.outlier_replace_value)
return input_data_features