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