(self, data_instances, validate_data=None)
Train lr model of role guest
        Parameters
        ----------
        data_instances: Table of Instance, input data
Train lr model of role guest
        Parameters
        ----------
        data_instances: Table of Instance, input data
Train lr model of role guest Parameters ---------- data_instances : Table of Instance input data
def fit(self, data_instances, validate_data=None):
        """
        Train lr model of role guest
        Parameters
        ----------
        data_instances: Table of Instance, input data
        """

        LOGGER.info("Enter hetero_lr_guest fit")
        self._abnormal_detection(data_instances)
        self.check_abnormal_values(data_instances)
        self.check_abnormal_values(validate_data)
        self.header = self.get_header(data_instances)

        classes = self.one_vs_rest_obj.get_data_classes(data_instances)

        if len(classes) > 2:
            self.need_one_vs_rest = True
            self.need_call_back_loss = False
            self.one_vs_rest_fit(train_data=data_instances, validate_data=validate_data)
        else:
            self.need_one_vs_rest = False
            self.fit_binary(data_instances, validate_data)
def fit ( self , data_instances , validate_data = None ) : LOGGER . info ( "Enter hetero_lr_guest fit" ) self . _abnormal_detection ( data_instances ) self . check_abnormal_values ( data_instances ) self . check_abnormal_values ( validate_data ) self . header = self . get_header ( data_instances ) classes = self . one_vs_rest_obj . get_data_classes ( data_instances ) if len ( classes ) > 2 : self . need_one_vs_rest = True self . need_call_back_loss = False self . one_vs_rest_fit ( train_data = data_instances , validate_data = validate_data ) else : self . need_one_vs_rest = False self . fit_binary ( data_instances , validate_data )