(self, y_train, y_validate, y_test, all_metas)
return all_metas
Tool to callback returned data label information
        Parameters
        ----------
        y_train: list, y
        y_validate: list, y
        y_test: list, y
        all_metas: dict, all meta info

        Returns
        -------
        None
Tool to callback returned data label information
        Parameters
        ----------
        y_train: list, y
        y_validate: list, y
        y_test: list, y
        all_metas: dict, all meta info
Tool to callback returned data label information Parameters ---------- y_train : list y y_validate : list y y_test : list y all_metas : dict all meta info
def callback_label_info(self, y_train, y_validate, y_test, all_metas):
        """
        Tool to callback returned data label information
        Parameters
        ----------
        y_train: list, y
        y_validate: list, y
        y_test: list, y
        all_metas: dict, all meta info

        Returns
        -------
        None

        """
        metas = {}
        y_all = y_train + y_validate + y_test

        label_names = None
        if self.split_points is None:
            label_names = list(set(y_all))

        original_freq_dict = DataSplitter.get_class_freq(y_all, self.split_points, label_names)
        metas["original"] = original_freq_dict

        train_freq_dict = DataSplitter.get_class_freq(y_train, self.split_points, label_names)
        metas["train"] = train_freq_dict

        validate_freq_dict = DataSplitter.get_class_freq(y_validate, self.split_points, label_names)
        metas["validate"] = validate_freq_dict

        test_freq_dict = DataSplitter.get_class_freq(y_test, self.split_points, label_names)
        metas["test"] = test_freq_dict

        if self.split_points is not None:
            metas["split_points"] = self.split_points
            metas["continuous_label"] = True
        else:
            metas["label_names"] = label_names
            metas["continuous_label"] = False

        metric_name = f"{self.metric_name}_label_info"
        all_metas[metric_name] = metas

        return all_metas
def callback_label_info ( self , y_train , y_validate , y_test , all_metas ) : metas = { } y_all = y_train + y_validate + y_test label_names = None if self . split_points is None : label_names = list ( set ( y_all ) ) original_freq_dict = DataSplitter . get_class_freq ( y_all , self . split_points , label_names ) metas [ "original" ] = original_freq_dict train_freq_dict = DataSplitter . get_class_freq ( y_train , self . split_points , label_names ) metas [ "train" ] = train_freq_dict validate_freq_dict = DataSplitter . get_class_freq ( y_validate , self . split_points , label_names ) metas [ "validate" ] = validate_freq_dict test_freq_dict = DataSplitter . get_class_freq ( y_test , self . split_points , label_names ) metas [ "test" ] = test_freq_dict if self . split_points is not None : metas [ "split_points" ] = self . split_points metas [ "continuous_label" ] = True else : metas [ "label_names" ] = label_names metas [ "continuous_label" ] = False metric_name = f"{self.metric_name}_label_info" all_metas [ metric_name ] = metas return all_metas