def prepare(self):
    'Prepare dataset for the usage.\n\n        Returns\n        -------\n        self\n\n        '
    if (not self.meta_container.exists()):
        meta_data = {}
        for fold in range(1, self.crossvalidation_folds):
            fold_data = MetaDataContainer(filename=self.evaluation_setup_filename(setup_part='train', fold=fold)).load()
            fold_data += MetaDataContainer(filename=self.evaluation_setup_filename(setup_part='evaluate', fold=fold)).load()
            for item in fold_data:
                if (item.filename not in meta_data):
                    self.process_meta_item(item=item, absolute_path=False)
                    meta_data[item.filename] = item
        MetaDataContainer(list(meta_data.values())).save(filename=self.meta_file)
        self.load()
    return self
Prepare dataset for the usage.

Returns
-------
self
def prepare(self):
    'Prepare dataset for the usage.\n\n        Returns\n        -------\n        self\n\n        '
    if (not self.meta_container.exists()):
        meta_data = {}
        for fold in range(1, self.crossvalidation_folds):
            fold_data = MetaDataContainer(filename=self.evaluation_setup_filename(setup_part='train', fold=fold)).load()
            fold_data += MetaDataContainer(filename=self.evaluation_setup_filename(setup_part='evaluate', fold=fold)).load()
            for item in fold_data:
                if (item.filename not in meta_data):
                    self.process_meta_item(item=item, absolute_path=False)
                    meta_data[item.filename] = item
        MetaDataContainer(list(meta_data.values())).save(filename=self.meta_file)
        self.load()
    return self