def per_combination_results_export(combination_name: str, data_split: str, metrics_dataframe: pd.DataFrame):
    'Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,\n    then the folder is created.\n\n        Args:\n            combination_name: Name of the current combination of modalities and skeleton pose model.\n            data_split: Name of the split the subset of the dataset belongs to.\n            metrics_dataframe: A dataframe containing the mean of all the metrics for all the hyperparameters & models.\n\n        Returns:\n            None.\n    '
    directory_path = '{}/{}'.format('../results/combination_results', combination_name)
    if (not os.path.isdir(directory_path)):
        os.mkdir(directory_path)
    file_path = '{}/{}.csv'.format(directory_path, data_split)
    metrics_dataframe.to_csv(file_path, index=False)
Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,
then the folder is created.

    Args:
        combination_name: Name of the current combination of modalities and skeleton pose model.
        data_split: Name of the split the subset of the dataset belongs to.
        metrics_dataframe: A dataframe containing the mean of all the metrics for all the hyperparameters & models.

    Returns:
        None.
def per_combination_results_export(combination_name: str, data_split: str, metrics_dataframe: pd.DataFrame):
    'Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,\n    then the folder is created.\n\n        Args:\n            combination_name: Name of the current combination of modalities and skeleton pose model.\n            data_split: Name of the split the subset of the dataset belongs to.\n            metrics_dataframe: A dataframe containing the mean of all the metrics for all the hyperparameters & models.\n\n        Returns:\n            None.\n    '
    directory_path = '{}/{}'.format('../results/combination_results', combination_name)
    if (not os.path.isdir(directory_path)):
        os.mkdir(directory_path)
    file_path = '{}/{}.csv'.format(directory_path, data_split)
    metrics_dataframe.to_csv(file_path, index=False)