def _summarize_scores(scores):
    'Computes a summary of the scores obtained by a synthesizer.\n\n    The raw scores returned by the ``compute_benchmark`` function are summarized\n    by grouping them by dataset and computing the average.\n\n    The results are then put in a ``pandas.Series`` object with one value per\n    dataset and metric.\n\n    As an example, the summary of a synthesizer that has been evaluated on the\n    ``adult`` and the ``asia`` datasets produces the following output::\n\n        adult/accuracy          0.8765\n        adult/f1_micro          0.7654\n        adult/f1_macro          0.7654\n        asia/syn_likelihood    -2.5364\n        asia/test_likelihood   -2.4321\n        dtype: float64\n\n    Args:\n        scores (pandas.DataFrame):\n            Raw Scores dataframe as returned by the ``compute_benchmark`` function.\n\n    Returns:\n        pandas.Series:\n            Summarized scores series in the format described above.\n    '
    scores = scores.drop(['distance', 'iteration', 'name'], axis=1, errors='ignore')
    grouped = scores.groupby('dataset').apply(_dataset_summary)
    if isinstance(grouped, pd.Series):
        return grouped.droplevel(0)
    return grouped.iloc[0]
Computes a summary of the scores obtained by a synthesizer.

The raw scores returned by the ``compute_benchmark`` function are summarized
by grouping them by dataset and computing the average.

The results are then put in a ``pandas.Series`` object with one value per
dataset and metric.

As an example, the summary of a synthesizer that has been evaluated on the
``adult`` and the ``asia`` datasets produces the following output::

    adult/accuracy          0.8765
    adult/f1_micro          0.7654
    adult/f1_macro          0.7654
    asia/syn_likelihood    -2.5364
    asia/test_likelihood   -2.4321
    dtype: float64

Args:
    scores (pandas.DataFrame):
        Raw Scores dataframe as returned by the ``compute_benchmark`` function.

Returns:
    pandas.Series:
        Summarized scores series in the format described above.
def _summarize_scores(scores):
    'Computes a summary of the scores obtained by a synthesizer.\n\n    The raw scores returned by the ``compute_benchmark`` function are summarized\n    by grouping them by dataset and computing the average.\n\n    The results are then put in a ``pandas.Series`` object with one value per\n    dataset and metric.\n\n    As an example, the summary of a synthesizer that has been evaluated on the\n    ``adult`` and the ``asia`` datasets produces the following output::\n\n        adult/accuracy          0.8765\n        adult/f1_micro          0.7654\n        adult/f1_macro          0.7654\n        asia/syn_likelihood    -2.5364\n        asia/test_likelihood   -2.4321\n        dtype: float64\n\n    Args:\n        scores (pandas.DataFrame):\n            Raw Scores dataframe as returned by the ``compute_benchmark`` function.\n\n    Returns:\n        pandas.Series:\n            Summarized scores series in the format described above.\n    '
    scores = scores.drop(['distance', 'iteration', 'name'], axis=1, errors='ignore')
    grouped = scores.groupby('dataset').apply(_dataset_summary)
    if isinstance(grouped, pd.Series):
        return grouped.droplevel(0)
    return grouped.iloc[0]