def load_mlcomp(name_or_id, set_="raw", mlcomp_root=None, **kwargs):

    if mlcomp_root is None:
        try:
            mlcomp_root = os.environ['MLCOMP_DATASETS_HOME']
        except KeyError:
            raise ValueError("MLCOMP_DATASETS_HOME env variable is undefined")

    mlcomp_root = os.path.expanduser(mlcomp_root)
    mlcomp_root = os.path.abspath(mlcomp_root)
    mlcomp_root = os.path.normpath(mlcomp_root)

    if not os.path.exists(mlcomp_root):
        raise ValueError("Could not find folder: " + mlcomp_root)

    # dataset lookup
    if isinstance(name_or_id, numbers.Integral):
        # id lookup
        dataset_path = os.path.join(mlcomp_root, str(name_or_id))
    else:
        # assume name based lookup
        dataset_path = None
        expected_name_line = "name: " + name_or_id
        for dataset in os.listdir(mlcomp_root):
            metadata_file = os.path.join(mlcomp_root, dataset, 'metadata')
            if not os.path.exists(metadata_file):
                continue
            with open(metadata_file) as f:
                for line in f:
                    if line.strip() == expected_name_line:
                        dataset_path = os.path.join(mlcomp_root, dataset)
                        break
        if dataset_path is None:
            raise ValueError("Could not find dataset with metadata line: " +
                             expected_name_line)

    # loading the dataset metadata
    metadata = dict()
    metadata_file = os.path.join(dataset_path, 'metadata')
    if not os.path.exists(metadata_file):
        raise ValueError(dataset_path + ' is not a valid MLComp dataset')
    with open(metadata_file) as f:
        for line in f:
            if ":" in line:
                key, value = line.split(":", 1)
                metadata[key.strip()] = value.strip()

    format = metadata.get('format', 'unknow')
    loader = LOADERS.get(format)
    if loader is None:
        raise ValueError("No loader implemented for format: " + format)
    return loader(dataset_path, metadata, set_=set_, **kwargs)
Returns a list of audit logs.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/datasets/mlcomp.py/load_mlcomp--crossover with--dataset/ETHPy150Open Nextdoor/ndscheduler/ndscheduler/core/datastore/providers/base.py/DatastoreBase.get_audit_logs