def preprocess_data()

in prediction_generation/original-project/analysis/scripts/rank_common.py [0:0]

• 30 lines of code
• 18 McCabe index (conditional complexity)

def preprocess_data(data, _type):
    methods = set([m for dset in data.keys() for m in data[dset].keys()])
    methods = sorted(methods)

    # filter out rbocpdms on "best" (uni or multi)
    if _type == "best":
        warning(
            "\nWarning: Filtering out RBOCPDMS due to insufficient results.\n"
        )
        methods = [m for m in methods if not m == "rbocpdms"]

    # filter out methods that have no results on any dataset
    methods_no_result = set()
    for m in methods:
        if all(data[d][m] is None for d in data):
            methods_no_result.add(m)
    if methods_no_result:
        print(
            "\nWarning: Filtering out %r due to no results on any series\n"
            % methods_no_result,
            file=sys.stderr,
        )
        methods = [m for m in methods if not m in methods_no_result]

    data_w_methods = {}
    for dset in data:
        data_w_methods[dset] = {}
        for method in methods:
            data_w_methods[dset][method] = data[dset][method]

    data_no_missing = {}
    for dset in data_w_methods:
        if any((x is None for x in data_w_methods[dset].values())):
            continue
        data_no_missing[dset] = data_w_methods[dset]
    return data_no_missing, methods