def f_measure(reference, test, alpha=0.5):
    p = precision(reference, test)
    r = recall(reference, test)
    if p is None or r is None:
        return None
    if p == 0 or r == 0:
        return 0
    return 1.0 / (alpha / p + (1-alpha) / r)
Start the signal listener
dataset/ETHPy150Open nltk/nltk/nltk/metrics/scores.py/f_measure--crossover with--dataset/ETHPy150Open veegee/guv/pyuv_cffi/__init__.py/Signal.start