fscore.py 文件源码

def exact_expected_fscore_naive(probs, thresh):
  """NB: This algorithm is exponential in the size of probs!
  Based on initial measurements, less than 15 items is
  sub-second. 16 = 2s, 17=4s, 18=8s, and, well, you know
  the rest...
  possible relaxation to allow larger number of products:
  force items with sufficiently low probs (e.g. < 1%) off
  in groundtruths.
  """
  probs = np.asarray(probs)
  n = len(probs)
  expected = 0
  p_none = np.product(1-probs)
  predict_none = p_none > thresh
  predictions = (probs >= thresh).astype(np.int8)
  for gt in itertools.product([0,1], repeat=n):
    gt = np.array(gt)
    fs = fscore(predictions, gt, predict_none)
    p = gt_prob(gt, probs)
    expected += fs * p
  return expected