def measure(self, target: Labelling, predicted) -> float:
avg_prec = 0.0
npos = target.positives()
if (npos == 0):
return 1.0
pos_idxs = target.where_positive()
for i in pos_idxs:
yi_rank = predicted[i]
s = sum([1 for j in pos_idxs if predicted[j] <= yi_rank])
avg_prec += s / yi_rank
return avg_prec / npos
def measure(self, target: Labelling, predicted) -> float:
positives_idxs = target.where_positive()
return sum([1 / predicted[pi] for pi in positives_idxs]) / sum(
1 / np.arange(1,
target.positives() + 1))
def measure(self, target: Labelling, predicted) -> float:
positives_idxs = target.where_positive()
if (len(positives_idxs) == 0):
return 0.0
return max([predicted[pi] for pi in positives_idxs
]) # / len(target) / target.positives()