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 risk(self, prediction: Labelling,
Pdist: ProbabilityDistribution) -> float:
n = Pdist.getNumberOfLabels()
k = prediction.positives()
if (k == 0):
return 1 - Pdist.fulljoint([0] * n)
p = 0.0
for i in range(n):
if (prediction[i] == 1):
p += sum([
Pdist.getProbSize(i, j) / (k + j) for j in range(1, n + 1)
])
return 1 - 2 * p
def measure(self, target: Labelling, predicted) -> float:
s = 0
for ri, yi in zip(predicted, target):
if (yi == 0):
continue
for rj, yj in zip(predicted, target):
if (yj == 1):
continue
if (ri > rj):
s += 1
num_pos = target.positives()
n = len(target)
if (self.normalized == 2):
return s / (num_pos * (n - num_pos))
if (self.normalized == 1):
return 4 * s / (n * n)
return s
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))