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from allennlp.training.metrics import FBetaMeasure, Metric
@Metric.register('fbeta_mix')
class FBetaMixMeasure(FBetaMeasure):
def __init__(self, null_idx, **kwargs):
super().__init__(**kwargs)
self.null_idx = null_idx
def get_metric(self, reset: bool = False):
tp = float(self._true_positive_sum.sum() - self._true_positive_sum[self.null_idx])
total_pred = float(self._pred_sum.sum() - self._pred_sum[self.null_idx])
total_gold = float(self._true_sum.sum() - self._true_sum[self.null_idx])
beta2 = self._beta ** 2
p = 0. if total_pred == 0 else tp / total_pred
r = 0. if total_pred == 0 else tp / total_gold
f = 0. if p == 0. or r == 0. else ((1 + beta2) * p * r / (p * beta2 + r))
mix_f = {
'p': p * 100,
'r': r * 100,
'f': f * 100
}
if reset:
self.reset()
return mix_f
def add_false_negative(self, labels):
for lab in labels:
self._true_sum[lab] += 1
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