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