def compute(self): if self.sum_inst == 0: raise NotComputableError( 'Accuracy must have at least one example before it can be computed' ) return self.sum_metric / self.sum_inst
def compute(self): if self._num_examples == 0: raise NotComputableError('FalsePositiveRate must have at least one example before it can be computed.') return self._false_positive_num/ self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError( "GeometricMeanAbsoluteError must have at " "least one example before it can be computed.") return torch.exp(self._sum_of_errors / self._num_examples).item()
def compute(self): if self._num_examples == 0: raise NotComputableError( 'Score must have at least one example before it can be computed.' ) return self._sum / self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError('WER must have at least one example before it can be computed') return (self._total_edit_distance / self._num_examples) * 100
def compute(self) -> torch.Tensor: if self._num_examples == 0: raise NotComputableError( "PSNR must have at least one example before it can be computed." ) return self._sum_of_batchwise_psnr / self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError( "TopKCategoricalAccuracy must have at" "least one example before it can be computed.") return self._num_correct / self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError( 'MeanSquaredError must have at least one example before it can be computed.' ) return self._sum_of_squared_errors / self._num_examples
def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "Loss must have at least one example before it can be computed." ) return self._sum.item() / self._num_examples
def compute(self): if self._max_of_absolute_errors < 0: raise NotComputableError( "MaximumAbsoluteError must have at least one example before it can be computed." ) return self._max_of_absolute_errors
def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( "TopKCategoricalAccuracy must have at least one example before it can be computed." ) return self._num_correct.item() / self._num_examples
def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("R2Score must have at least one example before it can be computed.") return 1 - self._sum_of_errors.item() / (self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples)
def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( "MeanSquaredError must have at least one example before it can be computed." ) return self._sum_of_squared_errors.item() / self._num_examples
def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( 'MeanAbsoluteError must have at least one example before it can be computed.' ) return self._sum_of_distances / self._num_examples
def compute(self) -> torch.Tensor: if self._num_sentences == 0: raise NotComputableError( "Bleu must have at least one example before it can be computed." ) return self._sum_of_bleu / self._num_sentences
def compute(self): if self._num_samples == 0: raise NotComputableError( "MeanAbsoluteRelativeError must have at least" "one sample before it can be computed.") return self._sum_of_absolute_relative_errors / self._num_samples
def compute(self): if self._num_examples == 0: raise NotComputableError( 'FractionalBias must have at least one example before it can be computed.' ) return self._sum_of_errors / self._num_examples
def compute(self): if self._num_ex == 0: raise NotComputableError( 'Loss must have at least one example before it can be computed.') return {k: v * 100 / self._num_ex for k, v in self._data.items()}
def compute(self): if self._count < 2: raise NotComputableError( 'Covariance must have at least two samples before it can be computed.' ) return self._comoment / (self._count - 1)
def compute(self): if self._num_examples == 0: raise NotComputableError( "MeanNormalizedBias must have at least one example before it can be computed." ) return self._sum_of_errors / self._num_examples
def compute(self): if self._count < 1: raise NotComputableError( 'Mean must have at least one sample before it can be computed.' ) return self._mean
def compute(self): if self.pred_label is None or self.gold_label is None: raise NotComputableError( 'Microf1 must have at least one example before it can be computed' ) return f1_score(self.gold_label, self.pred_label, average='micro')
def compute(self): if self._num_examples == 0: raise NotComputableError('MeanAbsoluteError must have at least one example before it can be computed') return self._sum_of_absolute_errors / self._num_examples
def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "FractionalAbsoluteError must have at least one example before it can be computed." ) return self._sum_of_errors.item() / self._num_examples
def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "Accuracy must have at least one example before it can be computed." ) return self._num_correct.item() / self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError( 'Accuracy must have at least one example before it can be computed.' ) return self._num_correct / self._num_examples
def compute(self): if self._num_examples == 0: raise NotComputableError( 'AverageError must have at least one example before it can be computed.' ) return torch.tensor(self.__average_errors).mean()