87a61ad92a9e0129e5c81c242f0ea96d77e6b0af,allennlp/training/metrics/pearson_correlation.py,PearsonCorrelation,get_metric,#PearsonCorrelation#Any#,71

Before Change

        predictions_variance = self._predictions_variance.get_metric(reset=reset)
        labels_variance = self._labels_variance.get_metric(reset=reset)
        denominator = math.sqrt(predictions_variance) * math.sqrt(labels_variance)
        if is_distributed():
            // Note: this gives an approximate aggregation of the covariance.

            device = self._device
            _covariance = torch.tensor(covariance).to(device)
            dist.all_reduce(_covariance, op=dist.ReduceOp.SUM)
            covariance = _covariance.item()
            _denominator = torch.tensor(denominator).to(device)
            dist.all_reduce(_denominator, op=dist.ReduceOp.SUM)
            denominator = _denominator.item()
        if reset:
            self.reset()

        if np.around(denominator, decimals=5) == 0:

After Change

        The accumulated sample Pearson correlation.
        
        if is_distributed():
            raise RuntimeError(
                "Distributed aggregation for PearsonCorrelation is currently not supported."
            )
        covariance = self._predictions_labels_covariance.get_metric(reset=reset)
        predictions_variance = self._predictions_variance.get_metric(reset=reset)
        labels_variance = self._labels_variance.get_metric(reset=reset)
        denominator = math.sqrt(predictions_variance) * math.sqrt(labels_variance)

Instances