allennlp.training.metrics.pearson_correlation#

PearsonCorrelation#

PearsonCorrelation(self) -> None

This Metric calculates the sample Pearson correlation coefficient (r) between two tensors. Each element in the two tensors is assumed to be a different observation of the variable (i.e., the input tensors are implicitly flattened into vectors and the correlation is calculated between the vectors).

This implementation is mostly modeled after the streaming_pearson_correlation function in Tensorflow. See https://github.com/tensorflow/tensorflow/blob/v1.10.1/tensorflow/contrib/metrics/python/ops/metric_ops.py#L3267

This metric delegates to the Covariance metric the tracking of three [co]variances:

  • covariance(predictions, labels), i.e. covariance
  • covariance(predictions, predictions), i.e. variance of predictions
  • covariance(labels, labels), i.e. variance of labels

If we have these values, the sample Pearson correlation coefficient is simply:

r = covariance / (sqrt(predictions_variance) * sqrt(labels_variance))

if predictions_variance or labels_variance is 0, r is 0

get_metric#

PearsonCorrelation.get_metric(self, reset:bool=False)

Returns

The accumulated sample Pearson correlation.

reset#

PearsonCorrelation.reset(self)

Reset any accumulators or internal state.