[ allennlp.training.metrics.pearson_correlation ]
class PearsonCorrelation(Metric): | def __init__(self) -> None
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
covariance(labels, labels), i.e. variance of
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
| def get_metric(self, reset: bool = False)
- The accumulated sample Pearson correlation.
| @overrides | def reset(self)