pearson_correlation

allennlp.training.metrics.pearson_correlation

PearsonCorrelation¶

@Metric.register("pearson_correlation")
class PearsonCorrelation(Metric):
 | def __init__(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

call¶

class PearsonCorrelation(Metric):
 | ...
 | def __call__(
 |     self,
 |     predictions: torch.Tensor,
 |     gold_labels: torch.Tensor,
 |     mask: Optional[torch.BoolTensor] = None
 | )

Parameters¶

predictions : torch.Tensor
A tensor of predictions of shape (batch_size, ...).
gold_labels : torch.Tensor
A tensor of the same shape as predictions.
mask : torch.BoolTensor, optional (default = None)
A tensor of the same shape as predictions.

get_metric¶

class PearsonCorrelation(Metric):
 | ...
 | def get_metric(self, reset: bool = False)

Returns¶

The accumulated sample Pearson correlation.

reset¶

class PearsonCorrelation(Metric):
 | ...
 | def reset(self)

pearson_correlation