class PearsonCorrelation(Metric):
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
self._device = torch.device("cpu")
def __call__(
self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.BoolTensor] = None,
):
predictions, gold_labels, mask = self.detach_tensors(predictions, gold_labels, mask)
self._device = gold_labels.device
self._predictions_labels_covariance(predictions, gold_labels, mask)
self._predictions_variance(predictions, predictions, mask)
self._labels_variance(gold_labels, gold_labels, mask)
def get_metric(self, reset: bool = False):
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)
if is_distributed():
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:
pearson_r = 0
else:
pearson_r = covariance / denominator
return pearson_r
@overrides
def reset(self):
self._predictions_labels_covariance.reset()
self._predictions_variance.reset()
reveal_type(self._labels_variance)
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
class PearsonCorrelation(Metric):
"""
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
"""
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
def __call__(
self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.Tensor] = None,
):
"""
# Parameters
predictions : `torch.Tensor`, required.
A tensor of predictions of shape (batch_size, ...).
gold_labels : `torch.Tensor`, required.
A tensor of the same shape as `predictions`.
mask : `torch.Tensor`, optional (default = None).
A tensor of the same shape as `predictions`.
"""
predictions, gold_labels, mask = self.unwrap_to_tensors(predictions, gold_labels, mask)
self._predictions_labels_covariance(predictions, gold_labels, mask)
self._predictions_variance(predictions, predictions, mask)
self._labels_variance(gold_labels, gold_labels, mask)
def get_metric(self, reset: bool = False):
"""
# Returns
The accumulated sample Pearson correlation.
"""
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)
if reset:
self.reset()
denominator = math.sqrt(predictions_variance) * math.sqrt(labels_variance)
if np.around(denominator, decimals=5) == 0:
pearson_r = 0
else:
pearson_r = covariance / denominator
return pearson_r
@overrides
def reset(self):
self._predictions_labels_covariance.reset()
self._predictions_variance.reset()
self._labels_variance.reset()
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
self._device = torch.device("cpu")
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
class PearsonCorrelation(Metric):
"""
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))
"""
def __init__(self) -> None:
self._predictions_labels_covariance = Covariance()
self._predictions_variance = Covariance()
self._labels_variance = Covariance()
def __call__(self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.Tensor] = None):
"""
Parameters
----------
predictions : ``torch.Tensor``, required.
A tensor of predictions of shape (batch_size, ...).
gold_labels : ``torch.Tensor``, required.
A tensor of the same shape as ``predictions``.
mask: ``torch.Tensor``, optional (default = None).
A tensor of the same shape as ``predictions``.
"""
predictions, gold_labels, mask = self.unwrap_to_tensors(predictions, gold_labels, mask)
self._predictions_labels_covariance(predictions, gold_labels, mask)
self._predictions_variance(predictions, predictions, mask)
self._labels_variance(gold_labels, gold_labels, mask)
def get_metric(self, reset: bool = False):
"""
Returns
-------
The accumulated sample Pearson correlation.
"""
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)
if reset:
self.reset()
pearson_r = covariance / (math.sqrt(predictions_variance) * math.sqrt(labels_variance))
return pearson_r
@overrides
def reset(self):
self._predictions_labels_covariance.reset()
self._predictions_variance.reset()
self._labels_variance.reset()