metrics

Functions:

Name	Description
MetricFunc	Metric function protocol.
mae	Mean Absolute Error.
pearson_correlation	Mean Pearson correlation over the target dimensions.
rmse	Root Mean Squared Error.

MetricFunc

Bases: Protocol

Metric function protocol.

Accepts predictions and observations of the same shape and returns a metric value.

mae

mae(predictions, observations)

Mean Absolute Error.

Parameters:

Name	Type	Description	Default
predictions	ndarray or Tensor	(N,), (N, D), or (B, N, D).	required
observations	ndarray or Tensor	Same shape as predictions.	required

Returns:

Type	Description
ndarray or Tensor	() for 1D/2D input, (B,) for 3D input.

Raises:

Type	Description
ValueError	- If predictions and observations have different shapes. - If the inputs are not 1D, 2D, or 3D.

pearson_correlation

pearson_correlation(predictions, observations)

Mean Pearson correlation over the target dimensions.

The correlation is computed per target dimension along the sample axis, then averaged over the dimensions.

Parameters:

Name	Type	Description	Default
predictions	ndarray or Tensor	(N,), (N, D), or (B, N, D).	required
observations	ndarray or Tensor	Same shape as predictions.	required

Returns:

Type	Description
ndarray or Tensor	() for 1D/2D input, (B,) for 3D input.

Raises:

Type	Description
ValueError	- If predictions and observations have different shapes. - If the inputs are not 1D, 2D, or 3D.

rmse

rmse(predictions, observations)

Root Mean Squared Error.

Parameters:

Name	Type	Description	Default
predictions	ndarray or Tensor	(N,), (N, D), or (B, N, D).	required
observations	ndarray or Tensor	Same shape as predictions.	required

Returns:

Type	Description
ndarray or Tensor	() for 1D/2D input, (B,) for 3D input.

Raises:

Type	Description
ValueError	- If predictions and observations have different shapes. - If the inputs are not 1D, 2D, or 3D.