# MaximumAbsoluteError#

class ignite.contrib.metrics.regression.MaximumAbsoluteError(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#

Calculates the Maximum Absolute Error.

$\text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right)$

where $A_j$ is the ground truth and $P_j$ is the predicted value.

More details can be found in Botchkarev 2018.

• update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

• y and y_pred must be of same shape (N, ) or (N, 1).

Parameters are inherited from Metric.__init__.

Parameters
• output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as (y_pred, y) or {'y_pred': y_pred, 'y': y}.

• device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

Changed in version 0.4.5: - Works with DDP.

Methods

 compute Computes the metric based on it's accumulated state. reset Resets the metric to it's initial state.
compute()[source]#

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None