# ignite.contrib.metrics#

Contribution module of metrics

class ignite.contrib.metrics.AveragePrecision(activation=None, output_transform=<function AveragePrecision.<lambda>>)[source]#

Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score

Parameters
• activation (Callable, optional) – optional function to apply on prediction tensors, e.g. activation=torch.sigmoid to transform logits.

• output_transform (callable, optional) – a callable that is used to transform the ignite.engine.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.

class ignite.contrib.metrics.MaximumAbsoluteError(output_transform=<function Metric.<lambda>>)[source]#

Calculates the maximum absolute error.

• update must receive output of the form (y_pred, y).

compute()[source]#

Computes the metric based on it’s accumulated state.

This is called at the end of each epoch.

Returns

the actual quantity of interest

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed

reset()[source]#

Resets the metric to to it’s initial state.

This is called at the start of each epoch.

update(output)[source]#

Updates the metric’s state using the passed batch output.

This is called once for each batch.

Parameters

output – the is the output from the engine’s process function

class ignite.contrib.metrics.ROC_AUC(activation=None, output_transform=<function ROC_AUC.<lambda>>)[source]#

Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.roc_auc_score

Parameters
• activation (Callable, optional) – optional function to apply on prediction tensors, e.g. activation=torch.sigmoid to transform logits.

• output_transform (callable, optional) – a callable that is used to transform the ignite.engine.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.