ignite.contrib.metrics

Contribution module of metrics

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

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>>)

Calculates the maximum absolute error.

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

compute()

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()

Resets the metric to to it’s initial state.

This is called at the start of each epoch.

update(output)

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>>)

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.