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Source code for ignite.contrib.metrics.roc_auc

from ignite.metrics import EpochMetric


def roc_auc_compute_fn(y_preds, y_targets):
    try:
        from sklearn.metrics import roc_auc_score
    except ImportError:
        raise RuntimeError("This contrib module requires sklearn to be installed.")

    y_true = y_targets.numpy()
    y_pred = y_preds.numpy()
    return roc_auc_score(y_true, y_pred)


[docs]class ROC_AUC(EpochMetric): """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 <http://scikit-learn.org/stable/modules/generated/ sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_ . Args: output_transform (callable, optional): a callable that is used to transform the :class:`~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. ROC_AUC expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def activated_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y roc_auc = ROC_AUC(activated_output_transform) """ def __init__(self, output_transform=lambda x: x): super(ROC_AUC, self).__init__(roc_auc_compute_fn, output_transform=output_transform)

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