Source code for catalyst.callbacks.metrics.auc

from typing import List

from catalyst.callbacks.metric import LoaderMetricCallback
from catalyst.metrics.auc import auc
from catalyst.metrics.functional import (

[docs]class AUCCallback(LoaderMetricCallback): """Calculates the AUC per class for each loader. .. note:: Currently, supports binary and multilabel cases. """
[docs] def __init__( self, input_key: str = "targets", output_key: str = "logits", prefix: str = "auc", activation: str = "Sigmoid", per_class: bool = False, class_args: List[str] = None, **kwargs, ): """ Args: input_key: input key to use for auc calculation specifies our ``y_true``. output_key: output key to use for auc calculation; specifies our ``y_pred``. prefix: key for the metric's name activation: An torch.nn activation applied to the outputs. Must be one of ``'none'``, ``'Sigmoid'``, or ``'Softmax'`` per_class: boolean flag to log per class metrics, or use mean/macro statistics otherwise class_args: class names to display in the logs. If None, defaults to indices for each class, starting from 0 **kwargs: key-value params to pass to the metric .. note:: For ``**kwargs`` info, please follow ``catalyst.callbacks.metric.LoaderMetricCallback`` and ``catalyst.metrics.auc.auc`` docs """ metric_fn = wrap_metric_fn_with_activation( metric_fn=auc, activation=activation ) metric_fn = wrap_class_metric2dict( metric_fn, per_class=per_class, class_args=class_args ) super().__init__( prefix=prefix, metric_fn=metric_fn, input_key=input_key, output_key=output_key, **kwargs, )
__all__ = ["AUCCallback"]