o  i@sPddlmZddlmZddlmZddlmZddlm Z GdddeZ dS) ) annotations)Callable)IgniteMetricHandler)ConfusionMatrixMetric)MetricReductioncs6eZdZdZdddejdddfdfdd ZZS)ConfusionMatrixz Compute confusion matrix related metrics from full size Tensor and collects average over batch, class-channels, iterations. Thit_rateFcCs|S)N)xr r a/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/handlers/confusion_matrix.py szConfusionMatrix.include_backgroundbool metric_namestrcompute_sample reductionMetricReduction | stroutput_transformr save_detailsreturnNonecs,t||||d}||_tj|||ddS)a Args: include_background: whether to include metric computation on the first channel of the predicted output. Defaults to True. metric_name: [``"sensitivity"``, ``"specificity"``, ``"precision"``, ``"negative predictive value"``, ``"miss rate"``, ``"fall out"``, ``"false discovery rate"``, ``"false omission rate"``, ``"prevalence threshold"``, ``"threat score"``, ``"accuracy"``, ``"balanced accuracy"``, ``"f1 score"``, ``"matthews correlation coefficient"``, ``"fowlkes mallows index"``, ``"informedness"``, ``"markedness"``] Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned), and you can also input those names instead. compute_sample: when reducing, if ``True``, each sample's metric will be computed based on each confusion matrix first. if ``False``, compute reduction on the confusion matrices first, defaults to ``False``. reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values, available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction. output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`. `engine.state` and `output_transform` inherit from the ignite concept: https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial: https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb. save_details: whether to save metric computation details per image, for example: TP/TN/FP/FN of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. See also: :py:meth:`monai.metrics.confusion_matrix` )r rrr) metric_fnrrN)rrsuper__init__)selfr rrrrrr __class__r r rs&zConfusionMatrix.__init__)r rrrrrrrrrrrrr)__name__ __module__ __qualname____doc__rMEANr __classcell__r r rr rsrN) __future__rcollections.abcrmonai.handlers.ignite_metricr monai.metricsrmonai.utils.enumsrrr r r r s