Source code for monai.handlers.confusion_matrix

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from typing import Callable

from monai.handlers.ignite_metric import IgniteMetric
from monai.metrics import ConfusionMatrixMetric
from monai.metrics.utils import MetricReduction

[docs]class ConfusionMatrix(IgniteMetric): """ Compute confusion matrix related metrics from full size Tensor and collects average over batch, class-channels, iterations. """
[docs] def __init__( self, include_background: bool = True, metric_name: str = "hit_rate", output_transform: Callable = lambda x: x, save_details: bool = True, ) -> None: """ Args: include_background: whether to skip 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. 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()`. for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`, output_transform can be `lambda x: (x["pred"], x["label"])`. 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` """ metric_fn = ConfusionMatrixMetric( include_background=include_background, metric_name=metric_name, compute_sample=False, reduction=MetricReduction.MEAN, ) self.metric_name = metric_name super().__init__( metric_fn=metric_fn, output_transform=output_transform, save_details=save_details, )