Source code for monai.handlers.confusion_matrix

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from __future__ import annotations

from import Callable

from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import ConfusionMatrixMetric
from monai.utils.enums import MetricReduction

[docs] class ConfusionMatrix(IgniteMetricHandler): """ 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", compute_sample: bool = False, reduction: MetricReduction | str = MetricReduction.MEAN, output_transform: Callable = lambda x: x, save_details: bool = True, ) -> None: """ 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:, explanation and usage example are in the tutorial: 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=compute_sample, reduction=reduction, ) self.metric_name = metric_name super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)