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# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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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.
"""
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,
)