Source code for monai.handlers.confusion_matrix

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Callable, Optional, Sequence

import torch

from monai.metrics import ConfusionMatrixMetric, compute_confusion_matrix_metric
from monai.metrics.utils import MetricReduction, do_metric_reduction
from monai.utils import exact_version, optional_import

NotComputableError, _ = optional_import("ignite.exceptions", "0.4.2", exact_version, "NotComputableError")
Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "reinit__is_reduced")
sync_all_reduce, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "sync_all_reduce")


[docs]class ConfusionMatrix(Metric): # type: ignore[valid-type, misc] # due to optional_import """ 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", compute_sample: bool = False, output_transform: Callable = lambda x: x, device: Optional[torch.device] = None, ) -> 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. compute_sample: if ``True``, each sample's metric will be computed first. If ``False``, the confusion matrix for all samples will be accumulated first. Defaults to ``False``. output_transform: transform the ignite.engine.state.output into [y_pred, y] pair. device: device specification in case of distributed computation usage. See also: :py:meth:`monai.metrics.confusion_matrix` """ super().__init__(output_transform, device=device) self.confusion_matrix = ConfusionMatrixMetric( include_background=include_background, metric_name=metric_name, compute_sample=compute_sample, reduction=MetricReduction.MEAN, ) self._sum = 0.0 self._num_examples = 0 self.compute_sample = compute_sample self.metric_name = metric_name self._total_tp = 0.0 self._total_fp = 0.0 self._total_tn = 0.0 self._total_fn = 0.0
[docs] @reinit__is_reduced def reset(self) -> None: self._sum = 0.0 self._num_examples = 0 self._total_tp = 0.0 self._total_fp = 0.0 self._total_tn = 0.0 self._total_fn = 0.0
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: """ Args: output: sequence with contents [y_pred, y]. Raises: ValueError: When ``output`` length is not 2. This metric can only support y_pred and y. """ if len(output) != 2: raise ValueError(f"output must have length 2, got {len(output)}.") y_pred, y = output if self.compute_sample is True: score, not_nans = self.confusion_matrix(y_pred, y) not_nans = int(not_nans.item()) # add all items in current batch self._sum += score.item() * not_nans self._num_examples += not_nans else: confusion_matrix = self.confusion_matrix(y_pred, y) confusion_matrix, _ = do_metric_reduction(confusion_matrix, MetricReduction.SUM) self._total_tp += confusion_matrix[0].item() self._total_fp += confusion_matrix[1].item() self._total_tn += confusion_matrix[2].item() self._total_fn += confusion_matrix[3].item()
[docs] @sync_all_reduce("_sum", "_num_examples", "_total_tp", "_total_fp", "_total_tn", "_total_fn") def compute(self): """ Raises: NotComputableError: When ``compute`` is called before an ``update`` occurs. """ if self.compute_sample is True: if self._num_examples == 0: raise NotComputableError( "ConfusionMatrix metric must have at least one example before it can be computed." ) return self._sum / self._num_examples confusion_matrix = torch.tensor([self._total_tp, self._total_fp, self._total_tn, self._total_fn]) return compute_confusion_matrix_metric(self.metric_name, confusion_matrix)