# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
else:
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)