# 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.
# 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 Union, cast
import numpy as np
import torch
from monai.utils import Average
from .metric import CumulativeIterationMetric
[docs]class ROCAUCMetric(CumulativeIterationMetric):
"""
Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
The input `y_pred` and `y` can be a list of `channel-first` Tensor or a `batch-first` Tensor.
Args:
average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
Type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average,
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
"""
def __init__(self, average: Union[Average, str] = Average.MACRO) -> None:
super().__init__()
self.average = average
def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor): # type: ignore
return y_pred, y
[docs] def aggregate(self): # type: ignore
"""
As AUC metric needs to execute on the overall data, so usually users accumulate `y_pred` and `y`
of every iteration, then execute real computation and reduction on the accumulated data.
"""
y_pred, y = self.get_buffer()
# compute final value and do metric reduction
if not isinstance(y_pred, torch.Tensor) or not isinstance(y, torch.Tensor):
raise ValueError("y_pred and y must be PyTorch Tensor.")
return compute_roc_auc(y_pred=y_pred, y=y, average=self.average)
def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
if not (y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(y_pred)):
raise AssertionError("y and y_pred must be 1 dimension data with same length.")
if not y.unique().equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
raise AssertionError("y values must be 0 or 1, can not be all 0 or all 1.")
n = len(y)
indices = y_pred.argsort()
y = y[indices].cpu().numpy()
y_pred = y_pred[indices].cpu().numpy()
nneg = auc = tmp_pos = tmp_neg = 0.0
for i in range(n):
y_i = cast(float, y[i])
if i + 1 < n and y_pred[i] == y_pred[i + 1]:
tmp_pos += y_i
tmp_neg += 1 - y_i
continue
if tmp_pos + tmp_neg > 0:
tmp_pos += y_i
tmp_neg += 1 - y_i
nneg += tmp_neg
auc += tmp_pos * (nneg - tmp_neg / 2)
tmp_pos = tmp_neg = 0
continue
if y_i == 1:
auc += nneg
else:
nneg += 1
return auc / (nneg * (n - nneg))
[docs]def compute_roc_auc(
y_pred: torch.Tensor,
y: torch.Tensor,
average: Union[Average, str] = Average.MACRO,
):
"""Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
Args:
y_pred: input data to compute, typical classification model output.
it must be One-Hot format and first dim is batch, example shape: [16] or [16, 2].
y: ground truth to compute ROC AUC metric, the first dim is batch.
example shape: [16, 1] will be converted into [16, 2] (where `2` is inferred from `y_pred`).
average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
Type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average,
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
Raises:
ValueError: When ``y_pred`` dimension is not one of [1, 2].
ValueError: When ``y`` dimension is not one of [1, 2].
ValueError: When ``average`` is not one of ["macro", "weighted", "micro", "none"].
Note:
ROCAUC expects y to be comprised of 0's and 1's. `y_pred` must be either prob. estimates or confidence values.
"""
y_pred_ndim = y_pred.ndimension()
y_ndim = y.ndimension()
if y_pred_ndim not in (1, 2):
raise ValueError("Predictions should be of shape (batch_size, n_classes) or (batch_size, ).")
if y_ndim not in (1, 2):
raise ValueError("Targets should be of shape (batch_size, n_classes) or (batch_size, ).")
if y_pred_ndim == 2 and y_pred.shape[1] == 1:
y_pred = y_pred.squeeze(dim=-1)
y_pred_ndim = 1
if y_ndim == 2 and y.shape[1] == 1:
y = y.squeeze(dim=-1)
if y_pred_ndim == 1:
return _calculate(y_pred, y)
if y.shape != y_pred.shape:
raise AssertionError("data shapes of y_pred and y do not match.")
average = Average(average)
if average == Average.MICRO:
return _calculate(y_pred.flatten(), y.flatten())
y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
auc_values = [_calculate(y_pred_, y_) for y_pred_, y_ in zip(y_pred, y)]
if average == Average.NONE:
return auc_values
if average == Average.MACRO:
return np.mean(auc_values)
if average == Average.WEIGHTED:
weights = [sum(y_) for y_ in y]
return np.average(auc_values, weights=weights)
raise ValueError(f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].')