# 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 Optional, Union
import torch
import warnings
import numpy as np
from monai.networks import one_hot
from monai.utils import Average
def _calculate(y, y_pred):
assert y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(
y_pred
), "y and y_pred must be 1 dimension data with same length."
assert y.unique().equal(
torch.tensor([0, 1], dtype=y.dtype, device=y.device)
), "y values must be 0 or 1, can not be all 0 or all 1."
n = len(y)
indexes = y_pred.argsort()
y = y[indexes].cpu().numpy()
y_pred = y_pred[indexes].cpu().numpy()
nneg = auc = tmp_pos = tmp_neg = 0
for i in range(n):
y_i = 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,
to_onehot_y: bool = False,
softmax: bool = False,
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 (torch.Tensor): 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 (torch.Tensor): 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`).
to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
softmax: whether to add softmax function to `y_pred` before computation. Defaults to False.
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: predictions should be of shape (batch_size, n_classes) or (batch_size, ).
ValueError: targets should be of shape (batch_size, n_classes) or (batch_size, ).
ValueError: unsupported average method.
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:
if to_onehot_y:
warnings.warn("y_pred has only one channel, to_onehot_y=True ignored.")
if softmax:
warnings.warn("y_pred has only one channel, softmax=True ignored.")
return _calculate(y, y_pred)
else:
n_classes = y_pred.shape[1]
if to_onehot_y:
y = one_hot(y, n_classes)
if softmax:
y_pred = y_pred.float().softmax(dim=1)
assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
average = Average(average)
if average == Average.MICRO:
return _calculate(y.flatten(), y_pred.flatten())
else:
y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
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("unsupported average method.")