# Copyright (c) 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 __future__ import annotations import warnings from typing import TYPE_CHECKING, cast import numpy as np if TYPE_CHECKING: import numpy.typing as npt import torch from monai.utils import Average, look_up_option from .metric import CumulativeIterationMetric class AveragePrecisionMetric(CumulativeIterationMetric): """ Computes Average Precision (AP). AP is a useful metric to evaluate a classifier when the classes are imbalanced. It can take values between 0.0 and 1.0, 1.0 being the best possible score. It summarizes a Precision-Recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight: .. math:: \\text{AP} = \\sum_n (R_n - R_{n-1}) P_n :label: ap where :math:`P_n` and :math:`R_n` are the precision and recall at the :math:`n^{th}` threshold. Referring to: `sklearn.metrics.average_precision_score `_. The input `y_pred` and `y` can be a list of `channel-first` Tensor or a `batch-first` Tensor. Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`. 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: Average | str = Average.MACRO) -> None: super().__init__() self.average = average def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: # type: ignore[override] return y_pred, y def aggregate(self, average: Average | str | None = None) -> np.ndarray | float | npt.ArrayLike: """ Typically `y_pred` and `y` are stored in the cumulative buffers at each iteration, This function reads the buffers and computes the Average Precision. Args: average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``} Type of averaging performed if not binary classification. Defaults to `self.average`. """ 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_average_precision(y_pred=y_pred, y=y, average=average or 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.") y_unique = y.unique() if len(y_unique) == 1: warnings.warn(f"y values can not be all {y_unique.item()}, skip AP computation and return `Nan`.") return float("nan") if not y_unique.equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)): warnings.warn(f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AP computation and return `Nan`.") return float("nan") n = len(y) indices = y_pred.argsort(descending=True) y = y[indices].cpu().numpy() # type: ignore[assignment] y_pred = y_pred[indices].cpu().numpy() # type: ignore[assignment] npos = ap = tmp_pos = 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 else: tmp_pos += y_i npos += tmp_pos ap += tmp_pos * npos / (i + 1) tmp_pos = 0 return ap / npos def compute_average_precision( y_pred: torch.Tensor, y: torch.Tensor, average: Average | str = Average.MACRO ) -> np.ndarray | float | npt.ArrayLike: """Computes Average Precision (AP). AP is a useful metric to evaluate a classifier when the classes are imbalanced. It summarizes a Precision-Recall according to equation :eq:`ap`. Referring to: `sklearn.metrics.average_precision_score `_. Args: y_pred: input data to compute, typical classification model output. the first dim must be batch, if multi-classes, it must be in One-Hot format. for example: shape `[16]` or `[16, 1]` for a binary data, shape `[16, 2]` for 2 classes data. y: ground truth to compute AP metric, the first dim must be batch. if multi-classes, it must be in One-Hot format. for example: shape `[16]` or `[16, 1]` for a binary data, shape `[16, 2]` for 2 classes data. 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: Average Precision 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( f"Predictions should be of shape (batch_size, num_classes) or (batch_size, ), got {y_pred.shape}." ) if y_ndim not in (1, 2): raise ValueError(f"Targets should be of shape (batch_size, num_classes) or (batch_size, ), got {y.shape}.") 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 ValueError(f"data shapes of y_pred and y do not match, got {y_pred.shape} and {y.shape}.") average = look_up_option(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) ap_values = [_calculate(y_pred_, y_) for y_pred_, y_ in zip(y_pred, y)] if average == Average.NONE: return ap_values if average == Average.MACRO: return np.mean(ap_values) if average == Average.WEIGHTED: weights = [sum(y_) for y_ in y] return np.average(ap_values, weights=weights) # type: ignore[no-any-return] raise ValueError(f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].')