Source code for

Maintains a confusion matrix for a given classification problem.
import numpy as np

import torch

from import meter

[docs]class ConfusionMeter(meter.Meter): """ ConfusionMeter constructs a confusion matrix for a multiclass classification problems. It does not support multilabel, multiclass problems: for such problems, please use MultiLabelConfusionMeter. """
[docs] def __init__(self, k: int, normalized: bool = False): """ Args: k: number of classes in the classification problem normalized: determines whether or not the confusion matrix is normalized or not """ super(ConfusionMeter, self).__init__() self.conf = np.ndarray((k, k), dtype=np.int32) self.normalized = normalized self.k = k self.reset()
[docs] def reset(self) -> None: """Reset confusion matrix, filling it with zeros.""" self.conf.fill(0)
[docs] def add(self, predicted: torch.Tensor, target: torch.Tensor) -> None: """Computes the confusion matrix of ``K x K`` size where ``K`` is no of classes. Args: predicted: Can be an N x K tensor of predicted scores obtained from the model for N examples and K classes or an N-tensor of integer values between 0 and K-1 target: Can be a N-tensor of integer values assumed to be integer values between 0 and K-1 or N x K tensor, where targets are assumed to be provided as one-hot vectors """ predicted = predicted.cpu().numpy() target = target.cpu().numpy() assert ( predicted.shape[0] == target.shape[0] ), "number of targets and predicted outputs do not match" if np.ndim(predicted) != 1: assert ( predicted.shape[1] == self.k ), "number of predictions does not match size of confusion matrix" predicted = np.argmax(predicted, 1) else: assert (predicted.max() < self.k) and ( predicted.min() >= 0 ), "predicted values are not between 1 and k" onehot_target = np.ndim(target) != 1 if onehot_target: assert ( target.shape[1] == self.k ), "Onehot target does not match size of confusion matrix" assert (target >= 0).all() and ( target <= 1 ).all(), "in one-hot encoding, target values should be 0 or 1" assert ( target.sum(1) == 1 ).all(), "multilabel setting is not supported" target = np.argmax(target, 1) else: assert (predicted.max() < self.k) and ( predicted.min() >= 0 ), "predicted values are not between 0 and k-1" # hack for bincounting 2 arrays together x = predicted + self.k * target bincount_2d = np.bincount( # noqa: WPS114 x.astype(np.int32), minlength=self.k ** 2 ) assert bincount_2d.size == self.k ** 2 conf = bincount_2d.reshape((self.k, self.k)) self.conf += conf
[docs] def value(self): """ Returns: Confusion matrix of K rows and K columns, where rows corresponds to ground-truth targets and columns corresponds to predicted targets. """ if self.normalized: conf = self.conf.astype(np.float32) return conf / conf.sum(1).clip(min=1e-12)[:, None] else: return self.conf
__all__ = ["ConfusionMeter"]