Source code for pywick.meters.confusionmeter

from . import meter
import numpy as np

[docs]class ConfusionMeter(meter.Meter): """Maintains a confusion matrix for a given classification problem. The ConfusionMeter constructs a confusion matrix for a multi-class classification problems. It does not support multi-label, multi-class problems: for such problems, please use MultiLabelConfusionMeter. :param k (int): number of classes in the classification problem :param normalized (boolean): Determines whether or not the confusion matrix is normalized or not """ def __init__(self, k, normalized=False): 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): self.conf.fill(0)
[docs] def add(self, predicted, target): """Computes the confusion matrix of K x K size where K is no of classes :param predicted (tensor): 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. :param target (tensor): 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() if predicted.shape[0] != target.shape[0]: raise AssertionError('number of targets and predicted outputs do not match') if np.ndim(predicted) != 1: if predicted.shape[1] != self.k: raise AssertionError('number of predictions does not match size of confusion matrix') predicted = np.argmax(predicted, 1) else: if not ((predicted.max() < self.k) and (predicted.min() >= 0)): raise AssertionError('predicted values are not between 1 and k') onehot_target = np.ndim(target) != 1 if onehot_target: if target.shape[1] != self.k: raise AssertionError('Onehot target does not match size of confusion matrix') if not ((target >= 0).all() and (target <= 1).all()): raise AssertionError('in one-hot encoding, target values should be 0 or 1') if not (target.sum(1) == 1).all(): raise AssertionError('multi-label setting is not supported') target = np.argmax(target, 1) else: if not ((predicted.max() < self.k) and (predicted.min() >= 0)): raise AssertionError('predicted values are not between 0 and k-1') # hack for bincounting 2 arrays together x = predicted + self.k * target bincount_2d = np.bincount(x.astype(np.int32), minlength=self.k ** 2) if bincount_2d.size != self.k ** 2: raise AssertionError conf = bincount_2d.reshape((self.k, self.k)) self.conf += conf
[docs] def value(self): """ Returns: Confustion 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