from . import meter
import numpy as np
[docs]class ConfusionMeter(meter.Meter):
"""Maintains a confusion matrix for a given calssification 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.
Args:
k (int): number of classes in the classification problem
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()
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
Args:
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.
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()
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(), \
'multi-label 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(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:
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