chainer.functions.classification_summary(y, t, label_num=None, beta=1.0, ignore_label=-1)[source]

Calculates Precision, Recall, F beta Score, and support.

This function calculates the following quantities for each class.

  • Precision: \(\frac{\mathrm{tp}}{\mathrm{tp} + \mathrm{fp}}\)
  • Recall: \(\frac{\mathrm{tp}}{\mathrm{tp} + \mathrm{tn}}\)
  • F beta Score: The weighted harmonic average of Precision and Recall.
  • Support: The number of instances of each ground truth label.

Here, tp, fp, and tn stand for the number of true positives, false positives, and true negative, respectively.

label_num specifies the number of classes, that is, each value in t must be an integer in the range of [0, label_num). If label_num is None, this function regards label_num as a maximum of in t plus one.

ignore_label determines which instances should be ignored. Specifically, instances with the given label are not taken into account for calculating the above quantities. By default, it is set to -1 so that all instances are taken into consideration, as labels are supposed to be non-negative integers. Setting ignore_label to a non-negative integer less than label_num is illegal and yields undefined behavior. In the current implementation, it arises RuntimeWarning and ignore_label-th entries in output arrays do not contain correct quantities.

  • y (Variable) – Variable holding a vector of scores.
  • t (Variable) – Variable holding a vector of ground truth labels.
  • label_num (int) – The number of classes.
  • beta (float) – The parameter which determines the weight of precision in the F-beta score.
  • ignore_label (int) – Instances with this label are ignored.

4-tuple of ~chainer.Variable of size (label_num,). Each element represents precision, recall, F beta score, and support of this minibatch.