class kenchi.metrics.LeeLiuScorer[source]

Bases: object

Lee-Liu scorer.

References

[1]Lee, W. S, and Liu, B., “Learning with positive and unlabeled examples using weighted Logistic Regression,” In Proceedings of ICML, pp. 448-455, 2003.
class kenchi.metrics.NegativeMVAUCScorer(data_max, data_min, interval=(0.9, 0.999), n_offsets=1000, n_uniform_samples=1000, random_state=None)[source]

Bases: object

Negative MV AUC scorer.

Parameters:
  • data_max (array-like of shape (n_features,)) – Per feature maximum seen in the data.
  • data_min (array-like of shape (n_features,)) – Per feature minimum seen in the data.
  • interval (tuple, default (0.9, 0.999)) – Interval of probabilities.
  • n_offsets (int, default 1000) – Number of offsets.
  • n_uniform_samples (int, default 1000) – Number of samples which are drawn from the uniform distribution over the hypercube enclosing the data.
  • random_state (int or RandomState instance, default None) – Seed of the pseudo random number generator.

References

[2]Goix, N., “How to evaluate the quality of unsupervised anomaly detection algorithms?” In ICML Anomaly Detection Workshop, 2016.