There is now a anomaly.base.AnomalyFilter base class for anomaly filtering methods. These allow to classify anomaly scores. They can also prevent models from learning on anomalous data, for instance by putting them as an initial step of a pipeline.
Added anomaly.ConstantFilter and QuantileFilter, which are the first anomaly filters.
Removed anomaly.ConstantThresholder and anomaly.QuantileThresholder, as they overlap with the new anomaly filtering mechanism.
Add the PeriodicTrigger detector, a baseline capable of producing drift signals in regular or random intervals.
The numpy usage was removed in drift.KSWIN in favor of collections.deque. Appending or deleting elements to numpy arrays imply creating another object.
Added the seed parameter to drift.KSWIN to control reproducibility.
The Kolmogorov-Smirnov test mode was changed to the default ("auto") to suppress warnings (drift.KSWIN).
Unnecessary usage of numpy was also removed in other concept drift detectors.
Added a iter_progressive_val_score function, which does the same as progressive_val_score, except that it yields rather than prints results at each step, which give more control to the user.