v0.2.0

• Moved to something more closely resembling semantic versioning, which is the main reason this is version 0.2.0.
• Builds and tests on Python 3.11 have been successful, so now supporting this version.
• Added custom 'alarm' Detector, which can be instantiated with a function and a warning to emit when the function returns True for a 1D array. You can easily write your own detectors with this class.
• Added make_detector_pipeline() which can take sequences of functions and warnings (or a mapping of functions to warnings) and returns a scikit-learn.pipeline.Pipeline containing a Detector for each function.
• Added RegressionMultimodalDetector to allow detection of non-unimodal distributions in features, when considered across the entire dataset. (Coming soon, a similar detector for classification tasks that will partition the data by class.)
• Redefined is_standardized (deprecated) as is_standard_normal, which implements the Kolmogorov–Smirnov test. It seems more reliable than assuming the data will have a mean of almost exactly 0 and standard deviation of exactly 1, when all we really care about is that the feature is roughly normal.
• Changed the wording slightly in the existing detector warning messages.
• No longer warning if y is None in, eg, ImportanceDetector, since you most likely know this.
• Some changes to ImportanceDetector. It now uses KNN estimators instead of SVMs as the third measure of importance; the SVMs were too unstable, causing numerical issues. It also now requires that the number of important features is less than the total number of features to be triggered. So if you have 2 features and both are important, it does not trigger.
• Improved is_continuous() which was erroneously classifying integer arrays with many consecutive values as non-continuous.
• Note that wasserstein no longer checks that the data are standardized; this check will probably return in the future, however.
• Added a Tutorial.ipynb notebook to the docs.
• Added a Copy button to code blocks in the docs.