v1.0.8

Added

• Add model_selection.outlier.RepeatedOutlierSplit to cross-validate
  outlier detection algorithms

Fixed

• Fix bug in filter of datasets.load_datasets
• Fix the number of outliers when setting n_outliers to None for
  KMeansLabeler
• Fix the number of outliers when setting n_outliers to float for
  MinorityLabeler

v1.0.7

Added

• Add filter keyword-argument to datasets.load_datasets

v1.0.6

Fixed

• Fix bug in source distribution

v1.0.5

Added

• Add MajorityLabeler to construct synthetic outlier datasets

Fixed

• Fix bug in IsolationShapeletForest where max_depth was incorrectly computed

v1.0.4

Added

• Model agnostic counterfactual explanations has been added.
• Shapelet forest counterfactual explanations has been refined.
• KNearestNeighbors counterfactual explanations has been refined.
• Synthetic generation of outlier detection datasets.
• IsolationShapeletForest has been added. A novel semi-supervised method for detecting
  time series outliers.
• Fast computation of scaled and unscaled dynamic time warping (using the UCRSuite algorithm).
• LB_Keogh lower bound and envelope.
• Add new class Repository which represents a collection of bundles
• datasets.set_cache_dir to globally change the default cache directory
• datasets.clear_cache to clear the cache

Deprecated

• datasets.load_all_datasets has been replaced by load_datasets

Changed

• wildboar.datasets.install_repository now installs a repository instead of a bundle
• Rename Repository to Bundle

v1.0.3

Added

• Added a counterfactual explainability module

v1.0.2

Fixes

• Binary builds for GNU/Linux

v0.3.4

Changed

• Complete rewrite of the shapelet tree representation to allow releasing GIL.
  The prediction of trees should be backwards compatible, i.e., trees fitted using
  the new versions are functionally equivalent to the old but with another internal
  representation.

v0.3.3

Fixes

• Binary builds

v0.3.2

Fixed

• Improved caching of lower-bound for DTW
  The DTW subsequence search implementation has been improved by caching
  DTW lower-bound information for repeated calls with the same
  subsequece. This slightly increases the memory requirement, but can
  give significantly improved performance under certain circumstances.

• Allow shapelet information to be extracted
  A new attribute ts_info is added to Shapelet (which is accessible
  from tree.root_node_.shapelet). ts_info returns a tuple
  (ts_index, ts_start, length) with information about the index (in
  the x used to fit, fit(x, y), the model) and the start position of
  the shapelet. For a shapelet tree/forest fit on x the shapelet in a
  particular node is given by x[ts_index, ts_start:(ts_start + length)].