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@ascillitoe ascillitoe released this 01 Feb 14:08
· 101 commits to master since this release
v0.11.0
e257e1f

v0.11.0

Full Changelog

Added

  • New feature The MMD and learned-kernel MMD drift detectors have been extended with KeOps backends to scale and speed up the detectors. See the example notebook for more info (#548 and #602).
  • New feature Added support for serializing detectors with PyTorch backends, and detectors containing PyTorch models in their proprocessing functions (#656).
  • New feature Added support for serializing detectors with scikit-learn and KeOps backends (#642 and #681).
  • New feature Added support for saving and loading online detectors' state. This allows a detector to be restarted from previously generated checkpoints (#604).
  • New feature Added a PyTorch version of the UAE preprocessing utility function (#656, (#705).
  • For the ClassifierDrift and SpotTheDiffDrift detectors, we can also return the out-of-fold instances of the reference and test sets. When using train_size for training the detector, this allows to associate the returned prediction probabilities with the correct instances (#665).

Changed

  • Minimum prophet version bumped to 1.1.0 (used by OutlierProphet). This upgrade removes the dependency on pystan as cmdstanpy is used instead. This version also comes with pre-built wheels for all major platforms and Python versions, making both installation and testing easier (#627).
  • Breaking change The configuration field config_spec has been removed. In order to load detectors serialized from previous Alibi Detect versions, the field will need to be deleted from the detector's config.toml file. However, in any case, serialization compatibility across Alibi Detect versions is not currently guranteed. (#641).
  • Added support for serializing tensorflow optimizers. Previously, tensorflow optimizers were not serialized, which meant the default optimizer kwarg would also be set when a detector was loaded with load_detector, regardless of the optimizer given to the original detector (#656).
  • Strengthened pydantic validation of detector configs. The flavour backend is now validated whilst taking into account the optional dependencies. For example, a ValidationError will be raised if flavour='pytorch' is given but PyTorch is not installed (#656).
  • If a categories_per_feature dictionary is not passed to TabularDrift, a warning is now raised to inform the user that all features are assumed to be numerical (#606).
  • For better clarity, the original error is now reraised when optional dependency errors are raised (#783).
  • The maximum tensorflow version has been bumped from 2.9 to 2.10 (#608).
  • The maximum torch version has been bumped from 1.12 to 1.13 (#669).

Fixed

  • Fixed an issue with the serialization of kernel_a and kernel_b in DeepKernel's (#656).
  • Fixed minor documentation issues (#636, #640, #651).
  • Fixed an issue with a warning being incorrectly raised when device='cpu' was passed to PyTorch based detectors (#698).
  • Fixed a bug that could cause IndexError's to be raised in the TensorFlow MMDDriftOnline detector when older numpy versions were installed (#710).

Development

  • UTF-8 decoding is enforced when README.md is opened by setup.py. This is to prevent pip install errors on systems with PYTHONIOENCODING set to use other encoders (#605).
  • Skip specific save/load tests that require downloading remote artefacts if the relevant URI(s) is/are down (#607).
  • CI test/ directories are now ignored when measuring testing code coverage. This has a side-effect of lowering the reported test coverage (#614).
  • Added codecov tags to measure to platform-specific code coverage (#615).
  • Added option to ssh into CI runs for debugging (#644).
  • Measure executation time of test runs in CI (#712).
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