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Change Log

v0.7.2 (2021-08-17)

Full Changelog

Added

  • Learned kernels drift detector with TensorFlow and PyTorch support: from alibi_detect.cd import LearnedKernelDrift
  • Spot-the-diff drift detector with TensorFlow and PyTorch support: from alibi_detect.cd import SpotTheDiffDrift
  • Online drift detection example on medical imaging data: https://github.com/SeldonIO/alibi-detect/blob/master/examples/cd_online_camelyon.ipynb

v0.7.1 (2021-07-22)

Full Changelog

Added

  • Extend allowed input type for drift detectors to include List[Any] with additional graph and text data examples.
  • Allow custom preprocessing steps within alibi_detect.utils.pytorch.prediction.predict_batch and alibi_detect.utils.tensorflow.prediction.predict_batch. This makes it possible to take List[Any] as input and combine instances in the list into batches of data in the right format for the model.

Removed

  • PCA preprocessing step for drift detectors.

Fixed

  • Improve numerical stability LSDD detectors (offline and online) to avoid overflow/underflow caused by higher dimensionality of the input data.
  • Spectral Residual outlier detector test.

v0.7.0 (2021-06-07)

Full Changelog

Added

  • Least squares density difference drift detector from alibi_detect.cd import LSDDDrift with TensorFlow and PyTorch support.
  • Online versions of the MMD and LSDD drift detectors: from alibi_detect.cd import MMDDriftOnline, LSDDDriftOnline with TensorFlow and PyTorch support.
  • Enable Python 3.9 support.

Fixed

  • Hidden layer output as preprocessing step for drift detectors for internal layers with higher dimensional shape, e.g. (B, C, H, W).

v0.6.2 (2021-05-06)

Full Changelog

Fixed

  • alibi-detect compatibility with transformers>=4.0.0
  • update slack link to point to alibi-detect channel

v0.6.1 (2021-04-26)

Full Changelog

Added

  • Classification and regression model uncertainty drift detectors for both PyTorch and TensorFlow models: from alibi_detect.cd import ClassifierUncertaintyDrift, RegressorUncertaintyDrift.
  • Return p-values for ClassifierDrift detectors using either a KS test on the classifier's probabilities or logits. The model predictions can also be binarised and a binomial test can be applied.
  • Allow unseen categories in the test batches for the categorical and tabular drift detectors: from alibi_detect.cd import ChiSquareDrift, TabularDrift.

v0.6.0 (2021-04-12)

Full Changelog

Added

  • Flexible backend support (TensorFlow and PyTorch) for drift detectors MMDDrift and ClassifierDrift as well as support for both frameworks for preprocessing steps (from alibi_detect.cd.tensorflow import HiddenOutput, preprocess_drift and from alibi_detect.models.tensorflow import TransformerEmbedding, replace tensorflow with pytorch for PyTorch support) and various utility functions (kernels and distance metrics) under alibi_detect.utils.tensorflow and alibi_detect.utils.pytorch.
  • Significantly faster implementation MMDDrift detector leveraging both GPU implementations in TensorFlow and PyTorch as well as making efficient use of the cached kernel matrix for the permutation tests.
  • Change test for ChiSquareDrift from goodness-of-fit of the observed data against the empirical distribution of the reference data to a test for homogeneity which does not bias p-values as much to extremes.
  • Include NumpyEncoder in library to facilitate json serialization.

Removed

  • As part of the introduction of flexible backends for various drift detectors, dask is no longer supported for the MMDDrift detector and distance computations.

Fixed

  • Update RTD theme version due to rendering bug.
  • Bug when using TabularDrift with categorical features and continuous numerical features. Incorrect indexing of categorical columns was performed.

Development

  • Pin pystan version to working release with prophet.

v0.5.1 (2021-03-05)

Full Changelog

This is a bug fix release.

Fixed

  • The order of the reference and test dataset for the TabularDrift and ChiSquareDrift was reversed leading to incorrect test statistics
  • The implementation of TabularDrift and ChiSquareDrift were not accounting for the different sample sizes between reference and test datasets leading to incorrect test statistics
  • Bumped required scipy version to 1.3.0 as older versions were missing the alternative keyword argument for ks_2samp function

v0.5.0 (2021-02-18)

Full Changelog

Added

  • Chi-square drift detector for categorical data: alibi_detect.cd.chisquare.ChiSquareDrift
  • Mixed-type tabular data drift detector: alibi_detect.cd.tabular.TabularDrift
  • Classifier-based drift detector: alibi_detect.cd.classifier.ClassifierDrift

Removed

  • DataTracker utility

Development

  • Docs build improvements, dependabot integration, daily build cronjob

v0.4.4 (2020-12-23)

Full Changelog

Added

  • Remove integrations directory
  • Extend return dict drift detector
  • Update saving functionality drift detectors

v0.4.3 (2020-10-08)

Full Changelog

Added

  • Make Prophet an optional dependency
  • Extend what is returned by the drift detectors to raw scores
  • Add licenses from dependencies

v0.4.2 (2020-09-09)

Full Changelog

Added

  • Text drift detector functionality for KS and MMD drift detectors
  • Add embedding extraction functionality for pretrained HuggingFace transformers models (alibi_detect.models.embedding)
  • Add Python 3.8 support

v0.4.1 (2020-05-12)

Full Changelog

Added

  • Likelihood ratio outlier detector (alibi_detect.od.llr.LLR) with image and genome dataset examples
  • Add genome dataset (alibi_detect.datasets.fetch_genome)
  • Add PixelCNN++ model (alibi_detect.models.pixelcnn.PixelCNN)

v0.4.0 (2020-04-02)

Full Changelog

Added

  • Kolmogorov-Smirnov drift detector (alibi_detect.cd.ks.KSDrift)
  • Maximum Mean Discrepancy drift detector (alibi_detect.cd.mmd.MMDDrift)

v0.3.1 (2020-02-26)

Full Changelog

Added

  • Adversarial autoencoder detection method (offline method, alibi_detect.ad.adversarialae.AdversarialAE)
  • Add pretrained adversarial and outlier detectors to Google Cloud Bucket and include fetch functionality
  • Add data/concept drift dataset (CIFAR-10-C) to Google Cloud Bucket and include fetch functionality
  • Update VAE loss function and log var layer
  • Fix tests for Prophet outlier detector on Python 3.6
  • Add batch sizes for all detectors

v0.3.0 (2020-01-17)

Full Changelog

Added

  • Multivariate time series outlier detection method OutlierSeq2Seq (offline method, alibi_detect.od.seq2seq.OutlierSeq2Seq)
  • ECG and synthetic data examples for OutlierSeq2Seq detector
  • Auto-Encoder outlier detector (offline method, alibi_detect.od.ae.OutlierAE)
  • Including tabular and categorical perturbation functions (alibi_detect.utils.perturbation)

v0.2.0 (2019-12-06)

Full Changelog

Added

  • Univariate time series outlier detection methods: Prophet (offline method, alibi_detect.od.prophet.OutlierProphet) and Spectral Residual (online method, alibi_detect.od.sr.SpectralResidual)
  • Function for fetching Numenta Anomaly Benchmark time series data (alibi_detect.datasets.fetch_nab)
  • Perturbation function for time series data (alibi_detect.utils.perturbation.inject_outlier_ts)
  • Roadmap

v0.1.0 (2019-11-19)

Added

  • Isolation Forest (Outlier Detection)
  • Mahalanobis Distance (Outlier Detection)
  • Variational Auto-Encoder (VAE, Outlier Detection)
  • Auto-Encoding Gaussian Mixture Model (AEGMM, Outlier Detection)
  • Variational Auto-Encoding Gaussian Mixture Model (VAEGMM, Outlier Detection)
  • Adversarial Variational Auto-Encoder (Adversarial Detection)