V0.8.3

v<0.8.2>, <07/04/2020> -- Add a set of utility functions.
v<0.8.2>, <08/30/2020> -- Add COPOD and MAD algorithm.
v<0.8.3>, <09/01/2020> -- Make decision score consistent.
v<0.8.3>, <09/19/2020> -- Add model persistence documentation (save and load).

Short summary, we add two new algorithms COPOD and MAD. Moreover, we now provide a short example regrading model save and load.

V0.8.1

This is a stable release. Python 2 support will be dropped in the next version.

v<0.8.0>, <05/18/2020> -- Update test frameworks by reflecting sklearn change.
v<0.8.1>, <07/11/2020> -- Bug fix and documentation update

V0.7.9

v<0.7.8.1>, <04/07/2020> -- Hot fix for SOD.
v<0.7.8.2>, <04/14/2020> -- Bug Fix for LODA.
v<0.7.9>, <04/20/2020> -- Relax the number of n_neighbors in ABOD and COF.
v<0.7.9>, <05/01/2020> -- Extend Vanilla VAE to Beta VAE by Dr Andrij Vasylenko.
v<0.7.9>, <05/01/2020> -- Add Conda Badge.

V0.7.8

Various changes have been made in these two releases:

v<0.7.7>, <12/21/2019> -- Refactor code for combination simplification on combo.
v<0.7.7>, <12/21/2019> -- Extended combination methods by median and majority vote.
v<0.7.7>, <12/22/2019> -- Code optimization and documentation update.
v<0.7.7>, <12/22/2019> -- Enable continuous integration for Python 3.7.
v<0.7.7.1>, <12/29/2019> -- Minor update for SUOD and warning fixes.
v<0.7.8>, <01/05/2019> -- Documentation update.
v<0.7.8>, <01/30/2019> -- Bug fix for kNN (#158).
v<0.7.8>, <03/14/2020> -- Add VAE (implemented by Dr Andrij Vasylenko).
v<0.7.8>, <03/17/2020> -- Add LODA (adapted from tilitools).

The major improvement includes the addition of VAE and LODA, along with multiple minor fixes.

v0.7.5

v<0.7.6>, <12/18/2019> -- Update Isolation Forest and LOF to be consistent with sklearn 0.22.
v<0.7.6>, <12/18/2019> -- Add Deviation-based Outlier Detection (LMDD).

The major update is about the compatibility fix for the newly released sklearn 0.22, and LMDD module built by @John-Almardeny

v0.7.5

This minor update includes the following items (most of them are bug fix and documentation improvement):

v<0.7.5>, <09/24/2019> -- Fix one dimensional data error in LSCP.
v<0.7.5>, <10/13/2019> -- Document kNN and Isolation Forest's incoming changes.
v<0.7.5>, <10/13/2019> -- SOD optimization (created by John-Almardeny in June).
v<0.7.5>, <10/13/2019> -- Documentation updates.

v0.7.0

Multiple bug fixes are introduced:

• Fix issue in CBLOF for n_cluster discrepancy.
• Fix issue #23 that kNN fails with Mahalanobis distance.
• Fix for sklearn new behaviour FutureWarning.

Improved documentation:

• Update docs with media coverage.
• Major documentation update for JMLR.
• Add License info and show support to 996.ICU!
• Redesign ReadMe for clarity.

Deprecate two key APIs: fit_predict and fit_predict_score.

Add some new utility functions, e.g., generate_data_clusters.

v.0.6.7

This release further improves package stability and comprehensiveness.

A set of new models are added:

• LSCP: Locally Selective Combination of Parallel Outlier Ensembles
• XGBOD: Extreme Boosting Based Outlier Detection (Supervised)
• SO_GAAL: Single-Objective Generative Adversarial Active Learning
• MO_GAAL: Multiple-Objective Generative Adversarial Active Learning

Bug fixes are also included, e.g., CBLOF.

Last but not least, a few functions/models are redesigned/optimized:

• Docstring is refactored to numpydoc
• LOCI is optimized with numba
• visualize function is redesigned

V6.0.5

Various exciting changes are made in this version.
Welcome Zain Nasrullah and Winston (Zheng) Li to join the core dev team!

New models are added:

• Stochastic Outlier Selection (SOS)
• Local Correlation Integral (LOCI)

New continuous integration tools are enabled:

• Appveyor CI
• CodeClimate
• CircleCI

Some bugs are fixed and README is rewritten in rst.

V0.6.1

In this release, there are multiple exciting new models are introduced, including:

• MCD
• CBLOF
• AutoEncoder

Several performance optimizations are also implemented:

• numba
• Parallelization for multi-core support in certain models

Besides, pyod is officially supporting Python 3.7 now. Multiple incremental changes are also in this release, and some corresponding updates due to the dependent library changed (sklearn LOF model) are also included.

Last but not least, welcome Zain Nasrullah to become a core developer for pyod. We are preparing a paper for JMLR. Hopefully, we could refer and cite the library shortly.

A new figure for selected models ->