pyod

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

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.

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.

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

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 ->

In this release, more algorithms are implemented, including PCA and Feature Bagging models. The documentation and test coverage are also improved. A comparison for PyOD algorithms is presented below:

This is the first Github release to sync with PyPI. The current version brings the following changes compared with v0.3.x:

• Supported both Python 2 and 3 now.
• Refactor all models to be sklearn compatible. The models can be easily cloned, modified and viewed.
• Simplified examples for easy reproduction.
• Optimized code for HBOS and ABOD. Codes are refactored for better stability.
• Added more tests to improve the coverage.
• Added two combination functions, Average() and Maximization()