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V0.9.1
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@yzhao062
yzhao062 authored Aug 14, 2021
2 parents 58b1286 + 674c055 commit bd4905c
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4 changes: 4 additions & 0 deletions CHANGES.txt
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Expand Up @@ -129,6 +129,10 @@ v<0.8.9>, <06/11/2021> -- Fix LMDD parameter (#307)
v<0.9.0>, <06/20/2021> -- Add clone test for models.
v<0.9.0>, <07/03/2021> -- ROD hot fix (#316).
v<0.9.0>, <07/04/2021> -- Improve COPOD plot with colunms parameter.
v<0.9.1>, <07/12/2021> -- Improve COPOD by dropping pandas dependency.
v<0.9.1>, <07/19/2021> -- Add memory efficienct COF.
v<0.9.1>, <08/01/2021> -- Fix Pytorch Dataset issue.
v<0.9.1>, <08/14/2021> -- Synchronize scikit-learn LOF parameters.



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105 changes: 48 additions & 57 deletions README.rst
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Expand Up @@ -52,9 +52,9 @@ Python Outlier Detection (PyOD)
:alt: Build status


.. image:: https://travis-ci.org/yzhao062/pyod.svg?branch=master
:target: https://travis-ci.org/yzhao062/pyod
:alt: Build Status
.. image:: https://travis-ci.com/yzhao062/pyod.svg?branch=master
:target: https://travis-ci.com/yzhao062/pyod
:alt: Travis CI


.. image:: https://circleci.com/gh/yzhao062/pyod.svg?style=svg
Expand Down Expand Up @@ -105,7 +105,7 @@ PyOD is featured for:
* **Compatible with both Python 2 & 3**.


**API Demo**\ :
**Outlier Detection with 5 Lines of Code**\ :


.. code-block:: python
Expand All @@ -117,14 +117,14 @@ PyOD is featured for:
clf.fit(X_train)
# get outlier scores
y_train_scores = clf.decision_scores_ # raw outlier scores
y_test_scores = clf.decision_function(X_test) # outlier scores
y_train_scores = clf.decision_scores_ # raw outlier scores on the train data
y_test_scores = clf.decision_function(X_test) # predict raw outlier scores on test
**Citing PyOD**\ :

`PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
`JMLR <http://www.jmlr.org/>`_ (machine learning open-source software track).
`Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>`_ (MLOSS track).
If you use PyOD in a scientific publication, we would appreciate
citations to the following paper::

Expand Down Expand Up @@ -172,14 +172,17 @@ or::
Installation
^^^^^^^^^^^^

It is recommended to use **pip** for installation. Please make sure
It is recommended to use **pip** or **conda** for installation. Please make sure
**the latest version** is installed, as PyOD is updated frequently:

.. code-block:: bash
pip install pyod # normal install
pip install --upgrade pyod # or update if needed
pip install --pre pyod # or include pre-release version for new features
.. code-block:: bash
conda install -c conda-forge pyod
Alternatively, you could clone and run setup.py file:

Expand All @@ -190,14 +193,6 @@ Alternatively, you could clone and run setup.py file:
pip install .
**Note on Python 2.7**\ :
The maintenance of Python 2.7 will be stopped by January 1, 2020 (see `official announcement <https://github.com/python/devguide/pull/344>`_)
To be consistent with the Python change and PyOD's dependent libraries, e.g., scikit-learn, we will
stop supporting Python 2.7 in the near future (dates are still to be decided). We encourage you to use
Python 3.5 or newer for the latest functions and bug fixes. More information can
be found at `Moving to require Python 3 <https://python3statement.org/>`_.


**Required Dependencies**\ :


Expand All @@ -206,9 +201,8 @@ be found at `Moving to require Python 3 <https://python3statement.org/>`_.
* joblib
* numpy>=1.13
* numba>=0.35
* pandas>=0.25
* scipy>=0.19.1
* scikit_learn>=0.19.1
* scikit_learn>=0.20.0
* statsmodels

**Optional Dependencies (see details below)**\ :
Expand All @@ -229,13 +223,9 @@ Instructions are provided: `neural-net FAQ <https://github.com/yzhao062/pyod/wik
Similarly, models depending on **xgboost**, e.g., XGBOD, would **NOT** enforce xgboost installation by default.

**Warning 2**\ :
Running examples needs **matplotlib**, which may throw errors in conda
virtual environment on mac OS. See reasons and solutions `mac_matplotlib <https://github.com/yzhao062/pyod/issues/6>`_.

**Warning 3**\ :
PyOD contains multiple models that also exist in scikit-learn. However, these two
libraries' API is not exactly the same--it is recommended to use only one of them
for consistency but not mix the results. Refer `Differences between sckit-learn and PyOD <https://pyod.readthedocs.io/en/latest/issues.html>`_
for consistency but not mix the results. Refer `Differences between scikit-learn and PyOD <https://pyod.readthedocs.io/en/latest/issues.html>`_
for more information.


Expand Down Expand Up @@ -307,39 +297,40 @@ PyOD toolkit consists of three major functional groups:

**(i) Individual Detection Algorithms** :

=================== ================ ====================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================ ====================================================================================================== ===== ========================================
Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_
Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
Linear Model OCSVM One-Class Support Vector Machines 2001 [#Scholkopf2001Estimating]_
Linear Model LMDD Deviation-based Outlier Detection (LMDD) 1996 [#Arning1996A]_
Proximity-Based LOF Local Outlier Factor 2000 [#Breunig2000LOF]_
Proximity-Based COF Connectivity-Based Outlier Factor 2002 [#Tang2002Enhancing]_
Proximity-Based CBLOF Clustering-Based Local Outlier Factor 2003 [#He2003Discovering]_
Proximity-Based LOCI LOCI: Fast outlier detection using the local correlation integral 2003 [#Papadimitriou2003LOCI]_
Proximity-Based HBOS Histogram-based Outlier Score 2012 [#Goldstein2012Histogram]_
Proximity-Based kNN k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score) 2000 [#Ramaswamy2000Efficient]_
Proximity-Based AvgKNN Average kNN (use the average distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based MedKNN Median kNN (use the median distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based SOD Subspace Outlier Detection 2009 [#Kriegel2009Outlier]_
Proximity-Based ROD Rotation-based Outlier Detection 2020 [#Almardeny2020A]_
Probabilistic ABOD Angle-Based Outlier Detection 2008 [#Kriegel2008Angle]_
Probabilistic COPOD COPOD: Copula-Based Outlier Detection 2020 [#Li2020COPOD]_
Probabilistic FastABOD Fast Angle-Based Outlier Detection using approximation 2008 [#Kriegel2008Angle]_
Probabilistic MAD Median Absolute Deviation (MAD) 1993 [#Iglewicz1993How]_
Probabilistic SOS Stochastic Outlier Selection 2012 [#Janssens2012Stochastic]_
Outlier Ensembles IForest Isolation Forest 2008 [#Liu2008Isolation]_
Outlier Ensembles Feature Bagging 2005 [#Lazarevic2005Feature]_
Outlier Ensembles LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles 2019 [#Zhao2019LSCP]_
Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection **(Supervised)** 2018 [#Zhao2018XGBOD]_
Outlier Ensembles LODA Lightweight On-line Detector of Anomalies 2016 [#Pevny2016Loda]_
Neural Networks AutoEncoder Fully connected AutoEncoder (use reconstruction error as the outlier score) [#Aggarwal2015Outlier]_ [Ch.3]
Neural Networks VAE Variational AutoEncoder (use reconstruction error as the outlier score) 2013 [#Kingma2013Auto]_
Neural Networks Beta-VAE Variational AutoEncoder (all customized loss term by varying gamma and capacity) 2018 [#Burgess2018Understanding]_
Neural Networks SO_GAAL Single-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
Neural Networks MO_GAAL Multiple-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
=================== ================ ====================================================================================================== ===== ========================================
=================== ================== ====================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================== ====================================================================================================== ===== ========================================
Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_
Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
Linear Model OCSVM One-Class Support Vector Machines 2001 [#Scholkopf2001Estimating]_
Linear Model LMDD Deviation-based Outlier Detection (LMDD) 1996 [#Arning1996A]_
Proximity-Based LOF Local Outlier Factor 2000 [#Breunig2000LOF]_
Proximity-Based COF Connectivity-Based Outlier Factor 2002 [#Tang2002Enhancing]_
Proximity-Based (Incremental) COF Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity) 2002 [#Tang2002Enhancing]_
Proximity-Based CBLOF Clustering-Based Local Outlier Factor 2003 [#He2003Discovering]_
Proximity-Based LOCI LOCI: Fast outlier detection using the local correlation integral 2003 [#Papadimitriou2003LOCI]_
Proximity-Based HBOS Histogram-based Outlier Score 2012 [#Goldstein2012Histogram]_
Proximity-Based kNN k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score) 2000 [#Ramaswamy2000Efficient]_
Proximity-Based AvgKNN Average kNN (use the average distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based MedKNN Median kNN (use the median distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_
Proximity-Based SOD Subspace Outlier Detection 2009 [#Kriegel2009Outlier]_
Proximity-Based ROD Rotation-based Outlier Detection 2020 [#Almardeny2020A]_
Probabilistic ABOD Angle-Based Outlier Detection 2008 [#Kriegel2008Angle]_
Probabilistic COPOD COPOD: Copula-Based Outlier Detection 2020 [#Li2020COPOD]_
Probabilistic FastABOD Fast Angle-Based Outlier Detection using approximation 2008 [#Kriegel2008Angle]_
Probabilistic MAD Median Absolute Deviation (MAD) 1993 [#Iglewicz1993How]_
Probabilistic SOS Stochastic Outlier Selection 2012 [#Janssens2012Stochastic]_
Outlier Ensembles IForest Isolation Forest 2008 [#Liu2008Isolation]_
Outlier Ensembles FB Feature Bagging 2005 [#Lazarevic2005Feature]_
Outlier Ensembles LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles 2019 [#Zhao2019LSCP]_
Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection **(Supervised)** 2018 [#Zhao2018XGBOD]_
Outlier Ensembles LODA Lightweight On-line Detector of Anomalies 2016 [#Pevny2016Loda]_
Neural Networks AutoEncoder Fully connected AutoEncoder (use reconstruction error as the outlier score) [#Aggarwal2015Outlier]_ [Ch.3]
Neural Networks VAE Variational AutoEncoder (use reconstruction error as the outlier score) 2013 [#Kingma2013Auto]_
Neural Networks Beta-VAE Variational AutoEncoder (all customized loss term by varying gamma and capacity) 2018 [#Burgess2018Understanding]_
Neural Networks SO_GAAL Single-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
Neural Networks MO_GAAL Multiple-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_
=================== ================== ====================================================================================================== ===== ========================================


**(ii) Outlier Ensembles & Outlier Detector Combination Frameworks**:
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7 changes: 6 additions & 1 deletion docs/about.rst
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Expand Up @@ -34,4 +34,9 @@ Antônio Pedro Camargo (University of Campinas)
Dr Andrij Vasylenko (Research Associate @ University of Liverpool)

- Joined in 2020 (implemented the VAE and extend to Beta-VAE)
- `Homepage (Dr Andrij Vasylenko) <https://www.liverpool.ac.uk/chemistry/staff/andrij-vasylenko/>`_
- `Homepage (Dr Andrij Vasylenko) <https://www.liverpool.ac.uk/chemistry/staff/andrij-vasylenko/>`_

Roel Bouman (Ph.D. Student @ Radboud University):

- Joined in 2021
- `LinkedIn (Roel Bouman) <https://nl.linkedin.com/in/roel-bouman-18b5b9167>`_
12 changes: 6 additions & 6 deletions docs/index.rst
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Expand Up @@ -57,9 +57,9 @@ Welcome to PyOD documentation!
:alt: Build status


.. image:: https://travis-ci.org/yzhao062/pyod.svg?branch=master
:target: https://travis-ci.org/yzhao062/pyod
:alt: Build Status
.. image:: https://travis-ci.com/yzhao062/pyod.svg?branch=master
:target: https://travis-ci.com/yzhao062/pyod
:alt: Travis CI


.. image:: https://circleci.com/gh/yzhao062/pyod.svg?style=svg
Expand Down Expand Up @@ -121,15 +121,15 @@ PyOD is featured for:
clf.fit(X_train)
# get outlier scores
y_train_scores = clf.decision_scores_ # raw outlier scores
y_test_scores = clf.decision_function(X_test) # outlier scores
y_train_scores = clf.decision_scores_ # raw outlier scores on the train data
y_test_scores = clf.decision_function(X_test) # predict raw outlier scores on test
**Citing PyOD**\ :

`PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
`JMLR <http://www.jmlr.org/>`_ (machine learning open-source software track).
`Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>`_ (MLOSS track).
If you use PyOD in a scientific publication, we would appreciate
citations to the following paper::

Expand Down
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