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A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
pip install pyod==1.0.1
Python Outlier Detection (PyOD)
===============================
**Deployment & Documentation & Stats & License**
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-----
Read Me First
^^^^^^^^^^^^^
Welcome to PyOD, a versatile Python library for detecting anomalies in multivariate data. Whether you're tackling a small-scale project or large datasets, PyOD offers a range of algorithms to suit your needs.
* **For time-series outlier detection**, please use `TODS <https://github.com/datamllab/tods>`_.
* **For graph outlier detection**, please use `PyGOD <https://pygod.org/>`_.
* **Performance Comparison \& Datasets**: We have a 45-page, the most comprehensive `anomaly detection benchmark paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-neurips-adbench.pdf>`_. The fully `open-sourced ADBench <https://github.com/Minqi824/ADBench>`_ compares 30 anomaly detection algorithms on 57 benchmark datasets.
* **Learn more about anomaly detection** \@ `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_
* **PyOD on Distributed Systems**: you could also run `PyOD on databricks <https://www.databricks.com/blog/2023/03/13/unsupervised-outlier-detection-databricks.html>`_.
----
About PyOD
^^^^^^^^^^
PyOD, established in 2017, has become a go-to **Python library** for **detecting anomalous/outlying objects** in
multivariate data. This exciting yet challenging field is commonly referred as
`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
PyOD includes more than 50 detection algorithms, from classical LOF (SIGMOD 2000) to
the cutting-edge ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD has been successfully used in numerous academic researches and
commercial products with more than `17 million downloads <https://pepy.tech/project/pyod>`_.
It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including
`Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_,
`KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_, and
`Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_.
**PyOD is featured for**:
* **Unified, User-Friendly Interface** across various algorithms.
* **Wide Range of Models**\, from classic techniques to the latest deep learning methods.
* **High Performance & Efficiency**, leveraging `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_ for JIT compilation and parallel processing.
* **Fast Training & Prediction**, achieved through the SUOD framework [#Zhao2021SUOD]_.
**Outlier Detection with 5 Lines of Code**\ :
.. code-block:: python
# Example: Training an ECOD detector
from pyod.models.ecod import ECOD
clf = ECOD()
clf.fit(X_train)
y_train_scores = clf.decision_scores_ # Outlier scores for training data
y_test_scores = clf.decision_function(X_test) # Outlier scores for test data
**Selecting the Right Algorithm:**. Unsure where to start? Consider these robust and interpretable options:
- `ECOD <https://github.com/yzhao062/pyod/blob/master/examples/ecod_example.py>`_: Example of using ECOD for outlier detection
- `Isolation Forest <https://github.com/yzhao062/pyod/blob/master/examples/iforest_example.py>`_: Example of using Isolation Forest for outlier detection
Alternatively, explore `MetaOD <https://github.com/yzhao062/MetaOD>`_ for a data-driven approach.
**Citing PyOD**\ :
`PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
`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::
@article{zhao2019pyod,
author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
title = {PyOD: A Python Toolbox for Scalable Outlier Detection},
journal = {Journal of Machine Learning Research},
year = {2019},
volume = {20},
number = {96},
pages = {1-7},
url = {http://jmlr.org/papers/v20/19-011.html}
}
or::
Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
For a broader perspective on anomaly detection, see our NeurIPS papers
`ADBench: Anomaly Detection Benchmark Paper <https://viterbi-web.usc.edu/~yzhao010/papers/22-neurips-adbench.pdf>`_ \& `ADGym: Design Choices for Deep Anomaly Detection <https://viterbi-web.usc.edu/~yzhao010/papers/23-neurips-adgym.pdf>`_::
@article{han2022adbench,
title={Adbench: Anomaly detection benchmark},
author={Han, Songqiao and Hu, Xiyang and Huang, Hailiang and Jiang, Minqi and Zhao, Yue},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={32142--32159},
year={2022}
}
@article{jiang2023adgym,
title={ADGym: Design Choices for Deep Anomaly Detection},
author={Jiang, Minqi and Hou, Chaochuan and Zheng, Ao and Han, Songqiao and Huang, Hailiang and Wen, Qingsong and Hu, Xiyang and Zhao, Yue},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2023}
}
**Table of Contents**\ :
* `Installation <#installation>`_
* `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
* `ADBench Benchmark and Datasets <#adbench-benchmark-and-datasets>`_
* `Model Save & Load <#model-save--load>`_
* `Fast Train with SUOD <#fast-train-with-suod>`_
* `Thresholding Outlier Scores <#thresholding-outlier-scores>`_
* `Implemented Algorithms <#implemented-algorithms>`_
* `Quick Start for Outlier Detection <#quick-start-for-outlier-detection>`_
* `How to Contribute <#how-to-contribute>`_
* `Inclusion Criteria <#inclusion-criteria>`_
----
Installation
^^^^^^^^^^^^
PyOD is designed for easy installation using either **pip** or **conda**.
We recommend using the latest version of PyOD due to frequent updates and enhancements:
.. code-block:: bash
pip install pyod # normal install
pip install --upgrade pyod # or update if needed
.. code-block:: bash
conda install -c conda-forge pyod
Alternatively, you could clone and run setup.py file:
.. code-block:: bash
git clone https://github.com/yzhao062/pyod.git
cd pyod
pip install .
**Required Dependencies**\ :
* Python 3.6 or higher
* joblib
* matplotlib
* numpy>=1.19
* numba>=0.51
* scipy>=1.5.1
* scikit_learn>=0.22.0
* six
**Optional Dependencies (see details below)**\ :
* combo (optional, required for models/combination.py and FeatureBagging)
* keras/tensorflow (optional, required for AutoEncoder, and other deep learning models)
* suod (optional, required for running SUOD model)
* xgboost (optional, required for XGBOD)
* pythresh (optional, required for thresholding)
**Warning**\ :
PyOD includes several neural network-based models, such as AutoEncoders, implemented in Tensorflow and PyTorch. These deep learning libraries are not automatically installed by PyOD to avoid conflicts with existing installations. If you plan to use neural-net based models, please ensure these libraries are installed. See the `neural-net FAQ <https://github.com/yzhao062/pyod/wiki/Setting-up-Keras-and-Tensorflow-for-Neural-net-Based-models>`_ for guidance. Additionally, xgboost is not installed by default but is required for models like XGBOD.
----
API Cheatsheet & Reference
^^^^^^^^^^^^^^^^^^^^^^^^^^
The full API Reference is available at `PyOD Documentation <https://pyod.readthedocs.io/en/latest/pyod.html>`_. Below is a quick cheatsheet for all detectors:
* **fit(X)**\ : Fit the detector. The parameter y is ignored in unsupervised methods.
* **decision_function(X)**\ : Predict raw anomaly scores for X using the fitted detector.
* **predict(X)**\ : Determine whether a sample is an outlier or not as binary labels using the fitted detector.
* **predict_proba(X)**\ : Estimate the probability of a sample being an outlier using the fitted detector.
* **predict_confidence(X)**\ : Assess the model's confidence on a per-sample basis (applicable in predict and predict_proba) [#Perini2020Quantifying]_.
**Key Attributes of a fitted model**:
* **decision_scores_**\ : Outlier scores of the training data. Higher scores typically indicate more abnormal behavior. Outliers usually have higher scores.
* **labels_**\ : Binary labels of the training data, where 0 indicates inliers and 1 indicates outliers/anomalies.
----
ADBench Benchmark and Datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We just released a 45-page, the most comprehensive `ADBench: Anomaly Detection Benchmark <https://arxiv.org/abs/2206.09426>`_ [#Han2022ADBench]_.
The fully `open-sourced ADBench <https://github.com/Minqi824/ADBench>`_ compares 30 anomaly detection algorithms on 57 benchmark datasets.
The organization of **ADBench** is provided below:
.. image:: https://github.com/Minqi824/ADBench/blob/main/figs/ADBench.png?raw=true
:target: https://github.com/Minqi824/ADBench/blob/main/figs/ADBench.png?raw=true
:alt: benchmark-fig
For a simpler visualization, we make **the comparison of selected models** via
`compare_all_models.py <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>`_\.
.. image:: https://github.com/yzhao062/pyod/blob/development/examples/ALL.png?raw=true
:target: https://github.com/yzhao062/pyod/blob/development/examples/ALL.png?raw=true
:alt: Comparison_of_All
----
Model Save & Load
^^^^^^^^^^^^^^^^^
PyOD takes a similar approach of sklearn regarding model persistence.
See `model persistence <https://scikit-learn.org/stable/modules/model_persistence.html>`_ for clarification.
In short, we recommend to use joblib or pickle for saving and loading PyOD models.
See `"examples/save_load_model_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/save_load_model_example.py>`_ for an example.
In short, it is simple as below:
.. code-block:: python
from joblib import dump, load
# save the model
dump(clf, 'clf.joblib')
# load the model
clf = load('clf.joblib')
It is known that there are challenges in saving neural network models.
Check `#328 <https://github.com//issues/328#issuecomment-917192704>`_
and `#88 <https://github.com//issues/88#issuecomment-615343139>`_
for temporary workaround.
----
Fast Train with SUOD
^^^^^^^^^^^^^^^^^^^^
**Fast training and prediction**: it is possible to train and predict with
a large number of detection models in PyOD by leveraging SUOD framework [#Zhao2021SUOD]_.
See `SUOD Paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/21-mlsys-suod.pdf>`_
and `SUOD example <https://github.com/yzhao062/pyod/blob/master/examples/suod_example.py>`_.
.. code-block:: python
from pyod.models.suod import SUOD
# initialized a group of outlier detectors for acceleration
detector_list = [LOF(n_neighbors=15), LOF(n_neighbors=20),
LOF(n_neighbors=25), LOF(n_neighbors=35),
COPOD(), IForest(n_estimators=100),
IForest(n_estimators=200)]
# decide the number of parallel process, and the combination method
# then clf can be used as any outlier detection model
clf = SUOD(base_estimators=detector_list, n_jobs=2, combination='average',
verbose=False)
----
Thresholding Outlier Scores
^^^^^^^^^^^^^^^^^^^^^^^^^^^
A more data based approach can be taken when setting the contamination level.
By using a thresholding method, guessing an abritrary value can be replaced
with tested techniques for seperating inliers and outliers. Refer to
`PyThresh <https://github.com/KulikDM/pythresh>`_ for
a more in depth look at thresholding.
.. code-block:: python
from pyod.models.knn import KNN
from pyod.models.thresholds import FILTER
# Set the outlier detection and thresholding methods
clf = KNN(contamination=FILTER())
----
Implemented Algorithms
^^^^^^^^^^^^^^^^^^^^^^
PyOD toolkit consists of four major functional groups:
**(i) Individual Detection Algorithms** :
=================== ================== ====================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================== ====================================================================================================== ===== ========================================
Probabilistic ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions 2022 [#Li2021ECOD]_
Probabilistic ABOD Angle-Based Outlier Detection 2008 [#Kriegel2008Angle]_
Probabilistic FastABOD Fast Angle-Based Outlier Detection using approximation 2008 [#Kriegel2008Angle]_
Probabilistic COPOD COPOD: Copula-Based Outlier Detection 2020 [#Li2020COPOD]_
Probabilistic MAD Median Absolute Deviation (MAD) 1993 [#Iglewicz1993How]_
Probabilistic SOS Stochastic Outlier Selection 2012 [#Janssens2012Stochastic]_
Probabilistic QMCD Quasi-Monte Carlo Discrepancy outlier detection 2001 [#Fang2001Wrap]_
Probabilistic KDE Outlier Detection with Kernel Density Functions 2007 [#Latecki2007Outlier]_
Probabilistic Sampling Rapid distance-based outlier detection via sampling 2013 [#Sugiyama2013Rapid]_
Probabilistic GMM Probabilistic Mixture Modeling for Outlier Analysis [#Aggarwal2015Outlier]_ [Ch.2]
Linear Model PCA Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_
Linear Model KPCA Kernel Principal Component Analysis 2007 [#Hoffmann2007Kernel]_
Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
Linear Model CD Use Cook's distance for outlier detection 1977 [#Cook1977Detection]_
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]_
Outlier Ensembles IForest Isolation Forest 2008 [#Liu2008Isolation]_
Outlier Ensembles INNE Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles 2018 [#Bandaragoda2018Isolation]_
Outlier Ensembles DIF Deep Isolation Forest for Anomaly Detection 2023 [#Xu2023Deep]_
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]_
Outlier Ensembles SUOD SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection **(Acceleration)** 2021 [#Zhao2021SUOD]_
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]_
Neural Networks DeepSVDD Deep One-Class Classification 2018 [#Ruff2018Deep]_
Neural Networks AnoGAN Anomaly Detection with Generative Adversarial Networks 2017 [#Schlegl2017Unsupervised]_
Neural Networks ALAD Adversarially learned anomaly detection 2018 [#Zenati2018Adversarially]_
Graph-based R-Graph Outlier detection by R-graph 2017 [#You2017Provable]_
Graph-based LUNAR LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks 2022 [#Goodge2022Lunar]_
=================== ================== ====================================================================================================== ===== ========================================
**(ii) Outlier Ensembles & Outlier Detector Combination Frameworks**:
=================== ================ ===================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
=================== ================ ===================================================================================================== ===== ========================================
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]_
Outlier Ensembles SUOD SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection **(Acceleration)** 2021 [#Zhao2021SUOD]_
Outlier Ensembles INNE Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles 2018 [#Bandaragoda2018Isolation]_
Combination Average Simple combination by averaging the scores 2015 [#Aggarwal2015Theoretical]_
Combination Weighted Average Simple combination by averaging the scores with detector weights 2015 [#Aggarwal2015Theoretical]_
Combination Maximization Simple combination by taking the maximum scores 2015 [#Aggarwal2015Theoretical]_
Combination AOM Average of Maximum 2015 [#Aggarwal2015Theoretical]_
Combination MOA Maximization of Average 2015 [#Aggarwal2015Theoretical]_
Combination Median Simple combination by taking the median of the scores 2015 [#Aggarwal2015Theoretical]_
Combination majority Vote Simple combination by taking the majority vote of the labels (weights can be used) 2015 [#Aggarwal2015Theoretical]_
=================== ================ ===================================================================================================== ===== ========================================
**(iii) Outlier Detection Score Thresholding Methods**:
================================== ================ ================================================================ ====================================================================================================================
Type Abbr Algorithm Documentation
================================== ================ ================================================================ ====================================================================================================================
Kernel-Based AUCP Area Under Curve Percentage `AUCP <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.AUCP>`_
Statistical Moment-Based BOOT Bootstrapping `BOOT <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.BOOT>`_
Normality-Based CHAU Chauvenet's Criterion `CHAU <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CHAU>`_
Linear Model CLF Trained Linear Classifier `CLF <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CLF>`_
cluster-Based CLUST Clustering Based `CLUST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CLUST>`_
Kernel-Based CPD Change Point Detection `CPD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.CPD>`_
Transformation-Based DECOMP Decomposition `DECOMP <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.DECOMP>`_
Normality-Based DSN Distance Shift from Normal `DSN <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.DSN>`_
Curve-Based EB Elliptical Boundary `EB <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.EB>`_
Kernel-Based FGD Fixed Gradient Descent `FGD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FGD>`_
Filter-Based FILTER Filtering Based `FILTER <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FILTER>`_
Curve-Based FWFM Full Width at Full Minimum `FWFM <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.FWFM>`_
Statistical Test-Based GESD Generalized Extreme Studentized Deviate `GESD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.GESD>`_
Filter-Based HIST Histogram Based `HIST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.HIST>`_
Quantile-Based IQR Inter-Quartile Region `IQR <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.IQR>`_
Statistical Moment-Based KARCH Karcher mean (Riemannian Center of Mass) `KARCH <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.KARCH>`_
Statistical Moment-Based MAD Median Absolute Deviation `MAD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MAD>`_
Statistical Test-Based MCST Monte Carlo Shapiro Tests `MCST <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MCST>`_
Ensembles-Based META Meta-model Trained Classifier `META <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.META>`_
Transformation-Based MOLL Friedrichs' Mollifier `MOLL <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MOLL>`_
Statistical Test-Based MTT Modified Thompson Tau Test `MTT <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.MTT>`_
Linear Model OCSVM One-Class Support Vector Machine `OCSVM <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.OCSVM>`_
Quantile-Based QMCD Quasi-Monte Carlo Discrepancy `QMCD <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.QMCD>`_
Linear Model REGR Regression Based `REGR <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.REGR>`_
Neural Networks VAE Variational Autoencoder `VAE <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.VAE>`_
Curve-Based WIND Topological Winding Number `WIND <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.WIND>`_
Transformation-Based YJ Yeo-Johnson Transformation `YJ <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.YJ>`_
Normality-Based ZSCORE Z-score `ZSCORE <https://pyod.readthedocs.io/en/latest/pyod.models.html#module-pyod.models.thresholds.ZSCORE>`_
================================== ================ ================================================================ ====================================================================================================================
**(iV) Utility Functions**:
=================== ====================== ===================================================================================================================================================== ======================================================================================================================================
Type Name Function Documentation
=================== ====================== ===================================================================================================================================================== ======================================================================================================================================
Data generate_data Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution `generate_data <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
Data generate_data_clusters Synthesized data generation in clusters; more complex data patterns can be created with multiple clusters `generate_data_clusters <https://pyod.readthedocs.io/en/latest/pyod.utils.html#pyod.utils.data.generate_data_clusters>`_
Stat wpearsonr Calculate the weighted Pearson correlation of two samples `wpearsonr <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
Utility get_label_n Turn raw outlier scores into binary labels by assign 1 to top n outlier scores `get_label_n <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
Utility precision_n_scores calculate precision @ rank n `precision_n_scores <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
=================== ====================== ===================================================================================================================================================== ======================================================================================================================================
----
Quick Start for Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PyOD has been well acknowledged by the machine learning community with a few featured posts and tutorials.
**Analytics Vidhya**: `An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_
**KDnuggets**: `Intuitive Visualization of Outlier Detection Methods <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_, `An Overview of Outlier Detection Methods from PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>`_
**Towards Data Science**: `Anomaly Detection for Dummies <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_
**Computer Vision News (March 2019)**: `Python Open Source Toolbox for Outlier Detection <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_
`"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>`_
demonstrates the basic API of using kNN detector. **It is noted that the API across all other algorithms are consistent/similar**.
More detailed instructions for running examples can be found in `examples directory <https://github.com/yzhao062/pyod/blob/master/examples>`_.
#. Initialize a kNN detector, fit the model, and make the prediction.
.. code-block:: python
from pyod.models.knn import KNN # kNN detector
# train kNN detector
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
# get the prediction label and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# it is possible to get the prediction confidence as well
y_test_pred, y_test_pred_confidence = clf.predict(X_test, return_confidence=True) # outlier labels (0 or 1) and confidence in the range of [0,1]
#. Evaluate the prediction by ROC and Precision @ Rank n (p@n).
.. code-block:: python
from pyod.utils.data import evaluate_print
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
#. See a sample output & visualization.
.. code-block:: python
On Training Data:
KNN ROC:1.0, precision @ rank n:1.0
On Test Data:
KNN ROC:0.9989, precision @ rank n:0.9
.. code-block:: python
visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred, show_figure=True, save_figure=False)
Visualization (\ `knn_figure <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png>`_\ ):
.. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
:target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
:alt: kNN example figure
----
How to Contribute
^^^^^^^^^^^^^^^^^
You are welcome to contribute to this exciting project:
* Please first check Issue lists for "help wanted" tag and comment the one
you are interested. We will assign the issue to you.
* Fork the master branch and add your improvement/modification/fix.
* Create a pull request to **development branch** and follow the pull request template `PR template <https://github.com/yzhao062/pyod/blob/master/PULL_REQUEST_TEMPLATE.md>`_
* Automatic tests will be triggered. Make sure all tests are passed. Please make sure all added modules are accompanied with proper test functions.
To make sure the code has the same style and standard, please refer to abod.py, hbos.py, or feature_bagging.py for example.
You are also welcome to share your ideas by opening an issue or dropping me an email at zhaoy@cmu.edu :)
Inclusion Criteria
^^^^^^^^^^^^^^^^^^
Similarly to `scikit-learn <https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms>`_,
We mainly consider well-established algorithms for inclusion.
A rule of thumb is at least two years since publication, 50+ citations, and usefulness.
However, we encourage the author(s) of newly proposed models to share and add your implementation into PyOD
for boosting ML accessibility and reproducibility.
This exception only applies if you could commit to the maintenance of your model for at least two year period.
----
Reference
^^^^^^^^^
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