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/yzhao062/pyod/issues/328#issuecomment-917192704>_ and #88 <https://github.com/yzhao062/pyod/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 =================== Probabilistic ECOD Probabilistic ABOD Probabilistic FastABOD Probabilistic COPOD Probabilistic MAD Probabilistic SOS Probabilistic QMCD Probabilistic KDE Probabilistic Sampling Probabilistic GMM Linear Model PCA Linear Model KPCA Linear Model MCD Linear Model CD Linear Model OCSVM Linear Model LMDD Proximity-Based LOF Proximity-Based COF Proximity-Based Proximity-Based CBLOF Proximity-Based LOCI Proximity-Based HBOS Proximity-Based kNN Proximity-Based AvgKNN Proximity-Based MedKNN Proximity-Based SOD Proximity-Based ROD Outlier Ensembles IForest Outlier Ensembles INNE Outlier Ensembles DIF Outlier Ensembles FB Outlier Ensembles LSCP Outlier Ensembles XGBOD Outlier Ensembles LODA Outlier Ensembles SUOD Neural Networks AutoEncoder Neural Networks VAE Neural Networks Beta-VAE Neural Networks SO_GAAL Neural Networks MO_GAAL Neural Networks DeepSVDD Neural Networks AnoGAN Neural Networks ALAD Graph-based R-Graph Graph-based LUNAR =================== ================== ====================================================================================================== ===== ======================================== Algorithm Year Ref ================== ====================================================================================================== ===== ======================================== Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions 2022 [#Li2021ECOD]_ Angle-Based Outlier Detection 2008 [#Kriegel2008Angle]_ Fast Angle-Based Outlier Detection using approximation 2008 [#Kriegel2008Angle]_ COPOD: Copula-Based Outlier Detection 2020 [#Li2020COPOD]_ Median Absolute Deviation (MAD) 1993 [#Iglewicz1993How]_ Stochastic Outlier Selection 2012 [#Janssens2012Stochastic]_ Quasi-Monte Carlo Discrepancy outlier detection 2001 [#Fang2001Wrap]_ Outlier Detection with Kernel Density Functions 2007 [#Latecki2007Outlier]_ Rapid distance-based outlier detection via sampling 2013 [#Sugiyama2013Rapid]_ Probabilistic Mixture Modeling for Outlier Analysis [#Aggarwal2015Outlier]_ [Ch.2] Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) 2003 [#Shyu2003A]_ Kernel Principal Component Analysis 2007 [#Hoffmann2007Kernel]_ Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) 1999 [#Hardin2004Outlier]_ [#Rousseeuw1999A]_ Use Cook's distance for outlier detection 1977 [#Cook1977Detection]_ One-Class Support Vector Machines 2001 [#Scholkopf2001Estimating]_ Deviation-based Outlier Detection (LMDD) 1996 [#Arning1996A]_ Local Outlier Factor 2000 [#Breunig2000LOF]_ Connectivity-Based Outlier Factor 2002 [#Tang2002Enhancing]_ (Incremental) COF Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity) 2002 [#Tang2002Enhancing]_ Clustering-Based Local Outlier Factor 2003 [#He2003Discovering]_ LOCI: Fast outlier detection using the local correlation integral 2003 [#Papadimitriou2003LOCI]_ Histogram-based Outlier Score 2012 [#Goldstein2012Histogram]_ k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score) 2000 [#Ramaswamy2000Efficient]_ Average kNN (use the average distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_ Median kNN (use the median distance to k nearest neighbors as the outlier score) 2002 [#Angiulli2002Fast]_ Subspace Outlier Detection 2009 [#Kriegel2009Outlier]_ Rotation-based Outlier Detection 2020 [#Almardeny2020A]_ Isolation Forest 2008 [#Liu2008Isolation]_ Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles 2018 [#Bandaragoda2018Isolation]_ Deep Isolation Forest for Anomaly Detection 2023 [#Xu2023Deep]_ Feature Bagging 2005 [#Lazarevic2005Feature]_ LSCP: Locally Selective Combination of Parallel Outlier Ensembles 2019 [#Zhao2019LSCP]_ Extreme Boosting Based Outlier Detection (Supervised) 2018 [#Zhao2018XGBOD]_ Lightweight On-line Detector of Anomalies 2016 [#Pevny2016Loda]_ SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection (Acceleration) 2021 [#Zhao2021SUOD]_ Fully connected AutoEncoder (use reconstruction error as the outlier score) [#Aggarwal2015Outlier]_ [Ch.3] Variational AutoEncoder (use reconstruction error as the outlier score) 2013 [#Kingma2013Auto]_ Variational AutoEncoder (all customized loss term by varying gamma and capacity) 2018 [#Burgess2018Understanding]_ Single-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_ Multiple-Objective Generative Adversarial Active Learning 2019 [#Liu2019Generative]_ Deep One-Class Classification 2018 [#Ruff2018Deep]_ Anomaly Detection with Generative Adversarial Networks 2017 [#Schlegl2017Unsupervised]_ Adversarially learned anomaly detection 2018 [#Zenati2018Adversarially]_ Outlier detection by R-graph 2017 [#You2017Provable]_ LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks 2022 [#Goodge2022Lunar]_ ================== ====================================================================================================== ===== ========================================

(ii) Outlier Ensembles & Outlier Detector Combination Frameworks:

(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:

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 ^^^^^^^^^

.. [#Aggarwal2015Outlier] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham.

.. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ ACM SIGKDD Explorations Newsletter\ , 17(1), pp.24-47.

.. [#Aggarwal2017Outlier] Aggarwal, C.C. and Sathe, S., 2017. Outlier ensembles: An introduction. Springer.

.. [#Almardeny2020A] Almardeny, Y., Boujnah, N. and Cleary, F., 2020. A Novel Outlier Detection Method for Multivariate Data. IEEE Transactions on Knowledge and Data Engineering.

.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery pp. 15-27.

.. [#Arning1996A] Arning, A., Agrawal, R. and Raghavan, P., 1996, August. A Linear Method for Deviation Detection in Large Databases. In KDD (Vol. 1141, No. 50, pp. 972-981).

.. [#Bandaragoda2018Isolation] Bandaragoda, T. R., Ting, K. M., Albrecht, D., Liu, F. T., Zhu, Y., and Wells, J. R., 2018, Isolation-based anomaly detection using nearest-neighbor ensembles. Computational Intelligence\ , 34(4), pp. 968-998.

.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. ACM Sigmod Record\ , 29(2), pp. 93-104.

.. [#Burgess2018Understanding] Burgess, Christopher P., et al. "Understanding disentangling in beta-VAE." arXiv preprint arXiv:1804.03599 (2018).

.. [#Cook1977Detection] Cook, R.D., 1977. Detection of influential observation in linear regression. Technometrics, 19(1), pp.15-18.

.. [#Fang2001Wrap] Fang, K.T. and Ma, C.X., 2001. Wrap-around L2-discrepancy of random sampling, Latin hypercube and uniform designs. Journal of complexity, 17(4), pp.608-624.

.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In KI-2012: Poster and Demo Track\ , pp.59-63.

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