Deployment & Documentation & Stats & License
News: We just released a 36-page, the most comprehensive anomaly detection benchmark paper. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 55 benchmark datasets.
PyOD is the most comprehensive and scalable Python library for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection.
PyOD includes more than 40 detection algorithms, from classical LOF (SIGMOD 2000) to the latest ECOD (TKDE 2022). Since 2017, PyOD has been successfully used in numerous academic researches and commercial products with more than 8 million downloads. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including Analytics Vidhya, KDnuggets, and Towards Data Science.
PyOD is featured for:
Outlier Detection with 5 Lines of Code:
# train an ECOD detector
from pyod.models.ecod import ECOD
clf = ECOD()
clf.fit(X_train)
# get 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
Personal suggestion on selecting an OD algorithm. If you do not know which algorithm to try, go with:
They are both fast and interpretable. Or, you could try more data-driven approach MetaOD.
Citing PyOD:
PyOD paper is published in Journal of Machine Learning Research (JMLR) (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.
Key Links and Resources:
Table of Contents:
It is recommended to use pip or conda for installation. Please make sure the latest version is installed, as PyOD is updated frequently:
pip install pyod # normal install
pip install --upgrade pyod # or update if needed
conda install -c conda-forge pyod
Alternatively, you could clone and run setup.py file:
git clone https://github.com/yzhao062/pyod.git
cd pyod
pip install .
Required Dependencies:
Optional Dependencies (see details below):
Warning: PyOD has multiple neural network based models, e.g., AutoEncoders, which are implemented in both Tensorflow and PyTorch. However, PyOD does NOT install these deep learning libraries for you. This reduces the risk of interfering with your local copies. If you want to use neural-net based models, please make sure these deep learning libraries are installed. Instructions are provided: neural-net FAQ. Similarly, models depending on xgboost, e.g., XGBOD, would NOT enforce xgboost installation by default.
Full API Reference: (https://pyod.readthedocs.io/en/latest/pyod.html). API cheatsheet for all detectors:
Key Attributes of a fitted model:
We just released a 36-page, the most comprehensive anomaly detection benchmark paper [14]. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 55 benchmark datasets.
The organization of ADBench is provided below:
The comparison of selected models is made available below (Figure, compare_all_models.py, Interactive Jupyter Notebooks). For Jupyter Notebooks, please navigate to "/notebooks/Compare All Models.ipynb".
PyOD takes a similar approach of sklearn regarding model persistence. See model persistence for clarification.
In short, we recommend to use joblib or pickle for saving and loading PyOD models. See "examples/save_load_model_example.py" for an example. In short, it is simple as below:
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 and #88 for temporary workaround.
Fast training and prediction: it is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework [45]. See SUOD Paper and SUOD example.
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)
PyOD toolkit consists of three major functional groups:
(i) Individual Detection Algorithms :
Type | Abbr | Algorithm | Year | Ref |
---|---|---|---|---|
Probabilistic | ECOD | Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions | 2022 | [26] |
Probabilistic | ABOD | Angle-Based Outlier Detection | 2008 | [20] |
Probabilistic | FastABOD | Fast Angle-Based Outlier Detection using approximation | 2008 | [20] |
Probabilistic | COPOD | COPOD: Copula-Based Outlier Detection | 2020 | [25] |
Probabilistic | MAD | Median Absolute Deviation (MAD) | 1993 | [17] |
Probabilistic | SOS | Stochastic Outlier Selection | 2012 | [18] |
Probabilistic | KDE | Outlier Detection with Kernel Density Functions | 2007 | [22] |
Probabilistic | Sampling | Rapid distance-based outlier detection via sampling | 2013 | [38] |
Probabilistic | GMM | Probabilistic Mixture Modeling for Outlier Analysis | [1] [Ch.2] | |
Linear Model | PCA | Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes) | 2003 | [37] |
Linear Model | MCD | Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) | 1999 | [15] [33] |
Linear Model | CD | Use Cook's distance for outlier detection | 1977 | [10] |
Linear Model | OCSVM | One-Class Support Vector Machines | 2001 | [36] |
Linear Model | LMDD | Deviation-based Outlier Detection (LMDD) | 1996 | [6] |
Proximity-Based | LOF | Local Outlier Factor | 2000 | [8] |
Proximity-Based | COF | Connectivity-Based Outlier Factor | 2002 | [39] |
Proximity-Based | (Incremental) COF | Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity) | 2002 | [39] |
Proximity-Based | CBLOF | Clustering-Based Local Outlier Factor | 2003 | [16] |
Proximity-Based | LOCI | LOCI: Fast outlier detection using the local correlation integral | 2003 | [29] |
Proximity-Based | HBOS | Histogram-based Outlier Score | 2012 | [11] |
Proximity-Based | kNN | k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score) | 2000 | [32] |
Proximity-Based | AvgKNN | Average kNN (use the average distance to k nearest neighbors as the outlier score) | 2002 | [5] |
Proximity-Based | MedKNN | Median kNN (use the median distance to k nearest neighbors as the outlier score) | 2002 | [5] |
Proximity-Based | SOD | Subspace Outlier Detection | 2009 | [21] |
Proximity-Based | ROD | Rotation-based Outlier Detection | 2020 | [4] |
Outlier Ensembles | IForest | Isolation Forest | 2008 | [27] |
Outlier Ensembles | INNE | Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles | 2018 | [7] |
Outlier Ensembles | FB | Feature Bagging | 2005 | [23] |
Outlier Ensembles | LSCP | LSCP: Locally Selective Combination of Parallel Outlier Ensembles | 2019 | [44] |
Outlier Ensembles | XGBOD | Extreme Boosting Based Outlier Detection (Supervised) | 2018 | [43] |
Outlier Ensembles | LODA | Lightweight On-line Detector of Anomalies | 2016 | [30] |
Outlier Ensembles | SUOD | SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection (Acceleration) | 2021 | [45] |
Neural Networks | AutoEncoder | Fully connected AutoEncoder (use reconstruction error as the outlier score) | [1] [Ch.3] | |
Neural Networks | VAE | Variational AutoEncoder (use reconstruction error as the outlier score) | 2013 | [19] |
Neural Networks | Beta-VAE | Variational AutoEncoder (all customized loss term by varying gamma and capacity) | 2018 | [9] |
Neural Networks | SO_GAAL | Single-Objective Generative Adversarial Active Learning | 2019 | [28] |
Neural Networks | MO_GAAL | Multiple-Objective Generative Adversarial Active Learning | 2019 | [28] |
Neural Networks | DeepSVDD | Deep One-Class Classification | 2018 | [34] |
Neural Networks | AnoGAN | Anomaly Detection with Generative Adversarial Networks | 2017 | [35] |
Neural Networks | ALAD | Adversarially learned anomaly detection | 2018 | [42] |
Graph-based | R-Graph | Outlier detection by R-graph | 2017 | [41] |
Graph-based | LUNAR | LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks | 2022 | [12] |
(ii) Outlier Ensembles & Outlier Detector Combination Frameworks:
Type | Abbr | Algorithm | Year | Ref |
---|---|---|---|---|
Outlier Ensembles | FB | Feature Bagging | 2005 | [23] |
Outlier Ensembles | LSCP | LSCP: Locally Selective Combination of Parallel Outlier Ensembles | 2019 | [44] |
Outlier Ensembles | XGBOD | Extreme Boosting Based Outlier Detection (Supervised) | 2018 | [43] |
Outlier Ensembles | LODA | Lightweight On-line Detector of Anomalies | 2016 | [30] |
Outlier Ensembles | SUOD | SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection (Acceleration) | 2021 | [45] |
Outlier Ensembles | INNE | Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles | 2018 | [7] |
Combination | Average | Simple combination by averaging the scores | 2015 | [2] |
Combination | Weighted Average | Simple combination by averaging the scores with detector weights | 2015 | [2] |
Combination | Maximization | Simple combination by taking the maximum scores | 2015 | [2] |
Combination | AOM | Average of Maximum | 2015 | [2] |
Combination | MOA | Maximization of Average | 2015 | [2] |
Combination | Median | Simple combination by taking the median of the scores | 2015 | [2] |
Combination | majority Vote | Simple combination by taking the majority vote of the labels (weights can be used) | 2015 | [2] |
(iii) 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 |
Data | generate_data_clusters | Synthesized data generation in clusters; more complex data patterns can be created with multiple clusters | generate_data_clusters |
Stat | wpearsonr | Calculate the weighted Pearson correlation of two samples | wpearsonr |
Utility | get_label_n | Turn raw outlier scores into binary labels by assign 1 to top n outlier scores | get_label_n |
Utility | precision_n_scores | calculate precision @ rank n | precision_n_scores |
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
KDnuggets: Intuitive Visualization of Outlier Detection Methods, An Overview of Outlier Detection Methods from PyOD
Towards Data Science: Anomaly Detection for Dummies
Computer Vision News (March 2019): Python Open Source Toolbox for Outlier Detection
"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.
Initialize a kNN detector, fit the model, and make the prediction.
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 ([email protected]).
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.
On Training Data:
KNN ROC:1.0, precision @ rank n:1.0
On Test Data:
KNN ROC:0.9989, precision @ rank n:0.9
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):
You are welcome to contribute to this exciting project:
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 [email protected] :)
Similarly to scikit-learn, 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.
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