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:
Unified APIs, detailed documentation, and interactive examples across various algorithms.
Advanced models, including classical distance and density estimation, latest deep learning methods, and emerging algorithms like ECOD.
Optimized performance with JIT and parallelization using numba and joblib.
# train an ECOD detectorfrompyod.models.ecodimportECODclf=ECOD()
clf.fit(X_train)
# get outlier scoresy_train_scores=clf.decision_scores_# raw outlier scores on the train datay_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:
@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.
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:
Python 3.6+
joblib
matplotlib
numpy>=1.19
numba>=0.51
scipy>=1.5.1
scikit_learn>=0.20.0
six
statsmodels
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)
pandas (optional, required for running benchmark)
suod (optional, required for running SUOD model)
xgboost (optional, required for XGBOD)
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.
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:
fromjoblibimportdump, load# save the modeldump(clf, 'clf.joblib')
# load the modelclf=load('clf.joblib')
It is known that there are challenges in saving neural network models.
Check #328
and #88
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 [45].
See SUOD Paper
and SUOD example.
frompyod.models.suodimportSUOD# initialized a group of outlier detectors for accelerationdetector_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 modelclf=SUOD(base_estimators=detector_list, n_jobs=2, combination='average',
verbose=False)
Implemented Algorithms
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
"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.
frompyod.models.knnimportKNN# kNN detector# train kNN detectorclf_name='KNN'clf=KNN()
clf.fit(X_train)
# get the prediction label and outlier scores of the training datay_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 datay_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 welly_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).
frompyod.utils.dataimportevaluate_print# evaluate and print the resultsprint("\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)
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
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,
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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Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C. and Chen, H. G. ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
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A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)