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.
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
or 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.
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, User-Friendly Interface across various algorithms.
Wide Range of Models, from classic techniques to the latest deep learning methods.
High Performance & Efficiency, leveraging numba and joblib for JIT compilation and parallel processing.
Fast Training & Prediction, achieved through the SUOD framework [48].
Outlier Detection with 5 Lines of Code:
# Example: Training an ECOD detectorfrompyod.models.ecodimportECODclf=ECOD()
clf.fit(X_train)
y_train_scores=clf.decision_scores_# Outlier scores for training datay_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:
@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.
@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}
}
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:
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 3.8 or higher
joblib
matplotlib
numpy>=1.19
numba>=0.51
scipy>=1.5.1
scikit_learn>=0.22.0
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)optional
API Cheatsheet & Reference
The full API Reference is available at PyOD Documentation. 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) [33].
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.
For a simpler visualization, we make the comparison of selected models via
compare_all_models.py.
Model Save & Load
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 [48].
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)
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 for
a more in depth look at thresholding.
frompyod.models.knnimportKNNfrompyod.models.thresholdsimportFILTER# Set the outlier detection and thresholding methodsclf=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
"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)
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About
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques