Python Deep Outlier/Anomaly Detection (DeepOD)

DeepOD is an open-source python library for Deep Learning-based Outlier Detection and Anomaly Detection. DeepOD supports tabular anomaly detection and time-series anomaly detection.

DeepOD includes 27 deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm). More baseline algorithms will be included later.

DeepOD is featured for:

• Unified APIs across various algorithms.
• SOTA models includes reconstruction-, representation-learning-, and self-superivsed-based latest deep learning methods.
• Comprehensive Testbed that can be used to directly test different models on benchmark datasets (highly recommend for academic research).
• Versatile in different data types including tabular and time-series data (DeepOD will support other data types like images, graph, log, trace, etc. in the future, welcome PR 🔭).
• Diverse Network Structures can be plugged into detection models, we now support LSTM, GRU, TCN, Conv, and Transformer for time-series data. (welcome PR as well ✨)

If you are interested in our project, we are pleased to have your stars and forks 👍 🍻 .

Installation

The DeepOD framework can be installed via:

pip install deepod

install a developing version (strongly recommend)

git clone https://github.com/xuhongzuo/DeepOD.git
cd DeepOD
pip install .

Usages

Directly use detection models in DeepOD:

DeepOD can be used in a few lines of code. This API style is the same with Sklean and PyOD.

for tabular anomaly detection:

# unsupervised methods
from deepod.models.tabular import DeepSVDD
clf = DeepSVDD()
clf.fit(X_train, y=None)
scores = clf.decision_function(X_test)

# weakly-supervised methods
from deepod.models.tabular import DevNet
clf = DevNet()
clf.fit(X_train, y=semi_y) # semi_y uses 1 for known anomalies, and 0 for unlabeled data
scores = clf.decision_function(X_test)

# evaluation of tabular anomaly detection
from deepod.metrics import tabular_metrics
auc, ap, f1 = tabular_metrics(y_test, scores)

for time series anomaly detection:

# time series anomaly detection methods
from deepod.models.time_series import TimesNet
clf = TimesNet()
clf.fit(X_train)
scores = clf.decision_function(X_test)

# evaluation of time series anomaly detection
from deepod.metrics import ts_metrics
from deepod.metrics import point_adjustment # execute point adjustment for time series ad
eval_metrics = ts_metrics(labels, scores)
adj_eval_metrics = ts_metrics(labels, point_adjustment(labels, scores))

Testbed usage:

Testbed contains the whole process of testing an anomaly detection model, including data loading, preprocessing, anomaly detection, and evaluation.

Please refer to testbed/

• testbed/testbed_unsupervised_ad.py is for testing unsupervised tabular anomaly detection models.
• testbed/testbed_unsupervised_tsad.py is for testing unsupervised time-series anomaly detection models.

Key arguments:

• --input_dir: name of the folder that contains datasets (.csv, .npy)
• --dataset: "FULL" represents testing all the files within the folder, or a list of dataset names using commas to split them (e.g., "10_cover*,20_letter*")
• --model: anomaly detection model name
• --runs: how many times running the detection model, finally report an average performance with standard deviation values

Example:

1. Download ADBench datasets.
2. modify the dataset_root variable as the directory of the dataset.
3. input_dir is the sub-folder name of the dataset_root, e.g., Classical or NLP_by_BERT.
4. use the following command in the bash

cd DeepOD
pip install .
cd testbed
python testbed_unsupervised_ad.py --model DeepIsolationForest --runs 5 --input_dir ADBench

Implemented Models

Tabular Anomaly Detection models:

Model	Venue	Year	Type	Title
Deep SVDD	ICML	2018	unsupervised	Deep One-Class Classification1
REPEN	KDD	2018	unsupervised	Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection2
RDP	IJCAI	2020	unsupervised	Unsupervised Representation Learning by Predicting Random Distances3
RCA	IJCAI	2021	unsupervised	RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection4
GOAD	ICLR	2020	unsupervised	Classification-Based Anomaly Detection for General Data5
NeuTraL	ICML	2021	unsupervised	Neural Transformation Learning for Deep Anomaly Detection Beyond Images6
ICL	ICLR	2022	unsupervised	Anomaly Detection for Tabular Data with Internal Contrastive Learning7
DIF	TKDE	2023	unsupervised	Deep Isolation Forest for Anomaly Detection8
SLAD	ICML	2023	unsupervised	Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning9
DevNet	KDD	2019	weakly-supervised	Deep Anomaly Detection with Deviation Networks10
PReNet	KDD	2023	weakly-supervised	Deep Weakly-supervised Anomaly Detection11
Deep SAD	ICLR	2020	weakly-supervised	Deep Semi-Supervised Anomaly Detection12
FeaWAD	TNNLS	2021	weakly-supervised	Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection13
RoSAS	IP&M	2023	weakly-supervised	RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision14

Time-series Anomaly Detection models:

Model	Venue	Year	Type	Title
DCdetector	KDD	2023	unsupervised	DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection15
TimesNet	ICLR	2023	unsupervised	TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis16
AnomalyTransformer	ICLR	2022	unsupervised	Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy17
NCAD	IJCAI	2022	unsupervised	Neural Contextual Anomaly Detection for Time Series18
TranAD	VLDB	2022	unsupervised	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data19
COUTA	arXiv	2022	unsupervised	Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection20
USAD	KDD	2020	unsupervised	USAD: UnSupervised Anomaly Detection on Multivariate Time Series
DIF	TKDE	2023	unsupervised	Deep Isolation Forest for Anomaly Detection21
TcnED	TNNLS	2021	unsupervised	An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series22
Deep SVDD (TS)	ICML	2018	unsupervised	Deep One-Class Classification23
DevNet (TS)	KDD	2019	weakly-supervised	Deep Anomaly Detection with Deviation Networks24
PReNet (TS)	KDD	2023	weakly-supervised	Deep Weakly-supervised Anomaly Detection25
Deep SAD (TS)	ICLR	2020	weakly-supervised	Deep Semi-Supervised Anomaly Detection26

NOTE:

• For Deep SVDD, DevNet, PReNet, and DeepSAD, we employ network structures that can handle time-series data. These models' classes have a parameter named network in these models, by changing it, you can use different networks.
• We currently support 'TCN', 'GRU', 'LSTM', 'Transformer', 'ConvSeq', and 'DilatedConv'.

Citation

If you use this library in your work, please cite this paper:

Hongzuo Xu, Guansong Pang, Yijie Wang and Yongjun Wang, "Deep Isolation Forest for Anomaly Detection," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2023.3270293.

You can also use the BibTex entry below for citation.

@ARTICLE{xu2023deep,
   author={Xu, Hongzuo and Pang, Guansong and Wang, Yijie and Wang, Yongjun},
   journal={IEEE Transactions on Knowledge and Data Engineering}, 
   title={Deep Isolation Forest for Anomaly Detection}, 
   year={2023},
   volume={},
   number={},
   pages={1-14},
   doi={10.1109/TKDE.2023.3270293}
}

Star History

Current stars:

Reference

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1. Ruff, Lukas, et al. "Deep one-class classification." ICML. 2018.↩

2. Pang, Guansong, et al. "Learning representations of ultrahigh-dimensional data for random distance-based outlier detection". KDD (pp. 2041-2050). 2018.↩

3. Wang, Hu, et al. "Unsupervised Representation Learning by Predicting Random Distances". IJCAI (pp. 2950-2956). 2020.↩

4. Liu, Boyang, et al. "RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection". IJCAI (pp. 1505-1511). 2021.↩

5. Bergman, Liron, and Yedid Hoshen. "Classification-Based Anomaly Detection for General Data". ICLR. 2020.↩

6. Qiu, Chen, et al. "Neural Transformation Learning for Deep Anomaly Detection Beyond Images". ICML. 2021.↩

7. Shenkar, Tom, et al. "Anomaly Detection for Tabular Data with Internal Contrastive Learning". ICLR. 2022.↩

8. Xu, Hongzuo et al. "Deep Isolation Forest for Anomaly Detection". TKDE. 2023.↩

9. Xu, Hongzuo et al. "Fascinating supervisory signals and where to find them: deep anomaly detection with scale learning". ICML. 2023.↩

10. Pang, Guansong, et al. "Deep Anomaly Detection with Deviation Networks". KDD. 2019.↩

11. Pang, Guansong, et al. "Deep Weakly-supervised Anomaly Detection". KDD. 2023.↩

12. Ruff, Lukas, et al. "Deep Semi-Supervised Anomaly Detection". ICLR. 2020.↩

13. Zhou, Yingjie, et al. "Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection". TNNLS. 2021.↩

14. Xu, Hongzuo et al. "RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision". IP&M. 2023.↩

15. Yang, Yiyuan, et al. "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection". KDD. 2023↩

16. Wu, Haixu, et al. "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis". ICLR. 2023.↩

17. Xu, Jiehui, et al. "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy". ICLR, 2022.↩

18. Carmona, Chris U., et al. "Neural Contextual Anomaly Detection for Time Series". IJCAI. 2022.↩

19. Tuli, Shreshth, et al. "TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data". VLDB. 2022.↩

20. Xu, Hongzuo et al. "Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection". arXiv:2207.12201. 2022.↩

21. Xu, Hongzuo et al. "Deep Isolation Forest for Anomaly Detection". TKDE. 2023.↩

22. Garg, Astha, et al. "An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series". TNNLS. 2021.↩

23. Ruff, Lukas, et al. "Deep one-class classification." ICML. 2018.↩

24. Pang, Guansong, et al. "Deep Anomaly Detection with Deviation Networks". KDD. 2019.↩

25. Pang, Guansong, et al. "Deep Weakly-supervised Anomaly Detection". KDD. 2023.↩

26. Ruff, Lukas, et al. "Deep Semi-Supervised Anomaly Detection". ICLR. 2020.↩