deepod 0.4.1

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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 25 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 :telescope:).

• 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 :sparkles:)

If you are interested in our project, we are pleased to have your stars and forks :thumbsup: :beers: .

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 DIF --runs 5 --input_dir ADBench

Implemented Models

Tabular Anomaly Detection models:

Model	Venue	Year	Type	Title
Deep SVDD	ICML	2018	unsupervised	Deep One-Class Classification [1]
REPEN	KDD	2018	unsupervised	Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection [2]
RDP	IJCAI	2020	unsupervised	Unsupervised Representation Learning by Predicting Random Distances [3]
RCA	IJCAI	2021	unsupervised	RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection [4]
GOAD	ICLR	2020	unsupervised	Classification-Based Anomaly Detection for General Data [5]
NeuTraL	ICML	2021	unsupervised	Neural Transformation Learning for Deep Anomaly Detection Beyond Images [6]
ICL	ICLR	2022	unsupervised	Anomaly Detection for Tabular Data with Internal Contrastive Learning
DIF	TKDE	2023	unsupervised	Deep Isolation Forest for Anomaly Detection
SLAD	ICML	2023	unsupervised	Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
DevNet	KDD	2019	weakly-supervised	Deep Anomaly Detection with Deviation Networks
PReNet	KDD	2023	weakly-supervised	Deep Weakly-supervised Anomaly Detection
Deep SAD	ICLR	2020	weakly-supervised	Deep Semi-Supervised Anomaly Detection
FeaWAD	TNNLS	2021	weakly-supervised	Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
RoSAS	IP&M	2023	weakly-supervised	RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

Time-series Anomaly Detection models:

Model	Venue	Year	Type	Title
TimesNet	ICLR	2023	unsupervised	TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis
AnomalyTransformer	ICLR	2022	unsupervised	Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
TranAD	VLDB	2022	unsupervised	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
COUTA	arXiv	2022	unsupervised	Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
USAD	KDD	2020	unsupervised	USAD: UnSupervised Anomaly Detection on Multivariate Time Series
DIF	TKDE	2023	unsupervised	Deep Isolation Forest for Anomaly Detection
TcnED	TNNLS	2021	unsupervised	An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Deep SVDD (TS)	ICML	2018	unsupervised	Deep One-Class Classification
DevNet (TS)	KDD	2019	weakly-supervised	Deep Anomaly Detection with Deviation Networks
PReNet (TS)	KDD	2023	weakly-supervised	Deep Weakly-supervised Anomaly Detection
Deep SAD (TS)	ICLR	2020	weakly-supervised	Deep Semi-Supervised Anomaly Detection

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}
}

Reference

[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.