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 👍 🍻 .

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 .

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 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

Tabular Anomaly Detection models:

Time-series Anomaly Detection models:

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'.

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}
}

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