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Python Deep Outlier/Anomaly Detection (DeepOD)

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DeepOD is an open-source Python framework for deep learning-based anomaly detection on multivariate/time-series data. DeepOD provides a unified implementation of different detection models based on PyTorch.

DeepOD includes 13 deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm) for now. More baseline algorithms will be included later.

🔭 We are working on a new feature -- by simply setting a few parameters, different deep anomaly detection models can handle different data types.

  • We have finished some attempts on partial models like Deep SVDD, DevNet, Deep SAD, PReNet, and DIF. These models can use temporal networks like LSTM, GRU, TCN, Conv, and Transformer to handle time series data.
  • Future work: we also want to implement several network structures, so as to process more data types like graphs and images by simply plugging in corresponding network architecture.

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 .

Supported Models

Detection models:

Model Venue Year Type Title
Deep SVDD ICML 2018 unsupervised Deep One-Class Classification
REPEN KDD 2018 unsupervised Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
RDP IJCAI 2020 unsupervised Unsupervised Representation Learning by Predicting Random Distances
RCA IJCAI 2021 unsupervised RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
GOAD ICLR 2020 unsupervised Classification-Based Anomaly Detection for General Data
NeuTraL ICML 2021 unsupervised Neural Transformation Learning for Deep Anomaly Detection Beyond Images
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

Usages

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

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

# weakly-supervised methods
from deepod.models.devnet 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)

Citation

If you use this library in your work, please use the BibTex entry below for citation.

@misc{deepod,
   author = {{Xu, Hongzuo}},
   title = {{DeepOD: Python Deep Outlier/Anomaly Detection}},
   url = {https://github.com/xuhongzuo/DeepOD}
}