Python Deep Outlier/Anomaly Detection (DeepOD)

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}
}