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 data. DeepOD provides unified low-code implementation of different detection models based on PyTorch.
DeepOD includes ten popular deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm) for now. More baseline algorithms will be included later.
Installation
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The DeepOD framework can be installed via:
.. code-block:: bash
pip install deepod
install a developing version
.. code-block:: bash
git clone https://github.com/xuhongzuo/DeepOD.git
cd DeepOD
pip install .
Supported Models
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**Detection models:**
.. csv-table::
:header: "Model", "Venue", "Year", "Type", "Title"
:widths: 4, 4, 4, 8, 20
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
DevNet, KDD, 2019, weakly-supervised, Deep Anomaly Detection with Deviation Networks
PReNet, ArXiv, 2020, weakly-supervised, Deep Weakly-supervised Anomaly Detection
Deep SAD, ICLR, 2020, weakly-supervised, Deep Semi-Supervised Anomaly Detection
Usages
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DeepOD can be used in a few lines of code. This API style is the same with sklearn and PyOD.
.. code-block:: python
# 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)
