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)