Metadata-Version: 2.1
Name: deepod
Version: 0.1.1
Home-page: https://github.com/xuhongzuo/DeepOD
Author: Hongzuo Xu
Author-email: hongzuoxu@126.com
License: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: BSD License
Description-Content-Type: text/x-rst

Python Deep Outlier/Anomaly Detection (DeepOD)
==================================================

**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 six popular deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm) for now. More baseline algorithms will be included later.


The DeepOD framework can be installed via:


.. code-block:: bash

    pip install deepod



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 use 1 for known anomalies, and 0 for unlabeled data
    scores = clf.decision_function(X_test)

