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:

pip install deepod

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