iclgg / deep-iforest

孤立森林异常检测iforest

Deep iForest

This repository is the source code of the paper "Deep Isolation Forest for Anomaly Detection" (see full paper at https://arxiv.org/abs/2206.06602 )
Please consider citing our paper if you find this repository useful.

@article{xu2022deep,
  title={Deep Isolation Forest for Anomaly Detection},
  author={Xu, Hongzuo and Pang, Guansong and Wang, Yijie and Wang, Yongjun},
  journal={arXiv preprint arXiv:2206.06602},
  year={2022}
}

DIF provides easy APIs like the sklearn style. We first instantiate the model class by giving the parameters
then, the instantiated model can be used to fit and predict data

from algorithms.dif import DIF
model_configs = {'n_ensemble':50, 'n_estimators':6}
model = DIF(**model_configs)
model.fit(X_train)
score = model.predict(X_test)

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