HIF

Hybrid Isolation Forest

The Hybrid Isolation Forest (HIF) is an extension of the [Isolation Forest (IF) algorithm] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html). IF and HIF are designed for detecting anomalies and outliers from a data point distribution. As is, they are alternative methods to the one-class Support Vector Machine.

HIF integrates two extensions dedicated to

overcome a drawback in the Isolation Forest (IF) algorithm that limits its use in the scope of anomaly detection
provide it with some supervised learning capability from few samples

The HIF is described (among other places) in this draft paper http://arxiv.org/abs/1705.03800. Please cite this draft paper if you use this code.

This is a simple package implementation for the HIF (inspired from this simple Python implementation of the Isolation Forest algorithm).

Installation

(It supports python3, posssibly python2)

$ sudo python3 setup.py install

Requirements

No extra requirements are needed.

Use

Launching the code

$ python3 -i testHIFDonuts.py

creating an instance of the donut dataset (normal data) and the anomaly clusters (red, green, cyan)

createDonutData(contamin=.005)

Creating the HIF

computeHIF(ntrees=512, sample_size=64)

Evaluating globally the HIF (AUC)

Outputs the best (HIF1) and <alpha1, alpha2> (HIF2) values

plotGlobalAucBis(contamin=True)

Evaluating globally the 1C-SVM (AUC)

testOneClassSVM(NU=.1, GAMMA=.1)

Evaluating globally the 2C-SVM (AUC)

testTwoClassSVM(C=.1, GAMMA=.1)

Evaluating cluster by cluster the IF, HIF(1,2), 1C-SVM, 2C-SVM (AUC)

plotDetailedResults(alpha0=.5, alpha1=.5, alpha2=.5)

pfmarteau/HIF

HIF