Metadata-Version: 1.0
Name: eif
Version: 1.0.1
Summary: Extended Isolation Forest for anomaly detection
Home-page: https://github.com/sahandha/eif
Author: Matias Carrasco Kind , Sahand Hariri
Author-email: mcarras2@illinois.edu , sahandha@gmail.com
License: License.txt
Description: # Extended Isolation Forest
        
        This is a simple package implementation for the Extended Isolation Forest method. It is an improvement on the original algorithm Isolation Forest which is described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution. The original code can be found at [https://github.com/mgckind/iso_forest](https://github.com/mgckind/iso_forest)
        
        For an *N* dimensional data set, Extended Isolation Forest has *N* levels of extension, with *0* being identical to the case of standard Isolation Forest, and *N-1* being the fully extended version.
        
        ## Installation
        
        
            pip install eif
        
        
        or directly from the repository
        
        
            pip install git+https://github.com/sahandha/eif.git
        
        
        ## Requirements
        
        - numpy
        
        No extra requirements are needed.
        In addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library. See the example for tree visualizations
        
        ## Use
        
        See these notebooks for examples on how to use it
        
        - [Basics](Notebooks/IsolationForest.ipynb)
        - [3D Example](Notebooks/general_3D_examples.ipynb)
        - [Tree visualizations](Notebooks/TreeVisualization.ipynb)
        
        ## Release
        
        ### v1.0.1
        #### 2018-AUG-08
        - Bugfix for multidimensional data
        
        ### v1.0.0
        #### 2018-JUL-15
        - Initial Release
        
Platform: UNKNOWN
