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        "author": "Matias Carrasco Kind , Sahand Hariri",
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        "description": "<a href=\"https://github.com/sahandha/eif/releases/tag/v1.0.2\"> <img src=\"https://img.shields.io/badge/release-v1.0.2-blue.svg\" alt=\"latest release\" /></a><a href=\"https://pypi.org/project/eif/1.0.2/\"><img src=\"https://img.shields.io/badge/pypi-v1.0.2-orange.svg\" alt=\"pypi version\"/></a>\n# Extended Isolation Forest\n\nThis 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)\n\nFor 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.\n\n## Installation\n\n\n    pip install eif\n\n\nor directly from the repository\n\n\n    pip install git+https://github.com/sahandha/eif.git\n\n\n## Requirements\n\n- numpy\n\nNo extra requirements are needed.\nIn addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library. See the example for tree visualizations\n\n## Use\n\nSee these notebooks for examples on how to use it\n\n- [Basics](Notebooks/IsolationForest.ipynb)\n- [3D Example](Notebooks/general_3D_examples.ipynb)\n- [Tree visualizations](Notebooks/TreeVisualization.ipynb)\n\n## Release\n\n### v1.0.2\n#### 2018-OCT-01\n- Added documentation, examples and software paper\n\n### v1.0.1\n#### 2018-AUG-08\n- Bugfix for multidimensional data\n\n### v1.0.0\n#### 2018-JUL-15\n- Initial Release\n",
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