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🌲 IsoTree - outlier/anomaly detection using Isolation Forest - for Ruby

🌲 IsoTree - outlier/anomaly detection using Isolation Forest - for Ruby
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Category: Ruby / Miscellaneous
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Star: 10
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Last update: Aug 13, 2020
Resource links
https://github.com/ankane/isotree

IsoTree

🌲 IsoTree - outlier/anomaly detection using Isolation Forest - for Ruby

Learn how Isolation Forest works

Build Status

Installation

Add this line to your application’s Gemfile:

gem 'isotree'

Getting Started

Prep your data

x = [[1, 2], [3, 4], [5, 6], [7, 8]]

Train a model

model = IsoTree::IsolationForest.new
model.fit(x)

Get outlier scores

model.predict(x)

Scores are between 0 and 1, with higher scores indicating outliers

Parameters

Pass parameters - default values below

IsoTree::IsolationForest.new(
  sample_size: nil,
  ntrees: 500,
  ndim: 3,
  ntry: 3,
  prob_pick_avg_gain: 0,
  prob_pick_pooled_gain: 0,
  prob_split_avg_gain: 0,
  prob_split_pooled_gain: 0,
  min_gain: 0,
  all_perm: false,
  coef_by_prop: false,
  sample_with_replacement: false,
  penalize_range: true,
  weigh_by_kurtosis: false,
  min_imp_obs: 3,
  random_seed: 1,
  nthreads: -1
)

See a detailed explanation

Data

Data can be an array of arrays

[[1, 2, 3], [4, 5, 6]]

Or a Numo array

Numo::NArray.cast([[1, 2, 3], [4, 5, 6]])

Performance

IsoTree uses OpenMP when possible for best performance. To enable OpenMP on Mac, run:

brew install libomp

Then reinstall the gem.

gem uninstall isotree --force
bundle install

History

View the changelog

Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

To get started with development:

git clone --recursive https://github.com/ankane/isotree.git
cd isotree
bundle install
bundle exec rake compile
bundle exec rake test

Contents

    vc1492a Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
    hoya012 A curated list of awesome anomaly detection resources
    arundo A Python toolkit for rule-based/unsupervised anomaly detection in time series
    hendrycks Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
    ankane Explainable outlier/anomaly detection for Ruby
    google A light-weight process isolation tool, making use of Linux namespaces and seccomp-bpf syscall filters (with help of the kafel bpf language)
    yzhao062 Anomaly detection related books, papers, videos, and toolboxes
    zurutech Anomaly detection using GANs.
    datamllab PyODDS is an end-to end Python system for outlier detection with database support
    adobe One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.
    demonzyj56 Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
    yzhao062 TOD: Tensor-based Outlier Detection
    yzhao062 A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
    yzhao062 (MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)
    numenta The Numenta Anomaly Benchmark
    chickenbestlover RNN based Time-series Anomaly detector model implemented in Pytorch.
    Teknasyon-Teknoloji An open-source outlier detection package by Getcontact Data Team
    masatoi Random forest in Common Lisp
    LiDan456 Applied generative adversarial networks (GANs) to do anomaly detection for time series data
    opendistro-for-elasticsearch A machine learning plugin in Open Distro for real time anomaly detection on streaming data.
    seominseok0429 Real-world Anomaly Detection in Surveillance Videos- pytorch Re-implementation
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