IsoTree

:evergreen_tree: IsoTree - outlier/anomaly detection using Isolation Forest - for Ruby

Learn how Isolation Forest works

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:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

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