Random Cut Forest CLI

Command-line interface for Random Cut Forest anomaly detection

Installation

Download the latest version:

• Linux - x86_64 or arm64
• Mac - x86_64 or arm64
• Windows - x86_64

You can also install it with Homebrew:

brew install ankane/brew/rcf

Getting Started

Prepare a CSV. Each row should be a data point. Here’s an example with three dimensions:

$ tail input.csv
5.0001,0.0006,-0.0084
-4.9998,0.0041,0.0025
-5.0096,-0.0085,-0.0007
4.9980,-0.0049,-0.0029
5.0083,0.0078,-0.0029
0.0001,-0.0052,0.0003
5.0051,0.0081,-0.0068
5.0044,-0.0030,-0.0016
-5.0028,-0.0067,-0.0056
4.9921,0.0092,0.0049

And run:

rcf < input.csv > output.csv

The program outputs the anomaly score as a new column. Anomalous data points are given a larger score.

$ tail output.csv
5.0001,0.0006,-0.0084,0.7980522827802555
-4.9998,0.0041,0.0025,0.7071138288972222
-5.0096,-0.0085,-0.0007,1.018823150444355
4.9980,-0.0049,-0.0029,0.7408316990007481
5.0083,0.0078,-0.0029,0.8277566916765301
0.0001,-0.0052,0.0003,2.8337001378872664
5.0051,0.0081,-0.0068,0.7709170753009097
5.0044,-0.0030,-0.0016,0.7204096423354174
-5.0028,-0.0067,-0.0056,0.8198039786393572
4.9921,0.0092,0.0049,0.9185893539878859

Options

  -d, --delimiter <DELIMITER>        The character used as a field delimiter [default: ,]
      --header-row                   Pass if the data contains a header row
  -n, --number-of-trees <TREES>      Number of trees to use in the forest [default: 100]
  -r, --random-seed <RANDOM_SEED>    Random seed to use [default: 42]
  -s, --sample-size <SAMPLE_SIZE>    Points to keep in sample for each tree [default: 256]
  -c, --shingle-cyclic               Use cyclic shingles instead of linear shingles
  -g, --shingle-size <SHINGLE_SIZE>  Shingle size to use [default: 1]
  -w, --window-size <WINDOW_SIZE>    Window size of the sample or 0 for no window [default: 0]
  -h, --help                         Print help information
  -V, --version                      Print version information

References

• Robust Random Cut Forest Based Anomaly Detection On Streams

Credits

This project is modeled after the Java CLI.

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 https://github.com/ankane/rcf-cli.git
cd rcf-cli
cargo run --release < example-data/input.csv