Rust port of the extended isolation forest algorithm for anomaly detection

Related tags

Machine learning machine-learning anomaly-detection isolation-forest
Overview

Extended Isolation Forest

Latest Version

This is a rust port of the anomaly detection algorithm described in Extended Isolation Forest and implemented in https://github.com/sahandha/eif. For a detailed description see the paper or the github repository.

This crate requires rust >= 1.51 as it makes use of min_const_generics.

Includes optional serde support with the serde feature.

Example

use rand::distributions::Uniform;
use rand::Rng;
use extended_isolation_forest::{Forest, ForestOptions};

fn make_f64_forest() -> Forest<f64, 3> {
    let rng = &mut rand::thread_rng();
    let distribution = Uniform::new(-4., 4.);
    let distribution2 = Uniform::new(10., 50.);
    let values: Vec<_> = (0..3000)
        .map(|_| [rng.sample(distribution), rng.sample(distribution), rng.sample(distribution2)])
        .collect();

    let options = ForestOptions {
        n_trees: 150,
        sample_size: 200,
        max_tree_depth: None,
        extension_level: 1,
    };
    Forest::from_slice(values.as_slice(), &options).unwrap()
}

fn main() {
    let forest = make_f64_forest();

    // no anomaly
    assert!(forest.score(&[1.0, 3.0, 25.0]) < 0.5);
    assert!(forest.score(&[-1.0, 3.0, 25.0]) < 0.5);

    // anomalies
    assert!(forest.score(&[-12.0, 6.0, 25.0]) > 0.5);
    assert!(forest.score(&[-1.0, 2.0, 60.0]) > 0.5);
    assert!(forest.score(&[-1.0, 2.0, 0.0]) > 0.5);
}
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Comments
  • serde support

    serde support

    To implement this, serde should get support for const_generics first.

    • https://github.com/serde-rs/serde/pull/1860
    • https://github.com/rust-lang/rust/pull/75644
    opened by nmandery 1
Releases(v0.2.1)
Owner
Nico Mandery
Nico Mandery
GitHub
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