rcf3

rcf3 is a Rust implementation of Random Cut Forest (RCF) for anomaly detection in streaming data, with Python bindings and an mStream detector for mixed numerical and categorical streams.

What it provides

• Random Cut Forest for online anomaly detection, feature attribution, neighborhood search, missing-value imputation, and time-series forecasting
• mStream for mixed numerical/categorical streaming anomaly detection with feature-level score decomposition
• Rust and Python APIs over the same core implementation

Documentation

• Documentation index
• Forest API guide
• MStream API guide

Quick orientation

Use Forest when your stream is represented as numerical observations and you want the broader RCF feature set:

use rcf3::Forest;

let mut forest = Forest::builder(2).build()?;
forest.update(&[1.5, 2.3])?;

Use MStream when each event has separate numerical and categorical aspects:

use rcf3::MStream;

let mut detector = MStream::builder(2, 1).build()?;
let score = detector.update_and_score(&[1.5, 2.0], &[7], 1)?;

Features

The crate supports several compile-time features:

std (enabled by default)

Enables use of the Rust standard library. Disable this for no_std environments:

[dependencies]
rcf3 = { version = "0.3", default-features = false }

serde (enabled by default)

Provides JSON serialization and deserialization support for persisted detector state:

[dependencies]
rcf3 = { version = "0.3", features = ["serde", "std"] }

python (optional)

Builds Python bindings using PyO3, enabling use from Python. Automatically enables serde and std:

[dependencies]
rcf3 = { version = "0.3", features = ["python"] }

To use just the core algorithm without serialization:

[dependencies]
rcf3 = { version = "0.3", default-features = false, features = ["std"] }

License

Licensed under either of the Apache License 2.0 or MIT license, at your option.