rcf3 0.5.1

Streaming anomaly detection algorithms in Rust with Python bindings.

RCF3

rcf3 provides streaming anomaly detectors in Rust with Python bindings.

What it provides

• Random Cut Forest for online anomaly detection, feature attribution, neighborhood search, missing-value imputation, and time-series forecasting
• Online Isolation Forest for numerical streams with sliding-window updates
• mStream for mixed numerical/categorical streaming anomaly detection with feature-level score decomposition
• FeatureSketch for sparse feature-name streams whose schema can grow or shrink over time
• Rust and Python APIs over the same core implementation

Documentation

• Documentation index
• Forest API guide
• OnlineIForest API guide
• MStream API guide
• FeatureSketch 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 OnlineIForest when you want a compact numerical detector with sliding-window updates:

use rcf3::OnlineIForest;

let mut detector = OnlineIForest::builder(2).build()?;
let score = detector.update_and_score(&[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)?;

Use FeatureSketch when each event is a sparse set of named features and the feature universe is not fixed:

use rcf3::FeatureSketch;

let mut detector = FeatureSketch::builder().build()?;
let score = detector.update_and_score([
    ("endpoint:/login", 1.0),
    ("status:401", 1.0),
    ("bytes", 812.0),
])?;

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 = "0.4", default-features = false }

serde (enabled by default)

Provides JSON serialization and deserialization support for persisted detector state:

[dependencies]
rcf3 = { version = "0.4", features = ["serde", "std"] }

python (optional)

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

[dependencies]
rcf3 = { version = "0.4", features = ["python"] }

To use just the core algorithm without serialization:

[dependencies]
rcf3 = { version = "0.4", default-features = false, features = ["std"] }

License

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