irithyll 9.5.1

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Streaming machine learning in Rust -- gradient boosted trees, neural streaming architectures (reservoir computing, state space models, spiking networks), kernel methods, linear models, and composable pipelines, all learning one sample at a time.

use irithyll::{pipe, normalizer, sgbt, StreamingLearner};

let mut model = pipe(normalizer()).learner(sgbt(50, 0.01));
model.train(&[100.0, 0.5], 42.0);
let prediction = model.predict(&[100.0, 0.5]);

Workspace

irithyll is structured as a Cargo workspace with three crates:

Crate	Description	`no_std`	Allocator
`irithyll`	Training, streaming algorithms, pipelines, I/O, async	No	std
`irithyll-core`	Packed inference engine (12-byte nodes, branch-free traversal)	Yes	Zero-alloc
`irithyll-python`	PyO3 Python bindings for `irithyll`	No	std

irithyll-core cross-compiles for bare-metal targets (verified: cargo check --target thumbv6m-none-eabi) and has zero dependencies.

Why irithyll?

30+ streaming algorithms under one unified StreamingLearner trait
One sample at a time -- O(1) memory per model, no batches, no windows, no retraining
Embedded deployment -- train with irithyll, export to packed binary, infer with irithyll-core on bare metal
Composable pipelines -- chain preprocessors and learners with a builder API
Concept drift adaptation -- automatic model replacement when the data distribution shifts
Confidence intervals -- prediction uncertainty from RLS and conformal methods
Production-grade -- async streaming, SIMD acceleration, Arrow/Parquet I/O, ONNX export
Neural streaming architectures -- reservoir computing (NG-RC, ESN), state space models (Mamba), spiking neural networks (e-prop)
Streaming AutoML -- champion-challenger hyperparameter racing with bandit-guided search
Pure Rust -- zero unsafe in irithyll, deterministic, serializable, 2,500+ tests

Algorithms

Every algorithm implements StreamingLearner -- train and predict with the same two-method interface.

Algorithm	Type	Use Case	Per-Sample Cost
`SGBT`	Gradient boosted trees	General regression/classification	O(n_steps * depth)
`AdaptiveSGBT`	SGBT + LR scheduling	Decaying/cycling learning rates	O(n_steps * depth)
`MulticlassSGBT`	One-vs-rest SGBT	Multi-class classification	O(classes * n_steps * depth)
`MultiTargetSGBT`	Independent SGBTs	Multi-output regression	O(targets * n_steps * depth)
`DistributionalSGBT`	Mean + variance SGBT	Prediction uncertainty	O(2 * n_steps * depth)
`KRLS`	Kernel recursive LS	Nonlinear regression (sin, exp, ...)	O(budget^2)
`RecursiveLeastSquares`	RLS with confidence	Linear regression + uncertainty	O(d^2)
`StreamingLinearModel`	SGD linear model	Fast linear baseline	O(d)
`StreamingPolynomialRegression`	Polynomial SGD	Polynomial curve fitting	O(d * degree)
`GaussianNB`	Naive Bayes	Text/categorical classification	O(d * classes)
`MondrianForest`	Random forest variant	Streaming ensemble regression	O(n_trees * depth)
`LocallyWeightedRegression`	Memory-based	Locally varying relationships	O(window)

Preprocessing (implements StreamingPreprocessor):

Preprocessor	Description
`IncrementalNormalizer`	Welford's online standardization
`OnlineFeatureSelector`	Streaming mutual-information feature selection
`CCIPCA`	O(kd) streaming PCA without covariance matrices

Neural Streaming Architectures

v9 introduces three families of neural architectures, all implementing StreamingLearner -- train and predict one sample at a time, compose in pipelines, no batching required.

Reservoir Computing

Model	Description	Per-Sample Cost
`NextGenRC`	Polynomial features of time-delayed observations + RLS readout. No random matrices. Trains in <10 samples. Based on Gauthier et al. 2021 (Nature Communications).	O(k * s * d^degree)
`EchoStateNetwork`	Deterministic cycle/ring reservoir (O(N) weights, not O(N^2)) with leaky integration + RLS readout. Based on Rodan & Tino 2010, Martinuzzi 2025.	O(N^2) readout
`ESNPreprocessor`	Use ESN as a pipeline preprocessor feeding any downstream learner.	O(N) reservoir step

State Space Models (SSM / Mamba)

Model	Description	Per-Sample Cost
`StreamingMamba`	Selective state space model with input-dependent B, C, Delta. ZOH discretization, diagonal A matrix. First streaming SSM implementation in Rust. Based on Gu & Dao 2023.	O(D * N)
`MambaPreprocessor`	SSM temporal features feeding SGBT or other learners in a pipeline.	O(D * N)

Spiking Neural Networks (SNN / e-prop)

Model	Description	Per-Sample Cost
`SpikeNet`	LIF neurons with e-prop online learning rule. Delta spike encoding for continuous features. Based on Bellec et al. 2020 (Nature Communications), Neftci et al. 2019.	O(N_hidden^2)
`SpikeNetFixed`	Full `no_std` training in Q1.14 integer arithmetic. 64 neurons fits in 22KB (Cortex-M0+ 32KB SRAM). Lives in `irithyll-core`.	O(N_hidden^2)

All neural models also have preprocessor variants (ESNPreprocessor, MambaPreprocessor, SpikePreprocessor) that implement StreamingPreprocessor for pipeline composition.

Streaming AutoML

v9.5 introduces online hyperparameter optimization via champion-challenger racing -- the first streaming AutoML framework in Rust.

Component	Description
`AutoTuner`	Top-level orchestrator (implements `StreamingLearner`). Champion always predicts; challengers race in parallel.
`ModelFactory`	Trait for creating model instances from hyperparameter configs. Built-in factories for SGBT, ESN, Mamba, Attention, SpikeNet.
`ConfigSpace`	Hyperparameter search space with float (linear/log), integer, and categorical params.
`DiscountedThompsonSampling`	Non-stationary bandit with exponential forgetting for config-space exploration.

use irithyll::{auto_tune, automl::SgbtFactory, StreamingLearner};

// One-liner: auto-tune SGBT hyperparameters online
let mut tuner = auto_tune(SgbtFactory::new(5));
for i in 0..1000 {
    let x = [i as f64 * 0.01; 5];
    tuner.train(&x, x[0].sin());
}
let pred = tuner.predict(&[0.5; 5]);

Based on Wu et al. (2021) ChaCha, Qi et al. (2023) Discounted Thompson Sampling.

Quick Start

cargo add irithyll

Factory Functions

The fastest way to get started -- one-liner construction for every algorithm:

use irithyll::{sgbt, rls, krls, gaussian_nb, mondrian, linear, StreamingLearner};

let mut trees  = sgbt(50, 0.01);        // 50 boosting steps, lr=0.01
let mut kernel = krls(1.0, 100, 1e-4);  // RBF gamma=1.0, budget=100
let mut bayes  = gaussian_nb();          // Gaussian Naive Bayes
let mut forest = mondrian(10);           // 10 Mondrian trees
let mut lin    = linear(0.01);           // SGD linear model, lr=0.01
let mut rls_m  = rls(0.99);             // RLS, forgetting factor=0.99

// All share the same interface
trees.train(&[1.0, 2.0], 3.0);
let pred = trees.predict(&[1.0, 2.0]);

Neural Architectures

Reservoir computing, state space models, and spiking networks -- same StreamingLearner interface:

use irithyll::*;

// NG-RC: time-delay + polynomial features
let mut model = ngrc(2, 1, 2);
model.train(&[1.0], 2.0);
let pred = model.predict(&[1.0]);

// Echo State Network
let mut model = esn(100, 0.9);

// SSM as feature extractor -> gradient boosted trees
let mut model = pipe(mamba_preprocessor(5, 16)).learner(sgbt(50, 0.01));

// Spiking neural network
let mut model = spikenet(64);
model.train(&[0.5, -0.3], 1.0);
let pred = model.predict(&[0.5, -0.3]);

Composable Pipelines

Chain preprocessors and learners with zero boilerplate:

use irithyll::{pipe, normalizer, sgbt, ccipca, StreamingLearner};

// Normalize → reduce to 5 components → gradient boosted trees
let mut model = pipe(normalizer())
    .pipe(ccipca(5))
    .learner(sgbt(50, 0.01));

model.train(&[100.0, 0.001, 50_000.0, 0.5, 1e-6, 42.0, 7.7, 0.3], 3.14);
let pred = model.predict(&[100.0, 0.001, 50_000.0, 0.5, 1e-6, 42.0, 7.7, 0.3]);

Kernel Methods (KRLS)

Learn nonlinear functions with automatic dictionary sparsification:

use irithyll::{krls, StreamingLearner};

let mut model = krls(1.0, 100, 1e-4);  // RBF kernel, budget=100

for i in 0..500 {
    let x = i as f64 * 0.01;
    model.train(&[x], x.sin());
}

let pred = model.predict(&[1.5708]);  // sin(pi/2) ~ 1.0

Prediction Intervals (RLS)

Get calibrated confidence intervals that narrow as data arrives:

use irithyll::{rls, StreamingLearner, RecursiveLeastSquares};

let mut model = rls(0.99);

for i in 0..1000 {
    let x = i as f64 * 0.01;
    model.train(&[x], 2.0 * x + 1.0);
}

let (pred, lo, hi) = model.predict_interval(&[5.0], 1.96);
// 95% CI: prediction is between lo and hi

Full Builder Pattern

For complete control over SGBT hyperparameters:

use irithyll::{SGBTConfig, SGBT, Sample};

let config = SGBTConfig::builder()
    .n_steps(100)
    .learning_rate(0.0125)
    .max_depth(6)
    .n_bins(64)
    .lambda(1.0)
    .grace_period(200)
    .feature_names(vec!["price".into(), "volume".into()])
    .build()
    .expect("valid config");

let mut model = SGBT::new(config);

for i in 0..500 {
    let x = i as f64 * 0.01;
    model.train_one(&Sample::new(vec![x], 2.0 * x + 1.0));
}

// TreeSHAP explanations
let shap = model.explain(&[3.0]);
if let Some(named) = model.explain_named(&[3.0]) {
    for (name, value) in &named.values {
        println!("{}: {:.4}", name, value);
    }
}

Concept Drift Detection

Automatic adaptation when the data distribution shifts:

use irithyll::{SGBTConfig, SGBT, Sample};
use irithyll::drift::adwin::Adwin;

let config = SGBTConfig::builder()
    .n_steps(50)
    .learning_rate(0.1)
    .drift_detector(Adwin::default())
    .build()
    .expect("valid config");

let mut model = SGBT::new(config);
// When drift is detected, trees are automatically replaced

Async Streaming

Tokio-native with bounded channels and concurrent prediction:

use irithyll::{SGBTConfig, Sample};
use irithyll::stream::AsyncSGBT;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = SGBTConfig::builder()
        .n_steps(30)
        .learning_rate(0.1)
        .build()?;

    let mut runner = AsyncSGBT::new(config);
    let sender = runner.sender();
    let predictor = runner.predictor();

    let handle = tokio::spawn(async move { runner.run().await });

    for i in 0..500 {
        let x = i as f64 * 0.01;
        sender.send(Sample::new(vec![x], 2.0 * x)).await?;
    }

    let pred = predictor.predict(&[3.0]);
    drop(sender);
    handle.await??;
    Ok(())
}

Python

import numpy as np
from irithyll_python import StreamingGBTConfig, StreamingGBT

config = StreamingGBTConfig().n_steps(50).learning_rate(0.1)
model = StreamingGBT(config)

for i in range(500):
    x = np.array([i * 0.01])
    model.train_one(x, 2.0 * x[0] + 1.0)

pred = model.predict(np.array([3.0]))
shap = model.explain(np.array([3.0]))

Packed Inference (`irithyll-core`)

Train with the full irithyll crate, export to a compact binary, and run inference on embedded targets with zero allocation.

Node Format

Each PackedNode is 12 bytes (5 nodes per 64-byte cache line):

Field	Size	Description
`value`	4B	Split threshold (internal) or prediction with learning rate baked in (leaf)
`children`	4B	Packed left/right child u16 indices
`feature_flags`	2B	Bit 15 = is_leaf, bits 14:0 = feature index
`_reserved`	2B	Padding for future use

Export and Deploy

use irithyll::{SGBTConfig, SGBT, Sample};
use irithyll::export_embedded::export_packed;

// 1. Train on a host machine
let config = SGBTConfig::builder()
    .n_steps(50)
    .learning_rate(0.01)
    .max_depth(4)
    .build()
    .unwrap();
let mut model = SGBT::new(config);
for sample in training_data {
    model.train_one(&sample);
}

// 2. Export to packed binary (learning rate baked into leaf values)
let packed: Vec<u8> = export_packed(&model, n_features);
std::fs::write("model.bin", &packed).unwrap();

// 3. Load on embedded target (no_std, zero-alloc)
use irithyll_core::EnsembleView;

// Zero-copy: borrows the buffer, no heap allocation
let model_bytes: &[u8] = include_bytes!("model.bin");
let view = EnsembleView::from_bytes(model_bytes).unwrap();

let prediction: f32 = view.predict(&[1.0, 2.0, 3.0]);

Performance

Benchmarked on x86-64 (single core, 50 trees, max depth 4, 10 features):

Operation	Latency	Throughput
Packed single predict (`irithyll-core`)	66 ns	15.2M pred/s
Packed batch predict (x4 interleave)	--	5.3M pred/s
Training-time predict (`irithyll` SoA)	533 ns	1.9M pred/s

The 8x speedup comes from the 12-byte AoS node layout (vs training-time SoA vectors), branch-free child selection (compiles to cmov on x86), and f32 arithmetic with pre-baked learning rate.

The SGBT Algorithm

The core gradient boosting engine is based on Gunasekara et al., 2024. The ensemble maintains n_steps boosting stages, each owning a streaming Hoeffding tree and a drift detector. For each sample (x, y):

Compute the ensemble prediction F(x) = base + lr * sum(tree_s(x))
For each boosting step, compute gradient/hessian of the loss at the residual
Update the tree's histogram accumulators and evaluate splits via Hoeffding bound
Feed the standardized error to the drift detector
If drift is detected, replace the tree with a fresh alternate

Beyond the paper, irithyll adds EWMA leaf decay, lazy O(1) histogram decay, proactive tree replacement, and EFDT-style split re-evaluation for long-running non-stationary systems.

Architecture

irithyll/                 Workspace root
  src/
    ensemble/             SGBT variants, config, multi-class/target, parallel, adaptive, distributional
    learners/             KRLS, RLS, Gaussian NB, Mondrian forests, linear/polynomial models
    pipeline/             Composable preprocessor + learner chains (StreamingPreprocessor trait)
    preprocessing/        IncrementalNormalizer, OnlineFeatureSelector, CCIPCA
    tree/                 Hoeffding-bound streaming decision trees
    histogram/            Streaming histogram binning (uniform, quantile, k-means)
    drift/                Concept drift detectors (Page-Hinkley, ADWIN, DDM)
    loss/                 Differentiable loss functions (squared, logistic, softmax, Huber)
    explain/              TreeSHAP, StreamingShap, importance drift monitoring
    stream/               Async tokio-based training runner and predictor handles
    metrics/              Online regression/classification metrics, conformal intervals, EWMA
    anomaly/              Half-space trees for streaming anomaly detection
    automl/               Champion-challenger racing, config space, model factories, reward normalization
    reservoir/            NG-RC (time-delay polynomial) and ESN (cycle reservoir) + preprocessors
    ssm/                  StreamingMamba (selective SSM) + MambaPreprocessor
    snn/                  SpikeNet (f64 wrapper), SpikePreprocessor
    serde_support/        Model checkpoint/restore (JSON, bincode)
    export_embedded.rs    SGBT -> packed binary export for irithyll-core

  irithyll-core/          #![no_std] training engine + zero-alloc inference
    packed.rs             12-byte PackedNode, EnsembleHeader, TreeEntry
    traverse.rs           Branch-free tree traversal (single + x4 batch)
    view.rs               EnsembleView<'a> -- zero-copy inference from &[u8]
    quantize.rs           f64 -> f32 quantization utilities
    error.rs              FormatError (no_std compatible)
    reservoir/            NG-RC delay buffer, polynomial features, cycle reservoir, PRNG (alloc)
    ssm/                  Selective SSM: diagonal state, ZOH discretization, projections (alloc)
    snn/                  SpikeNetFixed: Q1.14 LIF neurons, e-prop, delta encoding (alloc)

  irithyll-python/        PyO3 Python bindings

Configuration

Parameter	Default	Description
`n_steps`	100	Number of boosting steps (trees in ensemble)
`learning_rate`	0.0125	Shrinkage factor applied to each tree output
`feature_subsample_rate`	0.75	Fraction of features sampled per tree
`max_depth`	6	Maximum depth of each streaming tree
`n_bins`	64	Number of histogram bins per feature
`lambda`	1.0	L2 regularization on leaf weights
`gamma`	0.0	Minimum gain required to make a split
`grace_period`	200	Minimum samples before evaluating splits
`delta`	1e-7	Hoeffding bound confidence parameter
`drift_detector`	PageHinkley(0.005, 50.0)	Drift detection algorithm for tree replacement
`variant`	Standard	Computational variant (Standard, Skip, MI)
`leaf_half_life`	None (disabled)	EWMA decay half-life for leaf statistics
`max_tree_samples`	None (disabled)	Proactive tree replacement threshold
`split_reeval_interval`	None (disabled)	Re-evaluation interval for max-depth leaves

Feature Flags

These flags apply to the irithyll crate. irithyll-core has no required features (it is no_std with zero dependencies by default; an optional std feature is available).

Feature	Default	Description
`serde-json`	Yes	JSON model serialization
`serde-bincode`	No	Compact binary serialization (bincode)
`parallel`	No	Rayon-based parallel tree training (`ParallelSGBT`)
`simd`	No	AVX2 histogram acceleration
`kmeans-binning`	No	K-means histogram binning strategy
`arrow`	No	Apache Arrow RecordBatch integration
`parquet`	No	Parquet file I/O
`onnx`	No	ONNX model export
`neural-leaves`	No	Experimental MLP leaf models
`full`	No	Enable all features

Neural streaming modules (reservoir, ssm, snn) compile unconditionally -- no feature flag required.

Examples

Run any example with cargo run --example <name>:

Example	Description
`basic_regression`	Linear regression with RMSE tracking
`classification`	Binary classification with logistic loss
`async_ingestion`	Tokio-native async training with concurrent prediction
`custom_loss`	Implementing a custom loss function
`drift_detection`	Abrupt concept drift with recovery analysis
`model_checkpointing`	Save/restore models with prediction verification
`streaming_metrics`	Prequential evaluation with windowed metrics
`krls_nonlinear`	Kernel regression on sin(x) with ALD sparsification
`ccipca_reduction`	Streaming PCA dimensionality reduction
`rls_confidence`	RLS prediction intervals narrowing over time
`pipeline_composition`	Normalizer + SGBT composable pipeline

Documentation

API Reference -- full docs on docs.rs (all features enabled)
Rustdoc (GitHub Pages) -- latest from master

Minimum Supported Rust Version

The MSRV is 1.75. This is checked in CI and will only be raised in minor version bumps.

References

Gunasekara, N., Pfahringer, B., Gomes, H. M., & Bifet, A. (2024). Gradient boosted trees for evolving data streams. Machine Learning, 113, 3325-3352.

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Rodan, A., & Tino, P. (2010). Minimum complexity echo state network. IEEE Transactions on Neural Networks, 23(1), 131-144.

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Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R., & Maass, W. (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications, 11, 3625.

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Yang, S., et al. (2024). Gated Delta Networks: Improving Mamba2 with Delta Rule. arXiv preprint arXiv:2412.06464.

Peng, B., et al. (2024). Eagle and Finch: RWKV with matrix-valued states and dynamic recurrence. arXiv preprint arXiv:2404.05892.

Beck, M., et al. (2024). xLSTM: Extended long short-term memory. NeurIPS 2024.

Sun, Y., et al. (2023). Retentive network: A successor to transformer for large language models. arXiv preprint arXiv:2307.08621.

De, S., Smith, S. L., et al. (2024). Griffin: Mixing gated linear recurrences with local attention for efficient language models. arXiv preprint arXiv:2402.19427.

Kirkpatrick, J., et al. (2017). Overcoming catastrophic forgetting in neural networks. PNAS, 114(13), 3521-3526.

Dohare, S., et al. (2024). Loss of plasticity in deep continual learning. Nature, 632, 768-774.

Angelopoulos, A. N., Candes, E. J., & Tibshirani, R. J. (2023). Conformal PID control for time series prediction. NeurIPS 2023.

Bhatnagar, A., Wang, H., Xiong, C., & Bai, Y. (2023). Improved online conformal prediction via strongly adaptive online learning. ICML 2023.

Gupta, C., & Ramdas, A. (2023). Online Platt scaling with calibeating. ICML 2023.

Wu, Q., Iyer, C., & Wang, C. (2021). ChaCha for online AutoML. ICML 2021.

Qi, Y., et al. (2023). Discounted Thompson Sampling for non-stationary bandits. arXiv preprint arXiv:2305.10718.

License

Licensed under either of

Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.