#boosting #machine-learning


MiniBoosts: A collection of boosting algorithms written in Rust 🦀

2 unstable releases

0.2.0 Mar 13, 2023
0.1.0 Feb 11, 2023

#39 in Machine learning

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MIT license



A collection of boosting algorithms written in Rust 🦀.

Training loss comparison Soft margin objective comparison

Some boosting algorithms use Gurobi optimizer, so you must acquire a license to use this library. If you have the license, you can use these boosting algorithms (boosters) by specifying features = ["extended"] in Cargo.toml.

Note! If you are trying to use the extended feature without a Gurobi license, the compilation fails.


Currently, I implemented the following Boosters and Weak Learners.

If you invent a new boosting algorithm, you can introduce it by implementing Booster trait. See cargo doc --open for details.


by Freund and Schapire, 1997
by Rätsch and Warmuth, 2005
by Rocco A. Servedio, 2003
by Warmuth, Liao, and Rätsch, 2006
by Demiriz, Bennett, and Shawe-Taylor, 2002
by Warmuth, Glocer, and Rätsch, 2007
by Warmuth and Glocer, and Vishwanathan, 2008
CERLPBoost (Corrective ERLPBoost)
by Shalev-Shwartz and Singer, 2010
by Mitsuboshi, Hatano, and Takimoto, 2022
GBM (Gradient Boosting Machine),
by Jerome H. Friedman

Weak Learners

DTree (Decision Tree)
RTree (Regression Tree)

Future work

  • Boosters

  • Weak Learners

    • Bag of words
    • TF-IDF
    • Two-Layer Neural Network
    • RBF-Net
  • Others

    • Parallelization
    • LP/QP solver (This work allows you to use extended features without a license).

How to use

You can see the document by cargo doc --open command.

You need to write the following line to Cargo.toml.

miniboosts = { git = "https://github.com/rmitsuboshi/miniboosts" }

If you want to use extended features, such as LPBoost, specify the option:

miniboosts = { git = "https://github.com/rmitsuboshi/miniboosts", features = ["extended"] }

Here is a sample code:

use miniboosts::prelude::*;

fn main() {
    // Set file name
    let file = "/path/to/input/data.csv";

    // Read a CSV file
    // The column named `class` is corresponds to the labels (targets).
    let has_header = true;
    let sample = Sample::from_csv(file, has_header)

    // Set tolerance parameter
    let tolerance: f64 = 0.01;

    // Initialize Booster
    let mut booster = AdaBoost::init(&sample)
        .tolerance(tol); // Set the tolerance parameter.

    // Initialize Weak Learner
    // For decision tree, the default `max_depth` is `None` so that 
    // The tree grows extremely large.
    let weak_learner = DTree::init(&sample)
        .max_depth(2) // Specify the max depth (default is not specified)
        .criterion(Criterion::Edge); // Choose the split criterion

    // Run boosting algorithm
    // Each booster returns a combined hypothesis.
    let f = booster.run(&weak_learner);

    // Get the batch prediction for all examples in `data`.
    let predictions = f.predict_all(&sample);

    // You can predict the `i`th instance.
    let i = 0_usize;
    let prediction = f.predict(&sample, i);

If you use boosting for soft margin optimization, initialize booster like this:

let n_sample = sample.shape().0;
let nu = n_sample as f64 * 0.2;
let lpboost = LPBoost::init(&sample)
    .nu(nu); // Setting the capping parameter.

Note that the capping parameter must satisfies 1 <= nu && nu <= n_sample.

Research feature

When you invent a new boosting algorithm and write a paper, you need to compare it to previous works to show the effectiveness of your one. One way to compare the algorithms is to plot the curve for objective value or train/test loss. This crate can output a CSV file for such values in each step.

Here is an example:

use miniboosts::prelude::*;
use miniboosts::research::Logger;
use miniboosts::common::objective_functions::SoftMarginObjective;

// Define a loss function
fn zero_one_loss<H>(sample: &Sample, f: &CombinedHypothesis<H>) -> f64
    where H: Classifier
    let n_sample = sample.shape().0 as f64;

    let target = sample.target();

        .map(|(fx, &y)| if fx != y as i64 { 1.0 } else { 0.0 })
        / n_sample

fn main() {
    // Read the training data
    let path = "/path/to/train/data.csv";
    let train = Sample::from_csv(path, true)

    // Set some parameters used later.
    let n_sample = train.shape().0 as f64;
    let nu = 0.01 * n_sample;

    // Read the test data
    let path = "/path/to/test/data.csv";
    let test = Sample::from_csv(path, true)

    let booster = LPBoost::init(&train);
    let weak_learner = DTree::init(&train)

    let mut logger = Logger::new(
        booster, tree, objective, zero_one_loss, &train, &test

    // Each line of `lpboost.csv` contains the following four information:
    // Objective value, Train loss, Test loss, Time per iteration
    // The returned value `f` is the combined hypothesis.
    let f = logger.run("lpboost.csv");

Further, one can log your algorithm by implementing Research trait.

Run cargo doc --open to see more information.


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