Rust bindings for XGBoost.

Related tags

Machine learning rust machine-learning ffi xgboost
Overview

rust-xgboost

Travis Build Status Documentation link

Rust bindings for the XGBoost gradient boosting library.

Basic usage example:

extern crate xgboost;

use xgboost::{parameters, dmatrix::DMatrix, booster::Booster};

fn main() {
    // training matrix with 5 training examples and 3 features
    let x_train = &[1.0, 1.0, 1.0,
                    1.0, 1.0, 0.0,
                    1.0, 1.0, 1.0,
                    0.0, 0.0, 0.0,
                    1.0, 1.0, 1.0];
    let num_rows = 5;
    let y_train = &[1.0, 1.0, 1.0, 0.0, 1.0];

    // convert training data into XGBoost's matrix format
    let mut dtrain = DMatrix::from_dense(x_train, num_rows).unwrap();

    // set ground truth labels for the training matrix
    dtrain.set_labels(y_train).unwrap();

    // test matrix with 1 row
    let x_test = &[0.7, 0.9, 0.6];
    let num_rows = 1;
    let y_test = &[1.0];
    let mut dtest = DMatrix::from_dense(x_test, num_rows).unwrap();
    dtest.set_labels(y_test).unwrap();

    // build overall training parameters
    let params = parameters::ParametersBuilder::default().build().unwrap();

    // specify datasets to evaluate against during training
    let evaluation_sets = &[(&dtrain, "train"), (&dtest, "test")];

    // train model, and print evaluation data
    let bst = Booster::train(&params, &dtrain, 3, evaluation_sets).unwrap();

    println!("{:?}", bst.predict(&dtest).unwrap());
}

See the examples directory for more detailed examples of different features.

Status

Currently in a very early stage of development, so the API is changing as usability issues occur, or new features are supported.

Builds against XGBoost 0.81.

Platforms

Tested:

  • Linux
  • Mac OS

Unsupported:

  • Windows
Comments
  • Will this repository be maintained?

    Will this repository be maintained?

    @davechallis @jonathanstrong Hi.

    Do you have any plans to continue maintaining this repository?

    I'm working on LightGBM's rust bindings. https://github.com/vaaaaanquish/lightgbm-rs I have an issue where I want to integrate the interface with XGBoost.

    If the maintenance continues, I can provide some pull requests. I have the energy to take over as maintainer if you want.

    Please provide a policy. I am waiting for a good answer :)

    opened by vaaaaanquish 3
  • thread 'main' panicked at 'assertion failed: !out_result.is_null()'

    thread 'main' panicked at 'assertion failed: !out_result.is_null()'

    Hello, I'm getting this error /Users/<name>/.cargo/registry/src/github.com-1ecc6299db9ec823/xgboost-0.1.4/src/booster.rs:367:9 when trying to run a prediction on a loaded xgboost model that was trained via Python. Here is the sample code:

    extern crate xgboost;

use xgboost::{Booster, DMatrix};

fn main() -> Result<(), String> {
    let model = Booster::load("model.bst").unwrap();

    let data = &[
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
        100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1.0, 0.0, 0.0, 0.0, 0.0,
        0.0, 0.0, 0.0, 0.0, 0.0,
    ];

    let dmat = DMatrix::from_dense(data, 1).unwrap();

    let score = model.predict(&dmat).unwrap();
    return Ok(());
}

    Any idea why this happens?

    opened by amackillop 1
  • Expose raw probabilities from MultiSoftmax learning objective.

    Expose raw probabilities from MultiSoftmax learning objective.

    It would be nice to have predict_proba functionality similar to that provided by scikit, to see the probability of each class in a multiclass classification model.

    opened by montanalow 1
  • Update XGBoost 1.4.2

    Update XGBoost 1.4.2

    @davechallis @jonathanstrong Hi.

    Overview

    • fix: https://github.com/davechallis/rust-xgboost/issues/8
      • upgrade xgboost 1.4.2
      • update bindgen
      • update example

    Requires

    This PR requires the following merge

    • [x] ~https://github.com/davechallis/rust-xgboost/pull/13~
      • Included in this PR
    • [ ] https://github.com/davechallis/xgboost/pull/1
    • [x] https://github.com/dmlc/xgboost/pull/7265

    Confirmed that example/runall.sh is working.

    $ head xgboost-sys/xgboost/NEWS.md
XGBoost Change Log
==================

This file records the changes in xgboost library in reverse chronological order.

## v1.4.2 (2021.05.13)

$ ./runall.sh
...
Compiling xgboost-sys v0.2.0 (/app/rust-xgboost/xgboost-sys)
...

    Description

    • Using CMake because removed build.sh https://github.com/dmlc/xgboost/commit/207f0587111a951f5cd6c609ade001587f242bd1
    • XGBoosterPredict's args has been changed
      • https://xgboost.readthedocs.io/en/latest/dev/c__api_8h.html#adc14afaedd5f1add105d18942a4de33c
      • 0 for predict
      • 1 for custom metric training
    • remove root_index
      • https://github.com/dmlc/xgboost/pull/5138#discussion_r360838092
    • add group_ptr
      • https://github.com/dmlc/xgboost/pull/5187
    • demo file to start with 1 index
      • https://github.com/dmlc/xgboost/commit/a894ab6898f5ed525ca7dec89a79ea1fa7f49f5a#diff-b1f466d8b5ac867fc06e6d49115839b7b8428f5c3eb9f7f11488dd677f6f7dbc
    opened by vaaaaanquish 1
  • Incorrect sequence of updaters

    Incorrect sequence of updaters

    When using the xgboost module with the basic example I am getting the below warning

    [12:49:10] DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
[12:49:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 0 pruned nodes, max_depth=0
[0]	test-error:0	train-error:0.2
[12:49:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 0 pruned nodes, max_depth=0
[1]	test-error:0	train-error:0.2
    opened by infinite-Joy 1
  • Update to XGBoost 1.62

    Update to XGBoost 1.62

    This PR:

    • updates to use the upstream XGBoost version 1.62
    • vendors xgboost-sys into the crate for easier distribution (I'm not sure if this is the right way to do this?)
    opened by levkk 0
  • Increment Bindgen Version

    Increment Bindgen Version

    Hi guys. Any thoughts on incrementing bindgen version? Got into an issue where another library was using a newer version of bindgen, which link to a newer version of clang.

    opened by asdacap 0
  • Change types for some `TreeBoosterParameters` to match types in XGBoost

    Change types for some `TreeBoosterParameters` to match types in XGBoost

    While working on a personal project using this repo I noticed that some of the types for the parameters in TreeBoosterParameters don't seem to match up with what's in XGBoost? You can check them out here.

    Changelog

    • Change types for a few TreeBoosterParameters to match types in XGBoost:
      • gamma: u32 -> f32
      • min_child_weight: u32 -> f32
      • max_delta_step: u32 -> f32
      • lambda: u32 -> f32
      • alpha: u32 -> f32
    • Add some missing parameters:
      • colsample_bynode: Subsample columns for each split.
      • num_parallel_tree: Number of trees to train in parallel ("boosted random forest")
    • Change updater parameter to only pass a value if user specifies one (fix: #7)
    • Add impls of From<String> and From<&str> for TreeMethod.
    • Removed some muts to suppress compiler warnings.

    I'm keen to hear feedback on the changes for the updater parameter; the main design goal for the change was "make the warning message go away", so would not be surprised if my changes aren't the best way of handling things.

    Closes #7

    opened by KyussCaesar 0
  • upgrade xgboost-sys

    upgrade xgboost-sys

    Hi there, Thank you for your great work! Is it possible to upgrade xgboost-sys which depends on an old version of clang-sys, and causes conflict with boringssl?

    error: multiple packages link to native library `clang`, but a native library can be linked only once

package `clang-sys v0.22.0`
    ... which satisfies dependency `clang-sys = "^0.22.0"` (locked to 0.22.0) of package `bindgen v0.36.1`
    ... which satisfies dependency `bindgen = "^0.36"` (locked to 0.36.1) of package `xgboost-sys v0.1.2`
    ... which satisfies dependency `xgboost-sys = "^0.1.2"` (locked to 0.1.2) of package `xgboost v0.1.4`
    ... which satisfies dependency `xgboost = "^0.1.4"` (locked to 0.1.4) of package ...
...
links to native library `clang`

package `clang-sys v1.3.1`
    ... which satisfies dependency `clang-sys = "^1"` (locked to 1.3.1) of package `bindgen v0.59.2`
    ... which satisfies dependency `bindgen = "^0.59"` (locked to 0.59.2) of package `boring-sys v2.0.0`
    ... which satisfies dependency `boring-sys = ">=1.1.0, <3.0.0"` (locked to 2.0.0) of package `boring v2.0.0`

...
    opened by 1a1a11a 0
  • Python trained model deployment

    Python trained model deployment

    Hi guys,

    Thanks for all your work on this wrapper for XGboost, its awesome!

    Is it possible to take a model file trained with the xgboost python package and load it for predictions in urst using crate?

    Cheers!

    opened by CharlieBickerton 1
  • I am getting this when I tuned my parameters

    I am getting this when I tuned my parameters

    DANGER AHEAD: You have manually specified updater parameter. The tree_method parameter will be ignored. Incorrect sequence of updaters will produce undefined behaviour. For common uses, we recommend usingtree_method parameter instead.

    Thanks in advance for the great model

    opened by dataxsaiyan3 0
  • Not able to build on ubuntu machine

    Not able to build on ubuntu machine

    I have tried to use xgboost 0.1.4 on a mac and it works fine in a Mac, but not building in Ubuntu machines. This is probably because the build procedure uses clang in the backend. Building the original Xgboost (https://github.com/dmlc/xgboost) with make and cmake. I can see that there is a platform dependent build step. https://github.com/davechallis/rust-xgboost/blob/master/xgboost-sys/build.rs#L60. Not sure if this is the source of the problem.

    opened by infinite-Joy 1
  • help on safe implementation of XGBoosterGetModelRaw?

    help on safe implementation of XGBoosterGetModelRaw?

    hey -

    I was trying to implement the other side of the load/save from buffer, XGBoosterGetModelRaw, but I'm stuck, and thought maybe you could help.

    This is where I am (in Booster impl):

        /// Returns a `Vec<u8>` with model weights.
    pub fn to_vec(&self) -> XGBResult<Vec<u8>> {
        debug!("Writing Booster to_vec");
        let mut out_len = 0; 
        let mut out_dptr = ptr::null_mut();
        xgb_call!(xgboost_sys::XGBoosterGetModelRaw(self.handle, &mut out_len, out_dptr))?;
        // let bytes: &[u8] = unsafe {
        //     let length: u64 = *(out_len as *const _);
        //     std::slice::from_raw_parts(out_dptr as *const _, length as usize)
        // };
        // let mut out: Vec<u8> = vec![0u8; bytes.len()];
        // out[..].copy_from_slice(bytes);
        // Ok(out)
    }

    The commented out section is because I can't get past calling the xgboost function.

    I have tried a fairly wide variety of various pointer things as it relates to calling XGBoosterGetModelRaw, but I get SIGSEGV no matter what.

    The xgboost api is defined as:

    /*!
 * \brief save model into binary raw bytes, return header of the array
 * user must copy the result out, before next xgboost call
 * \param handle handle
 * \param out_len the argument to hold the output length
 * \param out_dptr the argument to hold the output data pointer
 * \return 0 when success, -1 when failure happens
 */
XGB_DLL int XGBoosterGetModelRaw(BoosterHandle handle,
                                 bst_ulong *out_len,
                                 const char **out_dptr);

    Any ideas? Thanks!

    opened by jonathanstrong 1
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Dave Challis
Dave Challis
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