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Introduction

leaves is a library implementing prediction code for GBRT (Gradient Boosting Regression Trees) models in pure Go. The goal of the project - make it possible to use models from popular GBRT frameworks in Go programs without C API bindings.

NOTE: Before 1.0.0 release the API is a subject to change.

Features

Usage examples

In order to start, go get this repository:

go get github.com/dmitryikh/leaves

Minimal example:

package main

import (
	"fmt"

	"github.com/dmitryikh/leaves"
)

func main() {
	// 1. Read model
	useTransformation := true
	model, err := leaves.LGEnsembleFromFile("lightgbm_model.txt", useTransformation)
	if err != nil {
		panic(err)
	}

	// 2. Do predictions!
	fvals := []float64{1.0, 2.0, 3.0}
	p := model.PredictSingle(fvals, 0)
	fmt.Printf("Prediction for %v: %f\n", fvals, p)
}

In order to use XGBoost model, just change leaves.LGEnsembleFromFile, to leaves.XGEnsembleFromFile.

Documentation

Documentation is hosted on godoc (link). Documentation contains complex usage examples and full API reference. Some additional information about usage examples can be found in leaves_test.go.

Compatibility

Most leaves features are tested to be compatible with old and coming versions of GBRT libraries. In compatibility.md one can found detailed report about leaves correctness against different versions of external GBRT libraries.

Some additional information on new features and backward compatibility can be found in NOTES.md.

Benchmark

Below are comparisons of prediction speed on batches (~1000 objects in 1 API call). Hardware: MacBook Pro (15-inch, 2017), 2,9 GHz Intel Core i7, 16 ГБ 2133 MHz LPDDR3. C API implementations were called from python bindings. But large batch size should neglect overhead of python bindings. leaves benchmarks were run by means of golang test framework: go test -bench. See benchmark for mode details on measurments. See testdata/README.md for data preparation pipelines.

Single thread:

Test CaseFeaturesTreesBatch sizeC APIleaves
LightGBM MS LTR137500100049ms51ms
LightGBM Higgs28500100050ms50ms
LightGBM KDD Cup 99*411200100070ms85ms
XGBoost Higgs28500100044ms50ms

4 threads:

Test CaseFeaturesTreesBatch sizeC APIleaves
LightGBM MS LTR137500100014ms14ms
LightGBM Higgs28500100014ms14ms
LightGBM KDD Cup 99*411200100019ms24ms
XGBoost Higgs285001000?14ms

(?) - currenly I'm unable to utilize multithreading form XGBoost predictions by means of python bindings

(*) - KDD Cup 99 problem involves continuous and categorical features simultaneously

Limitations

Contacts

In case if you are interested in the project or if you have questions, please contact with me by email: khdmitryi at gmail.com