# xgb2code

**Repository Path**: mirrors_maxmind/xgb2code

## Basic Information

- **Project Name**: xgb2code
- **Description**: A converter for xgboost model dumps to code.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-24
- **Last Updated**: 2026-09-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# xgb2code

`xgb2code` - generate code for an XGB model

## Description

This program takes an XGB model (in JSON format) and generates code for it.

Generating code for a model avoids having to call out to a different language
(e.g., C) as well as avoids the need for using the XGB libraries at runtime.

## Model Support

The following XGBoost objectives are supported:

- Binary classification: `binary:logistic` and `binary:logitraw`.
- Regression: `reg:logistic`, `reg:squarederror`, `reg:linear`,
  `reg:absoluteerror`, `reg:pseudohubererror`, and `reg:quantileerror`.

Both numeric and categorical splits (models trained with `enable_categorical`)
are supported. Categorical features must be passed to the generated function as
their integer category codes, the same encoding XGBoost uses internally; a
missing feature is represented by a `nil` entry in the `data` slice.

## Supported Languages

Currently `xgb2code` supports generating Go code.

## Usage

```bash
$ ./xgb2code -h
Usage of ./xgb2code:
  -function-name string
        The function name to use. Must be a valid Go function name.
  -go-package-name string
        The package name to use when generating Go code. Must be a valid Go package name.
  -input-json string
        Path to the model as JSON
  -language string
        Language to generate code for. Currently 'go' is supported. (default "go")
  -output-file string
        The file to write to
```

## Example Usage

```bash
$ ./xgb2code -function-name predict \
             -go-package-name main \
             -input-json testdata/small-model/model.json \
             -language go \
             -output-file predict.go
```

produces a file `predict.go` where the primary model prediction function has the
signature:

```go
func predict(data []*float32, predMargin bool) float32 {
```

When `predMargin` is true, the function returns the raw margin (the summed tree
outputs plus the `base_score` intercept). Otherwise, the sigmoid is applied for
the logistic objectives (`binary:logistic` and `reg:logistic`); for all other
objectives the margin is the final prediction, so `predMargin` has no effect.

## Library Usage

The code generation functionality is also available as a library via the `gen`
package:

```go
package main

import (
	"log"

	"github.com/maxmind/xgb2code/gen"
)

func main() {
	err := gen.GenerateFile(
		"testdata/small-model/model.json", // input model JSON
		"main",                            // Go package name
		"predict",                         // function name
		"predict.go",                      // output file
	)
	if err != nil {
		log.Fatal(err)
	}
}
```

## Installation

[Release binaries and packages](https://github.com/maxmind/xgb2code/releases)
have been made available for several popular platforms. Simply download the
binary for your platform and run it.

## Bug Reports

Please report bugs by filing an issue with our GitHub issue tracker at
[https://github.com/maxmind/xgb2code/issues](https://github.com/maxmind/xgb2code/issues).

## Copyright and License

This software is Copyright (c) 2022 - 2026 by MaxMind, Inc.

This is free software, licensed under the
[Apache License, Version 2.0](LICENSE-APACHE) or the [MIT License](LICENSE-MIT),
at your option.