# LGBM-rs
[](https://crates.io/crates/lgbm)
[](https://docs.rs/lgbm/)
[](https://github.com/frozenlib/lgbm-rs/actions)
Unofficial Rust bindings for [LightGBM](https://lightgbm.readthedocs.io/en/latest/)
## Requirement
### Windows or Linux
1. Install LightGBM and build according to [LightGBM Documentation](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).
2. Set the environment variable `LIGHTGBM_LIB_DIR` to the directory containing the build output (`.dll` and `.lib` on Windows, `.so` on Linux).
### MacOS
1. Run `brew install lightgbm` and install LightGBM on your system.
2. Set the environment variable `LIGHTGBM_LIB_DIR` to the directory containing `lib_lightgbm.dylib`.
```sh
brew install lightgbm
export LIGHTGBM_LIB_DIR=/opt/homebrew/Cellar/lightgbm/4.6.0/lib/
```
## Example
**Cargo.toml**
```toml
[dependencies]
lgbm = "0.0.6"
```
**main.rs**
```rust
use lgbm::{
parameters::{Objective, Verbosity},
Booster, Dataset, Field, MatBuf, Parameters, PredictType,
};
use std::sync::Arc;
fn main() -> anyhow::Result<()> {
let mut p = Parameters::new();
p.push("num_class", 3);
p.push("objective", Objective::Multiclass);
p.push("verbosity", Verbosity::Fatal);
let mut train = Dataset::from_mat(&MatBuf::from_rows(train_features()), None, &p)?;
train.set_field(Field::LABEL, &train_labels())?;
let mut valid = Dataset::from_mat(&MatBuf::from_rows(valid_features()), Some(&train), &p)?;
valid.set_field(Field::LABEL, &valid_labels())?;
let mut b = Booster::new(Arc::new(train), &p)?;
b.add_valid_data(Arc::new(valid))?;
for _ in 0..100 {
if b.update_one_iter()? {
break;
}
}
let p = Parameters::new();
let rs = b.predict_for_mat(
&MatBuf::from_rows(test_features()),
PredictType::Normal,
0,
None,
&p,
)?;
println!("\n{rs:.5}");
Ok(())
}
fn train_features() -> Vec<[f64; 1]> {
(0..128).map(|x| [(x % 3) as f64]).collect()
}
fn train_labels() -> Vec<f32> {
(0..128).map(|x| (x % 3) as f32).collect()
}
fn valid_features() -> Vec<[f64; 1]> {
(0..64).map(|x| [(x % 3) as f64]).collect()
}
fn valid_labels() -> Vec<f32> {
(0..64).map(|x| (x % 3) as f32).collect()
}
fn test_features() -> Vec<[f64; 1]> {
(0..4).map(|x| [(x % 3) as f64]).collect()
}
```
**output**
```txt
num_data : 4
num_class : 3
num_2 : 1
0 | 0.99998 | 0.00001 | 0.00001 |
1 | 0.00001 | 0.99998 | 0.00001 |
2 | 0.00001 | 0.00001 | 0.99998 |
3 | 0.99998 | 0.00001 | 0.00001 |
```
## Static linking or dynamic linking
The following types of linking are supported.
| Windows | ✔ | ✔ |
| Linux | ✔ | ✔ |
| MacOS | | ✔ |
On Windows, if `lib_lightgbm.dll` exists in the directory specified by `LIGHTGBM_LIB_DIR`, it will be dynamically linked. Otherwise, it will be statically linked.
On Linux, if `lib_lightgbm.a` exists in the directory specified by `LIGHTGBM_LIB_DIR`, it is statically linked. Otherwise, it is dynamically linked.
## License
This project is licensed under MIT. See the LICENSE files for details.
## Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you will be under the MIT license, without any additional terms or conditions.