Struct lightgbm::Booster

pub struct Booster { /* fields omitted */ }

Core model in LightGBM, containing functions for training, evaluating and predicting.

Implementations

impl Booster

pub fn from_file(filename: &str) -> Result<Self>

Init from model file.

pub fn train(dataset: Dataset, parameter: &Value) -> Result<Self>

Create a new Booster model with given Dataset and parameters.

Example

extern crate serde_json;
use lightgbm::{Dataset, Booster};
use serde_json::json;

let data = vec![vec![1.0, 0.1, 0.2, 0.1],
               vec![0.7, 0.4, 0.5, 0.1],
               vec![0.9, 0.8, 0.5, 0.1],
               vec![0.2, 0.2, 0.8, 0.7],
               vec![0.1, 0.7, 1.0, 0.9]];
let label = vec![0.0, 0.0, 0.0, 1.0, 1.0];
let dataset = Dataset::from_mat(data, label).unwrap();
let params = json!{
   {
        "num_iterations": 3,
        "objective": "binary",
        "metric": "auc"
    }
};
let bst = Booster::train(dataset, &params).unwrap();

pub fn predict(&self, data: Vec<Vec<f64>>) -> Result<Vec<Vec<f64>>>

Predict results for given data.

Input data example

let data = vec![vec![1.0, 0.1, 0.2],
               vec![0.7, 0.4, 0.5],
               vec![0.1, 0.7, 1.0]];

Output data example

let output = vec![vec![1.0, 0.109, 0.433]];

pub fn num_feature(&self) -> Result<i32>

Get Feature Num.

pub fn feature_name(&self) -> Result<Vec<String>>

Get Feature Names.

pub fn feature_importance(&self) -> Result<Vec<f64>>

pub fn save_file(&self, filename: &str) -> Result<()>

Save model to file.

Trait Implementations

impl Drop for Booster

fn drop(&mut self)

Executes the destructor for this type.