Struct Model 

pub struct Model {
    pub objective: ModelObjective,
    pub feature_names: Vec<String>,
    pub feature_infos: Vec<String>,
    pub trees: Vec<Tree>,
    pub parameters: Vec<(String, String)>,
    pub pandas_categorical: Option<String>,
}

Fields

objective: ModelObjective

feature_names: Vec<String>

feature_infos: Vec<String>

BinMapper::info_string per feature.

trees: Vec<Tree>

parameters: Vec<(String, String)>

[key: value] lines of the parameters: block.

pandas_categorical: Option<String>

The JSON after pandas_categorical: (LightGBM’s Python package stores pandas category levels there).

Implementations

impl Model

pub fn predict_raw_row(&self, row: &[f64], num_trees: Option<usize>) -> f64

Sum of the leaf values row reaches in the first num_trees trees (all when None).

row holds at least num_features() values; NaN is a missing value.

pub fn predict( &self, matrix: &DenseMatrix<'_>, raw_score: bool, num_trees: Option<usize>, ) -> Vec<f64>

Predictions for every row, parallel over rows; raw_score = false applies the objective’s transform.

Panics

When matrix has fewer columns than the model has features.

pub fn feature_importance(&self, kind: ImportanceType) -> Vec<f64>

LightGBM GBDT::FeatureImportance: per feature, the number of splits (Split) or their summed gain (Gain), counting only splits with positive gain.

impl Model

pub fn to_lightgbm_string(&self) -> String

The model file LightGBM 3.3.5’s Python Booster.model_to_string() returns, byte for byte.

pub fn from_lightgbm_string(text: &str) -> Result<Model>

Parses a LightGBM v3 model string (written by silvic or by LightGBM 3.x/4.x).

impl Model

pub fn num_features(&self) -> usize

pub fn num_trees(&self) -> usize

Trait Implementations

impl Clone for Model

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Model

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for Model

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for Model