Struct Tree 

pub struct Tree {

    pub num_leaves: usize,
    pub num_cat: usize,
    pub split_feature: Vec<i32>,
    pub split_gain: Vec<f32>,
    pub threshold: Vec<f64>,
    pub decision_type: Vec<i8>,
    pub left_child: Vec<i32>,
    pub right_child: Vec<i32>,
    pub leaf_value: Vec<f64>,
    pub leaf_weight: Vec<f64>,
    pub leaf_count: Vec<i64>,
    pub internal_value: Vec<f64>,
    pub internal_weight: Vec<f64>,
    pub internal_count: Vec<i64>,
    pub cat_boundaries: Vec<i32>,
    pub cat_threshold: Vec<u32>,
    pub shrinkage: f64,
    pub leaf_parent: Vec<i32>,
    pub leaf_depth: Vec<i32>,
}

Fields

num_leaves: usize

num_cat: usize

split_feature: Vec<i32>

Input (real) feature index of each internal node.

split_gain: Vec<f32>

threshold: Vec<f64>

Numerical: the value <= which goes left. Categorical: index into cat_boundaries.

decision_type: Vec<i8>

left_child: Vec<i32>

right_child: Vec<i32>

leaf_value: Vec<f64>

leaf_weight: Vec<f64>

leaf_count: Vec<i64>

internal_value: Vec<f64>

internal_weight: Vec<f64>

internal_count: Vec<i64>

cat_boundaries: Vec<i32>

num_cat + 1 offsets into cat_threshold.

cat_threshold: Vec<u32>

Bitsets of category values going left, 32 per word.

shrinkage: f64

leaf_parent: Vec<i32>

Training only: each leaf’s parent node (-1 for the root).

leaf_depth: Vec<i32>

Training only: each leaf’s depth (root 0).

Implementations

impl Tree

pub fn leaf_index(&self, row: &[f64]) -> usize

The leaf row reaches, as Model::predict_raw_row walks it: row holds raw feature values (NaN missing).

impl Tree

pub fn new() -> Tree

A single leaf of value 0.

pub fn split_numerical( &mut self, leaf: usize, feature: usize, threshold: f64, default_left: bool, missing: MissingType, children: &SplitChildren, ) -> usize

Tree::Split for a numerical feature; returns the new (right) leaf’s index.

pub fn split_categorical( &mut self, leaf: usize, feature: usize, categories: &[i32], missing: MissingType, children: &SplitChildren, ) -> usize

Tree::SplitCategorical: categories are the category values going left.

pub fn shrink(&mut self, rate: f64)

Tree::Shrinkage.

pub fn add_bias(&mut self, bias: f64)

Tree::AddBias: folds the initial score into the first tree, whose shrinkage LightGBM then writes as 1.

pub fn as_constant(&mut self, value: f64)

Tree::AsConstantTree.

pub fn set_leaf_output(&mut self, leaf: usize, value: f64)

Tree::SetLeafOutput.

pub fn max_depth(&self) -> i32

Depth of the deepest leaf.

Trait Implementations

impl Clone for Tree

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 Tree

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

Formats the value using the given formatter.

impl Default for Tree

fn default() -> Self

Returns the “default value” for a type.

impl PartialEq for Tree

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

Equality operator ==.

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

Inequality operator !=.

impl StructuralPartialEq for Tree