Struct forust_ml::splitter::MissingBranchSplitter

pub struct MissingBranchSplitter {
    pub l2: f32,
    pub gamma: f32,
    pub min_leaf_weight: f32,
    pub learning_rate: f32,
    pub allow_missing_splits: bool,
    pub constraints_map: ConstraintMap,
}

Missing branch splitter Always creates a separate branch for the missing values of a feature. This results, in every node having a specific “missing”, direction. If this node is able, it will be split further, otherwise it will a leaf node will be generated.

Fields

l2: f32

gamma: f32

min_leaf_weight: f32

learning_rate: f32

allow_missing_splits: bool

constraints_map: ConstraintMap

Trait Implementations

impl Splitter for MissingBranchSplitter

fn get_constraint(&self, feature: &usize) -> Option<&Constraint>

fn get_gamma(&self) -> f32

fn get_l2(&self) -> f32

fn get_learning_rate(&self) -> f32

fn evaluate_split( &self, left_gradient: f32, left_hessian: f32, right_gradient: f32, right_hessian: f32, missing_gradient: f32, missing_hessian: f32, lower_bound: f32, upper_bound: f32, constraint: Option<&Constraint> ) -> Option<(NodeInfo, NodeInfo, MissingInfo)>

Evaluate a split, returning the node info for the left, and right splits, as well as the node info the missing data of a feature.

fn handle_split_info( &self, split_info: SplitInfo, n_nodes: &usize, node: &mut SplittableNode, index: &mut [usize], data: &Matrix<'_, u16>, cuts: &JaggedMatrix<f64>, grad: &[f32], hess: &[f32], parallel: bool ) -> Vec<SplittableNode>

Handle the split info, creating the children nodes, this function will return a vector of new splitable nodes, that can be added to the growable stack, and further split, or converted to leaf nodes.

fn best_split(&self, node: &SplittableNode) -> Option<SplitInfo>

Find the best possible split, considering all feature histograms. If we wanted to add Column sampling, this is probably where we would need to do it, otherwise, it would be at the tree level.

fn best_feature_split( &self, node: &SplittableNode, feature: usize ) -> Option<SplitInfo>

fn split_node( &self, n_nodes: &usize, node: &mut SplittableNode, index: &mut [usize], data: &Matrix<'_, u16>, cuts: &JaggedMatrix<f64>, grad: &[f32], hess: &[f32], parallel: bool ) -> Vec<SplittableNode>

Split the node, if we cant find a best split, we will need to return an empty vector, this node is a leaf.