Struct ParallelUtils 

pub struct ParallelUtils;

Parallel utilities for tree algorithms

Implementations

impl ParallelUtils

pub fn maybe_parallel_map<T, U, F>(items: Vec<T>, f: F) -> Vec<U>

where F: Fn(T) -> U,

Execute a function sequentially if parallel feature is disabled

pub fn parallel_bootstrap_samples( x: &Array2<f64>, y: &Array1<i32>, n_estimators: usize, n_samples: usize, random_seeds: &[u64], ) -> Vec<(Array2<f64>, Array1<i32>)>

Sequential bootstrap sampling fallback

pub fn parallel_predict_proba_aggregate( predictions: Vec<Array2<f64>>, ) -> Result<Array2<f64>>

Sequential prediction aggregation fallback

pub fn parallel_permutation_importance<F>( x: &Array2<f64>, y: &Array1<i32>, baseline_score: f64, scoring_fn: F, n_repeats: usize, random_seeds: &[u64], ) -> Result<Array1<f64>>

where F: Fn(&Array2<f64>, &Array1<i32>) -> Result<f64>,

Sequential feature importance calculation fallback

pub fn optimal_n_threads(n_jobs: Option<i32>) -> usize

Determine optimal number of threads to use

pub fn with_thread_pool<T, F>(n_threads: usize, f: F) -> T

where F: FnOnce() -> T,

Sequential execution fallback

pub fn parallel_find_best_split( x: &Array2<f64>, y: &Array1<f64>, sample_indices: &[usize], config: &ParallelFeatureConfig, ) -> Result<Option<FeatureSplit>>

Sequential fallback for finding best split

pub fn parallel_find_best_classification_split( x: &Array2<f64>, y: &Array1<i32>, sample_indices: &[usize], n_classes: usize, config: &ParallelFeatureConfig, ) -> Result<Option<FeatureSplit>>

Sequential fallback for classification splits

pub fn parallel_compute_feature_stats( x: &Array2<f64>, sample_indices: &[usize], feature_indices: &[usize], ) -> Vec<FeatureStats>

Sequential fallback for feature statistics