Struct WeightedRandomSampler 

pub struct WeightedRandomSampler { /* private fields */ }

Weighted random sampler for imbalanced datasets

This sampler allows sampling indices according to specified weights, which is particularly useful for handling imbalanced datasets or implementing custom sampling strategies.

Examples

use torsh_data::sampler::{WeightedRandomSampler, Sampler};

// Higher weight for the last class (imbalanced dataset)
let weights = vec![0.1, 0.1, 0.1, 0.1, 0.6];
let sampler = WeightedRandomSampler::new(weights, 100, true).with_generator(42);

let indices: Vec<usize> = sampler.iter().collect();
assert_eq!(indices.len(), 100);

Implementations

impl WeightedRandomSampler

pub fn new(weights: Vec<f64>, num_samples: usize, replacement: bool) -> Self

Create a new weighted random sampler

Arguments

• weights - Sampling weights for each index
• num_samples - Number of samples to generate
• replacement - Whether to sample with replacement

Panics

Panics if weights are empty, contain negative values, or don’t sum to a positive finite value

pub fn with_generator(self, seed: u64) -> Self

Set random generator seed

Arguments

• seed - Random seed for reproducible sampling

pub fn weights(&self) -> &[f64]

Get the weights

pub fn num_samples(&self) -> usize

Get the number of samples

pub fn replacement(&self) -> bool

Check if sampling with replacement

pub fn generator(&self) -> Option<u64>

Get the generator seed if set

Trait Implementations

impl Clone for WeightedRandomSampler

fn clone(&self) -> WeightedRandomSampler

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for WeightedRandomSampler

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

Formats the value using the given formatter.

impl Sampler for WeightedRandomSampler

type Iter = SamplerIterator

Iterator type returned by the sampler

fn iter(&self) -> Self::Iter

Create an iterator over indices

fn len(&self) -> usize

Total number of samples that will be yielded

fn is_empty(&self) -> bool

Check if sampler is empty

fn into_batch_sampler( self, batch_size: usize, drop_last: bool, ) -> BatchingSampler<Self>

where Self: Sized,

Convert this sampler into a batch sampler

fn into_distributed( self, num_replicas: usize, rank: usize, ) -> DistributedWrapper<Self>

where Self: Sized,

Create a distributed version of this sampler