Imputers

Missing-value completion. Live in datarust::imputer. Both implement Transformer.

Missing values are represented as f64::NAN in the Matrix.

SimpleImputer

Fill missing values with a per-column statistic or a constant.

#![allow(unused)]
fn main() {
use datarust::imputer::{SimpleImputer, ImputeStrategy};

// Mean (default), median, most_frequent, or constant
let mut imp = SimpleImputer::new()
    .strategy(ImputeStrategy::Median); // or Mean, MostFrequent, Constant(0.0)
let out = imp.fit_transform(&x_with_nans)?;
}

Strategies:

• Mean — column mean (default)
• Median — column median, robust to outliers
• MostFrequent — most common value
• Constant(v) — a fixed fill value

KnnImputer

Impute using k-Nearest Neighbors. Distance is computed over co-observed features only (features where neither sample is missing).

#![allow(unused)]
fn main() {
use datarust::imputer::{KnnImputer, KnnWeights};

let mut imp = KnnImputer::new()
    .n_neighbors(5)
    .weights(KnnWeights::Distance); // or Uniform
let out = imp.fit_transform(&x_with_nans)?;
}

KnnImputer is more accurate than SimpleImputer when features are correlated, but slower — it computes pairwise distances over all samples.

When to use which?