Module: aprender::preprocessing

Public module of the aprender-core crate.

Source

crates/aprender-core/src/preprocessing.rs or directory.

Example

use aprender::preprocessing::{StandardScaler, MinMaxScaler, RobustScaler, PCA, TSNE};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::preprocessing is the feature-engineering layer. It owns the three canonical scalers (StandardScaler for z-score normalisation, MinMaxScaler for range scaling, RobustScaler for median + IQR), plus linear (PCA) and non-linear (t-SNE) dimensionality reduction. All of these implement Transformer so they slot into pipelines, follow the fit / transform / fit_transform contract, and support inverse_transform where mathematically defined.

Key types

TypeDescription
StandardScalerZero-mean, unit-variance scaling. Builder: with_mean, with_std. Supports inverse_transform.
MinMaxScalerLinear rescaling to [min, max]. Builder: with_range.
RobustScalerMedian + IQR scaling that is insensitive to outliers. Builder: with_centering, with_scaling.
PCAPrincipal Component Analysis. explained_variance, explained_variance_ratio, and components accessors.
TSNEt-distributed Stochastic Neighbor Embedding for 2-D / 3-D visualisation.

Usage patterns

Pattern 1: StandardScaler in a pipeline

use aprender::preprocessing::StandardScaler;
use aprender::primitives::Matrix;
use aprender::traits::Transformer;

let x = Matrix::from_vec(4, 2, vec![
    1.0, 100.0,
    2.0, 200.0,
    3.0, 300.0,
    4.0, 400.0,
]).expect("4x2");

let mut scaler = StandardScaler::new();
let x_scaled = scaler.fit_transform(&x).expect("standardize");

// After fitting, each column should have ~zero mean and ~unit variance.
println!("means: {:?}", scaler.mean());
println!("stds:  {:?}", scaler.std());

// Round-trip via inverse_transform.
let recovered = scaler.inverse_transform(&x_scaled).expect("inverse");
println!("recovered[0,0] = {:.2}", recovered.get(0, 0));

Pattern 2: PCA dimensionality reduction

use aprender::preprocessing::PCA;
use aprender::primitives::Matrix;
use aprender::traits::Transformer;

// 6 samples in 3-D, projected down to 2 principal components.
let x = Matrix::from_vec(6, 3, vec![
    1.0, 2.0, 3.0,
    2.0, 4.0, 6.0,
    3.0, 6.0, 9.0,
    1.1, 2.1, 3.1,
    2.2, 4.2, 6.2,
    3.3, 6.3, 9.3,
]).expect("6x3");

let mut pca = PCA::new(2);
let x_low = pca.fit_transform(&x).expect("PCA fit_transform");
assert_eq!(x_low.shape(), (6, 2));

if let Some(ratio) = pca.explained_variance_ratio() {
    println!("explained variance ratio: {:?}", ratio);
}

See also

  • traits — Transformer contract these types implement
  • decomposition — ICA for blind source separation
  • data — DataFrame as the input pipeline upstream of scaling
  • cluster — scale features before clustering for stable results

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.