Module: aprender::traits

Public module of the aprender-core crate.

Source

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

Example

use aprender::traits::{Estimator, Transformer, UnsupervisedEstimator};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::traits defines the three abstract contracts every ML algorithm in the framework satisfies: Estimator for supervised learning (regression, classification), UnsupervisedEstimator for clustering / anomaly detection, and Transformer for stateful preprocessing. Every model elsewhere in aprender — linear regression, random forest, KMeans, scalers — implements at least one of these traits, which is what makes pipelines and cross-validation generic over algorithm choice.

Key types

These are also re-exported at the crate root (aprender::{Estimator, Transformer, UnsupervisedEstimator}) and through aprender::prelude::*.

Usage patterns

Pattern 1: Writing generic code over Estimator

use aprender::prelude::*;
use aprender::traits::Estimator;

fn fit_and_score<E: Estimator>(
    model: &mut E,
    x: &Matrix<f32>,
    y: &Vector<f32>,
) -> f32 {
    model.fit(x, y).expect("fit succeeds on valid data");
    model.score(x, y)
}

let x = Matrix::from_vec(4, 1, vec![1.0, 2.0, 3.0, 4.0]).expect("4x1");
let y = Vector::from_slice(&[3.0, 5.0, 7.0, 9.0]);

let mut lr = LinearRegression::new();
let score = fit_and_score(&mut lr, &x, &y);
assert!(score > 0.99);

Pattern 2: Implementing Transformer

use aprender::primitives::Matrix;
use aprender::traits::Transformer;
use aprender::error::Result;

struct Identity;

impl Transformer for Identity {
    fn fit(&mut self, _x: &Matrix<f32>) -> Result<()> { Ok(()) }
    fn transform(&self, x: &Matrix<f32>) -> Result<Matrix<f32>> {
        let data: Vec<f32> = x.as_slice().to_vec();
        Matrix::from_vec(x.n_rows(), x.n_cols(), data)
            .map_err(|e| aprender::error::AprenderError::ValidationError {
                message: e.to_string(),
            })
    }
}

// The default `fit_transform` is provided automatically.
let mut t = Identity;
let x = Matrix::from_vec(2, 2, vec![1.0, 2.0, 3.0, 4.0]).expect("2x2");
let y = t.fit_transform(&x).expect("identity transform");
assert_eq!(y.n_rows(), 2);

See also

• primitives — Matrix and Vector used in every signature
• error — AprenderError returned by all fallible trait methods
• linear_model — flagship Estimator implementations
• preprocessing — flagship Transformer implementations
• cluster — flagship UnsupervisedEstimator implementations

Full API

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