Crate hyperopt

hyperopt

Tree-of-Parzen-estimators hyperparameter optimization for Rust

Examples

Continuous

use std::f64::consts::{FRAC_PI_2, PI};

use approx::assert_abs_diff_eq;
use fastrand::Rng;
use ordered_float::NotNan;

use hyperopt::Optimizer;
use hyperopt::kernel::continuous::Epanechnikov;
use hyperopt::kernel::universal::Uniform;

fn main() {
    let min = NotNan::new(FRAC_PI_2).unwrap();
    let max = NotNan::new(PI + FRAC_PI_2).unwrap();
    let mut optimizer = Optimizer::new(
        min, max,                                      // parameter search limits
        Uniform::<NotNan<f64>>::with_bounds(min, max), // our initial guess is just as bad
    );

    // Run 100 trials for the cosine function and try to find the point `(π, -1)`:
    let mut rng = Rng::with_seed(42);
    for _ in 0..50 {
        // Generate new trials using Epanechnikov kernel:
        let x = optimizer.new_trial::<Epanechnikov<_>, NotNan<f64>>(&mut rng);
        optimizer.feed_back(x, NotNan::new(x.cos()).unwrap());
    }

    let best_trial = optimizer.best_trial().unwrap();
    assert_abs_diff_eq!(best_trial.parameter.into_inner(), PI, epsilon = 0.02);
    assert_abs_diff_eq!(best_trial.metric.into_inner(), -1.0, epsilon = 0.01);
}

Discrete

use fastrand::Rng;
use ordered_float::OrderedFloat;

use hyperopt::Optimizer;
use hyperopt::kernel::discrete::Binomial;
use hyperopt::kernel::universal::Uniform;

fn main() {
    let mut optimizer = Optimizer::new(
        -100, 100,
        Uniform::with_bounds(-100, 100),
    );

    let mut rng = Rng::with_seed(42);
    for _ in 0..30 {
        let x = optimizer.new_trial::<Binomial<i32, OrderedFloat<f64>>, _>(&mut rng);
        optimizer.feed_back(x, x * x - 4 * x); // https://www.wolframalpha.com/input?i=x%5E2+-+4x
    }

    let best_trial = optimizer.best_trial().unwrap();
    assert_eq!(best_trial.parameter, 2);
    assert_eq!(best_trial.metric, -4);
}

Features

• ordered-float enables support for OrderedFloat and NotNan types

Re-exports

• pub use self::kernel::Density;
• pub use self::kernel::Sample;

Modules

• consts
  Additional constants that are missing in the standard library.
• kde
  Kernel density estimator implementation.
• kernel
  Different kernels for crate::kde::KernelDensityEstimator.

Structs

• Optimizer
  ✨ Hyperparameter optimizer.