tpe

A library that implements TPE, a hyperparameter optimization algorithm

3 unstable releases

 0.2.0 Jan 10, 2022 Mar 14, 2021 Sep 27, 2020

#421 in Science

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tpe

This crate provides a hyperparameter optimization algorithm using TPE (Tree-structured Parzen Estimator).

Examples

Minimize the result of a quadratic function

An example optimizing a simple quadratic function which has one numerical and one categorical parameters.

``````use rand::SeedableRng as _;

let choices = [1, 10, 100];
let mut optim0 =
tpe::TpeOptimizer::new(tpe::parzen_estimator(), tpe::range(-5.0, 5.0)?);
let mut optim1 =
tpe::TpeOptimizer::new(tpe::histogram_estimator(), tpe::categorical_range(choices.len())?);

fn objective(x: f64, y: i32) -> f64 {
x.powi(2) + y as f64
}

let mut best_value = std::f64::INFINITY;
let mut rng = rand::rngs::StdRng::from_seed(Default::default());
for _ in 0..100 {

let v = objective(x, choices[y as usize]);
optim0.tell(x, v)?;
optim1.tell(y, v)?;
best_value = best_value.min(v);
}

assert_eq!(best_value, 1.000098470725203);
``````

`kurobako` benchmark

There is an example examples/tpe-solver.rs which implements the `kurobako` solver interface, so you can run a benchmark using TPE as follows:

``````\$ PROBLEMS=\$(kurobako problem-suite sigopt auc)
\$ SOLVERS="\$(kurobako solver command -- cargo run --release --example tpe-solver) \$(kurobako solver optuna)"
\$ kurobako studies --solvers \$SOLVERS --problems \$PROBLEMS --repeats 30 --budget 80 | kurobako run > result.json
\$ cat result.json | kurobako report > report.md
``````

The result (`report.md`) of the above commands is shown here.

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