odesign is an optimal design of experiments library written in pure rust.
There are at least these following use cases:
- Fast calculation of optimal designs of arbitrary linear models with custom design bounds and optimalities.
- Research in area of optimal designs; e.g. I am working on a new optimal design feature selection algorithm, a mixture of SFFS, D-, C- and Measurements-Costs-Optimality, allowing to perform model feature selection and measurements alternating.
Please have a look at the book on odesign.rs for a high level introduction and theoretical background and at the docs on docs.rs/odesign for the implementation details.
- Optimalities:
- D-Optimality
- C-Optimality
- Custom Optimality
- A-Optimality
- Costs Optimality
- Design Bounds:
- Cubic Bounds
- Custom Bounds
- Documentation:
- documentation of the optimal design solver backed by "adaptive grid semidefinite programming for finding optimal designs" (doi: 10.1007/s11222-017-9741-y)
- Research:
- New optimal design feature selection algorithm, a mixture of SFFS, D-, C- and Measurements-Costs-Optimality, allowing to perform model feature selection and measurements alternating
Run the example:
cargo run --release --example basicIn short, this is a basic example of an optimal design of the simple polynomial 1 + x within design bounds [-1, +1] and 101 equally distributed grid points as an init design.
use nalgebra::{SVector, Vector1};
use num_dual::DualNum;
use odesign::{
DOptimality, Feature, FeatureFunction, FeatureSet, LinearModel, OptimalDesign, Result,
};
use std::sync::Arc;
#[derive(Feature)]
#[dimension = 1]
struct Monomial {
i: i32,
}
impl FeatureFunction<1> for Monomial {
fn f<D: DualNum<f64>>(&self, x: &SVector<D, 1>) -> D {
x[0].powi(self.i)
}
}
// f(x): 1 + x
fn main() -> Result<()> {
let mut fs = FeatureSet::new();
let c: Arc<_> = Monomial { i: 0 }.into();
fs.push(c);
let c: Arc<_> = Monomial { i: 1 }.into();
fs.push(c);
let lm = LinearModel::new(fs.features);
let optimality: Arc<_> = DOptimality::new(lm.into()).into();
let lower = Vector1::new(-1.0);
let upper = Vector1::new(1.0);
let q = Vector1::new(101);
let mut od = OptimalDesign::new()
.with_optimality(optimality)
.with_bound_args(lower, upper)?
.with_init_design_grid_args(lower, upper, q)?;
od.solve();
println!("{od}");
Ok(())
}
// Output
// ---------- Design ----------
// Weight Support Vector
// 0.5000 [ -1.0000 ]
// 0.5000 [ +1.0000 ]
// -------- Statistics --------
// Optimality measure: 1.000000
// No. support vectors: 2
// Iterations: 1
// ----------------------------Run the example:
cargo run --release --example polynomial-3dimAs a more complex example please take a look at this 3 dimensional polynomial example with monoms lower than a degree of 3 within a design space of [-1, -1, -1] x [+1, +1, +1] and an initial design grid of 11 x 11 x 11 points.
use nalgebra::{SVector, Vector3};
use num_dual::DualNum;
use odesign::{
DOptimality, Feature, FeatureFunction, FeatureSet, LinearModel, OptimalDesign, Result,
};
use std::sync::Arc;
#[derive(Feature)]
#[dimension = 3]
struct Monomial {
i: i32,
j: i32,
k: i32,
}
impl FeatureFunction<3> for Monomial {
fn f<D: DualNum<f64>>(&self, x: &SVector<D, 3>) -> D {
x[0].powi(self.i) * x[1].powi(self.j) * x[2].powi(self.k)
}
}
// f(x, y, z): 1 + x + y + z
// + x * y + x * z + y * y
// + x ^ 2 + y ^ 2 + z ^ 2
fn main() -> Result<()> {
let mut fs = FeatureSet::new();
for i in 0..3 {
for j in 0..3 {
for k in 0..3 {
if i + j + k < 3 {
let c: Arc<_> = Monomial { i, j, k }.into();
fs.push(c);
}
}
}
}
let lm = LinearModel::new(fs.features);
let q = Vector3::new(11, 11, 11);
let lower = Vector3::new(-1., -1., -1.);
let upper = Vector3::new(1., 1., 1.);
let optimality = Arc::new(DOptimality::new(lm.into()));
let mut od = OptimalDesign::new()
.with_optimality(optimality)
.with_bound_args(lower, upper)?
.with_init_design_grid_args(lower, upper, q)?;
od.solve();
println!("{od}");
Ok(())
}
// Output
// -------------- Design ---------------
// Weight Support Vector
// 0.0689 [ -1.0000, -1.0000, -1.0000 ]
// 0.0251 [ -1.0000, -1.0000, +0.0000 ]
// 0.0689 [ -1.0000, -1.0000, +1.0000 ]
// 0.0251 [ -1.0000, +0.0000, -1.0000 ]
// 0.0205 [ -1.0000, +0.0000, +0.0000 ]
// 0.0251 [ -1.0000, +0.0000, +1.0000 ]
// 0.0689 [ -1.0000, +1.0000, -1.0000 ]
// 0.0251 [ -1.0000, +1.0000, +0.0000 ]
// 0.0689 [ -1.0000, +1.0000, +1.0000 ]
// 0.0251 [ +0.0000, -1.0000, -1.0000 ]
// 0.0205 [ +0.0000, -1.0000, +0.0000 ]
// 0.0251 [ +0.0000, -1.0000, +1.0000 ]
// 0.0205 [ +0.0000, +0.0000, -1.0000 ]
// 0.0245 [ +0.0000, +0.0000, +0.0000 ]
// 0.0205 [ +0.0000, +0.0000, +1.0000 ]
// 0.0251 [ +0.0000, +1.0000, -1.0000 ]
// 0.0205 [ +0.0000, +1.0000, +0.0000 ]
// 0.0251 [ +0.0000, +1.0000, +1.0000 ]
// 0.0689 [ +1.0000, -1.0000, -1.0000 ]
// 0.0251 [ +1.0000, -1.0000, +0.0000 ]
// 0.0689 [ +1.0000, -1.0000, +1.0000 ]
// 0.0251 [ +1.0000, +0.0000, -1.0000 ]
// 0.0205 [ +1.0000, +0.0000, +0.0000 ]
// 0.0251 [ +1.0000, +0.0000, +1.0000 ]
// 0.0689 [ +1.0000, +1.0000, -1.0000 ]
// 0.0251 [ +1.0000, +1.0000, +0.0000 ]
// 0.0689 [ +1.0000, +1.0000, +1.0000 ]
// ------------ Statistics -------------
// Optimality measure: 0.474478
// No. support vectors: 27
// Iterations: 2
// -------------------------------------- odesign-announce: Low-volume mailing list for announcements
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The tracker on todo.sr.ht/~maunke/odesign is for confirmed bugs and confirmed feature requests only.
Before creating a ticket, search for existing (possibly already fixed) issues, on the docs or in the mailing list archives: odesign-discuss, odesign-devel.
If you cannot find anything describing your issue or if you have a question, ask on one of the the mailing lists first. You will be asked to file a ticket if appropriate.
