test(sobol): add integration tests for SobolSampler via Study API

2026-02-12 16:02:46 +01:00
parent 12efbaabab
commit 0e0e9ff38f
2 changed files with 316 additions and 0 deletions
@@ -12,4 +12,6 @@ mod differential_evolution;
 mod gp;
 mod multivariate_tpe;
 mod random;
+#[cfg(feature = "sobol")]
+mod sobol;
 mod tpe;
@@ -0,0 +1,314 @@
+use optimizer::prelude::*;
+use optimizer::sampler::random::RandomSampler;
+use optimizer::sampler::sobol::SobolSampler;
+
+#[test]
+fn sphere_function() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let x = FloatParam::new(-5.0, 5.0).name("x");
+    let y = FloatParam::new(-5.0, 5.0).name("y");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let xv = x.suggest(trial)?;
+            let yv = y.suggest(trial)?;
+            Ok::<_, Error>(xv * xv + yv * yv)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    assert!(
+        best.value < 100.0,
+        "sphere best value should be < 100.0, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn bounds_respected() {
+    let sampler = SobolSampler::with_seed(123);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let x = FloatParam::new(-2.0, 3.0).name("x");
+    let y = FloatParam::new(0.0, 10.0).name("y");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let xv = x.suggest(trial)?;
+            let yv = y.suggest(trial)?;
+            Ok::<_, Error>(xv + yv)
+        })
+        .unwrap();
+
+    for trial in study.trials() {
+        let xv: f64 = trial.get(&x).unwrap();
+        let yv: f64 = trial.get(&y).unwrap();
+        assert!((-2.0..=3.0).contains(&xv), "x = {xv} out of bounds [-2, 3]");
+        assert!((0.0..=10.0).contains(&yv), "y = {yv} out of bounds [0, 10]");
+    }
+}
+
+#[test]
+fn integer_params() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let n = IntParam::new(1, 20).name("n");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let nv = n.suggest(trial)?;
+            Ok::<_, Error>(((nv - 10) * (nv - 10)) as f64)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    let best_n: i64 = best.get(&n).unwrap();
+    assert!(
+        (1..=20).contains(&best_n),
+        "integer value {best_n} out of bounds"
+    );
+    assert!(
+        best.value < 10.0,
+        "integer optimization should find a good value, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn log_scale_params() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let lr = FloatParam::new(1e-5, 1.0).log_scale().name("lr");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let lrv = lr.suggest(trial)?;
+            Ok::<_, Error>((lrv.ln() - 0.01_f64.ln()).powi(2))
+        })
+        .unwrap();
+
+    for trial in study.trials() {
+        let lrv: f64 = trial.get(&lr).unwrap();
+        assert!(
+            (1e-5..=1.0).contains(&lrv),
+            "log-scale value {lrv} out of bounds"
+        );
+    }
+}
+
+#[test]
+fn categorical_params() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let cat = CategoricalParam::new(vec!["a", "b", "c"]).name("cat");
+
+    study
+        .optimize(50, |trial: &mut optimizer::Trial| {
+            let cv = cat.suggest(trial)?;
+            let val = match cv {
+                "a" => 0.0,
+                "b" => 1.0,
+                _ => 2.0,
+            };
+            Ok::<_, Error>(val)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    assert!(
+        best.value < 2.0,
+        "categorical optimization should find a good value, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn mixed_params() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let x = FloatParam::new(-5.0, 5.0).name("x");
+    let n = IntParam::new(1, 10).name("n");
+    let cat = CategoricalParam::new(vec!["a", "b", "c"]).name("cat");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let xv = x.suggest(trial)?;
+            let nv = n.suggest(trial)?;
+            let cv = cat.suggest(trial)?;
+            let penalty = match cv {
+                "a" => 0.0,
+                "b" => 1.0,
+                _ => 2.0,
+            };
+            Ok::<_, Error>(xv * xv + nv as f64 + penalty)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    assert!(
+        best.value < 20.0,
+        "mixed-param optimization should find a reasonable value, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn single_dimension() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let x = FloatParam::new(-10.0, 10.0).name("x");
+
+    study
+        .optimize(100, |trial: &mut optimizer::Trial| {
+            let xv = x.suggest(trial)?;
+            Ok::<_, Error>((xv - 3.0).powi(2))
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    assert!(
+        best.value < 5.0,
+        "1-D optimization should find a decent value, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn many_dimensions() {
+    let sampler = SobolSampler::with_seed(42);
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    let params: Vec<FloatParam> = (0..8)
+        .map(|i| FloatParam::new(-5.0, 5.0).name(format!("x{i}")))
+        .collect();
+
+    study
+        .optimize(200, |trial: &mut optimizer::Trial| {
+            let mut sum = 0.0;
+            for p in &params {
+                let v = p.suggest(trial)?;
+                sum += v * v;
+            }
+            Ok::<_, Error>(sum)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    // With 8 dimensions and 200 quasi-random trials the best won't be amazing,
+    // but it should be noticeably below the worst case (8 * 25 = 200).
+    assert!(
+        best.value < 150.0,
+        "8-D optimization should find something reasonable, got {}",
+        best.value
+    );
+}
+
+#[test]
+fn seeded_reproducibility() {
+    let x = FloatParam::new(-5.0, 5.0).name("x");
+    let y = FloatParam::new(-5.0, 5.0).name("y");
+
+    let run = |seed: u64| {
+        let sampler = SobolSampler::with_seed(seed);
+        let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+        study
+            .optimize(50, |trial: &mut optimizer::Trial| {
+                let xv = x.suggest(trial)?;
+                let yv = y.suggest(trial)?;
+                Ok::<_, Error>(xv * xv + yv * yv)
+            })
+            .unwrap();
+        study.trials().iter().map(|t| t.value).collect::<Vec<_>>()
+    };
+
+    let results1 = run(42);
+    let results2 = run(42);
+    assert_eq!(results1, results2, "same seed should produce same results");
+}
+
+#[test]
+fn different_seeds_different_results() {
+    let x = FloatParam::new(-5.0, 5.0).name("x");
+    let y = FloatParam::new(-5.0, 5.0).name("y");
+
+    let run = |seed: u64| {
+        let sampler = SobolSampler::with_seed(seed);
+        let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+        study
+            .optimize(20, |trial: &mut optimizer::Trial| {
+                let xv = x.suggest(trial)?;
+                let yv = y.suggest(trial)?;
+                Ok::<_, Error>(xv * xv + yv * yv)
+            })
+            .unwrap();
+        study.trials().iter().map(|t| t.value).collect::<Vec<_>>()
+    };
+
+    let results1 = run(42);
+    let results2 = run(99);
+    assert_ne!(
+        results1, results2,
+        "different seeds should produce different results"
+    );
+}
+
+#[test]
+fn better_coverage_than_random() {
+    let n_trials = 30;
+    let n_bins = 10;
+
+    let x = FloatParam::new(0.0, 1.0).name("x");
+
+    // Count bins filled by Sobol.
+    let sobol_study: Study<f64> =
+        Study::with_sampler(Direction::Minimize, SobolSampler::with_seed(0));
+    sobol_study
+        .optimize(n_trials, |trial: &mut optimizer::Trial| {
+            let v = x.suggest(trial)?;
+            Ok::<_, Error>(v)
+        })
+        .unwrap();
+
+    let mut sobol_bins = vec![0u32; n_bins];
+    for trial in sobol_study.trials() {
+        let v: f64 = trial.get(&x).unwrap();
+        let bin = ((v * n_bins as f64).floor() as usize).min(n_bins - 1);
+        sobol_bins[bin] += 1;
+    }
+    let sobol_filled = sobol_bins.iter().filter(|&&c| c > 0).count();
+
+    // Count bins filled by Random.
+    let random_study: Study<f64> =
+        Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(0));
+    random_study
+        .optimize(n_trials, |trial: &mut optimizer::Trial| {
+            let v = x.suggest(trial)?;
+            Ok::<_, Error>(v)
+        })
+        .unwrap();
+
+    let mut random_bins = vec![0u32; n_bins];
+    for trial in random_study.trials() {
+        let v: f64 = trial.get(&x).unwrap();
+        let bin = ((v * n_bins as f64).floor() as usize).min(n_bins - 1);
+        random_bins[bin] += 1;
+    }
+    let random_filled = random_bins.iter().filter(|&&c| c > 0).count();
+
+    assert!(
+        sobol_filled >= random_filled,
+        "Sobol should fill at least as many bins as random: sobol={sobol_filled}, random={random_filled}"
+    );
+    // Sobol with 30 samples in 10 bins should fill all or nearly all bins.
+    assert!(
+        sobol_filled >= 9,
+        "Sobol should fill at least 9/10 bins, got {sobol_filled}: {sobol_bins:?}"
+    );
+}