Manuel Raimann
748 lines
22 KiB
Rust
748 lines
22 KiB
Rust
//! Integration tests for the optimize library.
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use optimize::{Direction, RandomSampler, Study, TpeError, TpeSampler, Trial};
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// =============================================================================
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// Test: optimize simple quadratic function with TPE, finds near-optimal
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// =============================================================================
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#[test]
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fn test_tpe_optimizes_quadratic_function() {
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// Minimize f(x) = (x - 3)^2 where x ∈ [-10, 10]
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// Optimal: x = 3, f(3) = 0
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let sampler = TpeSampler::builder()
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.seed(42)
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.n_startup_trials(5) // Quick startup for test
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.n_ei_candidates(24)
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.build();
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let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
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study
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.optimize_with_sampler(50, |trial| {
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let x = trial.suggest_float("x", -10.0, 10.0)?;
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Ok::<_, TpeError>((x - 3.0).powi(2))
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})
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.expect("optimization should succeed");
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let best = study.best_trial().expect("should have at least one trial");
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// TPE should find a value close to optimal (x ≈ 3)
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// We expect the best value to be small (close to 0)
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assert!(
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best.value < 1.0,
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"TPE should find near-optimal: best value {} should be < 1.0",
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best.value
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);
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}
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#[test]
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fn test_tpe_optimizes_multivariate_function() {
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// Minimize f(x, y) = x^2 + y^2 where x, y ∈ [-5, 5]
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// Optimal: (0, 0), f(0, 0) = 0
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let sampler = TpeSampler::builder().seed(123).n_startup_trials(10).build();
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let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
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study
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.optimize_with_sampler(100, |trial| {
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let x = trial.suggest_float("x", -5.0, 5.0)?;
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let y = trial.suggest_float("y", -5.0, 5.0)?;
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Ok::<_, TpeError>(x * x + y * y)
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})
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.expect("optimization should succeed");
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let best = study.best_trial().expect("should have at least one trial");
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// TPE should find a reasonably good solution
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assert!(
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best.value < 5.0,
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"TPE should find near-optimal: best value {} should be < 5.0",
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best.value
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);
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}
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#[test]
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fn test_tpe_maximization() {
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// Maximize f(x) = -(x - 2)^2 + 10 where x ∈ [-10, 10]
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// Optimal: x = 2, f(2) = 10
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let sampler = TpeSampler::builder().seed(456).n_startup_trials(5).build();
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let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
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study
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.optimize_with_sampler(50, |trial| {
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let x = trial.suggest_float("x", -10.0, 10.0)?;
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Ok::<_, TpeError>(-(x - 2.0).powi(2) + 10.0)
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})
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.expect("optimization should succeed");
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let best = study.best_trial().expect("should have at least one trial");
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// For maximization, best value should be better than a random baseline
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// The function ranges from -90 (at x=-10 or x=10, when far from x=2) to 10 (at x=2)
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// A random approach would average around 0, so finding >5 is a reasonable check
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assert!(
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best.value > 5.0,
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"TPE should find reasonably good solution: best value {} should be > 5.0",
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best.value
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);
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}
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// =============================================================================
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// Test: RandomSampler samples uniformly across range
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// =============================================================================
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#[test]
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fn test_random_sampler_uniform_float_distribution() {
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// Test that RandomSampler samples uniformly by running multiple trials
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// and checking the distribution of sampled values
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let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(42));
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let n_samples = 1000;
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let mut samples = Vec::with_capacity(n_samples);
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study
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.optimize(n_samples, |trial| {
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let x = trial.suggest_float("x", 0.0, 1.0)?;
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samples.push(x);
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Ok::<_, TpeError>(x)
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})
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.unwrap();
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// All samples should be in range
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for &s in &samples {
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assert!((0.0..=1.0).contains(&s), "sample {s} out of range [0, 1]");
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}
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// Check distribution is roughly uniform by looking at quartiles
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samples.sort_by(|a, b| a.partial_cmp(b).unwrap());
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let q1 = samples[n_samples / 4];
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let q2 = samples[n_samples / 2];
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let q3 = samples[3 * n_samples / 4];
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// For uniform distribution, quartiles should be approximately 0.25, 0.5, 0.75
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assert!((q1 - 0.25).abs() < 0.1, "Q1 {q1} should be close to 0.25");
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assert!(
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(q2 - 0.5).abs() < 0.1,
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"Q2 (median) {q2} should be close to 0.5"
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);
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assert!((q3 - 0.75).abs() < 0.1, "Q3 {q3} should be close to 0.75");
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}
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#[test]
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fn test_random_sampler_uniform_int_distribution() {
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let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(123));
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let n_samples = 1000;
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let mut counts = [0u32; 10]; // counts for values 1-10
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study
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.optimize(n_samples, |trial| {
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let n = trial.suggest_int("n", 1, 10)?;
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assert!((1..=10).contains(&n), "sample {n} out of range [1, 10]");
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counts[(n - 1) as usize] += 1;
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Ok::<_, TpeError>(n as f64)
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})
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.unwrap();
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// Each value should appear roughly n_samples / 10 times
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let expected = n_samples as f64 / 10.0;
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for (i, &count) in counts.iter().enumerate() {
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let diff = (count as f64 - expected).abs() / expected;
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assert!(
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diff < 0.3,
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"value {} appeared {} times, expected ~{}, diff = {:.1}%",
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i + 1,
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count,
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expected,
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diff * 100.0
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);
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}
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}
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#[test]
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fn test_random_sampler_uniform_categorical_distribution() {
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let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(456));
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let n_samples = 1000;
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let mut counts = [0u32; 4];
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let choices = ["a", "b", "c", "d"];
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study
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.optimize(n_samples, |trial| {
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let choice = trial.suggest_categorical("cat", &choices)?;
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let idx = choices.iter().position(|&c| c == choice).unwrap();
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counts[idx] += 1;
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Ok::<_, TpeError>(idx as f64)
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})
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.unwrap();
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// Each category should appear roughly n_samples / 4 times
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let expected = n_samples as f64 / 4.0;
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for (i, &count) in counts.iter().enumerate() {
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let diff = (count as f64 - expected).abs() / expected;
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assert!(
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diff < 0.25,
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"category {} appeared {} times, expected ~{}, diff = {:.1}%",
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i,
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count,
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expected,
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diff * 100.0
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);
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}
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}
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#[test]
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fn test_random_sampler_reproducibility() {
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// Two studies with the same seed should produce the same sequence
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// NOTE: We must use optimize_with_sampler() for Study<f64> to get sampler integration
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let study1: Study<f64> =
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Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(999));
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let study2: Study<f64> =
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Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(999));
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let mut values1 = Vec::new();
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let mut values2 = Vec::new();
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study1
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.optimize_with_sampler(100, |trial| {
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let x = trial.suggest_float("x", 0.0, 100.0)?;
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values1.push(x);
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Ok::<_, TpeError>(x)
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})
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.unwrap();
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study2
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.optimize_with_sampler(100, |trial| {
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let x = trial.suggest_float("x", 0.0, 100.0)?;
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values2.push(x);
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Ok::<_, TpeError>(x)
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})
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.unwrap();
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for (i, (v1, v2)) in values1.iter().zip(values2.iter()).enumerate() {
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assert_eq!(
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v1, v2,
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"values at trial {i} should be identical with same seed: {v1} vs {v2}"
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);
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}
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}
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// =============================================================================
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// Test: suggest_* methods return cached values on repeated calls
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// =============================================================================
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#[test]
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fn test_suggest_float_caching() {
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let mut trial = Trial::new(0);
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let x1 = trial.suggest_float("x", 0.0, 10.0).unwrap();
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let x2 = trial.suggest_float("x", 0.0, 10.0).unwrap();
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let x3 = trial.suggest_float("x", 0.0, 10.0).unwrap();
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assert_eq!(x1, x2, "repeated suggest_float should return cached value");
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assert_eq!(x2, x3, "repeated suggest_float should return cached value");
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}
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#[test]
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fn test_suggest_float_log_caching() {
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let mut trial = Trial::new(0);
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let x1 = trial.suggest_float_log("lr", 1e-5, 1e-1).unwrap();
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let x2 = trial.suggest_float_log("lr", 1e-5, 1e-1).unwrap();
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assert_eq!(
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x1, x2,
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"repeated suggest_float_log should return cached value"
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);
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}
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#[test]
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fn test_suggest_float_step_caching() {
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let mut trial = Trial::new(0);
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let x1 = trial.suggest_float_step("step", 0.0, 1.0, 0.1).unwrap();
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let x2 = trial.suggest_float_step("step", 0.0, 1.0, 0.1).unwrap();
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assert_eq!(
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x1, x2,
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"repeated suggest_float_step should return cached value"
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);
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}
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#[test]
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fn test_suggest_int_caching() {
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let mut trial = Trial::new(0);
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let n1 = trial.suggest_int("n", 1, 100).unwrap();
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let n2 = trial.suggest_int("n", 1, 100).unwrap();
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assert_eq!(n1, n2, "repeated suggest_int should return cached value");
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}
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#[test]
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fn test_suggest_int_log_caching() {
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let mut trial = Trial::new(0);
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let n1 = trial.suggest_int_log("batch", 1, 1024).unwrap();
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let n2 = trial.suggest_int_log("batch", 1, 1024).unwrap();
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assert_eq!(
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n1, n2,
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"repeated suggest_int_log should return cached value"
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);
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}
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#[test]
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fn test_suggest_int_step_caching() {
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let mut trial = Trial::new(0);
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let n1 = trial.suggest_int_step("units", 32, 512, 32).unwrap();
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let n2 = trial.suggest_int_step("units", 32, 512, 32).unwrap();
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assert_eq!(
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n1, n2,
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"repeated suggest_int_step should return cached value"
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);
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}
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#[test]
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fn test_suggest_categorical_caching() {
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let mut trial = Trial::new(0);
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let choices = ["sgd", "adam", "rmsprop"];
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let c1 = trial.suggest_categorical("optimizer", &choices).unwrap();
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let c2 = trial.suggest_categorical("optimizer", &choices).unwrap();
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assert_eq!(
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c1, c2,
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"repeated suggest_categorical should return cached value"
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);
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}
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#[test]
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fn test_multiple_parameters_independent_caching() {
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let mut trial = Trial::new(0);
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// Suggest multiple parameters
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let x = trial.suggest_float("x", 0.0, 1.0).unwrap();
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let y = trial.suggest_float("y", 0.0, 1.0).unwrap();
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let n = trial.suggest_int("n", 1, 10).unwrap();
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let opt = trial.suggest_categorical("opt", &["a", "b"]).unwrap();
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// All should be cached independently
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assert_eq!(x, trial.suggest_float("x", 0.0, 1.0).unwrap());
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assert_eq!(y, trial.suggest_float("y", 0.0, 1.0).unwrap());
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assert_eq!(n, trial.suggest_int("n", 1, 10).unwrap());
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assert_eq!(opt, trial.suggest_categorical("opt", &["a", "b"]).unwrap());
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}
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// =============================================================================
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// Test: parameter conflict returns error
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// =============================================================================
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#[test]
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fn test_parameter_conflict_float_different_bounds() {
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let mut trial = Trial::new(0);
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trial.suggest_float("x", 0.0, 1.0).unwrap();
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let result = trial.suggest_float("x", 0.0, 2.0); // Different upper bound
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_float_vs_log() {
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let mut trial = Trial::new(0);
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trial.suggest_float("x", 0.1, 1.0).unwrap();
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let result = trial.suggest_float_log("x", 0.1, 1.0); // Same bounds but log scale
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_float_vs_step() {
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let mut trial = Trial::new(0);
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trial.suggest_float("x", 0.0, 1.0).unwrap();
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let result = trial.suggest_float_step("x", 0.0, 1.0, 0.1); // Same bounds but with step
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_int_different_bounds() {
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let mut trial = Trial::new(0);
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trial.suggest_int("n", 1, 10).unwrap();
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let result = trial.suggest_int("n", 1, 20); // Different upper bound
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_int_vs_log() {
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let mut trial = Trial::new(0);
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trial.suggest_int("n", 1, 100).unwrap();
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let result = trial.suggest_int_log("n", 1, 100); // Same bounds but log scale
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_categorical_different_n_choices() {
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let mut trial = Trial::new(0);
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trial.suggest_categorical("opt", &["a", "b", "c"]).unwrap();
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let result = trial.suggest_categorical("opt", &["x", "y"]); // Different number of choices
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_float_vs_int() {
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let mut trial = Trial::new(0);
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trial.suggest_float("x", 0.0, 10.0).unwrap();
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let result = trial.suggest_int("x", 0, 10); // Different type
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assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
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}
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#[test]
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fn test_parameter_conflict_returns_name() {
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let mut trial = Trial::new(0);
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trial.suggest_float("my_param", 0.0, 1.0).unwrap();
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let result = trial.suggest_float("my_param", 0.0, 2.0);
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match result {
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Err(TpeError::ParameterConflict { name, .. }) => {
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assert_eq!(name, "my_param");
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}
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_ => panic!("expected ParameterConflict error"),
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}
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}
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// =============================================================================
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// Test: empty categorical returns error
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// =============================================================================
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#[test]
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fn test_empty_categorical_returns_error() {
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let mut trial = Trial::new(0);
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let empty: &[&str] = &[];
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let result = trial.suggest_categorical("opt", empty);
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assert!(matches!(result, Err(TpeError::EmptyChoices)));
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}
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#[test]
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fn test_empty_categorical_vec_returns_error() {
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let mut trial = Trial::new(0);
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let empty: Vec<i32> = vec![];
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let result = trial.suggest_categorical("numbers", &empty);
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assert!(matches!(result, Err(TpeError::EmptyChoices)));
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}
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// =============================================================================
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// Additional integration tests
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// =============================================================================
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#[test]
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fn test_study_basic_workflow() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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study
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.optimize(10, |trial| {
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let x = trial.suggest_float("x", -5.0, 5.0)?;
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Ok::<_, TpeError>(x * x)
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})
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.expect("optimization should succeed");
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assert_eq!(study.n_trials(), 10);
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let best = study.best_trial().expect("should have best trial");
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assert!(best.value >= 0.0, "x^2 should be non-negative");
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}
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#[test]
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fn test_study_with_failures() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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// Every other trial fails
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let mut counter = 0;
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study
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.optimize(10, |trial| {
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counter += 1;
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if counter % 2 == 0 {
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return Err::<f64, &str>("intentional failure");
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}
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let x = trial
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.suggest_float("x", -5.0, 5.0)
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.map_err(|_| "param error")?;
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Ok(x * x)
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})
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.expect("optimization should succeed with some failures");
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// Only half the trials should have succeeded
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assert_eq!(study.n_trials(), 5, "only 5 trials should have completed");
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}
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#[test]
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fn test_no_completed_trials_error() {
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let study: Study<f64> = Study::new(Direction::Minimize);
|
|
|
|
let result = study.best_trial();
|
|
assert!(matches!(result, Err(TpeError::NoCompletedTrials)));
|
|
}
|
|
|
|
#[test]
|
|
fn test_invalid_bounds_errors() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
// low > high for float
|
|
let result = trial.suggest_float("x", 10.0, 5.0);
|
|
assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
|
|
|
|
// low > high for int
|
|
let result = trial.suggest_int("n", 100, 50);
|
|
assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
|
|
}
|
|
|
|
#[test]
|
|
fn test_invalid_log_bounds_errors() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
// low <= 0 for log float
|
|
let result = trial.suggest_float_log("x", 0.0, 1.0);
|
|
assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
|
|
|
|
let result = trial.suggest_float_log("y", -1.0, 1.0);
|
|
assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
|
|
|
|
// low < 1 for log int
|
|
let result = trial.suggest_int_log("n", 0, 100);
|
|
assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
|
|
}
|
|
|
|
#[test]
|
|
fn test_invalid_step_errors() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
// step <= 0 for float
|
|
let result = trial.suggest_float_step("x", 0.0, 1.0, 0.0);
|
|
assert!(matches!(result, Err(TpeError::InvalidStep)));
|
|
|
|
let result = trial.suggest_float_step("y", 0.0, 1.0, -0.1);
|
|
assert!(matches!(result, Err(TpeError::InvalidStep)));
|
|
|
|
// step <= 0 for int
|
|
let result = trial.suggest_int_step("n", 0, 100, 0);
|
|
assert!(matches!(result, Err(TpeError::InvalidStep)));
|
|
}
|
|
|
|
#[test]
|
|
fn test_tpe_with_categorical_parameter() {
|
|
let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
|
|
|
|
let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
|
|
|
|
// Optimization where the best choice depends on the categorical
|
|
study
|
|
.optimize_with_sampler(30, |trial| {
|
|
let choice = trial.suggest_categorical("model", &["linear", "quadratic", "cubic"])?;
|
|
let x = trial.suggest_float("x", 0.0, 2.0)?;
|
|
|
|
// cubic model is best at x=1
|
|
let value = match choice {
|
|
"linear" => x,
|
|
"quadratic" => x * x,
|
|
"cubic" => -((x - 1.0).powi(2)) + 10.0, // peak at x=1, max value 10
|
|
_ => unreachable!(),
|
|
};
|
|
Ok::<_, TpeError>(value)
|
|
})
|
|
.expect("optimization should succeed");
|
|
|
|
let best = study.best_trial().expect("should have best trial");
|
|
// The optimizer should find that "cubic" with x≈1 is best
|
|
assert!(
|
|
best.value > 5.0,
|
|
"should find good solution, got {}",
|
|
best.value
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_tpe_with_integer_parameters() {
|
|
let sampler = TpeSampler::builder().seed(789).n_startup_trials(5).build();
|
|
|
|
let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
|
|
|
|
// Minimize (n - 7)^2 where n ∈ [1, 10]
|
|
study
|
|
.optimize_with_sampler(30, |trial| {
|
|
let n = trial.suggest_int("n", 1, 10)?;
|
|
Ok::<_, TpeError>(((n - 7) as f64).powi(2))
|
|
})
|
|
.expect("optimization should succeed");
|
|
|
|
let best = study.best_trial().expect("should have best trial");
|
|
|
|
// Best value should be small (n close to 7)
|
|
assert!(
|
|
best.value < 5.0,
|
|
"should find n close to 7, best value = {}",
|
|
best.value
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_callback_early_stopping() {
|
|
use std::cell::Cell;
|
|
use std::ops::ControlFlow;
|
|
|
|
let study: Study<f64> = Study::new(Direction::Minimize);
|
|
let trials_run = Cell::new(0);
|
|
|
|
study
|
|
.optimize_with_callback(
|
|
100,
|
|
|trial| {
|
|
trials_run.set(trials_run.get() + 1);
|
|
let x = trial.suggest_float("x", 0.0, 10.0)?;
|
|
Ok::<_, TpeError>(x)
|
|
},
|
|
|_study, _trial| {
|
|
// Stop after 5 trials
|
|
if trials_run.get() >= 5 {
|
|
ControlFlow::Break(())
|
|
} else {
|
|
ControlFlow::Continue(())
|
|
}
|
|
},
|
|
)
|
|
.expect("optimization should succeed");
|
|
|
|
assert_eq!(study.n_trials(), 5, "should have stopped after 5 trials");
|
|
}
|
|
|
|
#[test]
|
|
fn test_study_trials_iteration() {
|
|
let study: Study<f64> = Study::new(Direction::Minimize);
|
|
|
|
study
|
|
.optimize(5, |trial| {
|
|
let x = trial.suggest_float("x", 0.0, 1.0)?;
|
|
Ok::<_, TpeError>(x)
|
|
})
|
|
.unwrap();
|
|
|
|
let trials = study.trials();
|
|
assert_eq!(trials.len(), 5);
|
|
|
|
for trial in &trials {
|
|
assert!(
|
|
!trial.params.is_empty(),
|
|
"each trial should have parameters"
|
|
);
|
|
assert!(
|
|
trial.params.contains_key("x"),
|
|
"each trial should have parameter 'x'"
|
|
);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn test_study_direction() {
|
|
let study_min: Study<f64> = Study::new(Direction::Minimize);
|
|
assert_eq!(study_min.direction(), Direction::Minimize);
|
|
|
|
let study_max: Study<f64> = Study::new(Direction::Maximize);
|
|
assert_eq!(study_max.direction(), Direction::Maximize);
|
|
}
|
|
|
|
#[test]
|
|
fn test_trial_state() {
|
|
use optimize::TrialState;
|
|
|
|
let trial = Trial::new(0);
|
|
assert_eq!(trial.state(), TrialState::Running);
|
|
}
|
|
|
|
#[test]
|
|
fn test_trial_params_access() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
trial.suggest_float("x", 0.0, 1.0).unwrap();
|
|
trial.suggest_int("n", 1, 10).unwrap();
|
|
|
|
let params = trial.params();
|
|
assert_eq!(params.len(), 2);
|
|
assert!(params.contains_key("x"));
|
|
assert!(params.contains_key("n"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_log_scale_float_range() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
let lr = trial.suggest_float_log("lr", 1e-5, 1e-1).unwrap();
|
|
assert!(
|
|
(1e-5..=1e-1).contains(&lr),
|
|
"log-scale value {lr} out of range"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_step_float_snaps_to_grid() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
let x = trial.suggest_float_step("x", 0.0, 1.0, 0.25).unwrap();
|
|
|
|
// x should be one of: 0.0, 0.25, 0.5, 0.75, 1.0
|
|
let valid_values = [0.0, 0.25, 0.5, 0.75, 1.0];
|
|
let is_valid = valid_values.iter().any(|&v| (x - v).abs() < 1e-10);
|
|
assert!(is_valid, "stepped float {x} should snap to grid");
|
|
}
|
|
|
|
#[test]
|
|
fn test_step_int_snaps_to_grid() {
|
|
let mut trial = Trial::new(0);
|
|
|
|
let n = trial.suggest_int_step("n", 0, 100, 25).unwrap();
|
|
|
|
// n should be one of: 0, 25, 50, 75, 100
|
|
assert!(
|
|
n % 25 == 0 && (0..=100).contains(&n),
|
|
"stepped int {n} should snap to grid"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn test_best_value() {
|
|
let study: Study<f64> = Study::new(Direction::Minimize);
|
|
|
|
study
|
|
.optimize(10, |trial| {
|
|
let x = trial.suggest_float("x", 0.0, 10.0)?;
|
|
Ok::<_, TpeError>(x)
|
|
})
|
|
.unwrap();
|
|
|
|
let best_value = study.best_value().expect("should have best value");
|
|
let best_trial = study.best_trial().expect("should have best trial");
|
|
|
|
assert_eq!(
|
|
best_value, best_trial.value,
|
|
"best_value should match best_trial.value"
|
|
);
|
|
}
|