Merge branch 'test/close-coverage-gaps'

Cover non-finite results and color-group disjointness, closing the two
test gaps #26 named.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
This commit is contained in:
2026-09-08 21:09:25 +02:00
co-authored by Claude Opus 5
2 changed files with 235 additions and 0 deletions
+118
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@@ -191,3 +191,121 @@ mod tests {
assert_eq!(cg.total_events(), 4);
}
}
#[cfg(test)]
mod properties {
use std::collections::HashSet;
use proptest::prelude::*;
use super::*;
/// The property the whole parallel sweep rests on: two events sharing a
/// competitor must never land in the same color, because a color group is
/// run concurrently and two events touching one competitor would race.
///
/// Hand-written cases cover the shapes someone thought of. This covers the
/// ones nobody did — the correctness of `sweep_color_groups` depends on it
/// holding for every input, not for five.
fn check(events: &[Vec<usize>]) {
let groups = color_greedy(events.len(), |ev| {
events[ev]
.iter()
.copied()
.map(Index::from)
.collect::<Vec<_>>()
});
// Disjointness *between events* within a color. Deduplicated per
// event, because one event legitimately naming a competitor twice is
// not a collision — `color_greedy` collects each event's members into
// a set for exactly that reason.
for color in 0..groups.n_colors() {
let mut seen: HashSet<usize> = HashSet::new();
for &ev in &groups.groups[color] {
let members: HashSet<usize> = events[ev].iter().copied().collect();
for competitor in members {
assert!(
seen.insert(competitor),
"competitor {competitor} shared by two events in color {color}"
);
}
}
}
// Every event is assigned exactly once. Without this, a partition that
// dropped events would satisfy disjointness trivially.
let mut assigned: Vec<usize> = groups.groups.iter().flatten().copied().collect();
assigned.sort_unstable();
assert_eq!(assigned, (0..events.len()).collect::<Vec<_>>());
assert_eq!(groups.total_events(), events.len());
// No empty colors: one would waste a sweep and make `n_colors`
// misleading.
for (color, group) in groups.groups.iter().enumerate() {
assert!(!group.is_empty(), "color {color} is empty");
}
// Contiguity is not a property of `color_greedy` — it holds only after
// `recompute_color_groups` reorders the events so each color occupies
// one range. What must always hold is that the reorder is *possible*:
// relabelling events in group order yields contiguous groups. The
// parallel sweep slices `&mut` sub-ranges from those, so if this ever
// failed the reorder would produce overlapping ranges.
let mut next = 0usize;
let relabelled: Vec<Vec<usize>> = groups
.groups
.iter()
.map(|group| {
group
.iter()
.map(|_| {
let i = next;
next += 1;
i
})
.collect()
})
.collect();
assert!(ColorGroups { groups: relabelled }.groups_are_contiguous());
}
proptest! {
#![proptest_config(ProptestConfig::with_cases(512))]
/// Small competitor pool, so collisions are common and colors are
/// forced to multiply.
#[test]
fn colors_are_disjoint_on_a_dense_pool(
events in prop::collection::vec(
prop::collection::vec(0usize..6, 1..4),
0..20,
)
) {
check(&events);
}
/// Wide pool, so most events are independent and land in one color.
#[test]
fn colors_are_disjoint_on_a_sparse_pool(
events in prop::collection::vec(
prop::collection::vec(0usize..200, 1..6),
0..30,
)
) {
check(&events);
}
/// Repeated competitors within one event must not confuse the
/// member-set bookkeeping.
#[test]
fn colors_are_disjoint_with_repeated_members(
events in prop::collection::vec(
prop::collection::vec(0usize..3, 1..8),
0..15,
)
) {
check(&events);
}
}
}
+117
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@@ -0,0 +1,117 @@
//! Inference must report numerical breakdown rather than call it convergence.
//!
//! The boundary rejects inputs that are *not numbers*, but finite inputs can
//! still overflow during inference — `beta.powi(2)` at 1e300 is infinite, and
//! infinity minus infinity is NaN. `NonFiniteResult` is the guard for that, and
//! it matters because the alternative is silent: NaN fails every comparison, so
//! a naive `step < epsilon` check reads a NaN step as *converged*.
//!
//! That is why the crate has `step_converged` / `step_is_finite` rather than
//! `!tuple_gt(..)`. These tests pin the guard from outside.
use smallvec::smallvec;
use trueskill_tt::{Event, Gaussian, History, InferenceError, Member, Outcome, Team};
fn scored_fit(
sigma: f64,
beta: f64,
score_sigma: f64,
scores: [f64; 2],
) -> Result<bool, InferenceError> {
let mut h = History::builder()
.mu(0.0)
.sigma(sigma)
.beta(beta)
.score_sigma(score_sigma)
.build();
h.add_events(vec![Event {
time: 1i64,
teams: smallvec![
Team::with_members([Member::new("a")]),
Team::with_members([Member::new("b")]),
],
outcome: Outcome::scores(scores),
}])?;
h.converge().map(|r| r.converged)
}
/// Every one of these is built from finite, individually legal parameters. The
/// overflow happens inside inference, which is exactly the case the boundary
/// checks cannot catch.
///
/// Matched rather than merely `is_err()`: an assertion that only checks "some
/// error" would keep passing if these started failing at the boundary for an
/// unrelated reason, and would then be testing nothing.
#[test]
fn overflow_during_inference_is_reported_not_hidden() {
let cases: [(&str, f64, f64, f64, [f64; 2]); 5] = [
("huge sigma", 1e300, 1.0, 1.0, [3.0, 1.0]),
("huge beta", 6.0, 1e300, 1.0, [3.0, 1.0]),
("tiny sigma", 1e-300, 1.0, 1.0, [3.0, 1.0]),
("tiny score_sigma", 6.0, 1.0, 1e-300, [3.0, 1.0]),
("huge scores", 6.0, 1.0, 1.0, [1e308, -1e308]),
];
for (name, sigma, beta, score_sigma, scores) in cases {
match scored_fit(sigma, beta, score_sigma, scores) {
Err(InferenceError::NonFiniteResult { context, step }) => {
assert_eq!(context, "History::converge", "{name}");
assert!(
!step.0.is_finite() || !step.1.is_finite(),
"{name}: reported NonFiniteResult with a finite step {step:?}"
);
}
other => panic!("{name}: expected NonFiniteResult, got {other:?}"),
}
}
}
/// The trap the invariant exists for: NaN fails every comparison, so a naive
/// `step < epsilon` test reads a NaN step as converged. A breakdown must never
/// come back as a successful fit.
#[test]
fn a_broken_fit_is_never_reported_as_converged() {
let mut h = History::builder().build();
h.add_events(vec![Event {
time: 1i64,
teams: smallvec![
Team::with_members([Member::new("a").with_prior(Gaussian::from_ms(1e300, 1e-300))]),
Team::with_members([Member::new("b")]),
],
outcome: Outcome::winner(0, 2),
}])
.unwrap();
let err = h.converge().unwrap_err();
assert!(
matches!(err, InferenceError::NonFiniteResult { .. }),
"a breakdown must not be reported as convergence: {err:?}"
);
// `converge_partial` must not launder it into an `Ok` either — the
// permissive path is permissive about *stopping short*, not about NaN.
let mut h2 = History::builder().build();
h2.add_events(vec![Event {
time: 1i64,
teams: smallvec![
Team::with_members([Member::new("a").with_prior(Gaussian::from_ms(1e300, 1e-300))]),
Team::with_members([Member::new("b")]),
],
outcome: Outcome::winner(0, 2),
}])
.unwrap();
assert!(matches!(
h2.converge_partial().unwrap_err(),
InferenceError::NonFiniteResult { .. }
));
}
/// The neighbouring case, so the tests above cannot pass by the fit simply
/// always failing: ordinary extreme-but-workable parameters still converge.
#[test]
fn merely_extreme_parameters_still_converge() {
assert!(scored_fit(1e6, 1.0, 1.0, [3.0, 1.0]).unwrap());
assert!(scored_fit(1e-6, 1.0, 1.0, [3.0, 1.0]).unwrap());
assert!(scored_fit(6.0, 1.0, 1e6, [3.0, 1.0]).unwrap());
assert!(scored_fit(6.0, 1.0, 1.0, [1e150, -1e150]).unwrap());
}