Files
trueskill-tt/tests/degenerate_inputs.rs
T
logaritmiskandClaude Opus 5 e4a68ba1a7 fix!: per-key queries report unknown keys instead of a plausible constant
Two accessors answered a question about a key the history had never seen
with a well-formed value indistinguishable from a real answer.

`log_evidence_for` filter_map'd unknown keys away. An empty target list
means "no restriction" downstream, so a list of *entirely* unknown keys
returned the whole-history evidence: measured on a two-cohort fixture,
`log_evidence_for(["typo"])` returned exactly `log_evidence()`. On the
one workload it is documented for — leave-one-out cross-validation —
that is the un-held-out score, a plausible number that silently
invalidates the comparison it was computed for. It now returns
`Err(UnknownKey)` naming the offending position.

`learning_curve` and `filtered_learning_curve` returned an empty `Vec`
both for a typo'd key and for a competitor who is registered but has not
played yet. They now return `Option`, so `None` is "never heard of it"
and `Some(vec![])` is "known, no appearances".

Tests carry a control case in each direction, so they cannot pass by
everything returning the same thing.

Closes #66, closes #70.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
2026-09-09 21:13:35 +02:00

439 lines
13 KiB
Rust

//! Degenerate, boundary, and error-path coverage.
//!
//! These run in both debug and release: the defects they pin were all
//! guarded only by `debug_assert!`, so a debug-only suite never saw them.
mod common;
use common::assert_finite;
use trueskill_tt::{
ConstantDrift, ConvergenceOptions, Game, GameOptions, Gaussian, History, InferenceError,
NullObserver, Outcome, Rating,
};
type R = Rating<i64, ConstantDrift>;
fn rating() -> R {
R::new(
Gaussian::from_ms(25.0, 25.0 / 3.0),
25.0 / 6.0,
ConstantDrift::new(25.0 / 300.0),
)
}
#[test]
fn record_draw_without_draw_probability_is_rejected() {
let mut h = History::default();
let err = h.record_draw(&"a", &"b", 1).unwrap_err();
assert!(matches!(
err,
InferenceError::TieWithoutDrawProbability { .. }
));
}
#[test]
fn builder_draw_without_draw_probability_is_rejected() {
let mut h = History::default();
let err = h
.event(1)
.team(["a"])
.team(["b"])
.draw()
.commit()
.unwrap_err();
assert!(matches!(
err,
InferenceError::TieWithoutDrawProbability { .. }
));
}
#[test]
fn draw_with_positive_draw_probability_is_finite() {
let mut h = History::builder().p_draw(0.25).build();
h.record_draw(&"a", &"b", 1).unwrap();
let report = h.converge().unwrap();
assert_finite(h.current_skill("a").unwrap(), "drawn competitor skill");
assert_finite(h.current_skill("b").unwrap(), "drawn competitor skill");
assert!(report.log_evidence.is_finite());
assert!(report.converged);
}
#[test]
fn game_ranked_rejects_tie_without_draw_probability() {
let a = [rating()];
let b = [rating()];
let teams: Vec<&[R]> = vec![&a, &b];
let err = Game::ranked(&teams, Outcome::draw(2), &GameOptions::default()).unwrap_err();
assert!(matches!(
err,
InferenceError::TieWithoutDrawProbability { .. }
));
}
/// `Outcome::winner(w, n)` ties every loser, so any n >= 3 free-for-all hits
/// the tie path even though the caller never asked for a draw.
#[test]
fn winner_of_three_or_more_requires_draw_probability() {
let a = [rating()];
let b = [rating()];
let c = [rating()];
let teams: Vec<&[R]> = vec![&a, &b, &c];
let err = Game::ranked(&teams, Outcome::winner(0, 3), &GameOptions::default()).unwrap_err();
assert!(matches!(
err,
InferenceError::TieWithoutDrawProbability { .. }
));
let opts = GameOptions {
p_draw: 0.1,
..GameOptions::default()
};
let game = Game::ranked(&teams, Outcome::winner(0, 3), &opts).unwrap();
for team in game.posteriors() {
for skill in team {
assert_finite(skill, "3-team winner posterior");
}
}
}
#[test]
fn full_ranking_without_ties_needs_no_draw_probability() {
let a = [rating()];
let b = [rating()];
let c = [rating()];
let teams: Vec<&[R]> = vec![&a, &b, &c];
let game = Game::ranked(&teams, Outcome::ranking([0, 1, 2]), &GameOptions::default()).unwrap();
for team in game.posteriors() {
for skill in team {
assert_finite(skill, "strict ranking posterior");
}
}
}
#[test]
fn empty_history_converges_trivially() {
let mut h = History::default();
let report = h.converge().unwrap();
assert_eq!(report.iterations, 0);
assert!(report.converged);
}
/// Issue #27's exact reproduction: a non-default key type reaching `converge`
/// with no events at all. The underflow it reported trapped in debug and
/// indexed out of bounds in release, so this must run in both profiles.
#[test]
fn converge_on_an_empty_history_with_owned_keys() {
let mut history: History<i64, ConstantDrift, NullObserver, String> = History::builder()
.key_type::<String>()
.score_sigma(5.0)
.build();
let report = history.converge().unwrap();
assert_eq!(report.iterations, 0);
assert!(report.converged);
}
/// A weights/team length mismatch used to be a `debug_assert!`, so release
/// builds ingested the event with the weights silently unapplied. This file's
/// CI job runs in release too, which is the point of pinning it here.
#[test]
fn event_builder_rejects_a_weights_length_mismatch() {
let mut h = History::default();
let err = h
.event(1)
.team(["a"])
.weights([1.0, 2.0])
.team(["b"])
.winner(0)
.commit()
.unwrap_err();
assert!(
matches!(
err,
InferenceError::MismatchedShape {
kind: "weights",
expected: 1,
got: 2,
..
}
),
"expected a weights MismatchedShape, got {err:?}"
);
}
/// The mismatch must not be applied even partially — a half-weighted team
/// reaching the history would be worse than the error.
#[test]
fn event_builder_weights_mismatch_leaves_the_history_untouched() {
let mut h = History::default();
// Two teams, so ingestion would otherwise succeed. A one-team event is
// rejected as `NotEnoughTeams` before the weights are ever examined, so
// building this with one team would pass vacuously.
let _ = h
.event(1)
.team(["a"])
.weights([1.0, 2.0])
.team(["b"])
.winner(0)
.commit();
// The rejected event never reached the history, so "a" was never interned.
// `None` is the honest answer, and it is distinguishable from a competitor
// that IS known but has no appearances yet.
assert!(h.learning_curve("a").is_none());
}
#[test]
fn empty_event_stream_then_converge() {
let mut h = History::default();
h.add_events(std::iter::empty()).unwrap();
let report = h.converge().unwrap();
assert_eq!(report.iterations, 0);
}
#[test]
fn empty_history_queries_do_not_panic() {
let h = History::default();
assert!(h.learning_curves().is_empty());
assert!(h.learning_curve("nobody").is_none());
assert!(h.current_skill("nobody").is_none());
}
#[test]
fn single_event_history_converges() {
let mut h = History::default();
h.record_winner(&"a", &"b", 1).unwrap();
let report = h.converge().unwrap();
assert!(report.converged);
assert_finite(h.current_skill("a").unwrap(), "single-event skill");
}
#[test]
fn scored_event_rejects_non_positive_sigma() {
let mut h = History::builder().score_sigma(2.0).build();
let err = h
.event(1)
.team(["a"])
.team(["b"])
.scores_with_sigma([3.0, 1.0], f64::NAN)
.commit()
.unwrap_err();
assert!(matches!(
err,
InferenceError::InvalidParameter {
name: "score_sigma",
..
}
));
}
#[test]
fn convergence_reports_are_finite_across_many_teams() {
let opts = GameOptions {
p_draw: 0.1,
convergence: ConvergenceOptions::default(),
..GameOptions::default()
};
let holders: Vec<[R; 1]> = (0..12).map(|_| [rating()]).collect();
let teams: Vec<&[R]> = holders.iter().map(|t| t.as_slice()).collect();
let game = Game::ranked(&teams, Outcome::ranking(0..12), &opts).unwrap();
assert!(
game.log_evidence().is_finite(),
"12-team log-evidence must be finite, got {}",
game.log_evidence()
);
for team in game.posteriors() {
for skill in team {
assert_finite(skill, "12-team posterior");
}
}
}
/// A long diff chain underflows a linear evidence product: each link
/// contributes a probability in (0, 1], so ~1000 links flush the product to
/// exactly 0.0 and `ln(0.0)` is `-inf`. Accumulating in log space keeps it
/// finite.
#[test]
fn log_evidence_survives_a_long_diff_chain() {
let holders: Vec<[R; 1]> = (0..1200).map(|_| [rating()]).collect();
let teams: Vec<&[R]> = holders.iter().map(|t| t.as_slice()).collect();
let game = Game::ranked(
&teams,
Outcome::ranking(0..holders.len() as u32),
&GameOptions::default(),
)
.unwrap();
let log_evidence = game.log_evidence();
assert!(
log_evidence.is_finite(),
"1200-team log-evidence must be finite, got {log_evidence}"
);
assert!(
log_evidence < 0.0,
"log-evidence of a probability must be negative, got {log_evidence}"
);
}
/// A near-certain outcome rounds the losing tail to exactly zero in the
/// `erfc` approximation; the evidence floor keeps `ln` finite.
#[test]
fn log_evidence_finite_for_near_certain_outcome() {
let overwhelming = R::new(
Gaussian::from_ms(5_000.0, 0.5),
1.0,
ConstantDrift::new(0.0),
);
let hopeless = R::new(
Gaussian::from_ms(-5_000.0, 0.5),
1.0,
ConstantDrift::new(0.0),
);
let a = [overwhelming];
let b = [hopeless];
let teams: Vec<&[R]> = vec![&a, &b];
let game = Game::ranked(&teams, Outcome::winner(0, 2), &GameOptions::default()).unwrap();
assert!(
game.log_evidence().is_finite(),
"got {}",
game.log_evidence()
);
// And the reverse — a colossal upset — must also stay finite.
let upset = Game::ranked(&teams, Outcome::winner(1, 2), &GameOptions::default()).unwrap();
assert!(
upset.log_evidence().is_finite(),
"upset log-evidence must be finite, got {}",
upset.log_evidence()
);
}
#[test]
fn empty_history_has_no_filtered_estimates() {
let history: History = History::builder().build();
assert_eq!(history.filtered_log_evidence(), 0.0);
assert!(history.filtered_learning_curves().is_empty());
assert!(history.filtered_learning_curve("nobody").is_none());
}
// --- Boundary inputs (#26) ----------------------------------------------
fn tight() -> ConvergenceOptions {
ConvergenceOptions {
max_iter: 2_000,
epsilon: 1e-12,
..ConvergenceOptions::default()
}
}
fn assert_curve_finite(h: &History, keys: &[&str], what: &str) {
for key in keys {
for (time, g) in h.learning_curve(*key).unwrap() {
assert!(
g.mu().is_finite() && g.sigma().is_finite(),
"{what}: non-finite posterior for {key} at t={time} (mu={} sigma={})",
g.mu(),
g.sigma()
);
}
}
}
/// A zero weight reaches `(m - performance.exclude(..)) * (1.0 / w)`, i.e. a
/// division by zero. The commit is accepted today, so this pins that the
/// resulting posterior is still finite rather than quietly NaN.
#[test]
fn zero_weight_does_not_produce_a_non_finite_posterior() {
let mut h = History::builder().build();
h.event(1)
.team(["a"])
.weights([0.0])
.team(["b"])
.winner(0)
.commit()
.expect("a zero weight is accepted today; update this test if that changes");
let _ = h.converge().unwrap();
assert_curve_finite(&h, &["a", "b"], "zero weight");
}
#[test]
fn negative_weight_does_not_produce_a_non_finite_posterior() {
let mut h = History::builder().build();
h.event(1)
.team(["a"])
.weights([-1.0])
.team(["b"])
.winner(0)
.commit()
.expect("a negative weight is accepted today; update this test if that changes");
let _ = h.converge().unwrap();
assert_curve_finite(&h, &["a", "b"], "negative weight");
}
/// Events supplied newest-first must land in the same slices as oldest-first:
/// ingestion sorts by time rather than trusting arrival order.
#[test]
fn out_of_order_timestamps_converge_to_the_same_answer() {
fn build(descending: bool) -> History {
let mut h = History::builder().convergence(tight()).build();
let mut times: Vec<i64> = (1..=6).collect();
if descending {
times.reverse();
}
for time in times {
h.record_winner(&"a", &"b", time).unwrap();
}
let _ = h.converge().unwrap();
h
}
let ascending = build(false);
let descending = build(true);
let one = ascending.current_skill("a").unwrap();
let other = descending.current_skill("a").unwrap();
assert!(
(one.mu() - other.mu()).abs() < 1e-8 && (one.sigma() - other.sigma()).abs() < 1e-8,
"arrival order changed the answer: ascending mu={} sigma={}, descending mu={} sigma={}",
one.mu(),
one.sigma(),
other.mu(),
other.sigma()
);
}
#[test]
fn extreme_beta_and_sigma_stay_finite() {
for (beta, sigma) in [(1e-6, 1e-6), (1e6, 1e6), (1e-6, 1e6), (1e6, 1e-6)] {
let mut h = History::builder().beta(beta).sigma(sigma).build();
h.record_winner(&"a", &"b", 1).unwrap();
h.record_winner(&"a", &"b", 2).unwrap();
let _ = h.converge().unwrap();
assert_curve_finite(&h, &["a", "b"], &format!("beta={beta} sigma={sigma}"));
}
}