Files
trueskill-tt/tests/competitor_config.rs
logaritmiskandClaude Opus 5 061c481aad refactor!: typed discriminators for InferenceError
Six of fifteen variants carried a `&'static str` discriminator, about
thirty magic strings between them, and the only thing a caller could do
with one was print it. Four new enums replace them:

    Parameter        13 variants, replacing 9 strings in InvalidParameter
    Shape             4 variants, replacing 10 in MismatchedShape
    OutcomeKind       2 variants, replacing WrongOutcomeKind's three fields
    CompetitorField   2 variants, replacing ConflictingCompetitorConfig's

`InvalidProbability` folds into `InvalidParameter` as
`Parameter::PDraw`. It was a bespoke variant for one scalar while every
other scalar shared `InvalidParameter`, and it omitted the parameter
name — so the same parameter had two mechanisms.

`JointUnavailable { reason: &'static str }` splits into `EmptyHistory`,
`JointRequiresScoredEvents` and `NotPositiveDefinite`. The three are
conditions a caller branches on differently — add events, use
`predict_win_probabilities`, or reconsider the priors — and telling them
apart used to mean string-matching English. One test already proved the
distinction was load-bearing: the blanket conversion mapped the
empty-history case onto the ranked one and `an_empty_history_has_no_joint`
caught it immediately.

`NonFiniteResult` splits into `NonFiniteStep { context, step }` and
`NonFiniteSkill { mu, sigma }`. One `step: (f64, f64)` field was
carrying a sweep step from `converge` and a skill's own moments from a
prediction — two situations in one variant, and a field name that could
only be right for one of them.

`InvalidParameter { name: "beta with point-mass skills" }` becomes
`NoPerformanceVariance`. It was never a parameter out of range: both
values are individually valid and it is their combination that leaves
nothing varying.

Three `Display` impls did not meet the standard the others set, and the
typed data is what makes fixing them possible:

    before  drift variance is invalid: NaN
    after   drift variance must be finite and non-negative (got NaN)

    before  kinds: expected length 3, got 2
    after   the outcome describes a different number of teams than the
            event has: expected 3, got 2

    before  Game::ranked: expected Outcome::Ranked, got Outcome::Scored
    after   expected Outcome::Ranked, got Outcome::Scored; call
            Game::scored for a scored outcome

`Parameter::range()` states each parameter's actual bounds, which no
`&'static str` name could have. `error::message_tests` renders every one
and asserts each is a sentence rather than a label, and that the three
above now carry a range or a next step.

The four internal `MismatchedShape` kinds — `results`, `times`, `kinds`,
and the weights array — collapse to `Shape::Internal`, whose `Display`
says plainly that reaching it is a bug in this crate. They are checks on
`add_events_with_prior`'s own parallel arrays and are unreachable
through the public API; they stay checked rather than becoming
`debug_assert!`s, because release is where this crate's defects hide.

Closes #74.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
2026-09-10 07:31:31 +02:00

226 lines
6.9 KiB
Rust

//! `Member::with_prior` / `with_drift_scale` — competitor configuration.
//!
//! Both were previously consumed only on the branch that *creates* a
//! competitor, so configuration supplied for a key the history already knew was
//! dropped with no error. `with_prior` had no coverage in this directory at
//! all, which is how that survived.
use smallvec::smallvec;
use trueskill_tt::{
ConvergenceOptions, Event, Gaussian, History, InferenceError, Member, Outcome, Team,
};
const CONVERGENCE: ConvergenceOptions = ConvergenceOptions {
max_iter: 2_000,
epsilon: 1e-12,
alpha: 1.0,
};
fn history() -> History {
History::builder()
.mu(25.0)
.sigma(25.0 / 3.0)
.beta(25.0 / 6.0)
.p_draw(0.0)
.convergence(CONVERGENCE)
.build()
}
/// One event, optionally configuring `a`.
fn bout(
a: &'static str,
b: &'static str,
time: i64,
prior: Option<Gaussian>,
scale: Option<f64>,
) -> Event<i64, &'static str> {
let mut member = Member::new(a);
if let Some(p) = prior {
member = member.with_prior(p);
}
if let Some(s) = scale {
member = member.with_drift_scale(s);
}
Event {
time,
teams: smallvec![
Team::with_members([member]),
Team::with_members([Member::new(b)]),
],
outcome: Outcome::winner(0, 2),
}
}
fn skill_of(h: &History, key: &str) -> Gaussian {
h.current_skill(&key).expect("key in history")
}
/// Baseline: the mechanism works at all on a competitor's first appearance.
#[test]
fn a_prior_applies_to_a_new_competitor() {
let seeded = Gaussian::from_ms(40.0, 1.0);
let mut with = history();
with.add_events(vec![bout("a", "b", 0, Some(seeded), None)])
.unwrap();
let _ = with.converge().unwrap();
let mut without = history();
without
.add_events(vec![bout("a", "b", 0, None, None)])
.unwrap();
let _ = without.converge().unwrap();
assert!(
(skill_of(&with, "a").mu() - skill_of(&without, "a").mu()).abs() > 1.0,
"a seeded prior should move the fit"
);
}
/// The defect in #10: a prior supplied for a competitor the history already
/// knows was silently discarded, and the caller got output computed from the
/// default prior with no indication anything had been dropped.
#[test]
fn a_prior_applies_to_a_competitor_the_history_already_knows() {
let seeded = Gaussian::from_ms(40.0, 1.0);
let mut late = history();
late.add_events(vec![bout("a", "b", 0, None, None)])
.unwrap();
// "a" now exists. Configuring it here used to do nothing whatsoever.
late.add_events(vec![bout("a", "b", 1, Some(seeded), None)])
.unwrap();
let _ = late.converge().unwrap();
let mut never = history();
never
.add_events(vec![
bout("a", "b", 0, None, None),
bout("a", "b", 1, None, None),
])
.unwrap();
let _ = never.converge().unwrap();
assert!(
(skill_of(&late, "a").mu() - skill_of(&never, "a").mu()).abs() > 1.0,
"a late prior must not be silently dropped: {} vs {}",
skill_of(&late, "a").mu(),
skill_of(&never, "a").mu()
);
}
/// Configuration is competitor-scoped, not event-scoped, and `converge` refits
/// from competitor state — so seeding late reaches the same fit as seeding from
/// the start. This is the documented scope, asserted rather than assumed.
#[test]
fn a_prior_is_whole_history_scoped_not_per_event() {
let seeded = Gaussian::from_ms(40.0, 1.0);
let mut late = history();
late.add_events(vec![bout("a", "b", 0, None, None)])
.unwrap();
late.add_events(vec![bout("a", "b", 1, Some(seeded), None)])
.unwrap();
let _ = late.converge().unwrap();
let mut early = history();
early
.add_events(vec![
bout("a", "b", 0, Some(seeded), None),
bout("a", "b", 1, Some(seeded), None),
])
.unwrap();
let _ = early.converge().unwrap();
let (l, e) = (skill_of(&late, "a"), skill_of(&early, "a"));
assert!(
(l.mu() - e.mu()).abs() < 1e-9 && (l.sigma() - e.sigma()).abs() < 1e-9,
"late seeding should refit the whole history: {l:?} vs {e:?}"
);
}
#[test]
fn repeating_the_same_prior_is_inert() {
let seeded = Gaussian::from_ms(40.0, 1.0);
let mut once = history();
once.add_events(vec![
bout("a", "b", 0, Some(seeded), None),
bout("a", "b", 1, None, None),
])
.unwrap();
let _ = once.converge().unwrap();
let mut every_time = history();
every_time
.add_events(vec![
bout("a", "b", 0, Some(seeded), None),
bout("a", "b", 1, Some(seeded), None),
])
.unwrap();
let _ = every_time.converge().unwrap();
let (o, e) = (skill_of(&once, "a"), skill_of(&every_time, "a"));
assert!(
(o.mu() - e.mu()).abs() < 1e-12 && (o.sigma() - e.sigma()).abs() < 1e-12,
"declaring the same prior repeatedly changed the fit: {o:?} vs {e:?}"
);
}
/// Events within a batch have no order, so two different values for one
/// competitor have no well-defined winner. Rejecting is what keeps the answer
/// independent of iteration order.
#[test]
fn a_batch_declaring_two_different_priors_is_rejected() {
let mut h = history();
let err = h
.add_events(vec![
bout("a", "b", 0, Some(Gaussian::from_ms(40.0, 1.0)), None),
bout("a", "b", 1, Some(Gaussian::from_ms(10.0, 1.0)), None),
])
.expect_err("two different priors for one competitor in one batch");
assert!(
matches!(
err,
InferenceError::ConflictingCompetitorConfig {
field: trueskill_tt::CompetitorField::Prior,
..
}
),
"got {err:?}"
);
}
/// A member setting only `drift_scale` must not also assert the default prior,
/// or it would silently undo a prior seeded earlier. This is why the collected
/// configuration tracks each field separately rather than a merged `Rating`.
#[test]
fn setting_one_field_late_leaves_the_other_alone() {
let seeded = Gaussian::from_ms(40.0, 1.0);
let mut h = history();
h.add_events(vec![bout("a", "b", 0, Some(seeded), None)])
.unwrap();
// Only the scale this time — the prior above must survive.
h.add_events(vec![bout("a", "b", 1, None, Some(0.5))])
.unwrap();
let _ = h.converge().unwrap();
let mut both_upfront = history();
both_upfront
.add_events(vec![
bout("a", "b", 0, Some(seeded), Some(0.5)),
bout("a", "b", 1, None, None),
])
.unwrap();
let _ = both_upfront.converge().unwrap();
let (a, b) = (skill_of(&h, "a"), skill_of(&both_upfront, "a"));
assert!(
(a.mu() - b.mu()).abs() < 1e-9 && (a.sigma() - b.sigma()).abs() < 1e-9,
"setting drift_scale late clobbered the earlier prior: {a:?} vs {b:?}"
);
}