Two names that described the wrong thing.
`scores_with_sigma(scores, sigma)` reads as "these scores have prior
sigma 2.0". The quantity is observation noise on the score *margin*, in
the units of the scores, and it is spelled `score_sigma` at every config
site — `HistoryBuilder::score_sigma`, `GameOptions::score_sigma`,
`EventKind::Scored { score_sigma }` — so this was the one place the
crate used a third meaning of "sigma" for it. Its own doc had to
disambiguate itself: "`sigma` overrides `HistoryBuilder::score_sigma`".
`scores_with_noise(scores, score_sigma)` on both `Outcome` and
`EventBuilder`.
`predict_quality` predicts nothing. Its own doc says it answers "is this
matchup *fair*", not "what will happen", and the `predict_*` family is
otherwise exactly the methods returning a probability or a distribution
over outcomes. `History::quality` also makes the free/method pair
consistent: free `quality` pairs with `History::quality` the way free
`expected_information_gain` already pairs with
`History::expected_information_gain`. The rule that was already being
followed and never stated — a free function scores a hypothetical from
explicit parameters, the same-named method asks it against the fit — is
now written on the method.
Closes #75. Refs #78 (part 4).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
153 lines
4.7 KiB
Rust
153 lines
4.7 KiB
Rust
//! No prediction path may answer from a fit it cannot answer from.
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//!
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//! `converge` grew a `NonFiniteResult` guard; nothing stopped a caller from
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//! ignoring that error and predicting anyway. The three failures that produced
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//! were each differently wrong: `Ok(NaN)`, a panic out of a `Result`-returning
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//! method, and `Ok([0.0, 0.0])` — finite, plausible, summing to zero against a
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//! doc that promises one.
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//!
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//! Every test here has a healthy control, so none can pass by everything
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//! returning `Err`.
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use trueskill_tt::{
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ConstantDrift, Event, Gaussian, History, InferenceError, Member, NullObserver, Outcome, Team,
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};
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type H = History<i64, ConstantDrift, NullObserver, &'static str>;
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fn build(beta: f64, prior: Option<Gaussian>, outcome: Outcome) -> H {
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let mut h: H = History::builder()
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.beta(beta)
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.drift(ConstantDrift::new(0.0))
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.build();
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let member = |k: &'static str| match prior {
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Some(p) => Member::new(k).with_prior(p),
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None => Member::new(k),
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};
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let _ = h.add_events(vec![Event {
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time: 1,
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teams: [
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Team::with_members([member("a")]),
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Team::with_members([member("b")]),
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]
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.into_iter()
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.collect(),
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outcome,
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}]);
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h
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}
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/// Point-mass priors with `beta(0.0)` on a *ranked* event: `converge` reports
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/// `NonFiniteResult` and the stored posteriors are `pi: NaN, tau: NaN`.
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fn nan_poisoned() -> H {
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let mut h = build(
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0.0,
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Some(Gaussian::from_ms(0.0, 0.0)),
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Outcome::winner(0, 2),
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);
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let err = h.converge().expect_err("this fixture must not converge");
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assert!(
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matches!(err, InferenceError::NonFiniteResult { .. }),
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"{err:?}"
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);
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h
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}
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/// The same degenerate parameters on a *scored* event, where inference
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/// converges cleanly and leaves legitimate point-mass posteriors behind. The
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/// fit is fine; it is prediction that has nothing to work with.
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fn degenerate_but_converged() -> H {
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let mut h = build(
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0.0,
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Some(Gaussian::from_ms(0.0, 0.0)),
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Outcome::scores([1.0, 0.0]),
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);
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h.converge().expect("this fixture converges");
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h
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}
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fn healthy() -> H {
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let mut h = build(1.0, None, Outcome::winner(0, 2));
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h.converge().expect("control converges");
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h
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}
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macro_rules! all_predictions {
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($h:ident, $f:expr) => {{
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let teams: &[&[&&'static str]] = &[&[&"a"], &[&"b"]];
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let f = $f;
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f("quality", $h.quality(teams).map(|_| ()));
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f(
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"predict_win_probabilities",
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$h.predict_win_probabilities(teams).map(|_| ()),
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);
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f("predict_outcome", $h.predict_outcome(teams).map(|_| ()));
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f(
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"predict_ranking",
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$h.predict_ranking(teams, &[0, 1]).map(|_| ()),
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);
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f(
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"expected_information_gain",
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$h.expected_information_gain(teams).map(|_| ()),
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);
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}};
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}
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#[test]
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fn a_nan_poisoned_fit_is_refused_by_every_prediction_path() {
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let h = nan_poisoned();
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all_predictions!(h, |name: &str, r: Result<(), InferenceError>| {
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match r {
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Err(InferenceError::NonFiniteResult { .. }) => {}
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other => panic!("{name} answered from a NaN fit: {other:?}"),
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}
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});
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}
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#[test]
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fn degenerate_performances_are_refused_rather_than_answered_wrongly() {
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let h = degenerate_but_converged();
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// The fit itself is sound — the posteriors are point masses, not NaN.
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let skill = h.current_skill("a").expect("a played");
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assert_eq!(skill.sigma(), 0.0);
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assert!(skill.mu().is_finite());
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// `quality` previously PANICKED here, out of a method that returns
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// `Result`: the contrast covariance is exactly singular when beta is zero
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// and every skill is a point mass.
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all_predictions!(h, |name: &str, r: Result<(), InferenceError>| {
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match r {
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Err(InferenceError::InvalidParameter { .. }) => {}
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other => panic!("{name} predicted from a degenerate fit: {other:?}"),
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}
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});
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}
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#[test]
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fn the_control_history_answers_every_prediction() {
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let h = healthy();
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all_predictions!(h, |name: &str, r: Result<(), InferenceError>| {
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assert!(r.is_ok(), "{name} failed on a healthy history: {r:?}");
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});
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}
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#[test]
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fn win_probabilities_sum_to_one_on_the_control() {
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// The promise the `Ok([0.0, 0.0])` case broke. Asserted on the control so
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// the guard above cannot be "fixed" by making every path error.
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let h = healthy();
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let p = h
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.predict_win_probabilities(&[&[&"a"], &[&"b"]])
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.expect("control predicts");
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let total: f64 = p.iter().sum();
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assert!(
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(total - 1.0).abs() < 1e-6,
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"win probabilities sum to {total}"
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);
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}
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