trueskill-tt/tests/prediction.rs

//! Prediction API: N-team outcomes, draw mass, and the error paths that used
//! to be panics or silent wrong answers.

use trueskill_tt::{History, InferenceError, MAX_PREDICTED_TEAMS};

fn history_with(names: &[&'static str], p_draw: f64) -> History {
    let mut h = History::builder().p_draw(p_draw).build();
    // Give every competitor a recorded skill by playing a small round robin.
    for pair in names.windows(2) {
        h.record_winner(&pair[0], &pair[1], 1).unwrap();
    }
    h.converge().unwrap();
    h
}

#[test]
fn unknown_keys_are_reported_not_silently_dropped() {
    let h = history_with(&["a", "b"], 0.0);

    let err = h
        .predict_outcome(&[&[&"a"], &[&"ghost"]])
        .expect_err("an unknown key must not yield a confident prediction");
    assert_eq!(err, InferenceError::UnknownKey { team: 1, member: 0 });

    // Every prediction entry point, not just one.
    assert!(
        h.predict_win_probabilities(&[&[&"a"], &[&"ghost"]])
            .is_err()
    );
    assert!(h.predict_quality(&[&[&"a"], &[&"ghost"]]).is_err());
    assert!(h.predict_ranking(&[&[&"a"], &[&"ghost"]], &[0, 1]).is_err());
}

#[test]
fn an_entirely_unknown_team_is_an_error() {
    let h = history_with(&["a", "b"], 0.0);
    let err = h.predict_outcome(&[&[&"a"], &[&"x", &"y"]]).unwrap_err();
    assert_eq!(err, InferenceError::UnknownKey { team: 1, member: 0 });
}

#[test]
fn degenerate_team_shapes_are_errors_rather_than_panics() {
    let h = history_with(&["a", "b"], 0.0);

    assert_eq!(
        h.predict_outcome(&[&[&"a"]]).unwrap_err(),
        InferenceError::NotEnoughTeams { got: 1 }
    );
    assert_eq!(
        h.predict_outcome(&[]).unwrap_err(),
        InferenceError::NotEnoughTeams { got: 0 }
    );
    assert_eq!(
        h.predict_outcome(&[&[&"a"], &[]]).unwrap_err(),
        InferenceError::EmptyTeam { team: 1 }
    );
}

#[test]
fn more_than_two_teams_no_longer_panics() {
    let h = history_with(&["a", "b", "c"], 0.0);
    let p = h
        .predict_outcome(&[&[&"a"], &[&"b"], &[&"c"]])
        .expect("three teams must be supported");
    assert!((p.total() - 1.0).abs() < 1e-6, "total = {}", p.total());
    // Three teams, no draws possible: exactly the six strict orderings.
    assert_eq!(p.outcomes().len(), 6);
}

#[test]
fn the_outcome_space_is_capped_rather_than_hanging() {
    let names: Vec<&'static str> = vec!["a", "b", "c", "d", "e", "f", "g", "h"];
    let h = history_with(&names, 0.0);

    let teams: Vec<&[&&'static str]> = Vec::new();
    let _ = teams;

    let too_many: Vec<Vec<&&str>> = names.iter().map(|n| vec![n]).collect();
    let refs: Vec<&[&&str]> = too_many.iter().map(Vec::as_slice).collect();

    let err = h.predict_outcome(&refs).unwrap_err();
    assert_eq!(
        err,
        InferenceError::TooManyTeams {
            got: 8,
            max: MAX_PREDICTED_TEAMS
        }
    );

    // The cheap paths stay available at any size.
    let wins = h.predict_win_probabilities(&refs).unwrap();
    assert_eq!(wins.len(), 8);
    assert!(
        (wins.iter().sum::<f64>() - 1.0).abs() < 1e-6,
        "win probabilities must still sum to one: {wins:?}"
    );
}

/// The defect that made every draw-enabled prediction wrong: `[p, 1 - p]`
/// allocated no mass to a draw even with `p_draw > 0`.
#[test]
fn a_draw_carries_probability_mass_when_p_draw_is_positive() {
    let h = history_with(&["a", "b"], 0.25);
    let p = h.predict_outcome(&[&[&"a"], &[&"b"]]).unwrap();

    let draw = p.probability_of(&[0, 0]);
    assert!(draw > 0.0, "a draw-enabled model must give draws mass");
    assert!((p.total() - 1.0).abs() < 1e-6, "total = {}", p.total());

    let wins = p.win_probabilities();
    assert!(
        (wins.iter().sum::<f64>() + draw - 1.0).abs() < 1e-6,
        "wins {wins:?} plus draw {draw} must be the whole space"
    );
    assert!(
        (p.shared_first_place() - draw).abs() < 1e-12,
        "a two-team draw is a shared first place"
    );
}

#[test]
fn a_zero_draw_probability_admits_no_ties() {
    let h = history_with(&["a", "b"], 0.0);
    let p = h.predict_outcome(&[&[&"a"], &[&"b"]]).unwrap();
    assert_eq!(p.probability_of(&[0, 0]), 0.0);
    assert!(p.shared_first_place() < 1e-12);
}

/// The two routes to a win probability run through entirely different
/// algorithms — adaptive quadrature versus the enumerated chain recursion —
/// so agreement between them is a real cross-check, not a tautology.
#[test]
fn the_cheap_and_exhaustive_paths_agree() {
    for p_draw in [0.0, 0.1] {
        let h = history_with(&["a", "b", "c"], p_draw);
        let teams: &[&[&&str]] = &[&[&"a"], &[&"b"], &[&"c"]];

        let cheap = h.predict_win_probabilities(teams).unwrap();
        let exhaustive = h.predict_outcome(teams).unwrap().win_probabilities();

        for (i, (a, b)) in cheap.iter().zip(&exhaustive).enumerate() {
            assert!(
                (a - b).abs() < 1e-6,
                "p_draw={p_draw} team {i}: quadrature {a} vs enumeration {b}"
            );
        }
    }
}

#[test]
fn predict_ranking_agrees_with_the_distribution() {
    let h = history_with(&["a", "b", "c"], 0.1);
    let teams: &[&[&&str]] = &[&[&"a"], &[&"b"], &[&"c"]];
    let dist = h.predict_outcome(teams).unwrap();

    for (ranks, expected) in dist.outcomes() {
        let direct = h.predict_ranking(teams, ranks).unwrap();
        assert!(
            (direct - expected).abs() < 1e-9,
            "ranks {ranks:?}: {direct} vs {expected}"
        );
    }
}

#[test]
fn predict_ranking_checks_its_shape() {
    let h = history_with(&["a", "b"], 0.0);
    let err = h
        .predict_ranking(&[&[&"a"], &[&"b"]], &[0, 1, 2])
        .unwrap_err();
    assert!(matches!(
        err,
        InferenceError::MismatchedShape {
            expected: 2,
            got: 3,
            ..
        }
    ));
}

#[test]
fn the_stronger_competitor_is_favoured() {
    let mut h = History::builder().build();
    for t in 1..=10 {
        h.record_winner(&"strong", &"weak", t).unwrap();
    }
    h.converge().unwrap();

    let p = h.predict_outcome(&[&[&"strong"], &[&"weak"]]).unwrap();
    let (best, _) = p.most_likely().expect("a most likely outcome");
    assert_eq!(best, &[0, 1], "the winner should be favoured");

    let wins = p.win_probabilities();
    assert!(wins[0] > wins[1], "{wins:?}");
}

/// Unequal team sizes change the draw margin, because inference derives it
/// from the teams' betas. Prediction has to follow, or it describes a
/// different model than the one that will be fitted.
#[test]
fn team_size_affects_the_prediction() {
    let mut h = History::builder().p_draw(0.2).build();
    h.event(1)
        .team(["a", "b"])
        .team(["c"])
        .winner(0)
        .commit()
        .unwrap();
    h.converge().unwrap();

    let p = h.predict_outcome(&[&[&"a", &"b"], &[&"c"]]).unwrap();
    assert!((p.total() - 1.0).abs() < 1e-6, "total = {}", p.total());
    assert!(p.probability_of(&[0, 0]) > 0.0);
}

// ---------------------------------------------------------------------------
// Expected information gain
// ---------------------------------------------------------------------------

/// The whole point of #39: "which comparison should I run next?" is a
/// different question from "who will win?" or "is this fair?".
#[test]
fn information_gain_prefers_the_uncertain_pairing() {
    let mut h = History::builder().build();

    // "known" and "rival" have played a lot; "newcomer" has played once.
    for t in 1..=15 {
        h.record_winner(&"known", &"rival", t).unwrap();
        h.record_winner(&"rival", &"known", t + 100).unwrap();
    }
    h.record_winner(&"known", &"newcomer", 500).unwrap();
    h.converge().unwrap();

    let settled = h
        .expected_information_gain(&[&[&"known"], &[&"rival"]])
        .unwrap();
    let unknown = h
        .expected_information_gain(&[&[&"known"], &[&"newcomer"]])
        .unwrap();

    assert!(
        unknown > settled,
        "pairing against the newcomer should teach more: {unknown} vs {settled}"
    );
}

/// The analytic ceiling, through the `History` entry point rather than the
/// standalone one.
#[test]
fn information_gain_respects_the_entropy_ceiling() {
    let h = history_with(&["a", "b", "c"], 0.0);

    let two = h.expected_information_gain(&[&[&"a"], &[&"b"]]).unwrap();
    assert!(
        (0.0..=std::f64::consts::LN_2).contains(&two),
        "two-team EIG {two} outside [0, ln 2]"
    );

    let three = h
        .expected_information_gain(&[&[&"a"], &[&"b"], &[&"c"]])
        .unwrap();
    assert!(
        (0.0..=6.0f64.ln()).contains(&three),
        "three-team EIG {three} outside [0, ln 6]"
    );
}

#[test]
fn information_gain_reports_unknown_keys() {
    let h = history_with(&["a", "b"], 0.0);
    assert_eq!(
        h.expected_information_gain(&[&[&"a"], &[&"ghost"]])
            .unwrap_err(),
        InferenceError::UnknownKey { team: 1, member: 0 }
    );
}

/// A draw-enabled history has three outcomes to weigh rather than two, so the
/// draw branch must actually be reachable through this path.
#[test]
fn information_gain_accounts_for_draws() {
    let with_draws = history_with(&["a", "b"], 0.25);
    let g = with_draws
        .expected_information_gain(&[&[&"a"], &[&"b"]])
        .unwrap();
    assert!(g > 0.0 && g <= 3.0f64.ln(), "{g}");

    // The draw outcome carries mass, so it is genuinely being weighed.
    let dist = with_draws.predict_outcome(&[&[&"a"], &[&"b"]]).unwrap();
    assert!(dist.probability_of(&[0, 0]) > 0.0);
}