trueskill-tt/tests/event_builder_members.rs

//! `EventBuilder::members` must reach exactly what the typed path reaches.
//!
//! Before this existed, `EventBuilder` could set weights and nothing else, so
//! `prior` and `drift_scale` were expressible only through `Event`/`Team`/
//! `Member` + `add_events`. Which ingestion route a competitor arrived through
//! decided whether it could be configured at all.

use smallvec::smallvec;
use trueskill_tt::{
    ConstantDrift, ConvergenceOptions, Event, Gaussian, History, InferenceError, Member, Outcome,
    Team,
};

type H = History;

fn history() -> H {
    History::builder()
        .mu(0.0)
        .sigma(6.0)
        .beta(1.0)
        .score_sigma(2.0)
        .drift(ConstantDrift::new(0.5))
        .convergence(ConvergenceOptions {
            max_iter: 20_000,
            epsilon: 1e-13,
            alpha: 1.0,
        })
        .build()
}

const PRIOR: Gaussian = Gaussian::from_ms(3.0, 1.5);

/// The contract that makes the escape hatch worth having: same configuration,
/// same fit, bit for bit.
#[test]
fn members_matches_the_typed_path_exactly() {
    let mut typed = history();
    typed
        .add_events(vec![Event {
            time: 1,
            teams: smallvec![
                Team::with_members([Member::new("player")]),
                Team::with_members([Member::new("layout_7")
                    .with_drift_scale(0.0)
                    .with_prior(PRIOR)]),
            ],
            outcome: Outcome::scores([5.0, 2.0]),
        }])
        .unwrap();
    assert!(typed.converge().unwrap().converged);

    let mut fluent = history();
    fluent
        .event(1)
        .team(["player"])
        .members([Member::new("layout_7")
            .with_drift_scale(0.0)
            .with_prior(PRIOR)])
        .scores([5.0, 2.0])
        .commit()
        .unwrap();
    assert!(fluent.converge().unwrap().converged);

    for key in ["player", "layout_7"] {
        let a = typed.current_skill(&key).unwrap();
        let b = fluent.current_skill(&key).unwrap();
        // Exact equality, on the public moments rather than the natural
        // parameters: `mu` and `variance` are `tau/pi` and `1/pi`, so
        // bit-equal natural parameters give bit-equal moments.
        assert_eq!(a.mu(), b.mu(), "{key} mu");
        assert_eq!(a.variance(), b.variance(), "{key} variance");
    }
}

/// The configuration has to actually take effect, not merely round-trip: a
/// competitor pinned with `drift_scale = 0.0` must not move across slices,
/// where an unpinned one does.
///
/// The comparison is against a control rather than against a fixed epsilon.
/// Pinned marginals are not bit-identical across slices — each slice combines
/// its own forward and backward messages, so the arithmetic order differs and
/// the last bit moves. What "pinned" promises is that no drift variance
/// accumulates, and the control is what makes that measurable.
#[test]
fn a_drift_scale_set_through_members_is_applied() {
    fn spread(h: &H, key: &'static str) -> f64 {
        let curve = h.learning_curve(&key).unwrap();
        assert!(curve.len() >= 2, "{key}: expected several appearances");
        let (lo, hi) = curve.iter().fold((f64::MAX, f64::MIN), |(lo, hi), (_, g)| {
            (lo.min(g.sigma()), hi.max(g.sigma()))
        });
        (hi - lo) / hi
    }

    let mut h = history();
    for t in 1..=4 {
        h.event(t)
            .team(["player"])
            .members([Member::new("pinned").with_drift_scale(0.0)])
            .scores([5.0, 2.0])
            .commit()
            .unwrap();
        // Same shape, no pinning: the control.
        h.event(t)
            .team(["rival"])
            .team(["drifting"])
            .scores([5.0, 2.0])
            .commit()
            .unwrap();
    }
    assert!(h.converge().unwrap().converged);

    let pinned = spread(&h, "pinned");
    let drifting = spread(&h, "drifting");
    assert!(pinned < 1e-9, "pinned competitor moved: {pinned:e}");
    assert!(
        drifting > 1e-3,
        "control did not move, so the test proves nothing: {drifting:e}"
    );
}

/// `weights` still applies to a team added through `members`, and still
/// records a mismatch rather than partially applying it.
#[test]
fn weights_still_guards_a_members_team() {
    let mut h = history();
    let err = h
        .event(1)
        .team(["a"])
        .members([Member::new("b"), Member::new("c")])
        .weights([1.0])
        .winner(0)
        .commit()
        .unwrap_err();
    assert!(
        matches!(
            err,
            InferenceError::MismatchedShape {
                shape: trueskill_tt::Shape::Weights,
                expected: 2,
                got: 1,
                ..
            }
        ),
        "{err:?}"
    );
    assert!(h.current_skill(&"b").is_none(), "nothing may reach history");
}

/// An invalid `drift_scale` surfaces from `commit`, not from a panic and not
/// silently.
#[test]
fn an_invalid_drift_scale_surfaces_from_commit() {
    for bad in [-1.0, f64::NAN, f64::INFINITY] {
        let mut h = history();
        let err = h
            .event(1)
            .team(["a"])
            .members([Member::new("b").with_drift_scale(bad)])
            .winner(0)
            .commit()
            .unwrap_err();
        assert!(
            matches!(
                err,
                InferenceError::InvalidParameter {
                    parameter: trueskill_tt::Parameter::DriftScale,
                    ..
                }
            ),
            "{bad}: {err:?}"
        );
        assert!(h.current_skill(&"b").is_none(), "{bad} reached the history");
    }
}

/// `members` and `team` compose in either order.
#[test]
fn members_and_team_interleave() {
    let mut h = history();
    h.event(1)
        .members([Member::new("a").with_prior(PRIOR)])
        .team(["b"])
        .scores([3.0, 1.0])
        .commit()
        .unwrap();
    h.event(2)
        .team(["b"])
        .members([Member::new("c").with_prior(PRIOR)])
        .scores([2.0, 4.0])
        .commit()
        .unwrap();
    assert!(h.converge().unwrap().converged);
    for key in ["a", "b", "c"] {
        assert!(h.current_skill(&key).is_some(), "{key} missing");
    }
}