trueskill-tt/tests/drift_scale.rs

//! Per-competitor drift scaling via `Member::with_drift_scale`.
//!
//! The scale multiplies the *variance* the history's `Drift` contributes for
//! that competitor, so `scale` is in the same units as `gamma`:
//! `ConstantDrift::new(g)` at `scale = s` behaves as `ConstantDrift::new(g * s)` would.
//! `scale = 0.0` pins a competitor still — an anchor, a rating floor, a course
//! difficulty — while everyone around them keeps drifting.

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

type Fit = History<i64, ConstantDrift, NullObserver, &'static str>;

const CONVERGENCE: ConvergenceOptions = ConvergenceOptions {
    max_iter: 64,
    epsilon: 1e-9,
    alpha: 1.0,
};

/// Two events separated by a long gap, so drift has room to matter.
fn distant_pair(anchor_scale: Option<f64>) -> Vec<Event<i64, &'static str>> {
    let anchor = |s: Option<f64>| match s {
        Some(scale) => Member::new("anchor").with_drift_scale(scale),
        None => Member::new("anchor"),
    };

    vec![
        Event {
            time: 0,
            teams: smallvec![
                Team::with_members([anchor(anchor_scale)]),
                Team::with_members([Member::new("player")]),
            ],
            outcome: Outcome::winner(0, 2),
        },
        Event {
            time: 1000,
            teams: smallvec![
                Team::with_members([anchor(anchor_scale)]),
                Team::with_members([Member::new("player")]),
            ],
            outcome: Outcome::winner(1, 2),
        },
    ]
}

fn fit(events: Vec<Event<i64, &'static str>>, gamma: f64) -> Fit {
    let mut h = History::builder()
        .mu(25.0)
        .sigma(25.0 / 3.0)
        .beta(25.0 / 6.0)
        .p_draw(0.0)
        .drift(ConstantDrift::new(gamma))
        .convergence(CONVERGENCE)
        .build();

    h.add_events(events).unwrap();
    let _ = h.converge().unwrap();
    h
}

fn curve(h: &Fit, key: &str) -> Vec<(i64, Gaussian)> {
    let mut c = h.learning_curves().remove(key).expect("key in curves");
    c.sort_by_key(|(t, _)| *t);
    c
}

/// A competitor at `scale = 0.0` is one latent skill observed twice, so the
/// posterior is the same distribution at both times — and strictly tighter
/// than the same competitor left to drift.
#[test]
fn zero_scale_pins_a_competitor_still() {
    let pinned = fit(distant_pair(Some(0.0)), 25.0 / 300.0);
    let drifting = fit(distant_pair(None), 25.0 / 300.0);

    let pinned_curve = curve(&pinned, "anchor");
    assert_eq!(pinned_curve.len(), 2);

    let (t0, first) = pinned_curve[0];
    let (t1, second) = pinned_curve[1];
    assert_eq!((t0, t1), (0, 1000));

    assert!(
        (first.sigma() - second.sigma()).abs() < 1e-9,
        "a pinned competitor's uncertainty must not move between t=0 and t=1000: \
         {} vs {}",
        first.sigma(),
        second.sigma()
    );
    assert!(
        (first.mu() - second.mu()).abs() < 1e-9,
        "a pinned competitor's mean must not move: {} vs {}",
        first.mu(),
        second.mu()
    );

    let drifting_curve = curve(&drifting, "anchor");
    assert!(
        drifting_curve[0].1.sigma() > first.sigma() + 1e-6,
        "drift must leave the anchor less certain than pinning does: {} vs {}",
        drifting_curve[0].1.sigma(),
        first.sigma()
    );
}

/// The scale is composable with `gamma`: scaling every competitor by `s` is
/// exactly the same fit as scaling the history's drift by `s`.
#[test]
fn scale_is_equivalent_to_scaling_gamma() {
    let scaled: Vec<Event<i64, &'static str>> = vec![
        Event {
            time: 0,
            teams: smallvec![
                Team::with_members([Member::new("a").with_drift_scale(0.5)]),
                Team::with_members([Member::new("b").with_drift_scale(0.5)]),
            ],
            outcome: Outcome::winner(0, 2),
        },
        Event {
            time: 400,
            teams: smallvec![
                Team::with_members([Member::new("b").with_drift_scale(0.5)]),
                Team::with_members([Member::new("a").with_drift_scale(0.5)]),
            ],
            outcome: Outcome::winner(0, 2),
        },
    ];

    let plain: Vec<Event<i64, &'static str>> = vec![
        Event {
            time: 0,
            teams: smallvec![
                Team::with_members([Member::new("a")]),
                Team::with_members([Member::new("b")]),
            ],
            outcome: Outcome::winner(0, 2),
        },
        Event {
            time: 400,
            teams: smallvec![
                Team::with_members([Member::new("b")]),
                Team::with_members([Member::new("a")]),
            ],
            outcome: Outcome::winner(0, 2),
        },
    ];

    let by_scale = fit(scaled, 0.3);
    let by_gamma = fit(plain, 0.15);

    for key in ["a", "b"] {
        let lhs = curve(&by_scale, key);
        let rhs = curve(&by_gamma, key);
        assert_eq!(lhs.len(), rhs.len());

        for ((t_l, g_l), (t_r, g_r)) in lhs.iter().zip(rhs.iter()) {
            assert_eq!(t_l, t_r);
            assert!(
                (g_l.mu() - g_r.mu()).abs() < 1e-9 && (g_l.sigma() - g_r.sigma()).abs() < 1e-9,
                "ConstantDrift::new(0.3) at scale 0.5 must equal ConstantDrift::new(0.15) for {key} at \
                 t={t_l}: ({}, {}) vs ({}, {})",
                g_l.mu(),
                g_l.sigma(),
                g_r.mu(),
                g_r.sigma()
            );
        }
    }
}

/// `None` means 1.0: an explicit unit scale changes nothing.
#[test]
fn unset_scale_matches_an_explicit_unit_scale() {
    let implicit = fit(distant_pair(None), 25.0 / 300.0);
    let explicit = fit(distant_pair(Some(1.0)), 25.0 / 300.0);

    for key in ["anchor", "player"] {
        let lhs = curve(&implicit, key);
        let rhs = curve(&explicit, key);
        assert_eq!(lhs.len(), rhs.len());

        for ((t_l, g_l), (t_r, g_r)) in lhs.iter().zip(rhs.iter()) {
            assert_eq!(t_l, t_r);
            assert_eq!(
                (g_l.mu(), g_l.sigma()),
                (g_r.mu(), g_r.sigma()),
                "an explicit scale of 1.0 must be bit-identical to leaving it unset, \
                 for {key} at t={t_l}"
            );
        }
    }
}

/// The use case from the issue: a static difficulty alongside drifting players,
/// in one graph. The anchor must hold still without absorbing drift through its
/// neighbours, and everything must stay finite.
#[test]
fn mixed_static_and_drifting_graph_converges() {
    let mut events: Vec<Event<i64, &'static str>> = Vec::new();
    let players = ["p0", "p1", "p2"];

    for (i, p) in players.iter().cycle().take(9).enumerate() {
        events.push(Event {
            time: (i as i64) * 100,
            teams: smallvec![
                Team::with_members([Member::new(*p)]),
                Team::with_members([Member::new("layout").with_drift_scale(0.0)]),
            ],
            outcome: Outcome::winner((i % 2) as u32, 2),
        });
    }

    let mut h = History::builder()
        .mu(25.0)
        .sigma(25.0 / 3.0)
        .beta(25.0 / 6.0)
        .p_draw(0.0)
        .drift(ConstantDrift::new(25.0 / 300.0))
        .convergence(CONVERGENCE)
        .build();

    h.add_events(events).unwrap();
    let report = h.converge().unwrap();
    assert!(report.converged, "mixed graph must converge: {report:?}");

    let curves = h.learning_curves();
    for (key, points) in &curves {
        for (t, g) in points {
            assert!(
                g.mu().is_finite() && g.sigma().is_finite() && g.sigma() > 0.0,
                "{key} at t={t} is not a usable posterior: mu={}, sigma={}",
                g.mu(),
                g.sigma()
            );
        }
    }

    let layout = curve(&h, "layout");
    assert_eq!(layout.len(), 9);
    let (_, first) = layout[0];
    for (t, g) in &layout {
        assert!(
            (g.sigma() - first.sigma()).abs() < 1e-9,
            "a static layout must not accumulate uncertainty; t={t} has sigma {} vs {}",
            g.sigma(),
            first.sigma()
        );
    }

    let p0 = curve(&h, "p0");
    assert!(
        p0.last().unwrap().1.sigma() > 0.0,
        "a drifting player should still have a proper posterior"
    );
}

fn reject(scale: f64) -> InferenceError {
    let mut h = History::builder()
        .drift(ConstantDrift::new(25.0 / 300.0))
        .build();

    let events: Vec<Event<i64, &'static str>> = vec![Event {
        time: 0,
        teams: smallvec![
            Team::with_members([Member::new("a").with_drift_scale(scale)]),
            Team::with_members([Member::new("b")]),
        ],
        outcome: Outcome::winner(0, 2),
    }];

    h.add_events(events)
        .expect_err("an out-of-range drift_scale must be rejected")
}

#[test]
fn negative_scale_is_rejected() {
    assert!(matches!(
        reject(-1.0),
        InferenceError::InvalidParameter { name: "drift_scale", value, .. }
            if value == -1.0
    ));
}

#[test]
fn non_finite_scale_is_rejected() {
    for scale in [f64::NAN, f64::INFINITY, f64::NEG_INFINITY] {
        assert!(
            matches!(
                reject(scale),
                InferenceError::InvalidParameter {
                    name: "drift_scale",
                    ..
                }
            ),
            "a drift_scale of {scale} must be rejected as an invalid parameter"
        );
    }
}

/// The scale must reach the filtering pass too, not just `converge()`.
/// `filtered_learning_curves` runs its own drift application, so a pinned
/// competitor has to stay pinned there as well.
#[test]
fn zero_scale_pins_a_competitor_in_the_filtered_pass() {
    let pinned = fit(distant_pair(Some(0.0)), 25.0 / 300.0);
    let drifting = fit(distant_pair(None), 25.0 / 300.0);

    let filtered = |h: &Fit| -> Vec<(i64, Gaussian)> {
        let mut c = h
            .filtered_learning_curves()
            .remove("anchor")
            .expect("anchor in filtered curves");
        c.sort_by_key(|(t, _)| *t);
        c
    };

    let pinned_curve = filtered(&pinned);
    let drifting_curve = filtered(&drifting);
    assert_eq!(pinned_curve.len(), 2);
    assert_eq!(drifting_curve.len(), 2);

    assert!(
        pinned_curve[1].1.sigma() < pinned_curve[0].1.sigma(),
        "a pinned competitor's filtered uncertainty must shrink with a second \
         observation, not be re-inflated by drift: {} then {}",
        pinned_curve[0].1.sigma(),
        pinned_curve[1].1.sigma()
    );

    assert!(
        pinned_curve[1].1.sigma() < drifting_curve[1].1.sigma() - 1e-6,
        "pinning must leave the filtered estimate tighter than drifting does: \
         {} vs {}",
        pinned_curve[1].1.sigma(),
        drifting_curve[1].1.sigma()
    );
}

/// `drift_scale` is competitor configuration, and configuration supplied for a
/// competitor the history already knows is now *applied* rather than dropped.
///
/// This test previously asserted the opposite. It was written as a deliberate
/// change-detector — "moving the capture would be a visible break, not a silent
/// one" — and that is exactly what happened: the capture moved, and the
/// assertion inverted rather than being deleted.
///
/// Because configuration lives on the competitor and `converge` refits from
/// competitor state, a late pin applies to the *whole* history, not just to
/// events after it. So a scale set on the second batch must reach the same fit
/// as one set from the very first event.
#[test]
fn drift_scale_applies_when_set_after_first_appearance() {
    let mut late = History::builder()
        .mu(25.0)
        .sigma(25.0 / 3.0)
        .beta(25.0 / 6.0)
        .p_draw(0.0)
        .drift(ConstantDrift::new(25.0 / 300.0))
        .convergence(CONVERGENCE)
        .build();

    // First batch creates "anchor" with the default scale.
    late.add_events(vec![Event {
        time: 0,
        teams: smallvec![
            Team::with_members([Member::new("anchor")]),
            Team::with_members([Member::new("player")]),
        ],
        outcome: Outcome::winner(0, 2),
    }])
    .unwrap();

    // Second batch asks for a pin. No longer too late.
    late.add_events(vec![Event {
        time: 1000,
        teams: smallvec![
            Team::with_members([Member::new("anchor").with_drift_scale(0.0)]),
            Team::with_members([Member::new("player")]),
        ],
        outcome: Outcome::winner(1, 2),
    }])
    .unwrap();
    let _ = late.converge().unwrap();

    let applied = curve(&late, "anchor");
    let pinned_from_the_start = curve(&fit(distant_pair(Some(0.0)), 25.0 / 300.0), "anchor");
    let never_pinned = curve(&fit(distant_pair(None), 25.0 / 300.0), "anchor");

    for ((t_l, g_l), (t_r, g_r)) in applied.iter().zip(pinned_from_the_start.iter()) {
        assert_eq!(t_l, t_r);
        assert!(
            (g_l.sigma() - g_r.sigma()).abs() < 1e-9,
            "a late pin should refit the whole history: t={t_l}, {} vs {}",
            g_l.sigma(),
            g_r.sigma()
        );
    }

    // And it must actually have done something.
    assert!(
        applied
            .iter()
            .zip(never_pinned.iter())
            .any(|((_, a), (_, b))| (a.sigma() - b.sigma()).abs() > 1e-9),
        "the pin had no effect at all — the silent drop is back"
    );
}

/// Re-declaring the same configuration must be inert. This is the shape a
/// caller gets when the configuration is a property of the domain — "layouts
/// are static" — so every ingestion path repeats it on every event.
///
/// Both histories see exactly the same events; only how many times the scale
/// is declared differs.
#[test]
fn repeating_the_same_configuration_changes_nothing() {
    let events = |declare_every_time: bool| {
        let anchor = |first: bool| {
            if first || declare_every_time {
                Member::new("anchor").with_drift_scale(0.0)
            } else {
                Member::new("anchor")
            }
        };
        vec![
            Event {
                time: 0,
                teams: smallvec![
                    Team::with_members([anchor(true)]),
                    Team::with_members([Member::new("player")]),
                ],
                outcome: Outcome::winner(0, 2),
            },
            Event {
                time: 1000,
                teams: smallvec![
                    Team::with_members([anchor(false)]),
                    Team::with_members([Member::new("player")]),
                ],
                outcome: Outcome::winner(1, 2),
            },
        ]
    };

    let once = curve(&fit(events(false), 25.0 / 300.0), "anchor");
    let every_time = curve(&fit(events(true), 25.0 / 300.0), "anchor");

    for ((t_l, a), (t_r, b)) in once.iter().zip(every_time.iter()) {
        assert_eq!(t_l, t_r);
        assert!(
            (a.sigma() - b.sigma()).abs() < 1e-12,
            "t={t_l}: declaring the same scale repeatedly changed the fit, {} vs {}",
            a.sigma(),
            b.sigma()
        );
    }
}

#[test]
fn a_batch_that_contradicts_itself_is_rejected() {
    let mut h = History::builder().convergence(CONVERGENCE).build();

    let err = h
        .add_events(vec![
            Event {
                time: 0,
                teams: smallvec![
                    Team::with_members([Member::new("anchor").with_drift_scale(0.0)]),
                    Team::with_members([Member::new("player")]),
                ],
                outcome: Outcome::winner(0, 2),
            },
            Event {
                time: 1,
                teams: smallvec![
                    Team::with_members([Member::new("anchor").with_drift_scale(1.0)]),
                    Team::with_members([Member::new("player")]),
                ],
                outcome: Outcome::winner(0, 2),
            },
        ])
        .expect_err("two different scales for one competitor in one batch");

    assert!(
        matches!(
            err,
            InferenceError::ConflictingCompetitorConfig {
                field: "drift_scale",
                ..
            }
        ),
        "got {err:?}"
    );
}