`Gaussian` publicly implemented `Mul`, `Div`, `Add` and `Sub`. They were
the EP product, cavity and variance-space convolutions, and every one of
them lies to a reader who takes the operator at face value:
a = N(10, 2) b = N(4, 3) c = N(1, 1)
a * b N(8.15, 1.66) not 40
a - b sigma GREW, 2 -> sqrt(4 + 9)
a * N(1, 0) mu = NaN "multiply by one"
a / c pi = -0.75 mu() prints a confident 0
The last is this crate's signature defect on a public operator. `Div` is
the cavity and can legitimately leave a negative precision, which is not
a distribution — and `mu()`/`sigma()` guard `pi <= 0` and report `0.0`
and `inf`, so it comes back as a plausible number with no panic, no
`Debug` marker and nothing to test against.
The four impls are now `pub(crate)` inherent methods that say what they
do: `ep_product`, `cavity`, `convolve`, `convolve_diff`, plus `scale`
for the one operation that genuinely is arithmetic. Nothing in a user's
workflow needed operator syntax; inference did, and it still has it.
`pi()` and `tau()` follow. Storing natural parameters is a performance
decision — it makes message passing two adds — not a contract. The
public surface is now exactly: `from_ms`, `from_mv`, `mu`, `sigma`,
`variance`, `probability_below`, `probability_above`. `from_mv` and
`variance` are promoted from `pub(crate)`; they are the honest pair for
callers who already hold a variance and should not pay a round trip
through the square root.
Four integration tests asserted bit-identity on `(pi, tau)`. They assert
it on `(mu, variance)` instead — still `assert_eq!`, still exact, and
`1/pi` and `tau/pi` are deterministic, so bit-equal natural parameters
give bit-equal moments. `a_nan_sigma_passes_through_from_ms` drops its
`|| g.pi().is_nan()` half: `sigma()` substitutes for `pi <= 0` and
`pi == inf`, so NaN survives to it only from a NaN precision.
`benches/gaussian.rs` is deleted. It timed two f64 additions through the
public operators, and keeping those public solely to feed it is the same
thing #73 objected to when a benchmark was dictating five public types.
The paths it covered are exercised by `batch` and `history_converge`
through the real call chain.
Closes #71.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
198 lines
6.1 KiB
Rust
198 lines
6.1 KiB
Rust
//! `EventBuilder::members` must reach exactly what the typed path reaches.
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//!
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//! Before this existed, `EventBuilder` could set weights and nothing else, so
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//! `prior` and `drift_scale` were expressible only through `Event`/`Team`/
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//! `Member` + `add_events`. Which ingestion route a competitor arrived through
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//! decided whether it could be configured at all.
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use smallvec::smallvec;
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use trueskill_tt::{
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ConstantDrift, ConvergenceOptions, Event, Gaussian, History, InferenceError, Member, Outcome,
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Team,
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};
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type H = History<i64, ConstantDrift, trueskill_tt::NullObserver, &'static str>;
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fn history() -> H {
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History::builder()
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.mu(0.0)
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.sigma(6.0)
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.beta(1.0)
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.score_sigma(2.0)
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.drift(ConstantDrift::new(0.5))
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.convergence(ConvergenceOptions {
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max_iter: 20_000,
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epsilon: 1e-13,
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alpha: 1.0,
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})
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.build()
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}
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const PRIOR: Gaussian = Gaussian::from_ms(3.0, 1.5);
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/// The contract that makes the escape hatch worth having: same configuration,
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/// same fit, bit for bit.
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#[test]
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fn members_matches_the_typed_path_exactly() {
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let mut typed = history();
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typed
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.add_events(vec![Event {
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time: 1,
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teams: smallvec![
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Team::with_members([Member::new("player")]),
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Team::with_members([Member::new("layout_7")
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.with_drift_scale(0.0)
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.with_prior(PRIOR)]),
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],
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outcome: Outcome::scores([5.0, 2.0]),
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}])
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.unwrap();
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assert!(typed.converge().unwrap().converged);
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let mut fluent = history();
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fluent
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.event(1)
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.team(["player"])
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.members([Member::new("layout_7")
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.with_drift_scale(0.0)
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.with_prior(PRIOR)])
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.scores([5.0, 2.0])
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.commit()
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.unwrap();
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assert!(fluent.converge().unwrap().converged);
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for key in ["player", "layout_7"] {
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let a = typed.current_skill(&key).unwrap();
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let b = fluent.current_skill(&key).unwrap();
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// Exact equality, on the public moments rather than the natural
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// parameters: `mu` and `variance` are `tau/pi` and `1/pi`, so
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// bit-equal natural parameters give bit-equal moments.
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assert_eq!(a.mu(), b.mu(), "{key} mu");
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assert_eq!(a.variance(), b.variance(), "{key} variance");
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}
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}
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/// The configuration has to actually take effect, not merely round-trip: a
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/// competitor pinned with `drift_scale = 0.0` must not move across slices,
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/// where an unpinned one does.
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///
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/// The comparison is against a control rather than against a fixed epsilon.
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/// Pinned marginals are not bit-identical across slices — each slice combines
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/// its own forward and backward messages, so the arithmetic order differs and
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/// the last bit moves. What "pinned" promises is that no drift variance
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/// accumulates, and the control is what makes that measurable.
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#[test]
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fn a_drift_scale_set_through_members_is_applied() {
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fn spread(h: &H, key: &'static str) -> f64 {
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let curve = h.learning_curve(&key).unwrap();
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assert!(curve.len() >= 2, "{key}: expected several appearances");
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let (lo, hi) = curve.iter().fold((f64::MAX, f64::MIN), |(lo, hi), (_, g)| {
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(lo.min(g.sigma()), hi.max(g.sigma()))
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});
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(hi - lo) / hi
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}
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let mut h = history();
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for t in 1..=4 {
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h.event(t)
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.team(["player"])
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.members([Member::new("pinned").with_drift_scale(0.0)])
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.scores([5.0, 2.0])
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.commit()
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.unwrap();
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// Same shape, no pinning: the control.
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h.event(t)
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.team(["rival"])
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.team(["drifting"])
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.scores([5.0, 2.0])
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.commit()
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.unwrap();
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}
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assert!(h.converge().unwrap().converged);
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let pinned = spread(&h, "pinned");
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let drifting = spread(&h, "drifting");
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assert!(pinned < 1e-9, "pinned competitor moved: {pinned:e}");
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assert!(
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drifting > 1e-3,
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"control did not move, so the test proves nothing: {drifting:e}"
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);
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}
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/// `weights` still applies to a team added through `members`, and still
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/// records a mismatch rather than partially applying it.
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#[test]
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fn weights_still_guards_a_members_team() {
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let mut h = history();
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let err = h
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.event(1)
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.team(["a"])
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.members([Member::new("b"), Member::new("c")])
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.weights([1.0])
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.winner(0)
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.commit()
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.unwrap_err();
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assert!(
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matches!(
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err,
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InferenceError::MismatchedShape {
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kind: "weights",
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expected: 2,
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got: 1,
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..
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}
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),
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"{err:?}"
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);
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assert!(h.current_skill(&"b").is_none(), "nothing may reach history");
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}
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/// An invalid `drift_scale` surfaces from `commit`, not from a panic and not
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/// silently.
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#[test]
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fn an_invalid_drift_scale_surfaces_from_commit() {
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for bad in [-1.0, f64::NAN, f64::INFINITY] {
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let mut h = history();
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let err = h
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.event(1)
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.team(["a"])
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.members([Member::new("b").with_drift_scale(bad)])
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.winner(0)
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.commit()
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.unwrap_err();
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assert!(
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matches!(
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err,
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InferenceError::InvalidParameter {
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name: "drift_scale",
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..
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}
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),
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"{bad}: {err:?}"
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);
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assert!(h.current_skill(&"b").is_none(), "{bad} reached the history");
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}
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}
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/// `members` and `team` compose in either order.
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#[test]
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fn members_and_team_interleave() {
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let mut h = history();
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h.event(1)
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.members([Member::new("a").with_prior(PRIOR)])
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.team(["b"])
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.scores([3.0, 1.0])
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.commit()
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.unwrap();
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h.event(2)
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.team(["b"])
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.members([Member::new("c").with_prior(PRIOR)])
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.scores([2.0, 4.0])
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.commit()
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.unwrap();
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assert!(h.converge().unwrap().converged);
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for key in ["a", "b", "c"] {
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assert!(h.current_skill(&key).is_some(), "{key} missing");
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}
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}
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