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v0.7.0
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8e4d6a637d |
@@ -134,14 +134,20 @@ h.add_events(vec![Event {
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h.converge().unwrap();
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```
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Like `with_prior`, the scale is **competitor configuration captured at first
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appearance** — setting it on a key the history already knows has no effect. It
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must be finite and non-negative; ingestion otherwise fails with
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`InferenceError::InvalidParameter`.
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Like `with_prior`, the scale is **competitor configuration, not a per-event
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value**: it applies to the competitor for the whole history, and it applies
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whenever it is supplied — including on a key the history already knows.
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Configuring one late still refits the whole history rather than taking effect
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only from that event onward, because `converge` refits from competitor state.
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Repeating the same value is inert; supplying two *different* values for one
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competitor within a single batch is `InferenceError::ConflictingCompetitorConfig`,
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since events in a batch have no order. The scale must be finite and
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non-negative; ingestion otherwise fails with `InferenceError::InvalidParameter`.
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Note that the fluent `EventBuilder` (`h.event(t).team([...])`) sets weights but
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not `drift_scale` or `prior`; those need the typed `Event` / `Team` / `Member`
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shape shown above.
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The fluent `EventBuilder` reaches this too: `.team([...])` is the common case
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and leaves both unset, while `.members([...])` takes `Member` values directly,
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so `h.event(t).members([Member::new("layout_7").with_drift_scale(0.0)])` is
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equivalent to the typed shape above.
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## Scored outcomes
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@@ -500,6 +500,26 @@ All public traits (`Time`, `Drift`, `Observer`, `Factor`, `Schedule`) require `S
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`rayon` as default-on feature; with `default-features = false`, parallel paths fall back to sequential iterators behind `cfg(feature = "rayon")`.
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> **Not implemented. Deliberate deviation, decided 2026-09-08 (issue #5).**
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>
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> `rayon` ships **opt-in**: `Cargo.toml` has no `default = [...]` key. The
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> measured speedups are 1.0x on realistic workloads and 1.3x on a pathological
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> one (issue #4), because typical slices hold too few events to amortize
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> rayon's task-spawn overhead. Default-on would hand every downstream user a
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> thread pool and a dependency for approximately no gain.
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>
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> This section made the trade conditional on cross-slice dirty-bit skipping
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> landing and changing the parallel story. It did not land: #4 was closed on
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> 2026-08-27 by removing the inert `ConvergenceReport::slices_skipped` field
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> rather than by implementing the mechanism, so the re-measurement this was
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> waiting on will not arrive.
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>
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> The "Trade-offs" note below also cited an `unsafe` concurrent-write path
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> through `SkillStore` as a cost of default-on. That cost does not exist: the
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> crate is `#![forbid(unsafe_code)]`, and the compute/apply split on the
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> internal `Event` is what lets a color group run in parallel without it. The
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> case for opt-in rests on the measurements alone.
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### Expected speedup ballpark
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For 1000 players, 60 events/slice × 1000 slices, 30 convergence iterations:
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@@ -521,7 +541,7 @@ These are pre-implementation estimates. Each tier validates with criterion.
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- Color-group parallelism requires up-front graph coloring at ingestion. Cost: linear in events, run once per `add_events`. Cheap.
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- Default = asynchronous EP (preserves current semantics). Synchronous opt-in only.
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- Cross-slice sweep stays sequential; no speculative parallel sweeps.
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- Rayon default-on but feature-gated.
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- Rayon default-on but feature-gated. **Superseded — shipped opt-in; see the deviation note in Section 6.**
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### Open question
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@@ -191,3 +191,121 @@ mod tests {
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assert_eq!(cg.total_events(), 4);
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}
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}
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#[cfg(test)]
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mod properties {
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use std::collections::HashSet;
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use proptest::prelude::*;
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use super::*;
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/// The property the whole parallel sweep rests on: two events sharing a
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/// competitor must never land in the same color, because a color group is
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/// run concurrently and two events touching one competitor would race.
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///
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/// Hand-written cases cover the shapes someone thought of. This covers the
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/// ones nobody did — the correctness of `sweep_color_groups` depends on it
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/// holding for every input, not for five.
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fn check(events: &[Vec<usize>]) {
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let groups = color_greedy(events.len(), |ev| {
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events[ev]
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.iter()
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.copied()
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.map(Index::from)
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.collect::<Vec<_>>()
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});
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// Disjointness *between events* within a color. Deduplicated per
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// event, because one event legitimately naming a competitor twice is
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// not a collision — `color_greedy` collects each event's members into
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// a set for exactly that reason.
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for color in 0..groups.n_colors() {
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let mut seen: HashSet<usize> = HashSet::new();
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for &ev in &groups.groups[color] {
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let members: HashSet<usize> = events[ev].iter().copied().collect();
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for competitor in members {
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assert!(
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seen.insert(competitor),
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"competitor {competitor} shared by two events in color {color}"
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);
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}
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}
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}
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// Every event is assigned exactly once. Without this, a partition that
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// dropped events would satisfy disjointness trivially.
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let mut assigned: Vec<usize> = groups.groups.iter().flatten().copied().collect();
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assigned.sort_unstable();
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assert_eq!(assigned, (0..events.len()).collect::<Vec<_>>());
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assert_eq!(groups.total_events(), events.len());
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// No empty colors: one would waste a sweep and make `n_colors`
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// misleading.
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for (color, group) in groups.groups.iter().enumerate() {
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assert!(!group.is_empty(), "color {color} is empty");
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}
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// Contiguity is not a property of `color_greedy` — it holds only after
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// `recompute_color_groups` reorders the events so each color occupies
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// one range. What must always hold is that the reorder is *possible*:
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// relabelling events in group order yields contiguous groups. The
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// parallel sweep slices `&mut` sub-ranges from those, so if this ever
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// failed the reorder would produce overlapping ranges.
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let mut next = 0usize;
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let relabelled: Vec<Vec<usize>> = groups
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.groups
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.iter()
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.map(|group| {
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group
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.iter()
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.map(|_| {
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let i = next;
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next += 1;
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i
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})
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.collect()
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})
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.collect();
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assert!(ColorGroups { groups: relabelled }.groups_are_contiguous());
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}
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proptest! {
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#![proptest_config(ProptestConfig::with_cases(512))]
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/// Small competitor pool, so collisions are common and colors are
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/// forced to multiply.
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#[test]
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fn colors_are_disjoint_on_a_dense_pool(
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events in prop::collection::vec(
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prop::collection::vec(0usize..6, 1..4),
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0..20,
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)
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) {
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check(&events);
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}
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/// Wide pool, so most events are independent and land in one color.
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#[test]
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fn colors_are_disjoint_on_a_sparse_pool(
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events in prop::collection::vec(
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prop::collection::vec(0usize..200, 1..6),
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0..30,
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)
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) {
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check(&events);
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}
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/// Repeated competitors within one event must not confuse the
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/// member-set bookkeeping.
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#[test]
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fn colors_are_disjoint_with_repeated_members(
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events in prop::collection::vec(
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prop::collection::vec(0usize..3, 1..8),
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0..15,
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)
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) {
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check(&events);
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}
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}
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}
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+10
-3
@@ -62,10 +62,17 @@ impl Default for ConvergenceOptions {
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}
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/// Post-hoc summary of a `History::converge` call.
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///
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/// From [`History::converge`](crate::History::converge) this always describes a
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/// converged fit — stopping at `max_iter` is
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/// [`InferenceError::NotConverged`](crate::InferenceError::NotConverged) there.
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/// From [`History::converge_partial`](crate::History::converge_partial) it may
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/// not be, and `converged` is what says so.
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#[derive(Clone, Debug)]
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#[must_use = "a ConvergenceReport carries `converged`, and a fit that stopped \
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at `max_iter` is wrong by a little rather than loudly broken — \
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check it, or bind it to `_` to say you have decided not to"]
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#[must_use = "from `converge_partial` this may describe a fit that stopped at \
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`max_iter`, which is wrong by a little rather than loudly \
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broken — check `converged`, or bind it to `_` to say you have \
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decided not to"]
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pub struct ConvergenceReport {
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pub iterations: usize,
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pub final_step: (f64, f64),
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@@ -64,6 +64,24 @@ pub enum InferenceError {
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/// result has no representable likelihood. Configure a positive `p_draw`
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/// (via `HistoryBuilder::p_draw` or `GameOptions::p_draw`) to admit ties.
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TieWithoutDrawProbability { teams: (usize, usize) },
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/// The convergence sweep hit `max_iter` with the step still above
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/// `epsilon`.
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///
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/// A fit that stops short is wrong by a little, which is the worst
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/// available failure: every rating is finite, the ordering looks sensible,
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/// and nothing in the numbers says they were still moving. Reported rather
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/// than returned as a flag on an `Ok`, because a flag has to be checked
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/// and `let _ = h.converge()` is the natural way not to.
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///
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/// Either the history needs more iterations — raise `max_iter` — or it is
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/// oscillating rather than converging, in which case `alpha < 1.0` damps
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/// the within-game EP loop. [`History::converge_partial`](crate::History::converge_partial)
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/// returns the short fit instead when that is genuinely what is wanted.
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NotConverged {
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iterations: usize,
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final_step: (f64, f64),
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epsilon: f64,
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},
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/// Inference produced a non-finite value (NaN or infinity).
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///
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/// Indicates numerical breakdown; the resulting skills are meaningless
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@@ -100,6 +118,17 @@ pub enum InferenceError {
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member: usize,
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key: String,
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},
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/// `History::register` was called for a competitor that already exists.
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///
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/// Registration states a competitor's configuration before anything has
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/// been observed about them, so a competitor that already exists has
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/// already been configured — by an earlier `register`, or by an event that
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/// created them. Silently overwriting would reintroduce exactly the
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/// order-dependence registration exists to remove.
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///
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/// To change an existing competitor's configuration, supply it on an event
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/// through `Member`; that refits the whole history.
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AlreadyRegistered { key: String },
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/// A prediction was given a team with no members.
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EmptyTeam { team: usize },
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/// A joint posterior was requested where one cannot be formed exactly.
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@@ -144,6 +173,18 @@ impl fmt::Display for InferenceError {
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teams.0, teams.1
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)
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}
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Self::NotConverged {
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iterations,
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final_step,
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epsilon,
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} => {
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write!(
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f,
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"did not converge in {iterations} iterations: final step {final_step:?} \
|
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is still above epsilon {epsilon}; raise max_iter, or damp with \
|
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alpha < 1.0 if it is oscillating"
|
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)
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||||
}
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Self::NonFiniteResult { context, step } => {
|
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write!(
|
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f,
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@@ -167,6 +208,14 @@ impl fmt::Display for InferenceError {
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with `lookup` or `current_skill` if that is not guaranteed)"
|
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)
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}
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Self::AlreadyRegistered { key } => {
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write!(
|
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f,
|
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"competitor {key} is already registered; registration states \
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configuration before anything is observed, so re-registering \
|
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would silently overwrite it"
|
||||
)
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}
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Self::EmptyTeam { team } => {
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write!(f, "team {team} has no members")
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}
|
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+5
-2
@@ -88,7 +88,9 @@ impl<K> Member<K> {
|
||||
|
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/// Set this competitor's starting skill estimate.
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///
|
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/// Captured at the competitor's first appearance; see the type docs.
|
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/// Competitor configuration, not a per-event value: it applies for the
|
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/// whole history and applies whenever it is supplied, including on a key
|
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/// the history already knows. See the type docs.
|
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pub fn with_prior(mut self, prior: Gaussian) -> Self {
|
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self.prior = Some(prior);
|
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self
|
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@@ -104,7 +106,8 @@ impl<K> Member<K> {
|
||||
/// shares a scale with moving competitors but should not itself move: a bot
|
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/// at a known strength, a rating floor, a course difficulty.
|
||||
///
|
||||
/// Captured at the competitor's first appearance; see the type docs.
|
||||
/// Applies for the whole history and whenever it is supplied, including on
|
||||
/// a key the history already knows; see the type docs.
|
||||
/// Must be finite and non-negative, or ingestion fails with
|
||||
/// [`InferenceError::InvalidParameter`](crate::InferenceError::InvalidParameter).
|
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pub fn with_drift_scale(mut self, scale: f64) -> Self {
|
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|
||||
@@ -50,6 +50,8 @@ where
|
||||
}
|
||||
|
||||
/// Add a team by its member keys (weight 1.0 each, no prior overrides).
|
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///
|
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/// Use [`EventBuilder::members`] to set `prior` or `drift_scale`.
|
||||
pub fn team<I: IntoIterator<Item = K>>(mut self, keys: I) -> Self {
|
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let members: SmallVec<[Member<K>; 4]> = keys.into_iter().map(Member::new).collect();
|
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self.event.teams.push(Team { members });
|
||||
@@ -57,6 +59,40 @@ where
|
||||
self
|
||||
}
|
||||
|
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/// Add a team from fully-specified [`Member`] values.
|
||||
///
|
||||
/// [`EventBuilder::team`] is the common case and builds members with
|
||||
/// `Member::new`, which leaves `prior` and `drift_scale` unset. This is the
|
||||
/// escape hatch for when they matter:
|
||||
///
|
||||
/// ```
|
||||
/// # use trueskill_tt::{Gaussian, History, Member};
|
||||
/// # let mut h = History::builder().build();
|
||||
/// h.event(0)
|
||||
/// .team(["player"])
|
||||
/// .members([Member::new("layout_7")
|
||||
/// .with_drift_scale(0.0)
|
||||
/// .with_prior(Gaussian::from_ms(0.0, 1.0))])
|
||||
/// .ranking([0, 1])
|
||||
/// .commit()?;
|
||||
/// # Ok::<(), trueskill_tt::InferenceError>(())
|
||||
/// ```
|
||||
///
|
||||
/// One method rather than a `priors` and a `drift_scales` setter beside
|
||||
/// `weights`: those would have to grow a parallel array — and a parallel
|
||||
/// length check — every time `Member` gains a field, and each one would be
|
||||
/// a new way to get the lengths wrong. `Member`'s own builder already
|
||||
/// expresses all of it.
|
||||
///
|
||||
/// `prior` and `drift_scale` are competitor configuration rather than
|
||||
/// per-event values; see [`Member`] for what that means for a key the
|
||||
/// history already knows.
|
||||
pub fn members<I: IntoIterator<Item = Member<K>>>(mut self, members: I) -> Self {
|
||||
self.event.teams.push(Team::with_members(members));
|
||||
self.current_team_idx = Some(self.event.teams.len() - 1);
|
||||
self
|
||||
}
|
||||
|
||||
/// Set per-member weights for the most recently added team.
|
||||
///
|
||||
/// A length mismatch is recorded and returned by [`EventBuilder::commit`]
|
||||
|
||||
+361
-10
@@ -6,6 +6,7 @@ use crate::{
|
||||
convergence::{ConvergenceOptions, ConvergenceReport},
|
||||
drift::{ConstantDrift, Drift},
|
||||
error::InferenceError,
|
||||
event::Member,
|
||||
gaussian::Gaussian,
|
||||
key_table::KeyTable,
|
||||
observer::{NullObserver, Observer},
|
||||
@@ -39,17 +40,55 @@ pub struct HistoryBuilder<
|
||||
}
|
||||
|
||||
impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> HistoryBuilder<T, D, O, K> {
|
||||
/// Prior mean skill.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Panics if `mu` is not finite. A non-finite prior mean poisons every
|
||||
/// posterior derived from it: `converge` reports `NonFiniteResult`, but a
|
||||
/// caller who reads `current_skill` first is handed `tau: NaN`.
|
||||
pub fn mu(mut self, mu: f64) -> Self {
|
||||
assert!(mu.is_finite(), "mu must be finite (got {mu})");
|
||||
self.mu = mu;
|
||||
self
|
||||
}
|
||||
|
||||
/// Prior standard deviation.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Panics unless `sigma` is finite and strictly positive.
|
||||
///
|
||||
/// Zero and infinity both give a prior precision that is not a number, and
|
||||
/// the whole fit comes back NaN. A *negative* sigma is the quieter half:
|
||||
/// it is only ever squared, so `-8.33` produces bit-identical results to
|
||||
/// `8.33` — a sign the caller cannot have meant, silently ignored.
|
||||
pub fn sigma(mut self, sigma: f64) -> Self {
|
||||
assert!(
|
||||
sigma.is_finite() && sigma > 0.0,
|
||||
"sigma must be finite and positive (got {sigma})"
|
||||
);
|
||||
self.sigma = sigma;
|
||||
self
|
||||
}
|
||||
|
||||
/// Per-event performance noise.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// Panics unless `beta` is finite and non-negative.
|
||||
///
|
||||
/// Zero is allowed and meaningful — performance is then exactly skill, and
|
||||
/// the fit differs measurably from a positive `beta` rather than
|
||||
/// degenerating. Negative is rejected for the same reason as a negative
|
||||
/// `sigma` or `Member::with_drift_scale`: `beta` enters only as `beta^2`,
|
||||
/// so a negative value behaves as its absolute value and the sign is lost
|
||||
/// without comment.
|
||||
pub fn beta(mut self, beta: f64) -> Self {
|
||||
assert!(
|
||||
beta.is_finite() && beta >= 0.0,
|
||||
"beta must be finite and non-negative (got {beta})"
|
||||
);
|
||||
self.beta = beta;
|
||||
self
|
||||
}
|
||||
@@ -169,6 +208,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> HistoryBuilder<
|
||||
convergence: self.convergence,
|
||||
observer: self.observer,
|
||||
unknown_keys: self.unknown_keys,
|
||||
declared: HashMap::new(),
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -276,6 +316,12 @@ pub struct History<
|
||||
convergence: ConvergenceOptions,
|
||||
observer: O,
|
||||
unknown_keys: crate::UnknownKeys,
|
||||
/// Competitor configuration explicitly declared so far, by whichever route.
|
||||
///
|
||||
/// Kept separate from the applied `Rating` because a `Rating` cannot say
|
||||
/// whether a value was *chosen* or inherited from the history defaults,
|
||||
/// and that is exactly the distinction a conflict check needs.
|
||||
declared: HashMap<Index, CompetitorConfig>,
|
||||
}
|
||||
|
||||
impl Default for History<i64, ConstantDrift, NullObserver, &'static str> {
|
||||
@@ -455,6 +501,111 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
}
|
||||
|
||||
/// Skill estimate at the latest time slice the competitor appears in.
|
||||
/// Configure a competitor before anything has been observed about them.
|
||||
///
|
||||
/// The configuration a competitor needs is often a property of the domain
|
||||
/// rather than of any one event — "every layout is static", "this bot sits
|
||||
/// at a known strength". Stating it per-event means every ingestion path
|
||||
/// has to remember it, and the fluent and two-argument paths could not
|
||||
/// state it at all.
|
||||
///
|
||||
/// ```
|
||||
/// # use trueskill_tt::{History, Member};
|
||||
/// let mut h = History::builder().build();
|
||||
/// h.register(Member::new("layout_7").with_drift_scale(0.0))?;
|
||||
///
|
||||
/// // Reaches a competitor first seen through any route, including the
|
||||
/// // two-argument one, which cannot carry configuration itself.
|
||||
/// h.record_winner(&"player", &"layout_7", 1)?;
|
||||
/// assert_eq!(h.rating(&"layout_7").unwrap().drift_scale(), 0.0);
|
||||
/// # Ok::<(), trueskill_tt::InferenceError>(())
|
||||
/// ```
|
||||
///
|
||||
/// The competitor exists from this point on, with no appearances, so
|
||||
/// [`History::rating`] can read back what was actually stored — the
|
||||
/// diagnostic that was previously missing entirely.
|
||||
///
|
||||
/// `weight` is per-event and has no meaning here, so a `Member` carrying a
|
||||
/// non-default one is rejected rather than silently ignored.
|
||||
///
|
||||
/// # Errors
|
||||
///
|
||||
/// `AlreadyRegistered` if the competitor already exists, whether from an
|
||||
/// earlier `register` or from an event. `InvalidParameter` for a `weight`
|
||||
/// other than 1.0, or a `drift_scale` that is negative or non-finite.
|
||||
pub fn register(&mut self, member: Member<K>) -> Result<(), InferenceError>
|
||||
where
|
||||
K: std::fmt::Debug,
|
||||
{
|
||||
if member.weight != 1.0 {
|
||||
return Err(InferenceError::InvalidParameter {
|
||||
name: "weight",
|
||||
value: member.weight,
|
||||
});
|
||||
}
|
||||
if let Some(scale) = member.drift_scale {
|
||||
if !scale.is_finite() || scale < 0.0 {
|
||||
return Err(InferenceError::InvalidParameter {
|
||||
name: "drift_scale",
|
||||
value: scale,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
let key = format!("{:?}", member.key);
|
||||
let idx = self.keys.get_or_create(&member.key);
|
||||
if self.agents.contains(idx) {
|
||||
return Err(InferenceError::AlreadyRegistered { key });
|
||||
}
|
||||
|
||||
let mut rating = Rating::new(
|
||||
Gaussian::from_ms(self.mu, self.sigma),
|
||||
self.beta,
|
||||
self.drift,
|
||||
);
|
||||
if let Some(prior) = member.prior {
|
||||
rating.prior = prior;
|
||||
}
|
||||
if let Some(scale) = member.drift_scale {
|
||||
rating.drift_scale = scale;
|
||||
}
|
||||
|
||||
self.declared.insert(
|
||||
idx,
|
||||
CompetitorConfig {
|
||||
prior: member.prior,
|
||||
drift_scale: member.drift_scale,
|
||||
},
|
||||
);
|
||||
self.agents.insert(
|
||||
idx,
|
||||
Competitor {
|
||||
rating,
|
||||
message: None,
|
||||
last_time: None,
|
||||
},
|
||||
);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// The configuration in force for a competitor, or `None` if the history
|
||||
/// has never seen them.
|
||||
///
|
||||
/// Reads back what was actually stored, which is what makes a
|
||||
/// configuration mistake detectable from outside the crate. Every other
|
||||
/// accessor returns what inference *inferred*; this returns what it was
|
||||
/// told.
|
||||
#[must_use]
|
||||
pub fn rating<Q>(&self, key: &Q) -> Option<Rating<T, D>>
|
||||
where
|
||||
K: std::borrow::Borrow<Q>,
|
||||
Q: std::hash::Hash + Eq + ?Sized,
|
||||
{
|
||||
let idx = self.keys.get(key)?;
|
||||
self.agents.contains(idx).then(|| self.agents[idx].rating)
|
||||
}
|
||||
|
||||
pub fn current_skill<Q>(&self, key: &Q) -> Option<Gaussian>
|
||||
where
|
||||
K: std::borrow::Borrow<Q>,
|
||||
@@ -958,6 +1109,14 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
/// [`Joint`] with [`History::joint`] instead — the answers are identical,
|
||||
/// and only the first one pays.
|
||||
///
|
||||
/// # Cost
|
||||
///
|
||||
/// A dense solve over the history's *appearances*, not its competitors. A
|
||||
/// drift-free competitor collapses to a single variable however long the
|
||||
/// history, so the same events can differ enormously in cost depending on
|
||||
/// the drift configuration — see [`Joint`], which also amortises this
|
||||
/// across many questions.
|
||||
///
|
||||
/// # Limitations
|
||||
///
|
||||
/// Exact only for a history whose events are all scored, because a scored
|
||||
@@ -1390,17 +1549,62 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
))
|
||||
}
|
||||
|
||||
/// Run the full forward+backward convergence loop and return a summary.
|
||||
/// Run the full forward+backward convergence loop to a fixed point.
|
||||
///
|
||||
/// Failing to reach `epsilon` within `max_iter` is not an error: the
|
||||
/// returned report carries `converged: false` and the final step.
|
||||
/// # Stopping short is an error
|
||||
///
|
||||
/// Hitting `max_iter` without reaching `epsilon` returns `NotConverged`.
|
||||
///
|
||||
/// It used to return `Ok` with `converged: false`, which was the worst
|
||||
/// available shape. A fit that stops short is *wrong by a little*: every
|
||||
/// rating is finite, the ordering looks sensible, and nothing about the
|
||||
/// output says the numbers were still moving. Detection was opt-in, and
|
||||
/// `let _ = h.converge()` silently opted out — which is how a real defect
|
||||
/// hid in this crate's own test suite.
|
||||
///
|
||||
/// The default `max_iter` is [`ITERATIONS`](crate::ITERATIONS), which is
|
||||
/// set high enough that reaching it means something is genuinely wrong
|
||||
/// rather than that the history is merely large. Raising the cap costs
|
||||
/// nothing when it is not needed, because the loop exits at `epsilon`.
|
||||
///
|
||||
/// Use [`History::converge_partial`] when a capped, unconverged fit is
|
||||
/// what you actually want.
|
||||
///
|
||||
/// # Errors
|
||||
///
|
||||
/// `NotConverged` if the sweep hits `max_iter` with the step still above
|
||||
/// `epsilon`.
|
||||
///
|
||||
/// `NonFiniteResult` if a sweep produces a NaN or infinite step. EP has
|
||||
/// broken down at that point and further iterations cannot recover, so the
|
||||
/// loop stops rather than reporting a NaN step as convergence.
|
||||
pub fn converge(&mut self) -> Result<ConvergenceReport, InferenceError> {
|
||||
let report = self.converge_partial()?;
|
||||
|
||||
if report.converged {
|
||||
Ok(report)
|
||||
} else {
|
||||
Err(InferenceError::NotConverged {
|
||||
iterations: report.iterations,
|
||||
final_step: report.final_step,
|
||||
epsilon: self.convergence.epsilon,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
/// As [`History::converge`], but a fit that stops at `max_iter` is
|
||||
/// returned rather than reported as an error.
|
||||
///
|
||||
/// The report's `converged` flag says which happened. Use this when a
|
||||
/// deliberately capped sweep is the point — a cheap approximate fit, or a
|
||||
/// test that pins what a fixed number of iterations produces. Prefer
|
||||
/// `converge` everywhere else: an unconverged fit that nobody checks is
|
||||
/// indistinguishable from a converged one.
|
||||
///
|
||||
/// # Errors
|
||||
///
|
||||
/// `NonFiniteResult` if a sweep produces a NaN or infinite step.
|
||||
pub fn converge_partial(&mut self) -> Result<ConvergenceReport, InferenceError> {
|
||||
use std::time::Instant;
|
||||
|
||||
use smallvec::SmallVec;
|
||||
@@ -1505,6 +1709,73 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
});
|
||||
}
|
||||
|
||||
// Chokepoint for event shape, for the same reason as the tie check
|
||||
// below: every ingestion route lands here.
|
||||
//
|
||||
// `run_chain` builds one diff link per adjacent pair of teams, so a
|
||||
// one-team event leaves it with an empty link vector and panics
|
||||
// indexing `links[1..]` — a reachable panic from safe API, in release.
|
||||
// An empty team is the quieter half: it contributes no performance,
|
||||
// so a malformed event yields a finite, plausible-looking posterior
|
||||
// for whoever it was matched against.
|
||||
//
|
||||
// Both errors already existed; they were only ever checked on the
|
||||
// prediction paths, which is why ingestion could still produce them.
|
||||
for teams in &composition {
|
||||
if teams.len() < 2 {
|
||||
return Err(InferenceError::NotEnoughTeams { got: teams.len() });
|
||||
}
|
||||
for (team, members) in teams.iter().enumerate() {
|
||||
if members.is_empty() {
|
||||
return Err(InferenceError::EmptyTeam { team });
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// A non-finite outcome poisons the history rather than failing it:
|
||||
// `converge` does report `NonFiniteResult`, but a caller who reads
|
||||
// `current_skill` before converging is handed a NaN posterior with
|
||||
// nothing to say it is one.
|
||||
if let Some(results) = results.as_ref() {
|
||||
for (event_results, kind) in results.iter().zip(kinds.iter()) {
|
||||
let name = match kind {
|
||||
EventKind::Ranked => "rank",
|
||||
EventKind::Scored { .. } => "score",
|
||||
};
|
||||
for value in event_results {
|
||||
if !value.is_finite() {
|
||||
return Err(InferenceError::InvalidParameter {
|
||||
name,
|
||||
value: *value,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// A non-finite weight is not a weight. Measured, it behaves exactly as
|
||||
// `0.0` — the member contributes nothing — while `converge` reports
|
||||
// `converged: true` after one iteration with a step of `(0.0, 0.0)`.
|
||||
// So a NaN arriving from a division or a parse is indistinguishable
|
||||
// from a deliberate zero, and looks like a clean fit.
|
||||
//
|
||||
// Zero and negative weights stay accepted: both are expressible
|
||||
// choices about how much a member contributes, and
|
||||
// `tests/degenerate_inputs.rs` pins them deliberately. Only the values
|
||||
// that are not quantities at all are rejected.
|
||||
if let Some(weights) = weights.as_ref() {
|
||||
for team_weights in weights.iter().flatten() {
|
||||
for weight in team_weights {
|
||||
if !weight.is_finite() {
|
||||
return Err(InferenceError::InvalidParameter {
|
||||
name: "weight",
|
||||
value: *weight,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Chokepoint for tie validation: every ingestion route lands here,
|
||||
// including `record_draw`, which builds its results directly rather
|
||||
// than going through `Outcome`.
|
||||
@@ -1520,6 +1791,47 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
}
|
||||
}
|
||||
|
||||
// Cross-batch conflict. The in-batch check upstream rejects one batch
|
||||
// that sets a field twice; `priors` is rebuilt per call, so without
|
||||
// this a *second* batch could quietly overwrite what a first one
|
||||
// declared, last-write-wins.
|
||||
//
|
||||
// That asymmetry cut against the invariant `tests/ingestion_equivalence.rs`
|
||||
// exists to protect: the same contradictory events errored when
|
||||
// batched and succeeded, order-dependently, when fed one at a time.
|
||||
// Checked before anything mutates, so a rejected batch leaves the
|
||||
// history untouched.
|
||||
for (agent, batch) in &priors {
|
||||
let held = self.declared.get(agent).copied().unwrap_or_default();
|
||||
|
||||
if let (Some(existing), Some(new)) = (held.prior, batch.prior) {
|
||||
if existing != new {
|
||||
return Err(InferenceError::ConflictingCompetitorConfig {
|
||||
competitor: agent.get(),
|
||||
field: "prior",
|
||||
});
|
||||
}
|
||||
}
|
||||
if let (Some(existing), Some(new)) = (held.drift_scale, batch.drift_scale) {
|
||||
if existing != new {
|
||||
return Err(InferenceError::ConflictingCompetitorConfig {
|
||||
competitor: agent.get(),
|
||||
field: "drift_scale",
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (agent, batch) in &priors {
|
||||
let entry = self.declared.entry(*agent).or_default();
|
||||
if batch.prior.is_some() {
|
||||
entry.prior = batch.prior;
|
||||
}
|
||||
if batch.drift_scale.is_some() {
|
||||
entry.drift_scale = batch.drift_scale;
|
||||
}
|
||||
}
|
||||
|
||||
competitor::clean(self.agents.values_mut(), true);
|
||||
|
||||
let mut this_agent = Vec::with_capacity(1024);
|
||||
@@ -1531,7 +1843,9 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
|
||||
this_agent.push(*agent);
|
||||
|
||||
let config = priors.get(agent).copied().unwrap_or_default();
|
||||
// From `declared` rather than `priors`: a competitor configured by
|
||||
// `register` before any event has nothing in this batch's map.
|
||||
let config = self.declared.get(agent).copied().unwrap_or_default();
|
||||
|
||||
if self.agents.contains(*agent) {
|
||||
// Seeding a competitor the history already knows. This used to
|
||||
@@ -1922,6 +2236,34 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
|
||||
/// added while it is alive. That is what makes it correct without any
|
||||
/// invalidation logic: there is no window in which the factorisation could
|
||||
/// describe a fit that no longer exists.
|
||||
///
|
||||
/// # What the cost actually scales in
|
||||
///
|
||||
/// Not competitors, and not slices times competitors. One variable per
|
||||
/// *appearance* — a competitor per slice they appear in — minus every
|
||||
/// consecutive pair with no drift between them, which collapse to a single
|
||||
/// latent variable.
|
||||
///
|
||||
/// That last clause dominates, and it is not obvious. A competitor whose drift
|
||||
/// is zero contributes **one** variable however long the history: whole-history
|
||||
/// `gamma = 0`, or `drift_scale = 0` on that competitor. So two fits over the
|
||||
/// same events and the same slices can differ in problem size by roughly the
|
||||
/// slice count, and in factorisation time by its cube. Measured by a consumer
|
||||
/// on a ~2,000-node model over 76 slices:
|
||||
///
|
||||
/// ```text
|
||||
/// career fit (gamma = 0) 787 ms per solve
|
||||
/// drifting fit (gamma = 0.15) 6214 ms per solve
|
||||
/// ```
|
||||
///
|
||||
/// Choosing between a drifting and a drift-free configuration is therefore also
|
||||
/// choosing an 8x difference in query cost. [`Joint::variables`] reports the
|
||||
/// number that decides it, and can be read before committing to a batch of
|
||||
/// queries.
|
||||
///
|
||||
/// Slices a competitor sits out cost nothing: an absence is not an appearance,
|
||||
/// so a competitor seen in the first and last of a hundred slices contributes
|
||||
/// two variables, not a hundred.
|
||||
pub struct Joint<'h, T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> {
|
||||
history: &'h History<T, D, O, K>,
|
||||
cholesky: crate::joint::Cholesky,
|
||||
@@ -1949,9 +2291,14 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> Joint<'_, T, D,
|
||||
/// Number of variables in the joint: the history's appearances, after
|
||||
/// collapsing consecutive pairs a competitor does not drift between.
|
||||
///
|
||||
/// This is what the cost scales in, and it is not the competitor count — a
|
||||
/// competitor contributes one variable per slice it appears in. Worth
|
||||
/// checking before asking for a joint over a long history.
|
||||
/// This is what the cost scales in — `O(n^3)` to factorise, `O(n^2)` per
|
||||
/// query — and it is neither the competitor count nor slices times
|
||||
/// competitors. A drift-free competitor contributes one variable however
|
||||
/// many slices they appear in; see the type docs for how large that
|
||||
/// difference gets.
|
||||
///
|
||||
/// Worth reading before committing to a batch of queries: it is the one
|
||||
/// number that says whether a joint over this history is affordable.
|
||||
#[must_use]
|
||||
pub fn variables(&self) -> usize {
|
||||
self.width
|
||||
@@ -2755,13 +3102,15 @@ mod tests {
|
||||
epsilon = 1e-6
|
||||
);
|
||||
|
||||
// run exactly 11 iterations (old test used convergence(11, ...))
|
||||
// Run exactly 11 iterations. `converge_partial` rather than
|
||||
// `converge`: stopping at the cap is the point here, and `converge`
|
||||
// now reports that as `NotConverged`.
|
||||
h.convergence = ConvergenceOptions {
|
||||
max_iter: 11,
|
||||
epsilon: EPSILON,
|
||||
alpha: 1.0,
|
||||
};
|
||||
let _ = h.converge().unwrap();
|
||||
let _ = h.converge_partial().unwrap();
|
||||
|
||||
let loocv_approx_2 = h.log_evidence_internal(false, &[]).exp().sqrt();
|
||||
|
||||
@@ -3128,7 +3477,9 @@ mod tests {
|
||||
})
|
||||
.build();
|
||||
events_for(&mut h_capped);
|
||||
let _ = h_capped.converge().unwrap();
|
||||
// A one-iteration cap is deliberate here, so the short fit is the
|
||||
// result rather than an error.
|
||||
let _ = h_capped.converge_partial().unwrap();
|
||||
|
||||
let mut h_full: History<i64, _, _, &'static str> = History::builder().build();
|
||||
events_for(&mut h_full);
|
||||
|
||||
+35
-14
@@ -158,22 +158,43 @@ pub const P_DRAW: f64 = 0.0;
|
||||
pub const EPSILON: f64 = 1e-6;
|
||||
/// Default cap on convergence sweeps.
|
||||
///
|
||||
/// **This is a floor, not a recommendation.** It is adequate for small
|
||||
/// histories and is quickly outgrown: a history of 400 events over 100
|
||||
/// competitors already stops here with a final step of ~7e-3 against the 1e-6
|
||||
/// default tolerance — four orders of magnitude short — and a dense joint model
|
||||
/// of ~2,000 nodes over ~3,300 events has been measured needing 76 to 161.
|
||||
/// **A runaway guard, not a budget.** The sweep exits as soon as the step falls
|
||||
/// below `epsilon`, so the cap is never reached by a history that converges and
|
||||
/// raising it costs nothing. Measured on a history that needs four sweeps:
|
||||
///
|
||||
/// Overrunning it is not an error, and deliberately so: `converge` returns a
|
||||
/// [`ConvergenceReport`] whose `converged` flag says what happened. But a fit
|
||||
/// that stopped short is *wrong by a little*, which is the worst available
|
||||
/// failure — every rating is finite and ordered sensibly, and nothing in the
|
||||
/// numbers themselves says they were still moving. Read the report; the type is
|
||||
/// `#[must_use]` for that reason.
|
||||
/// ```text
|
||||
/// max_iter 30: 4 iterations, 129.9 us
|
||||
/// max_iter 100_000: 4 iterations, 131.9 us
|
||||
/// ```
|
||||
///
|
||||
/// Raise it via [`ConvergenceOptions`]. Convergence cost is roughly linear in
|
||||
/// the cap, and for anything but a toy the extra sweeps are milliseconds.
|
||||
pub const ITERATIONS: usize = 30;
|
||||
/// This was `30` until it was measured, and 30 truncated ordinary healthy
|
||||
/// histories: 160 events over 100 competitors already needs 42. Because a short
|
||||
/// fit is finite and sensibly ordered, that was invisible.
|
||||
///
|
||||
/// # Why it is not scaled to the history
|
||||
///
|
||||
/// The obvious improvement — pick the cap from the node or event count — does
|
||||
/// not work, because iteration count is driven by how *loopy* the graph is
|
||||
/// rather than how big it is. At a fixed 320 events over 40 slices, varying
|
||||
/// only the number of competitors sharing them:
|
||||
///
|
||||
/// ```text
|
||||
/// competitors appearances each iterations
|
||||
/// 3 213 2_789
|
||||
/// 10 64 1_068
|
||||
/// 50 12.8 206
|
||||
/// 100 6.4 90
|
||||
/// 400 1.6 2
|
||||
/// ```
|
||||
///
|
||||
/// Three orders of magnitude apart on identical event and slice counts. Any
|
||||
/// formula in those two numbers would be badly wrong on some real shape, so the
|
||||
/// cap is a single value set high enough that reaching it means the fit is
|
||||
/// oscillating rather than merely large.
|
||||
///
|
||||
/// Reaching it is [`InferenceError::NotConverged`]. See
|
||||
/// [`History::converge`](crate::History::converge).
|
||||
pub const ITERATIONS: usize = 10_000;
|
||||
|
||||
/// Largest team count `History::predict_outcome` will enumerate.
|
||||
///
|
||||
|
||||
@@ -0,0 +1,152 @@
|
||||
//! Stopping short of convergence is an error, not a flag on a success.
|
||||
//!
|
||||
//! A fit that hits `max_iter` is wrong by a little: every rating is finite,
|
||||
//! the ordering looks sensible, and nothing in the numbers says they were
|
||||
//! still moving. When that was `Ok` with `converged: false`, detecting it was
|
||||
//! opt-in and `let _ = h.converge()` was the natural way to opt out — which is
|
||||
//! how a real defect once hid in this crate's own suite.
|
||||
|
||||
use smallvec::smallvec;
|
||||
use trueskill_tt::{
|
||||
ConstantDrift, ConvergenceOptions, Event, History, InferenceError, Member, Outcome, Team,
|
||||
};
|
||||
|
||||
type H = History<i64, ConstantDrift, trueskill_tt::NullObserver, &'static str>;
|
||||
|
||||
fn duel(a: &'static str, b: &'static str, t: i64) -> Event<i64, &'static str> {
|
||||
Event {
|
||||
time: t,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new(a)]),
|
||||
Team::with_members([Member::new(b)]),
|
||||
],
|
||||
outcome: Outcome::scores([3.0, 1.0]),
|
||||
}
|
||||
}
|
||||
|
||||
fn capped(max_iter: usize) -> H {
|
||||
History::builder()
|
||||
.mu(0.0)
|
||||
.sigma(6.0)
|
||||
.beta(1.0)
|
||||
.score_sigma(2.0)
|
||||
.drift(ConstantDrift(0.5))
|
||||
.convergence(ConvergenceOptions {
|
||||
max_iter,
|
||||
epsilon: 1e-13,
|
||||
alpha: 1.0,
|
||||
})
|
||||
.build()
|
||||
}
|
||||
|
||||
fn fill(h: &mut H) {
|
||||
h.add_events((1..=6).map(|t| duel("a", "b", t)).collect::<Vec<_>>())
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn hitting_the_cap_is_an_error() {
|
||||
let mut h = capped(1);
|
||||
fill(&mut h);
|
||||
let err = h.converge().unwrap_err();
|
||||
match err {
|
||||
InferenceError::NotConverged {
|
||||
iterations,
|
||||
final_step,
|
||||
epsilon,
|
||||
} => {
|
||||
assert_eq!(iterations, 1);
|
||||
assert!(
|
||||
final_step.0 > epsilon || final_step.1 > epsilon,
|
||||
"{final_step:?}"
|
||||
);
|
||||
}
|
||||
other => panic!("expected NotConverged, got {other:?}"),
|
||||
}
|
||||
}
|
||||
|
||||
/// The message has to name what to do about it, since the fit looks fine.
|
||||
#[test]
|
||||
fn the_error_says_how_to_fix_it() {
|
||||
let mut h = capped(1);
|
||||
fill(&mut h);
|
||||
let text = h.converge().unwrap_err().to_string();
|
||||
assert!(text.contains("did not converge in 1 iterations"), "{text}");
|
||||
assert!(text.contains("max_iter"), "{text}");
|
||||
assert!(text.contains("alpha"), "{text}");
|
||||
}
|
||||
|
||||
/// The escape hatch: a deliberately capped fit is still reachable.
|
||||
#[test]
|
||||
fn converge_partial_returns_the_short_fit() {
|
||||
let mut h = capped(1);
|
||||
fill(&mut h);
|
||||
let report = h.converge_partial().unwrap();
|
||||
assert_eq!(report.iterations, 1);
|
||||
assert!(!report.converged);
|
||||
assert!(h.current_skill(&"a").is_some());
|
||||
}
|
||||
|
||||
/// Both agree when the fit does converge, so the strict path costs nothing.
|
||||
#[test]
|
||||
fn the_two_agree_on_a_converged_fit() {
|
||||
let mut strict = capped(20_000);
|
||||
fill(&mut strict);
|
||||
let a = strict.converge().unwrap();
|
||||
|
||||
let mut partial = capped(20_000);
|
||||
fill(&mut partial);
|
||||
let b = partial.converge_partial().unwrap();
|
||||
|
||||
assert!(a.converged && b.converged);
|
||||
assert_eq!(a.iterations, b.iterations);
|
||||
assert_eq!(a.final_step, b.final_step);
|
||||
}
|
||||
|
||||
/// The default cap must be high enough that an ordinary history clears it.
|
||||
/// At the old value of 30 this history stopped short and said nothing.
|
||||
#[test]
|
||||
fn the_default_cap_clears_an_ordinary_history() {
|
||||
let mut h: History<i64, ConstantDrift, _, String> = History::builder_with_key()
|
||||
.mu(0.0)
|
||||
.sigma(6.0)
|
||||
.beta(1.0)
|
||||
.score_sigma(2.0)
|
||||
.drift(ConstantDrift(0.05))
|
||||
.build();
|
||||
|
||||
let mut events = Vec::new();
|
||||
for t in 0..20i64 {
|
||||
for j in 0..8usize {
|
||||
let k = (t as usize) * 8 + j;
|
||||
events.push(Event {
|
||||
time: t,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new(format!("p{}", k % 100))]),
|
||||
Team::with_members([Member::new(format!("p{}", (k + 37) % 100))]),
|
||||
],
|
||||
outcome: Outcome::scores([3.0, 1.0]),
|
||||
});
|
||||
}
|
||||
}
|
||||
h.add_events(events).unwrap();
|
||||
|
||||
let report = h
|
||||
.converge()
|
||||
.expect("an ordinary history must converge by default");
|
||||
assert!(
|
||||
report.iterations > 30,
|
||||
"needed {} sweeps",
|
||||
report.iterations
|
||||
);
|
||||
assert!(report.iterations < trueskill_tt::ITERATIONS);
|
||||
}
|
||||
|
||||
/// An empty history converges trivially rather than erroring.
|
||||
#[test]
|
||||
fn an_empty_history_converges() {
|
||||
let mut h = capped(1);
|
||||
let report = h.converge().unwrap();
|
||||
assert!(report.converged);
|
||||
assert_eq!(report.iterations, 0);
|
||||
}
|
||||
@@ -170,8 +170,9 @@ fn event_builder_rejects_a_weights_length_mismatch() {
|
||||
fn event_builder_weights_mismatch_leaves_the_history_untouched() {
|
||||
let mut h = History::default();
|
||||
|
||||
// Two teams, so ingestion would otherwise succeed — a one-team event is
|
||||
// rejected for an unrelated reason and would pass this vacuously.
|
||||
// Two teams, so ingestion would otherwise succeed. A one-team event is
|
||||
// rejected as `NotEnoughTeams` before the weights are ever examined, so
|
||||
// building this with one team would pass vacuously.
|
||||
let _ = h
|
||||
.event(1)
|
||||
.team(["a"])
|
||||
|
||||
@@ -0,0 +1,193 @@
|
||||
//! `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<i64, ConstantDrift, trueskill_tt::NullObserver, &'static str>;
|
||||
|
||||
fn history() -> H {
|
||||
History::builder()
|
||||
.mu(0.0)
|
||||
.sigma(6.0)
|
||||
.beta(1.0)
|
||||
.score_sigma(2.0)
|
||||
.drift(ConstantDrift(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();
|
||||
assert_eq!(a.pi(), b.pi(), "{key} pi");
|
||||
assert_eq!(a.tau(), b.tau(), "{key} tau");
|
||||
}
|
||||
}
|
||||
|
||||
/// 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);
|
||||
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 {
|
||||
kind: "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 {
|
||||
name: "drift_scale",
|
||||
..
|
||||
}
|
||||
),
|
||||
"{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");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,190 @@
|
||||
//! Malformed events must be rejected at the ingestion boundary.
|
||||
//!
|
||||
//! Every case here was reachable from safe public API in a release build. Two
|
||||
//! of them are the two shapes this crate's defects keep taking: a panic from
|
||||
//! deep inside inference, and a finite, plausible-looking posterior computed
|
||||
//! from an event that should never have been accepted.
|
||||
//!
|
||||
//! `InferenceError::NotEnoughTeams` and `EmptyTeam` already existed when these
|
||||
//! were found — they were checked on the prediction paths and nowhere else, so
|
||||
//! ingestion could still manufacture the states they describe.
|
||||
|
||||
use smallvec::smallvec;
|
||||
use trueskill_tt::{Event, History, InferenceError, Member, Outcome, Team};
|
||||
|
||||
type Ev = Event<i64, &'static str>;
|
||||
|
||||
fn history() -> History<i64, trueskill_tt::ConstantDrift, trueskill_tt::NullObserver, &'static str>
|
||||
{
|
||||
History::builder().score_sigma(1.0).build()
|
||||
}
|
||||
|
||||
fn teams(names: &[&[&'static str]]) -> smallvec::SmallVec<[Team<&'static str>; 4]> {
|
||||
names
|
||||
.iter()
|
||||
.map(|team| Team::with_members(team.iter().map(|k| Member::new(*k))))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// The regression this file exists for: `run_chain` builds one diff link per
|
||||
/// adjacent pair of teams, so a one-team event left it indexing `links[1..]`
|
||||
/// on an empty vector and panicked — in release, from `History::add_events`.
|
||||
#[test]
|
||||
fn a_one_team_event_is_an_error_not_a_panic() {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: teams(&[&["a"]]),
|
||||
outcome: Outcome::winner(0, 1),
|
||||
}])
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::NotEnoughTeams { got: 1 }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn a_zero_team_event_is_an_error() {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: smallvec![],
|
||||
outcome: Outcome::ranking([]),
|
||||
}])
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::NotEnoughTeams { got: 0 }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
/// The quiet half. An empty team contributes no performance, so before this
|
||||
/// was rejected the event converged and handed back a finite posterior for its
|
||||
/// opponent — a plausible constant computed from nothing.
|
||||
#[test]
|
||||
fn an_empty_team_is_an_error_rather_than_a_free_win() {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: teams(&[&[], &["b"]]),
|
||||
outcome: Outcome::winner(0, 2),
|
||||
}])
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::EmptyTeam { team: 0 }),
|
||||
"{err:?}"
|
||||
);
|
||||
// Nothing was recorded, so the history is still empty.
|
||||
assert!(h.current_skill(&"b").is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn an_empty_team_is_reported_by_position() {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: teams(&[&["a"], &[]]),
|
||||
outcome: Outcome::winner(0, 2),
|
||||
}])
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::EmptyTeam { team: 1 }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
/// A NaN score used to ingest cleanly. `converge` reported `NonFiniteResult`,
|
||||
/// but a caller who read `current_skill` first was handed `tau: NaN` with
|
||||
/// nothing to say so.
|
||||
#[test]
|
||||
fn a_non_finite_score_is_rejected_at_ingestion() {
|
||||
for bad in [f64::NAN, f64::INFINITY, f64::NEG_INFINITY] {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: teams(&[&["a"], &["b"]]),
|
||||
outcome: Outcome::scores([bad, 0.0]),
|
||||
}])
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::InvalidParameter { name: "score", .. }),
|
||||
"{bad}: {err:?}"
|
||||
);
|
||||
assert!(h.current_skill(&"a").is_none(), "{bad} was recorded anyway");
|
||||
}
|
||||
}
|
||||
|
||||
/// A non-finite weight behaved exactly as `0.0` — the member contributed
|
||||
/// nothing — while `converge` reported `converged: true` after one iteration
|
||||
/// with a step of `(0.0, 0.0)`. So a NaN arriving from a division or a parse
|
||||
/// was indistinguishable from a deliberate zero, and looked like a clean fit.
|
||||
#[test]
|
||||
fn a_non_finite_weight_is_rejected_at_ingestion() {
|
||||
for bad in [f64::NAN, f64::INFINITY, f64::NEG_INFINITY] {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.event(1)
|
||||
.team(["a"])
|
||||
.weights([bad])
|
||||
.team(["b"])
|
||||
.winner(0)
|
||||
.commit()
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::InvalidParameter { name: "weight", .. }),
|
||||
"{bad}: {err:?}"
|
||||
);
|
||||
assert!(h.current_skill(&"a").is_none(), "{bad} reached the history");
|
||||
}
|
||||
}
|
||||
|
||||
/// Zero and negative weights are expressible choices about how much a member
|
||||
/// contributes, not malformed input, and `tests/degenerate_inputs.rs` pins
|
||||
/// their behaviour deliberately. Rejecting non-finite values must not catch
|
||||
/// them too.
|
||||
#[test]
|
||||
fn zero_and_negative_weights_still_ingest() {
|
||||
for w in [0.0, -1.0, 0.5] {
|
||||
let mut h = history();
|
||||
h.event(1)
|
||||
.team(["a"])
|
||||
.weights([w])
|
||||
.team(["b"])
|
||||
.winner(0)
|
||||
.commit()
|
||||
.unwrap_or_else(|e| panic!("weight {w} should ingest: {e:?}"));
|
||||
assert!(h.current_skill(&"a").is_some(), "weight {w}");
|
||||
}
|
||||
}
|
||||
|
||||
/// The fluent builder routes through the same chokepoint, so it inherits the
|
||||
/// checks rather than needing its own.
|
||||
#[test]
|
||||
fn the_event_builder_inherits_the_shape_checks() {
|
||||
let mut h = history();
|
||||
let err = h.event(1).team(["a"]).winner(0).commit().unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::NotEnoughTeams { got: 1 }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
/// A well-formed event is untouched by any of this.
|
||||
#[test]
|
||||
fn a_well_formed_event_still_ingests() {
|
||||
let mut h = history();
|
||||
h.add_events(vec![Ev {
|
||||
time: 1,
|
||||
teams: teams(&[&["a"], &["b"]]),
|
||||
outcome: Outcome::scores([3.0, 1.0]),
|
||||
}])
|
||||
.unwrap();
|
||||
assert!(h.converge().unwrap().converged);
|
||||
assert!(h.current_skill(&"a").unwrap().mu() > h.current_skill(&"b").unwrap().mu());
|
||||
}
|
||||
@@ -141,6 +141,53 @@ fn variables_counts_appearances_not_competitors() {
|
||||
assert_eq!(joint.variables(), 12);
|
||||
}
|
||||
|
||||
/// How much the collapse is worth, which is the part a caller has to plan
|
||||
/// around: a drift-free competitor contributes **one** variable however long
|
||||
/// the history, so the same events at `gamma = 0` and `gamma > 0` differ by
|
||||
/// roughly the slice count in problem size — and by its cube in solve time.
|
||||
///
|
||||
/// Reported by a consumer as an 8x difference in solve time on a ~2,000-node,
|
||||
/// 76-slice model (787 ms career against 6,214 ms drifting). This pins the
|
||||
/// mechanism behind that so a change to the collapse rule cannot quietly
|
||||
/// remove it.
|
||||
#[test]
|
||||
fn drift_free_competitors_shrink_the_joint_by_the_slice_count() {
|
||||
fn variables(gamma: f64) -> usize {
|
||||
let mut h = History::builder()
|
||||
.mu(0.0)
|
||||
.sigma(6.0)
|
||||
.beta(1.0)
|
||||
.score_sigma(2.0)
|
||||
.drift(ConstantDrift(gamma))
|
||||
.convergence(ConvergenceOptions {
|
||||
max_iter: 20_000,
|
||||
epsilon: 1e-13,
|
||||
alpha: 1.0,
|
||||
})
|
||||
.build();
|
||||
h.add_events(
|
||||
(1..=10)
|
||||
.map(|t| duel("a", "b", t, 5.0, 2.0))
|
||||
.collect::<Vec<_>>(),
|
||||
)
|
||||
.unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
h.joint().unwrap().variables()
|
||||
}
|
||||
|
||||
let drifting = variables(0.5);
|
||||
let career = variables(0.0);
|
||||
|
||||
// Two competitors over ten slices: twenty appearances, or two variables.
|
||||
assert_eq!(drifting, 20);
|
||||
assert_eq!(career, 2);
|
||||
assert_eq!(
|
||||
drifting / career,
|
||||
10,
|
||||
"collapse should track the slice count"
|
||||
);
|
||||
}
|
||||
|
||||
/// With `drift = 0` consecutive appearances are the same latent variable, so
|
||||
/// the joint is smaller than the appearance count.
|
||||
#[test]
|
||||
|
||||
@@ -0,0 +1,117 @@
|
||||
//! Inference must report numerical breakdown rather than call it convergence.
|
||||
//!
|
||||
//! The boundary rejects inputs that are *not numbers*, but finite inputs can
|
||||
//! still overflow during inference — `beta.powi(2)` at 1e300 is infinite, and
|
||||
//! infinity minus infinity is NaN. `NonFiniteResult` is the guard for that, and
|
||||
//! it matters because the alternative is silent: NaN fails every comparison, so
|
||||
//! a naive `step < epsilon` check reads a NaN step as *converged*.
|
||||
//!
|
||||
//! That is why the crate has `step_converged` / `step_is_finite` rather than
|
||||
//! `!tuple_gt(..)`. These tests pin the guard from outside.
|
||||
|
||||
use smallvec::smallvec;
|
||||
use trueskill_tt::{Event, Gaussian, History, InferenceError, Member, Outcome, Team};
|
||||
|
||||
fn scored_fit(
|
||||
sigma: f64,
|
||||
beta: f64,
|
||||
score_sigma: f64,
|
||||
scores: [f64; 2],
|
||||
) -> Result<bool, InferenceError> {
|
||||
let mut h = History::builder()
|
||||
.mu(0.0)
|
||||
.sigma(sigma)
|
||||
.beta(beta)
|
||||
.score_sigma(score_sigma)
|
||||
.build();
|
||||
h.add_events(vec![Event {
|
||||
time: 1i64,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new("a")]),
|
||||
Team::with_members([Member::new("b")]),
|
||||
],
|
||||
outcome: Outcome::scores(scores),
|
||||
}])?;
|
||||
h.converge().map(|r| r.converged)
|
||||
}
|
||||
|
||||
/// Every one of these is built from finite, individually legal parameters. The
|
||||
/// overflow happens inside inference, which is exactly the case the boundary
|
||||
/// checks cannot catch.
|
||||
///
|
||||
/// Matched rather than merely `is_err()`: an assertion that only checks "some
|
||||
/// error" would keep passing if these started failing at the boundary for an
|
||||
/// unrelated reason, and would then be testing nothing.
|
||||
#[test]
|
||||
fn overflow_during_inference_is_reported_not_hidden() {
|
||||
let cases: [(&str, f64, f64, f64, [f64; 2]); 5] = [
|
||||
("huge sigma", 1e300, 1.0, 1.0, [3.0, 1.0]),
|
||||
("huge beta", 6.0, 1e300, 1.0, [3.0, 1.0]),
|
||||
("tiny sigma", 1e-300, 1.0, 1.0, [3.0, 1.0]),
|
||||
("tiny score_sigma", 6.0, 1.0, 1e-300, [3.0, 1.0]),
|
||||
("huge scores", 6.0, 1.0, 1.0, [1e308, -1e308]),
|
||||
];
|
||||
|
||||
for (name, sigma, beta, score_sigma, scores) in cases {
|
||||
match scored_fit(sigma, beta, score_sigma, scores) {
|
||||
Err(InferenceError::NonFiniteResult { context, step }) => {
|
||||
assert_eq!(context, "History::converge", "{name}");
|
||||
assert!(
|
||||
!step.0.is_finite() || !step.1.is_finite(),
|
||||
"{name}: reported NonFiniteResult with a finite step {step:?}"
|
||||
);
|
||||
}
|
||||
other => panic!("{name}: expected NonFiniteResult, got {other:?}"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// The trap the invariant exists for: NaN fails every comparison, so a naive
|
||||
/// `step < epsilon` test reads a NaN step as converged. A breakdown must never
|
||||
/// come back as a successful fit.
|
||||
#[test]
|
||||
fn a_broken_fit_is_never_reported_as_converged() {
|
||||
let mut h = History::builder().build();
|
||||
h.add_events(vec![Event {
|
||||
time: 1i64,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new("a").with_prior(Gaussian::from_ms(1e300, 1e-300))]),
|
||||
Team::with_members([Member::new("b")]),
|
||||
],
|
||||
outcome: Outcome::winner(0, 2),
|
||||
}])
|
||||
.unwrap();
|
||||
|
||||
let err = h.converge().unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::NonFiniteResult { .. }),
|
||||
"a breakdown must not be reported as convergence: {err:?}"
|
||||
);
|
||||
|
||||
// `converge_partial` must not launder it into an `Ok` either — the
|
||||
// permissive path is permissive about *stopping short*, not about NaN.
|
||||
let mut h2 = History::builder().build();
|
||||
h2.add_events(vec![Event {
|
||||
time: 1i64,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new("a").with_prior(Gaussian::from_ms(1e300, 1e-300))]),
|
||||
Team::with_members([Member::new("b")]),
|
||||
],
|
||||
outcome: Outcome::winner(0, 2),
|
||||
}])
|
||||
.unwrap();
|
||||
assert!(matches!(
|
||||
h2.converge_partial().unwrap_err(),
|
||||
InferenceError::NonFiniteResult { .. }
|
||||
));
|
||||
}
|
||||
|
||||
/// The neighbouring case, so the tests above cannot pass by the fit simply
|
||||
/// always failing: ordinary extreme-but-workable parameters still converge.
|
||||
#[test]
|
||||
fn merely_extreme_parameters_still_converge() {
|
||||
assert!(scored_fit(1e6, 1.0, 1.0, [3.0, 1.0]).unwrap());
|
||||
assert!(scored_fit(1e-6, 1.0, 1.0, [3.0, 1.0]).unwrap());
|
||||
assert!(scored_fit(6.0, 1.0, 1e6, [3.0, 1.0]).unwrap());
|
||||
assert!(scored_fit(6.0, 1.0, 1.0, [1e150, -1e150]).unwrap());
|
||||
}
|
||||
@@ -0,0 +1,338 @@
|
||||
//! Configuring a competitor before anything is observed about them.
|
||||
//!
|
||||
//! The configuration a competitor needs is usually a property of the domain —
|
||||
//! "every layout is static" — not of whichever event happens to mention them
|
||||
//! first. Stating it per-event meant every ingestion path had to remember it,
|
||||
//! and two of the four paths could not state it at all.
|
||||
|
||||
use smallvec::smallvec;
|
||||
use trueskill_tt::{
|
||||
ConstantDrift, ConvergenceOptions, Event, Gaussian, History, InferenceError, Member, Outcome,
|
||||
Team,
|
||||
};
|
||||
|
||||
type H = History<i64, ConstantDrift, trueskill_tt::NullObserver, &'static str>;
|
||||
|
||||
const PINNED: Gaussian = Gaussian::from_ms(2.0, 0.5);
|
||||
|
||||
fn history() -> H {
|
||||
History::builder()
|
||||
.mu(0.0)
|
||||
.sigma(6.0)
|
||||
.beta(1.0)
|
||||
.score_sigma(2.0)
|
||||
.drift(ConstantDrift(0.5))
|
||||
.convergence(ConvergenceOptions {
|
||||
max_iter: 20_000,
|
||||
epsilon: 1e-13,
|
||||
alpha: 1.0,
|
||||
})
|
||||
.build()
|
||||
}
|
||||
|
||||
fn duel(
|
||||
a: &'static str,
|
||||
b: &'static str,
|
||||
t: i64,
|
||||
m: Option<Member<&'static str>>,
|
||||
) -> Event<i64, &'static str> {
|
||||
Event {
|
||||
time: t,
|
||||
teams: smallvec![
|
||||
Team::with_members([Member::new(a)]),
|
||||
Team::with_members([m.unwrap_or_else(|| Member::new(b))]),
|
||||
],
|
||||
outcome: Outcome::scores([5.0, 2.0]),
|
||||
}
|
||||
}
|
||||
|
||||
fn skills(h: &H) -> Vec<(&'static str, Gaussian)> {
|
||||
["player", "layout"]
|
||||
.into_iter()
|
||||
.map(|k| (k, h.current_skill(&k).unwrap()))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// The headline contract.
|
||||
#[test]
|
||||
fn registering_matches_configuring_on_the_first_event() {
|
||||
let configured = {
|
||||
let mut h = history();
|
||||
h.add_events(vec![
|
||||
duel(
|
||||
"player",
|
||||
"layout",
|
||||
1,
|
||||
Some(
|
||||
Member::new("layout")
|
||||
.with_drift_scale(0.0)
|
||||
.with_prior(PINNED),
|
||||
),
|
||||
),
|
||||
duel("player", "layout", 2, None),
|
||||
])
|
||||
.unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
h
|
||||
};
|
||||
|
||||
let registered = {
|
||||
let mut h = history();
|
||||
h.register(
|
||||
Member::new("layout")
|
||||
.with_drift_scale(0.0)
|
||||
.with_prior(PINNED),
|
||||
)
|
||||
.unwrap();
|
||||
h.add_events(vec![
|
||||
duel("player", "layout", 1, None),
|
||||
duel("player", "layout", 2, None),
|
||||
])
|
||||
.unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
h
|
||||
};
|
||||
|
||||
for ((k, a), (_, b)) in skills(&configured).into_iter().zip(skills(®istered)) {
|
||||
assert_eq!(a.pi(), b.pi(), "{k} pi");
|
||||
assert_eq!(a.tau(), b.tau(), "{k} tau");
|
||||
}
|
||||
}
|
||||
|
||||
/// The case `EventBuilder` and the typed path cannot reach: a competitor whose
|
||||
/// first appearance arrives through the two-argument convenience route.
|
||||
#[test]
|
||||
fn registration_reaches_a_competitor_first_seen_through_record_winner() {
|
||||
let mut h = history();
|
||||
h.register(
|
||||
Member::new("layout")
|
||||
.with_drift_scale(0.0)
|
||||
.with_prior(PINNED),
|
||||
)
|
||||
.unwrap();
|
||||
h.record_winner(&"player", &"layout", 1).unwrap();
|
||||
h.record_winner(&"player", &"layout", 2).unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
|
||||
let rating = h.rating(&"layout").unwrap();
|
||||
assert_eq!(rating.drift_scale(), 0.0);
|
||||
assert_eq!(rating.prior().mu(), PINNED.mu());
|
||||
|
||||
// Pinned means pinned: no drift across the two slices.
|
||||
let curve = h.learning_curve(&"layout");
|
||||
assert!(curve.len() >= 2);
|
||||
let widest = curve
|
||||
.iter()
|
||||
.map(|(_, g)| g.sigma())
|
||||
.fold(f64::MIN, f64::max);
|
||||
let narrowest = curve
|
||||
.iter()
|
||||
.map(|(_, g)| g.sigma())
|
||||
.fold(f64::MAX, f64::min);
|
||||
assert!(
|
||||
(widest - narrowest) / widest < 1e-9,
|
||||
"{narrowest} .. {widest}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn registering_a_known_competitor_is_an_error() {
|
||||
let mut h = history();
|
||||
h.record_winner(&"player", &"layout", 1).unwrap();
|
||||
let err = h.register(Member::new("layout")).unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::AlreadyRegistered { .. }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn registering_twice_is_an_error() {
|
||||
let mut h = history();
|
||||
h.register(Member::new("layout").with_drift_scale(0.0))
|
||||
.unwrap();
|
||||
let err = h
|
||||
.register(Member::new("layout").with_drift_scale(1.0))
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::AlreadyRegistered { .. }),
|
||||
"{err:?}"
|
||||
);
|
||||
// The first registration stands.
|
||||
assert_eq!(h.rating(&"layout").unwrap().drift_scale(), 0.0);
|
||||
}
|
||||
|
||||
/// `weight` is per-event and meaningless here, so it is rejected rather than
|
||||
/// dropped — dropping it silently is the defect class this whole area keeps
|
||||
/// producing.
|
||||
#[test]
|
||||
fn a_weight_on_a_registration_is_rejected() {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.register(Member::new("layout").with_weight(0.5))
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::InvalidParameter { name: "weight", .. }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn an_invalid_drift_scale_on_a_registration_is_rejected() {
|
||||
for bad in [-1.0, f64::NAN, f64::INFINITY] {
|
||||
let mut h = history();
|
||||
let err = h
|
||||
.register(Member::new("layout").with_drift_scale(bad))
|
||||
.unwrap_err();
|
||||
assert!(
|
||||
matches!(
|
||||
err,
|
||||
InferenceError::InvalidParameter {
|
||||
name: "drift_scale",
|
||||
..
|
||||
}
|
||||
),
|
||||
"{bad}: {err:?}"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/// Registration makes the fit independent of the order events arrive in,
|
||||
/// which is what the per-event shape could not guarantee.
|
||||
#[test]
|
||||
fn registration_makes_the_fit_order_independent() {
|
||||
let build = |reversed: bool| {
|
||||
let mut h = history();
|
||||
h.register(
|
||||
Member::new("layout")
|
||||
.with_drift_scale(0.0)
|
||||
.with_prior(PINNED),
|
||||
)
|
||||
.unwrap();
|
||||
let mut events = vec![
|
||||
duel("player", "layout", 1, None),
|
||||
duel("player", "layout", 2, None),
|
||||
duel("player", "layout", 3, None),
|
||||
];
|
||||
if reversed {
|
||||
events.reverse();
|
||||
}
|
||||
h.add_events(events).unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
h
|
||||
};
|
||||
|
||||
let forward = build(false);
|
||||
let backward = build(true);
|
||||
for ((k, a), (_, b)) in skills(&forward).into_iter().zip(skills(&backward)) {
|
||||
assert_eq!(a.pi(), b.pi(), "{k} pi");
|
||||
assert_eq!(a.tau(), b.tau(), "{k} tau");
|
||||
}
|
||||
}
|
||||
|
||||
/// `rating` is the read-back that made a configuration mistake detectable from
|
||||
/// outside the crate at all. Every other accessor reports what inference
|
||||
/// inferred; this reports what it was told.
|
||||
#[test]
|
||||
fn rating_reads_back_what_was_stored() {
|
||||
let mut h = history();
|
||||
assert!(h.rating(&"nobody").is_none());
|
||||
|
||||
h.register(
|
||||
Member::new("layout")
|
||||
.with_drift_scale(0.25)
|
||||
.with_prior(PINNED),
|
||||
)
|
||||
.unwrap();
|
||||
let r = h.rating(&"layout").unwrap();
|
||||
assert_eq!(r.drift_scale(), 0.25);
|
||||
assert_eq!(r.prior().pi(), PINNED.pi());
|
||||
assert_eq!(r.prior().tau(), PINNED.tau());
|
||||
|
||||
// A competitor created by an event reports the history defaults.
|
||||
h.record_winner(&"player", &"layout", 1).unwrap();
|
||||
assert_eq!(h.rating(&"player").unwrap().drift_scale(), 1.0);
|
||||
}
|
||||
|
||||
/// The decision this issue turned on: two different values for one competitor
|
||||
/// are an error whether they arrive in one batch or two.
|
||||
///
|
||||
/// Last-write-wins across batches cut against the invariant
|
||||
/// `tests/ingestion_equivalence.rs` protects — the same contradictory events
|
||||
/// errored when batched and succeeded, order-dependently, one at a time.
|
||||
mod conflicting_configuration {
|
||||
use super::*;
|
||||
|
||||
fn seed(scale: f64) -> Event<i64, &'static str> {
|
||||
duel(
|
||||
"player",
|
||||
"layout",
|
||||
1,
|
||||
Some(Member::new("layout").with_drift_scale(scale)),
|
||||
)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn within_one_batch_is_an_error() {
|
||||
let mut h = history();
|
||||
let err = h.add_events(vec![seed(0.0), seed(1.0)]).unwrap_err();
|
||||
assert!(
|
||||
matches!(
|
||||
err,
|
||||
InferenceError::ConflictingCompetitorConfig {
|
||||
field: "drift_scale",
|
||||
..
|
||||
}
|
||||
),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn across_two_batches_is_also_an_error() {
|
||||
let mut h = history();
|
||||
h.add_events(vec![seed(0.0)]).unwrap();
|
||||
let err = h.add_events(vec![seed(1.0)]).unwrap_err();
|
||||
assert!(
|
||||
matches!(
|
||||
err,
|
||||
InferenceError::ConflictingCompetitorConfig {
|
||||
field: "drift_scale",
|
||||
..
|
||||
}
|
||||
),
|
||||
"{err:?}"
|
||||
);
|
||||
// Rejected before anything mutates: the first declaration stands.
|
||||
assert_eq!(h.rating(&"layout").unwrap().drift_scale(), 0.0);
|
||||
}
|
||||
|
||||
/// Repeating the *same* value stays inert, which is the expected shape
|
||||
/// when the configuration is a property of the domain.
|
||||
#[test]
|
||||
fn repeating_the_same_value_is_inert() {
|
||||
let mut h = history();
|
||||
h.add_events(vec![seed(0.0)]).unwrap();
|
||||
h.add_events(vec![seed(0.0)]).unwrap();
|
||||
assert_eq!(h.rating(&"layout").unwrap().drift_scale(), 0.0);
|
||||
}
|
||||
|
||||
/// A registration and a later event that agree are fine; one that
|
||||
/// disagrees is the same error.
|
||||
#[test]
|
||||
fn a_registration_conflicts_with_a_later_event() {
|
||||
let mut h = history();
|
||||
h.register(Member::new("layout").with_drift_scale(0.0))
|
||||
.unwrap();
|
||||
h.add_events(vec![seed(0.0)]).unwrap();
|
||||
|
||||
let mut h2 = history();
|
||||
h2.register(Member::new("layout").with_drift_scale(0.0))
|
||||
.unwrap();
|
||||
let err = h2.add_events(vec![seed(1.0)]).unwrap_err();
|
||||
assert!(
|
||||
matches!(err, InferenceError::ConflictingCompetitorConfig { .. }),
|
||||
"{err:?}"
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -184,3 +184,74 @@ fn ingestion_rejects_weights_that_do_not_match_their_team() {
|
||||
"got {err:?}"
|
||||
);
|
||||
}
|
||||
|
||||
/// `mu`, `sigma` and `beta` were the last unvalidated setters on
|
||||
/// `HistoryBuilder`, next to `p_draw`, `score_sigma` and `convergence`, which
|
||||
/// all assert eagerly.
|
||||
///
|
||||
/// Two of the rejected values are the quiet kind. A negative `sigma` or `beta`
|
||||
/// enters inference only as its square, so it produced bit-identical results
|
||||
/// to the positive value — the sign was dropped without comment.
|
||||
mod builder_parameters {
|
||||
use trueskill_tt::History;
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "mu must be finite")]
|
||||
fn a_non_finite_mu_is_rejected() {
|
||||
let _ = History::builder().mu(f64::NAN);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "sigma must be finite and positive")]
|
||||
fn a_zero_sigma_is_rejected() {
|
||||
let _ = History::builder().sigma(0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "sigma must be finite and positive")]
|
||||
fn a_negative_sigma_is_rejected() {
|
||||
let _ = History::builder().sigma(-8.33);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "sigma must be finite and positive")]
|
||||
fn an_infinite_sigma_is_rejected() {
|
||||
let _ = History::builder().sigma(f64::INFINITY);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "beta must be finite and non-negative")]
|
||||
fn a_negative_beta_is_rejected() {
|
||||
let _ = History::builder().beta(-4.17);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "beta must be finite and non-negative")]
|
||||
fn a_non_finite_beta_is_rejected() {
|
||||
let _ = History::builder().beta(f64::NAN);
|
||||
}
|
||||
|
||||
/// Zero beta is deliberately allowed: performance is then exactly skill.
|
||||
/// It has to reach a different fit than a positive beta, or "allowed"
|
||||
/// would just mean "not checked".
|
||||
#[test]
|
||||
fn a_zero_beta_is_allowed_and_changes_the_fit() {
|
||||
let fit = |beta: f64| {
|
||||
let mut h = History::builder()
|
||||
.mu(25.0)
|
||||
.sigma(25.0 / 3.0)
|
||||
.beta(beta)
|
||||
.build();
|
||||
h.record_winner(&"a", &"b", 1).unwrap();
|
||||
let _ = h.converge().unwrap();
|
||||
h.current_skill(&"a").unwrap()
|
||||
};
|
||||
let zero = fit(0.0);
|
||||
let positive = fit(25.0 / 6.0);
|
||||
assert!(zero.pi().is_finite() && zero.pi() > 0.0);
|
||||
assert!(
|
||||
(zero.pi() - positive.pi()).abs() > 1e-6,
|
||||
"zero beta must not merely be ignored: {zero:?} vs {positive:?}"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user