feat: add the missing trait impls and make #[must_use] consistent

Trait coverage (#76), all additive:

  History          Debug is still absent - see below
  HistoryBuilder   + Debug   (it derived Clone but not Debug)
  Rating           + PartialEq  (Gaussian had it; Rating is a Gaussian
                                 plus three scalars and had none)
  Event/Team/Member + PartialEq (input value types with no way to compare
                                 them, which made round-trip tests awkward)
  ConvergenceReport + PartialEq

`#[must_use]` (#67). The coverage had no rule: `filtered_log_evidence`
had it and `log_evidence` did not; `rating` had it and `current_skill`
did not; `Rating::with_drift_scale` had it and `Member::with_drift_scale`
did not.

Now on the types — `EventBuilder`, `HistoryBuilder`, `Prediction`,
`Gaussian`, `OwnedGame` — which covers most method returns at once, plus
the `History` accessors individually.

`EventBuilder` gets a message, because a dropped builder is the worst
case in the set: measured, `h.event(1).team(["x"]).team(["y"]).winner(0)`
without `.commit()` leaves `time_slices_len() == 0` and every skill
`None`, with no warning at all.

And `ConvergenceReport`'s `#[must_use]` moves off the TYPE onto
`converge_partial`, where its stated reason is true. It read "from
`converge_partial` this may describe a fit that stopped at max_iter" but
fired on `converge` too — where that is false, since `converge` returns
`Err(NotConverged)` in exactly that case. So the crate's own front-page
example warned, and every quickstart had to write `let _ =`. Verified
from a consumer crate: `h.converge()?;` now compiles clean.

Marking the types made eight method-level attributes redundant, which
clippy's `double_must_use` caught — that is the type-level marker doing
its job, and the eight are removed.

Refs #76, #67

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ

src/convergence.rs

@@ -68,11 +68,7 @@ impl Default for ConvergenceOptions {
 /// [`InferenceError::NotConverged`](crate::InferenceError::NotConverged) there.
 /// From [`History::converge_partial`](crate::History::converge_partial) it may
 /// not be, and `converged` is what says so.
-#[derive(Clone, Debug)]
-#[must_use = "from `converge_partial` this may describe a fit that stopped at \
-              `max_iter`, which is wrong by a little rather than loudly \
-              broken — check `converged`, or bind it to `_` to say you have \
-              decided not to"]
+#[derive(Clone, Debug, PartialEq)]
 pub struct ConvergenceReport {
     pub iterations: usize,
     pub final_step: (f64, f64),

src/event.rs

@@ -11,7 +11,7 @@ use smallvec::SmallVec;
 use crate::{gaussian::Gaussian, outcome::Outcome, time::Time};
 
 /// A single match at time `time` involving some number of teams.
-#[derive(Clone, Debug)]
+#[derive(Clone, Debug, PartialEq)]
 pub struct Event<T: Time, K> {
     pub time: T,
     pub teams: SmallVec<[Team<K>; 4]>,
@@ -19,7 +19,7 @@ pub struct Event<T: Time, K> {
 }
 
 /// A team: list of members competing together.
-#[derive(Clone, Debug)]
+#[derive(Clone, Debug, PartialEq)]
 pub struct Team<K> {
     pub members: SmallVec<[Member<K>; 4]>,
 }
@@ -61,7 +61,7 @@ impl<K> Default for Team<K> {
 /// for one competitor within a single batch is
 /// `InferenceError::ConflictingCompetitorConfig`: events in a batch have no
 /// order, so there would be no well-defined winner.
-#[derive(Clone, Debug)]
+#[derive(Clone, Debug, PartialEq)]
 pub struct Member<K> {
     pub key: K,
     pub weight: f64,

src/event_builder.rs

@@ -9,6 +9,8 @@ use crate::{
     time::Time,
 };
 
+#[must_use = "an event is only recorded by `.commit()`; a dropped builder \
+              silently ingests nothing"]
 pub struct EventBuilder<'h, T, D, O, K>
 where
     T: Time,

src/factor/mod.rs

@@ -44,7 +44,6 @@ impl VarStore {
         id
     }
 
-    #[must_use]
     pub fn get(&self, id: VarId) -> Gaussian {
         self.marginals[id.0 as usize]
     }

src/game.rs

@@ -92,6 +92,7 @@ impl Default for GameOptions {
 /// can be returned freely from public constructors. The inference inputs
 /// themselves are not retained — nothing reads them back.
 #[derive(Debug)]
+#[must_use]
 pub struct OwnedGame<T: Time, D: Drift<T>> {
     teams: Vec<Vec<Rating<T, D>>>,
     pub(crate) likelihoods: Vec<Vec<Gaussian>>,

src/gaussian.rs

@@ -11,6 +11,7 @@ use crate::{MU, N_INF, SIGMA};
 /// the stored fields with no `sqrt` or reciprocal in the hot path. `mu()` and
 /// `sigma()` are accessors computed on demand.
 #[derive(Clone, Copy, PartialEq, Debug)]
+#[must_use]
 pub struct Gaussian {
     pi: f64,
     tau: f64,
@@ -44,7 +45,6 @@ impl Gaussian {
     /// small truncated sigma and inference must not panic. It is worth knowing
     /// that such a `Gaussian` is not equal to itself, so two identical
     /// declarations of one can be reported as conflicting.
-    #[must_use]
     pub const fn from_ms(mu: f64, sigma: f64) -> Self {
         // NaN is admitted on purpose. A broken fit legitimately produces a NaN
         // sigma — `sqrt` of a negative truncated variance — and the design is
@@ -243,7 +243,6 @@ impl Gaussian {
     /// Used by within-game inference to stabilise oscillating fixed-point
     /// loops on hard graphs. `alpha = 1.0` returns `new` exactly;
     /// `alpha < 1.0` shrinks each per-step update.
-    #[must_use]
     pub fn damp_natural(self, new: Gaussian, alpha: f64) -> Gaussian {
         Gaussian::from_natural(
             alpha * new.pi() + (1.0 - alpha) * self.pi(),

src/history.rs

@@ -24,7 +24,8 @@ use crate::{
     tuple_gt, tuple_max,
 };
 
-#[derive(Clone)]
+#[derive(Clone, Debug)]
+#[must_use = "a builder does nothing until `.build()`"]
 pub struct HistoryBuilder<
     T: Time = i64,
     D: Drift<T> = ConstantDrift,
@@ -197,7 +198,6 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> HistoryBuilder<
     /// assert!(h.current_skill(&"alice").unwrap().mu() > 0.0);
     /// # Ok::<(), trueskill_tt::InferenceError>(())
     /// ```
-    #[must_use]
     pub fn time_type<T2>(self) -> HistoryBuilder<T2, D, O, K>
     where
         T2: Time,
@@ -232,7 +232,6 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> HistoryBuilder<
     /// h.record_winner(&"alice".to_string(), &"bob".to_string(), 1)?;
     /// # Ok::<(), trueskill_tt::InferenceError>(())
     /// ```
-    #[must_use]
     pub fn key_type<K2: Eq + Hash + Clone>(self) -> HistoryBuilder<T, D, O, K2> {
         HistoryBuilder {
             mu: self.mu,
@@ -418,7 +417,6 @@ impl Default for History<i64, ConstantDrift, NullObserver, &'static str> {
 }
 
 impl History<i64, ConstantDrift, NullObserver, &'static str> {
-    #[must_use]
     pub fn builder() -> HistoryBuilder<i64, ConstantDrift, NullObserver, &'static str> {
         HistoryBuilder::default()
     }
@@ -440,7 +438,6 @@ impl<T: Time, K: Eq + Hash + Clone> HistoryBuilder<T, ConstantDrift, NullObserve
     /// h.converge()?;
     /// # Ok::<(), trueskill_tt::InferenceError>(())
     /// ```
-    #[must_use]
     pub fn new() -> Self {
         Self::default()
     }
@@ -455,6 +452,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         self.keys.get_or_create(key)
     }
 
+    #[must_use]
     pub fn lookup<Q>(&self, key: &Q) -> Option<Index>
     where
         K: Borrow<Q>,
@@ -563,6 +561,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// assert_eq!(names, ["alice", "bob"]);
     /// # Ok::<(), trueskill_tt::InferenceError>(())
     /// ```
+    #[must_use]
     pub fn competitors(&self) -> impl ExactSizeIterator<Item = &K> {
         self.keys.keys()
     }
@@ -580,6 +579,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Learning curves for all competitors, keyed by their user-facing key.
+    #[must_use]
     pub fn learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
         #[cfg(feature = "rayon")]
         {
@@ -728,6 +728,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         self.agents.contains(idx).then(|| self.agents[idx].rating)
     }
 
+    #[must_use]
     pub fn current_skill<Q>(&self, key: &Q) -> Option<Gaussian>
     where
         K: std::borrow::Borrow<Q>,
@@ -741,6 +742,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Learning curve for a single key: (time, posterior) pairs in time order.
+    #[must_use]
     pub fn learning_curve<Q>(&self, key: &Q) -> Vec<(T, Gaussian)>
     where
         K: std::borrow::Borrow<Q>,
@@ -764,6 +766,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// Runs a full forward pass per call and caches nothing. This is the
     /// entry point for multi-key work — see `filtered_learning_curve` for
     /// why calling that once per key is far more expensive.
+    #[must_use]
     pub fn filtered_learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
         let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
 
@@ -786,6 +789,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// discarding every posterior but the requested key's. N keys fetched
     /// this way costs O(N * events); use `filtered_learning_curves` for
     /// multi-key work instead — it computes the same pass once.
+    #[must_use]
     pub fn filtered_learning_curve<Q>(&self, key: &Q) -> Vec<(T, Gaussian)>
     where
         K: Borrow<Q>,
@@ -841,12 +845,14 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Total log-evidence across the history.
+    #[must_use]
     pub fn log_evidence(&self) -> f64 {
         self.log_evidence_internal(false, &[])
     }
 
     /// Log-evidence restricted to time slices containing at least one of the
     /// given keys. Useful for leave-one-out cross-validation.
+    #[must_use]
     pub fn log_evidence_for<Q>(&self, keys: &[&Q]) -> f64
     where
         K: std::borrow::Borrow<Q>,
@@ -1759,6 +1765,8 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// # Errors
     ///
     /// `NonFiniteResult` if a sweep produces a NaN or infinite step.
+    #[must_use = "this fit may have stopped at `max_iter` — check `converged`, \
+                  or bind it to `_` to say you have decided not to"]
     pub fn converge_partial(&mut self) -> Result<ConvergenceReport, InferenceError> {
         use std::time::Instant;
 

src/lib.rs

@@ -104,12 +104,10 @@ use std::{
     f64::consts::{FRAC_1_SQRT_2, FRAC_2_SQRT_PI, SQRT_2},
 };
 
+mod acquisition;
 #[cfg(feature = "approx")]
 mod approx;
 pub(crate) mod arena;
-mod time;
-mod time_slice;
-mod acquisition;
 mod color_group;
 mod competitor;
 mod convergence;
@@ -130,6 +128,8 @@ mod predict;
 pub(crate) mod quadrature;
 mod rating;
 pub(crate) mod storage;
+mod time;
+mod time_slice;
 
 pub use acquisition::expected_information_gain;
 pub use convergence::{ConvergenceOptions, ConvergenceReport};

src/predict.rs

@@ -456,6 +456,7 @@ pub(crate) fn ranking_probability(
 /// `Game::ranked` asks "what would we believe if *this* happened", which is
 /// what an expected-information-gain calculation needs alongside the weight.
 #[derive(Clone, Debug, PartialEq)]
+#[must_use]
 pub struct Prediction {
     outcomes: Vec<(Vec<u32>, f64)>,
 }

src/rating.rs

@@ -11,7 +11,7 @@ use crate::{
 ///
 /// A configuration rather than a person: the per-history temporal state
 /// (messages, last appearance) lives on `Competitor`.
-#[derive(Clone, Copy, Debug)]
+#[derive(Clone, Copy, Debug, PartialEq)]
 pub struct Rating<T: Time = i64, D: Drift<T> = ConstantDrift> {
     pub(crate) prior: Gaussian,
     pub(crate) beta: f64,
@@ -61,7 +61,6 @@ impl<T: Time, D: Drift<T>> Rating<T, D> {
     }
 
     /// The configured prior skill estimate.
-    #[must_use]
     pub fn prior(&self) -> Gaussian {
         self.prior
     }