Merge feat/evidence-matrix: the missing evidence corner (#70)

src/history.rs

@@ -1,6 +1,6 @@
 use std::{
     borrow::Borrow,
-    collections::{BTreeMap, HashMap},
+    collections::{BTreeMap, HashMap, HashSet},
     hash::Hash,
     marker::PhantomData,
 };
@@ -744,6 +744,35 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             .find_map(|ts| ts.skills.get(idx).map(|sk| sk.posterior()))
     }
 
+    /// Every competitor's latest posterior, keyed by user-facing key.
+    ///
+    /// The plural of `current_skill`, and the cheap way to build a
+    /// leaderboard: the alternative was materialising every competitor's full
+    /// smoothed curve via `learning_curves` only to read the last point of
+    /// each.
+    ///
+    /// A competitor that is registered but has never played has no posterior
+    /// and is absent from the map, exactly as `current_skill` returns `None`
+    /// for them.
+    #[must_use]
+    pub fn current_skills(&self) -> HashMap<K, Gaussian> {
+        let mut latest: HashMap<Index, Gaussian> = HashMap::new();
+
+        // Time order, so a later slice overwrites an earlier one.
+        for slice in &self.time_slices {
+            for (competitor, skill) in slice.skills.iter() {
+                latest.insert(competitor, skill.posterior());
+            }
+        }
+
+        latest
+            .into_iter()
+            .filter_map(|(competitor, posterior)| {
+                self.keys.key(competitor).cloned().map(|k| (k, posterior))
+            })
+            .collect()
+    }
+
     /// Learning curve for a single key: (time, posterior) pairs in time order.
     ///
     /// `None` if the history has never seen the key; `Some(vec![])` if it is
@@ -778,7 +807,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     pub fn filtered_learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
         let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
 
-        for (time, step) in self.filtered_pass() {
+        for (time, step) in self.filtered_pass(&HashSet::new()) {
             for (competitor, posterior) in step.posteriors {
                 if let Some(key) = self.keys.key(competitor).cloned() {
                     data.entry(key).or_default().push((time, posterior));
@@ -808,7 +837,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         let idx = self.keys.get(key)?;
 
         Some(
-            self.filtered_pass()
+            self.filtered_pass(&HashSet::new())
                 .into_iter()
                 .filter_map(|(time, step)| {
                     step.posteriors
@@ -879,7 +908,20 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         K: std::borrow::Borrow<Q>,
         Q: std::hash::Hash + Eq + ?Sized + std::fmt::Debug,
     {
-        let mut targets: Vec<Index> = Vec::with_capacity(keys.len());
+        let targets: Vec<Index> = self.resolve_targets(keys)?.into_iter().collect();
+        Ok(self.log_evidence_internal(false, &targets))
+    }
+
+    /// Intern a key list, refusing the whole list if any key is unknown.
+    ///
+    /// Shared by the two `*_for` evidence accessors so they cannot drift apart
+    /// on how an unknown key is reported.
+    fn resolve_targets<Q>(&self, keys: &[&Q]) -> Result<HashSet<Index>, InferenceError>
+    where
+        K: Borrow<Q>,
+        Q: Hash + Eq + ?Sized + std::fmt::Debug,
+    {
+        let mut targets = HashSet::with_capacity(keys.len());
         for (member, key) in keys.iter().enumerate() {
             let idx = self
                 .keys
@@ -889,22 +931,25 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                     member,
                     key: format!("{key:?}"),
                 })?;
-            targets.push(idx);
+            targets.insert(idx);
         }
-        Ok(self.log_evidence_internal(false, &targets))
+        Ok(targets)
     }
 
     /// Walk the slices in time order carrying forward messages only.
     ///
     /// This is the forward half of `iteration` with the backward half never
     /// run. It reads `self` and mutates nothing.
-    fn filtered_pass(&self) -> Vec<(T, FilteredStep)> {
+    ///
+    /// `targets` restricts each step's evidence sum; the messages carried
+    /// forward are unaffected. See `TimeSlice::filtered_step`.
+    fn filtered_pass(&self, targets: &HashSet<Index>) -> Vec<(T, FilteredStep)> {
         let mut messages: HashMap<Index, Gaussian> = HashMap::new();
 
         let mut pass = Vec::with_capacity(self.time_slices.len());
 
         for slice in &self.time_slices {
-            let step = slice.filtered_step(&messages, &self.competitors);
+            let step = slice.filtered_step(&messages, &self.competitors, targets);
 
             for &(competitor, posterior) in &step.posteriors {
                 messages.insert(competitor, posterior);
@@ -930,12 +975,47 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// not depend on whether `converge` has been called.
     #[must_use]
     pub fn filtered_log_evidence(&self) -> f64 {
-        self.filtered_pass()
+        self.filtered_pass(&HashSet::new())
             .iter()
             .map(|(_, step)| step.log_evidence)
             .sum()
     }
 
+    /// Filtered log-evidence restricted to events involving at least one of
+    /// the given keys.
+    ///
+    /// The intersection of the two axes the other three evidence accessors
+    /// span: forward-only *and* key-restricted, which is what per-competitor
+    /// prequential scoring needs. `log_evidence_for` is the smoothed
+    /// counterpart, and its per-event priors carry information from events
+    /// that had not happened yet — so it is not the quantity for scoring a
+    /// competitor's history *as it unfolded*.
+    ///
+    /// Restricting filters which events are *scored*, not which are *run*: the
+    /// forward messages still absorb every event, so this is a held-out score
+    /// under the real history, not a score under a counterfactual one where
+    /// nobody else played.
+    ///
+    /// Runs a full forward pass per call and caches nothing.
+    ///
+    /// # Errors
+    ///
+    /// `UnknownKey` for any key the history has never seen — see
+    /// `log_evidence_for` for why an unknown key must not be skipped.
+    pub fn filtered_log_evidence_for<Q>(&self, keys: &[&Q]) -> Result<f64, InferenceError>
+    where
+        K: Borrow<Q>,
+        Q: Hash + Eq + ?Sized + std::fmt::Debug,
+    {
+        let targets = self.resolve_targets(keys)?;
+
+        Ok(self
+            .filtered_pass(&targets)
+            .iter()
+            .map(|(_, step)| step.log_evidence)
+            .sum())
+    }
+
     /// The configured observer.
     ///
     /// `History` takes its observer by value, so this is how a caller inspects

src/time_slice.rs

@@ -615,10 +615,18 @@ impl<T: Time> TimeSlice<T> {
     /// configured prior. The sweep runs on a scratch copy, so the real slice
     /// is untouched — which is what makes the filtered estimates independent
     /// of whether `History::converge` has run.
+    /// One forward-only step for this slice.
+    ///
+    /// `targets` restricts only the *evidence sum*, to events in which at
+    /// least one target competitor appears; an empty set means no restriction.
+    /// The forward messages are always built from every event in the slice —
+    /// restricting those instead would answer a different question (a history
+    /// in which the other events never happened), not a held-out one.
     pub(crate) fn filtered_step<D: Drift<T>>(
         &self,
         incoming: &HashMap<Index, Gaussian>,
         competitors: &CompetitorStore<T, D>,
+        targets: &std::collections::HashSet<Index>,
     ) -> FilteredStep {
         let mut scratch = TimeSlice {
             events: self.events.clone(),
@@ -674,7 +682,19 @@ impl<T: Time> TimeSlice<T> {
         scratch.iterate_to_convergence(competitors);
 
         FilteredStep {
-            log_evidence: scratch.events.iter().map(|event| event.log_evidence).sum(),
+            log_evidence: scratch
+                .events
+                .iter()
+                .filter(|event| {
+                    targets.is_empty()
+                        || event
+                            .teams
+                            .iter()
+                            .flat_map(|team| &team.items)
+                            .any(|item| targets.contains(&item.competitor))
+                })
+                .map(|event| event.log_evidence)
+                .sum(),
             posteriors: scratch
                 .skills
                 .iter()

tests/evidence_matrix.rs

@@ -0,0 +1,151 @@
+//! The evidence accessors span two independent axes — smoothed vs forward-only,
+//! all-keys vs key-restricted — and all four corners must exist and differ.
+//!
+//! `filtered_log_evidence_for` was the missing corner: the one a per-competitor
+//! prequential score needs.
+
+use trueskill_tt::{
+    ConstantDrift, Event, History, InferenceError, Member, NullObserver, Outcome, Team,
+};
+
+type H = History<i64, ConstantDrift, NullObserver, &'static str>;
+
+/// Two disjoint cohorts, so a key restriction is guaranteed to leave events out.
+fn two_cohorts() -> H {
+    let mut h = H::default();
+    let mut events = Vec::new();
+    for t in 1..=6 {
+        for (x, y) in [("a", "b"), ("c", "d")] {
+            events.push(Event {
+                time: t,
+                teams: [
+                    Team::with_members([Member::new(x)]),
+                    Team::with_members([Member::new(y)]),
+                ]
+                .into_iter()
+                .collect(),
+                outcome: Outcome::winner(0, 2),
+            });
+        }
+    }
+    h.add_events(events).expect("fixture ingests");
+    h.converge().expect("fixture converges");
+    h
+}
+
+#[test]
+fn all_four_corners_are_distinct_quantities() {
+    let h = two_cohorts();
+
+    let smoothed_all = h.log_evidence();
+    let smoothed_ab = h.log_evidence_for(&[&"a", &"b"]).unwrap();
+    let filtered_all = h.filtered_log_evidence();
+    let filtered_ab = h.filtered_log_evidence_for(&[&"a", &"b"]).unwrap();
+
+    for (name, v) in [
+        ("smoothed_all", smoothed_all),
+        ("smoothed_ab", smoothed_ab),
+        ("filtered_all", filtered_all),
+        ("filtered_ab", filtered_ab),
+    ] {
+        assert!(
+            v.is_finite() && v <= 0.0,
+            "{name} = {v} is not a log probability"
+        );
+    }
+
+    // Restricting to one cohort must drop the other cohort's events. Half the
+    // events, and the two cohorts are symmetric, so it lands near half.
+    assert!(
+        smoothed_ab > smoothed_all,
+        "restricting must drop evidence terms: {smoothed_ab} vs {smoothed_all}"
+    );
+    assert!(filtered_ab > filtered_all);
+
+    // The forward-only corner is a genuinely different quantity from the
+    // smoothed one, not an alias for it.
+    assert!(
+        (filtered_ab - smoothed_ab).abs() > 1e-9,
+        "filtered and smoothed restricted evidence coincide ({filtered_ab} vs {smoothed_ab}); \
+         one of them is not computing what it claims"
+    );
+}
+
+#[test]
+fn restricting_to_both_cohorts_recovers_the_unrestricted_value() {
+    let h = two_cohorts();
+
+    // Control on the filter itself: naming every competitor must restrict
+    // nothing, so this catches a filter that drops events it should keep.
+    let all_named = h
+        .filtered_log_evidence_for(&[&"a", &"b", &"c", &"d"])
+        .unwrap();
+    assert!(
+        (all_named - h.filtered_log_evidence()).abs() < 1e-12,
+        "naming everyone changed the answer: {all_named} vs {}",
+        h.filtered_log_evidence()
+    );
+}
+
+/// The restriction selects *events*, not competitors: naming one member of a
+/// pair that only ever plays each other selects the same events as naming both.
+#[test]
+fn naming_either_member_of_a_pair_selects_the_same_events() {
+    let h = two_cohorts();
+
+    let ab = h.filtered_log_evidence_for(&[&"a"]).unwrap();
+    let ab_pair = h.filtered_log_evidence_for(&[&"a", &"b"]).unwrap();
+    assert!(
+        (ab - ab_pair).abs() < 1e-12,
+        "a and b only ever play each other, so naming either or both selects \
+         the same events: {ab} vs {ab_pair}"
+    );
+}
+
+#[test]
+fn an_unknown_key_is_an_error_here_too() {
+    let h = two_cohorts();
+
+    let err = h
+        .filtered_log_evidence_for(&[&"typo"])
+        .expect_err("unknown key");
+    assert!(matches!(err, InferenceError::UnknownKey { .. }), "{err:?}");
+
+    // Control: the same call on a known key succeeds.
+    h.filtered_log_evidence_for(&[&"a"]).expect("a is known");
+}
+
+#[test]
+fn current_skills_agrees_with_current_skill() {
+    let h = two_cohorts();
+
+    let all = h.current_skills();
+    assert_eq!(all.len(), 4, "four competitors played");
+
+    for key in ["a", "b", "c", "d"] {
+        let one = h.current_skill(key).expect("played");
+        let from_map = all[key];
+        assert_eq!(
+            (one.mu(), one.sigma()),
+            (from_map.mu(), from_map.sigma()),
+            "current_skills disagrees with current_skill for {key}"
+        );
+    }
+}
+
+#[test]
+fn current_skills_omits_a_registered_but_unplayed_competitor() {
+    let mut h = two_cohorts();
+    h.register(Member::new("e")).expect("e is new");
+
+    let all = h.current_skills();
+    assert!(
+        !all.contains_key("e"),
+        "a competitor with no appearances has no posterior to report"
+    );
+    assert!(
+        h.current_skill("e").is_none(),
+        "control: the singular agrees"
+    );
+    assert_eq!(all.len(), 4);
+}