logaritmiskandClaude Opus 5
e4a68ba1a7
Two accessors answered a question about a key the history had never seen with a well-formed value indistinguishable from a real answer. `log_evidence_for` filter_map'd unknown keys away. An empty target list means "no restriction" downstream, so a list of *entirely* unknown keys returned the whole-history evidence: measured on a two-cohort fixture, `log_evidence_for(["typo"])` returned exactly `log_evidence()`. On the one workload it is documented for — leave-one-out cross-validation — that is the un-held-out score, a plausible number that silently invalidates the comparison it was computed for. It now returns `Err(UnknownKey)` naming the offending position. `learning_curve` and `filtered_learning_curve` returned an empty `Vec` both for a typo'd key and for a competitor who is registered but has not played yet. They now return `Option`, so `None` is "never heard of it" and `Some(vec![])` is "known, no appearances". Tests carry a control case in each direction, so they cannot pass by everything returning the same thing. Closes #66, closes #70. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
297 lines
7.8 KiB
Rust
297 lines
7.8 KiB
Rust
//! Tests for the new T2 public API surface: typed add_events(iter) and the
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//! fluent event builder (added in Task 16).
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use smallvec::smallvec;
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use trueskill_tt::{ConstantDrift, ConvergenceOptions, Event, History, Member, Outcome, Team};
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#[test]
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fn add_events_bulk_via_iter() {
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let mut h = History::builder()
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.mu(0.0)
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.sigma(2.0)
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.beta(1.0)
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.p_draw(0.0)
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.drift(ConstantDrift::new(0.0))
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.convergence(ConvergenceOptions {
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max_iter: 30,
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epsilon: 1e-6,
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alpha: 1.0,
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})
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.build();
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let events: Vec<Event<i64, &'static str>> = vec![
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Event {
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time: 1,
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teams: smallvec![
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Team::with_members([Member::new("a")]),
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Team::with_members([Member::new("b")]),
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],
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outcome: Outcome::winner(0, 2),
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},
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Event {
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time: 2,
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teams: smallvec![
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Team::with_members([Member::new("b")]),
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Team::with_members([Member::new("c")]),
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],
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outcome: Outcome::winner(0, 2),
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},
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];
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h.add_events(events).unwrap();
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let report = h.converge().unwrap();
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assert!(report.converged);
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assert!(h.lookup(&"a").is_some());
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assert!(h.lookup(&"b").is_some());
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assert!(h.lookup(&"c").is_some());
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}
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#[test]
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fn add_events_draw() {
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.25)
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.drift(ConstantDrift::new(25.0 / 300.0))
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.build();
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let events: Vec<Event<i64, &'static str>> = vec![Event {
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time: 1,
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teams: smallvec![
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Team::with_members([Member::new("alice")]),
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Team::with_members([Member::new("bob")]),
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],
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outcome: Outcome::draw(2),
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}];
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h.add_events(events).unwrap();
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let _ = h.converge().unwrap();
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}
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#[test]
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fn add_events_rejects_mismatched_outcome_ranks() {
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use trueskill_tt::InferenceError;
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let mut h: History = History::builder().build();
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let events: Vec<Event<i64, &'static str>> = vec![Event {
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time: 1,
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teams: smallvec![
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Team::with_members([Member::new("a")]),
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Team::with_members([Member::new("b")]),
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],
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outcome: Outcome::ranking([0, 1, 2]), // 3 ranks but 2 teams
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}];
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let err = h.add_events(events).unwrap_err();
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assert!(matches!(err, InferenceError::MismatchedShape { .. }));
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}
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#[test]
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fn fluent_event_builder_basic() {
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.0)
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.build();
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h.event(1)
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.team(["alice", "bob"])
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.weights([1.0, 0.7])
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.team(["carol"])
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.ranking([1, 0])
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.commit()
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.unwrap();
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let report = h.converge().unwrap();
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assert!(report.converged);
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assert!(h.lookup(&"alice").is_some());
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assert!(h.lookup(&"bob").is_some());
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assert!(h.lookup(&"carol").is_some());
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}
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#[test]
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fn fluent_event_builder_winner_convenience() {
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.0)
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.build();
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h.event(1)
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.team(["alice"])
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.team(["bob"])
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.winner(0)
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.commit()
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.unwrap();
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let _ = h.converge().unwrap();
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}
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#[test]
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fn fluent_event_builder_draw() {
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.25)
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.build();
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h.event(1)
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.team(["alice"])
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.team(["bob"])
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.draw()
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.commit()
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.unwrap();
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let _ = h.converge().unwrap();
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}
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#[test]
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fn current_skill_and_learning_curve() {
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use trueskill_tt::History;
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.0)
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.build();
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h.record_winner(&"a", &"b", 1).unwrap();
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h.record_winner(&"a", &"b", 2).unwrap();
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let _ = h.converge().unwrap();
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let a = h.current_skill(&"a").unwrap();
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assert!(a.mu() > 25.0);
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let b = h.current_skill(&"b").unwrap();
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assert!(b.mu() < 25.0);
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let a_curve = h.learning_curve(&"a").unwrap();
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assert_eq!(a_curve.len(), 2);
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assert_eq!(a_curve[0].0, 1);
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assert_eq!(a_curve[1].0, 2);
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let all = h.learning_curves();
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assert_eq!(all.len(), 2);
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assert!(all.contains_key("a"));
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assert!(all.contains_key("b"));
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}
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#[test]
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fn log_evidence_total_vs_subset() {
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use trueskill_tt::{ConstantDrift, History};
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let mut h = History::builder()
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.mu(0.0)
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.sigma(6.0)
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.beta(1.0)
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.p_draw(0.0)
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.drift(ConstantDrift::new(0.0))
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.build();
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h.record_winner(&"a", &"b", 1).unwrap();
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h.record_winner(&"b", &"a", 2).unwrap();
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let total = h.log_evidence();
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let a_only = h.log_evidence_for(&[&"a"]).unwrap();
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assert!(total.is_finite());
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assert!(a_only.is_finite());
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}
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#[test]
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fn predict_quality_two_teams() {
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use trueskill_tt::History;
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.0)
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.build();
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h.record_winner(&"a", &"b", 1).unwrap();
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let _ = h.converge().unwrap();
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let q = h.predict_quality(&[&[&"a"], &[&"b"]]).unwrap();
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assert!(q > 0.0 && q <= 1.0);
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}
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#[test]
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fn predict_outcome_two_teams_sums_to_one() {
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use trueskill_tt::History;
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.p_draw(0.0)
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.build();
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h.record_winner(&"a", &"b", 1).unwrap();
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let _ = h.converge().unwrap();
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let p = h.predict_outcome(&[&[&"a"], &[&"b"]]).unwrap();
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let wins = p.win_probabilities();
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assert_eq!(wins.len(), 2);
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// With p_draw == 0 there is no draw outcome, so the two win
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// probabilities are the whole space.
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assert!((p.total() - 1.0).abs() < 1e-9, "total = {}", p.total());
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assert!((wins[0] + wins[1] - 1.0).abs() < 1e-9);
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assert!(wins[0] > wins[1]);
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}
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#[test]
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fn fluent_event_builder_scores() {
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use trueskill_tt::ConstantDrift;
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let mut h = History::builder()
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.mu(25.0)
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.sigma(25.0 / 3.0)
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.beta(25.0 / 6.0)
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.drift(ConstantDrift::new(0.0))
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.build();
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h.event(1)
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.team(["alice"])
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.team(["bob"])
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.scores([12.0, 4.0])
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.commit()
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.unwrap();
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let _ = h.converge().unwrap();
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let a = h.current_skill(&"alice").unwrap();
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let b = h.current_skill(&"bob").unwrap();
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assert!(a.mu() > b.mu());
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}
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/// Every field of `ConvergenceReport` must carry real information.
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///
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/// `slices_skipped` was public, hardcoded to `0`, and reported a plausible
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/// value for a feature that never existed — the same shape as the inert
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/// `online` flag in #19. It was removed in #33. This pins the remaining fields
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/// so the next always-constant member has to survive an assertion rather than
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/// just a reviewer's attention.
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#[test]
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fn every_convergence_report_field_is_populated() {
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let mut h = History::builder().build();
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for time in 1..=6i64 {
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h.record_winner(&"a", &"b", time).unwrap();
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}
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let report = h.converge().unwrap();
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assert!(
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report.iterations > 0,
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"iterations is zero on a real converge"
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);
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assert!(report.converged, "fixture must converge");
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assert!(
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report.final_step.0.is_finite() && report.final_step.1.is_finite(),
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"final_step is not finite: {:?}",
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report.final_step
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);
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assert!(
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report.log_evidence.is_finite() && report.log_evidence < 0.0,
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"log_evidence is not a finite negative log probability: {}",
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report.log_evidence
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);
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assert_eq!(
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report.per_iteration_time.len(),
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report.iterations,
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"per_iteration_time must carry one duration per iteration"
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);
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}
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