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test: translate in-crate tests to new T2 API; delete legacy methods

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Every #[cfg(test)] mod tests in src/history.rs now uses the new public
API: add_events(iter) / converge() / learning_curve() / current_skill()
/ log_evidence(). No golden value changed.

Legacy methods removed:
- History::convergence(iters, eps, verbose) → use converge()
- History::learning_curves_by_index() → use learning_curve() / learning_curves()
- HistoryBuilder::gamma(f64) → use .drift(ConstantDrift(g))
- add_events_with_prior downgraded from pub to pub(crate)

Added:
- History::builder_with_key() for custom key types (used by atp example)
- tests/equivalence.rs: Game-level golden integration tests

examples/atp.rs rewritten in new API (Event<i64, String>, converge(),
learning_curve(), drift(ConstantDrift(...))).

Bench Batch::iteration: 21.4 µs (T1 reference: 22.88 µs).

Part of T2 of docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Anders Olsson
2026-04-24 13:10:10 +02:00
parent e8c9d4ed29
commit a6aaa93fd0
3 changed files with 489 additions and 464 deletions
Download Patch File Download Diff File Expand all files Collapse all files

102
examples/atp.rs
View File

@@ -1,53 +1,61 @@
use std::collections::HashMap;
use plotters::prelude::*;
use smallvec::smallvec;
use time::{Date, Month};
use trueskill_tt::{History, KeyTable};
use trueskill_tt::{Event, History, Member, Outcome, Team, drift::ConstantDrift};
fn main() {
let mut csv = csv::Reader::open("examples/atp.csv").unwrap();
let mut composition = Vec::new();
let mut results = Vec::new();
let mut times = Vec::new();
let from = Date::from_calendar_date(1900, Month::January, 1).unwrap();
let time_format = time::format_description::parse("[year]-[month]-[day]").unwrap();
let mut index_map = KeyTable::new();
let mut events: Vec<Event<i64, String>> = Vec::new();
for row in csv.records() {
if &row["double"] == "t" {
let w1_id = index_map.get_or_create(&row["w1_id"]);
let w2_id = index_map.get_or_create(&row["w2_id"]);
let l1_id = index_map.get_or_create(&row["l1_id"]);
let l2_id = index_map.get_or_create(&row["l2_id"]);
composition.push(vec![vec![w1_id, w2_id], vec![l1_id, l2_id]]);
} else {
let w1_id = index_map.get_or_create(&row["w1_id"]);
let l1_id = index_map.get_or_create(&row["l1_id"]);
composition.push(vec![vec![w1_id], vec![l1_id]]);
}
results.push(vec![1.0, 0.0]);
let date = Date::parse(&row["time_start"], &time_format).unwrap();
let time = (date - from).whole_days();
times.push((date - from).whole_days());
if &row["double"] == "t" {
events.push(Event {
time,
teams: smallvec![
Team::with_members([
Member::new(row["w1_id"].to_owned()),
Member::new(row["w2_id"].to_owned()),
]),
Team::with_members([
Member::new(row["l1_id"].to_owned()),
Member::new(row["l2_id"].to_owned()),
]),
],
outcome: Outcome::winner(0, 2),
});
} else {
events.push(Event {
time,
teams: smallvec![
Team::with_members([Member::new(row["w1_id"].to_owned())]),
Team::with_members([Member::new(row["l1_id"].to_owned())]),
],
outcome: Outcome::winner(0, 2),
});
}
}
let mut hist = History::builder().sigma(1.6).gamma(0.036).build();
let mut hist: History<i64, _, _, String> = History::builder_with_key()
.sigma(1.6)
.drift(ConstantDrift(0.036))
.convergence(trueskill_tt::ConvergenceOptions {
max_iter: 10,
epsilon: 0.01,
})
.build();
hist.add_events_with_prior(composition, results, times, vec![], HashMap::new())
.unwrap();
hist.convergence(10, 0.01, true);
hist.add_events(events).unwrap();
hist.converge().unwrap();
let players = [
("aggasi", "a092", 38800),
("aggasi", "a092", 38800i64),
("borg", "b058", 30300),
("connors", "c044", 31250),
("courier", "c243", 35750),
@@ -64,21 +72,16 @@ fn main() {
("wilander", "w023", 32600),
];
let curves = hist.learning_curves_by_index();
let mut x_spec = (f64::MAX, f64::MIN);
let mut y_spec = (f64::MAX, f64::MIN);
for (id, cutoff) in players
.iter()
.map(|&(_, id, cutoff)| (index_map.get_or_create(id), cutoff))
{
for (ts, gs) in &curves[&id] {
if *ts >= cutoff {
for &(_, id, cutoff) in &players {
for (ts, gs) in hist.learning_curve(id) {
if ts >= cutoff {
continue;
}
let ts = *ts as f64;
let ts = ts as f64;
if ts < x_spec.0 {
x_spec.0 = ts;
@@ -114,24 +117,19 @@ fn main() {
chart.configure_mesh().draw().unwrap();
for (idx, (player, id, cutoff)) in players
.iter()
.map(|&(player, id, cutoff)| (player, index_map.get_or_create(id), cutoff))
.enumerate()
{
for (idx, &(player, id, cutoff)) in players.iter().enumerate() {
let mut data = Vec::new();
let mut upper = Vec::new();
let mut lower = Vec::new();
for (ts, gs) in curves[&id].iter() {
if *ts >= cutoff {
for (ts, gs) in hist.learning_curve(id) {
if ts >= cutoff {
continue;
}
data.push((*ts as f64, gs.mu()));
upper.push((*ts as f64, gs.mu() + gs.sigma()));
lower.push((*ts as f64, gs.mu() - gs.sigma()));
data.push((ts as f64, gs.mu()));
upper.push((ts as f64, gs.mu() + gs.sigma()));
lower.push((ts as f64, gs.mu() - gs.sigma()));
}
let color = Palette99::pick(idx);