logaritmiskandClaude Opus 5
9e8515b7cd
The CLAUDE.md architecture section still described the pre-redesign engine: its data flow named `Batch`, `Agent`, `Player` and `message.rs`, none of which have existed since T2, and the public API it listed did not match `lib.rs`. It is the first thing a fresh session reads, so it was actively misleading. Rewritten against the current module layout, with the invariants that are easy to violate — ties needing a positive `p_draw`, NaN never being convergence, log-space evidence, color contiguity, `forbid(unsafe_code)`, and ingestion-order equivalence — written down. The README Todo list had five entries that were already done, including "Time needs to be an enum": `Time` has been a trait since T2, and the `batch::compute_elapsed()` it pointed at no longer exists. The genuinely open item — cross-checking `quality()` against sublee/trueskill — stays. `benches/ingest.rs` measures one-event-per-call against a single batched call. The rest of the suite only measured batched construction, which is why the quadratic fixed earlier on this branch went unnoticed for so long. `TimeSlice::log_evidence` also hashes its target set once instead of scanning the slice per player per event, so `log_evidence_for` with many keys is no longer quadratic. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej
104 lines
3.6 KiB
Markdown
104 lines
3.6 KiB
Markdown
# TrueSkill - Through Time
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Rust port of [TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillThroughTime.py).
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## Other implementations
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- [ttt-scala](https://github.com/ankurdave/ttt-scala)
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- [ChessAnalysis #F](https://github.com/lucasmaystre/ChessAnalysis)
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- [TrueSkillThroughTime.jl](https://github.com/glandfried/TrueSkillThroughTime.jl)
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- [TrueSkillThroughTime.R](https://github.com/glandfried/TrueSkillThroughTime.R)
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- [TrueSkill Through Time: Revisiting the History of Chess](https://www.microsoft.com/en-us/research/wp-content/uploads/2008/01/NIPS2007_0931.pdf)
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- [TrueSkill Through Time. The full scientific documentation](https://glandfried.github.io/publication/landfried2021-learning/)
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## Drift
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Skill drift models how a player's true skill can change between appearances. Each time a player reappears after a gap, their skill uncertainty is widened by the drift model before the new evidence is incorporated.
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Drift is represented by the `Drift` trait:
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```rust
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pub trait Drift: Copy + Debug {
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fn variance_delta(&self, elapsed: i64) -> f64;
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}
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```
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`variance_delta` returns the amount to add to `σ²` given the elapsed time since the player last played. Internally, `Gaussian::forget` uses this to compute the new sigma: `σ_new = sqrt(σ² + variance_delta)`.
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### ConstantDrift
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The built-in `ConstantDrift` implements a linear random walk — skill uncertainty grows proportionally to time:
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```
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variance_delta = elapsed * γ²
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```
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This is the standard TrueSkill Through Time model. Use it by passing a `ConstantDrift(gamma)` when constructing a `Player`:
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```rust
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use trueskill_tt::{Player, Gaussian, drift::ConstantDrift};
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// gamma = 0.1 means skill can shift ~0.1 per time unit
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let player = Player::new(Gaussian::from_ms(0.0, 6.0), 1.0, ConstantDrift(0.1));
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```
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### Custom drift
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Implement `Drift` to express any other model. For example, a drift that saturates after a long absence (uncertainty grows with the square root of elapsed time instead of linearly):
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```rust
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use trueskill_tt::drift::Drift;
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#[derive(Clone, Copy, Debug)]
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struct SqrtDrift {
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gamma: f64,
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}
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impl Drift for SqrtDrift {
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fn variance_delta(&self, elapsed: i64) -> f64 {
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(elapsed as f64).sqrt() * self.gamma * self.gamma
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}
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}
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let player = Player::new(Gaussian::from_ms(0.0, 6.0), 1.0, SqrtDrift { gamma: 0.5 });
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```
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To use a custom drift type with `History`, use the `.drift()` builder method instead of `.gamma()`:
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```rust
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let h = History::builder()
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.drift(SqrtDrift { gamma: 0.5 })
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.build();
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```
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## Scored outcomes
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Use `Outcome::scores([...])` when you have continuous per-team scores rather
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than just ranks. Adjacent score margins flow into a `MarginFactor` that adds
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soft Gaussian evidence about the latent performance diff. Configure
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`HistoryBuilder::score_sigma(σ)` to control how much you trust the margins
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(smaller σ = more trust).
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```rust
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use trueskill_tt::{History, Outcome};
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let mut h = History::builder().score_sigma(2.0).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([21.0, 9.0])
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.commit()
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.unwrap();
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h.converge().unwrap();
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```
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## Todo
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- [x] Implement approx for Gaussian
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- [x] Add more tests from `TrueSkillThroughTime.jl`
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- [x] Generalise a time axis — `Time` is now a trait (`Untimed`, `i64`), not an enum
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- [x] Add examples (`examples/atp.rs`, `examples/scored.rs`)
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- [x] Add Observer (`Observer` / `NullObserver`)
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- [x] Benchmark the inference loop (`benches/batch.rs`, `benches/history_converge.rs`, `benches/ingest.rs`)
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- [ ] Cross-check `quality()` against [sublee/trueskill](https://github.com/sublee/trueskill/tree/master) — N-group support works and is covered by invariants, but no reference values are asserted
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