50e11cfbfad5c64dbc9aefff8a58938002236c50
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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 logaritmisk
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50e11cfbfa |
feat: add filtered learning curves
learning_curve returns post-convergence posteriors, so every point is smoothed: the estimate at a given date incorporates rounds played years later. On ustat's data that starts six players' curves already spread apart at sigma 0.9-1.6 against a prior of 6.0, barely moving thereafter. filtered_learning_curve plots the same competitor on forward-only information, so everyone starts at the prior and fans out. It could not be reconstructed from the public API before: a caller could only refit over events[0..k] for every k, which is O(n^2) fits for something one forward pass already computes. |
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logaritmisk
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d4af048914 |
feat: add filtered_log_evidence
Scores every event on what was known before it, rather than on priors that carry information from events which had not happened yet. This is the quantity HistoryBuilder::online promised and never delivered. The pass walks slices in time order carrying its own forward messages, and per slice runs the unmodified production sweep on a scratch copy whose backward message is left improper. Reusing iterate_to_convergence rather than reimplementing inference means a competitor playing twice at one time is handled by the same within-slice EP that converge() uses, instead of being approximated the way the old evidence paths approximated it. Nothing is stored on Skill and nothing on self is mutated, so the result is independent of whether converge() has run — the property a stored field cannot have. |
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 logaritmiskandClaude Opus 5
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355cdb7e05 |
perf(gaussian): drop the sqrt round-trip from variance-space operations
`Add`, `Sub`, `exclude` and `forget` combined variances by way of standard
deviations: `sigma()` takes a square root, `.powi(2)` squares it away,
`var.sqrt()` takes another, and `from_ms` squares that one back. Three roots
to compute a value that is `1/pi` all along.
They now go through `variance()` and a new `from_mv(mu, var)`, which skip
both conversions. `Sub` is the hot one — `RankDiffFactor::propagate` is
`a - b`, run for every adjacent team pair on every forward and backward
sweep of every EP iteration.
`run_chain` also stopped recomputing each team's weighted performance in the
likelihood loop; the fold is already in `arena.team_prior`, indexed by the
sorted position the loop has in hand. Each `performance()` is itself a
`forget`, so the duplicate cost scaled with players per team.
Measured on this machine, before and after, same fixtures:
Batch::iteration 23.57us -> 19.31us (-18%)
scored_history_60_events 1.071ms -> 983us (-8%)
The `Gaussian::add`/`sub` microbenchmarks cannot resolve the change: they
sit at ~234ps against a ~218ps floor that `mul`/`div` also hit, so the
harness overhead dominates a single operation.
One golden moved. Two identical competitors drawing must land on their
shared prior mean exactly, by symmetry; the root-free path now returns
25.0 where the reference transcription recorded 24.999999 — that value
rounded to six decimals. Asserting a six-decimal transcription at
epsilon 1e-6 left no headroom, so the expectation is now the exact value.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej
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logaritmiskandClaude Opus 5
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c088214fed |
fix(history): stop reprocessing the slice that was just appended to
`add_events_with_prior` advanced `k` past the slice it had written when it
created a new one, but not when it appended to an existing one. The trailing
forward-refresh loop therefore started *on* the slice just modified and ran
`new_forward_info` over it again.
That is not merely redundant work. The loop immediately above it sets each
agent's message to `forward * likelihood` for that slice, and
`new_forward_info` then assigns `skill.forward = message.forget(drift)` —
folding the slice's own likelihood back into its own forward prior. The
skills it produced depended on how events had been batched.
Ingesting one event at a time now converges to the same fixed point as
ingesting the same events in a single call, which it previously did not:
for five events sharing a timestamp, competitor `a` converged to
mu=7.44 sigma=3.90 batched versus mu=7.99 sigma=3.10 incrementally. Both
runs had converged; the gap was not a convergence residual.
The numerical goldens never caught this because they all ingest in one call
with a distinct timestamp per event, so the append-to-existing-slice branch
is never taken. `tests/ingestion_equivalence.rs` covers it directly, and
asserts convergence before comparing so that a residual cannot be mistaken
for agreement.
Removing the redundant re-inference also removes the dominant cost of
incremental ingestion, which was quadratic in the number of events already
in the slice:
events before after speedup
500 45.8ms 1.1ms 42x
1000 179.5ms 2.8ms 64x
2000 721.8ms 9.9ms 73x
4000 2.9s 35.4ms 82x
Ingesting one at a time is now 1.8x a single batched call, down from 148x.
Two supporting changes are included:
- Color groups are rebuilt lazily rather than on every append. Nothing
reads the partition between an append and the next full sweep, so the
per-append rebuild was pure waste.
- `ColorGroups::groups_are_contiguous` is asserted after each rebuild and
in `color_range`. The parallel sweep derives one `&mut` sub-slice per
color from those ranges and relies on them being disjoint; that invariant
was established by construction but never checked.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej
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logaritmiskandClaude Opus 5
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0f1a1b8911 |
fix(evidence): accumulate in log space and floor the per-link value
Per-link evidence was multiplied in linear space and logged only at the end. Each link contributes a probability in (0, 1], so the product over an n-team game decays geometrically: around a thousand links it flushes to exactly 0.0 and `ln(0.0)` is `-inf`, which then propagates through the sum in `History::log_evidence_internal` and takes the whole history with it. `Game::free_for_all` builds one team per player, so this is reachable at the competitor counts the T3 benchmarks target. `Game`, `OwnedGame`, and `time_slice::Event` now carry `log_evidence` directly, summed over links rather than multiplied then logged. The cached per-link evidence is also floored at `f64::MIN_POSITIVE`. It could legitimately reach zero or go negative: `1.0 - cdf(..)` rounds to zero for a near-certain outcome, and the `erfc` approximation carries ~1e-7 error so `cdf` can exceed 1.0 and make the difference negative — `ln` of which is NaN. Existing log-evidence goldens are unchanged, confirming the accumulation is numerically equivalent in the range where the old form worked. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej |
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logaritmiskandClaude Opus 5
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0d32690fcc |
fix(quality): support any number of rating groups
`quality()` was two-group-only in three separate ways, and
`History::predict_quality` inherited all of them.
- The contrast-matrix column counter tracked two positions with two
variables that only agree on the first row, so three or more groups wrote
past the end of the row and panicked with an out-of-bounds index. The
negative block always begins immediately after the positive one, so the
second counter is unnecessary.
- `Matrix::inverse` was implemented only for the 1x1 case and otherwise
`panic!("eh, okey")`. It now uses LU decomposition with partial pivoting,
which also replaces the recursive cofactor `determinant` — that was O(n!)
and allocated a `Vec` per minor, so a 10-team match needed 362,880 terms.
- Degenerate inputs (zero groups, one group, empty groups) underflowed or
produced NaN. They now assert with a message naming the requirement.
`Matrix` also gains dimension checks on multiply/add and bounds checks on
indexing, and loses the now-unused `adjugate`/`minor` cofactor path.
The two-group golden is unchanged. N-group behaviour is covered by
invariants — permutation invariance, and quality falling as a skill gap
widens — since no reference values were available to compare against; the
sublee/trueskill cross-check remains open.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej
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logaritmiskandClaude Opus 5
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f4e2922d59 |
fix: reject ties without draw probability; never report NaN as converged
A tie with `p_draw == 0.0` produced NaN posteriors in release builds and `converge()` reported `converged: true`, because every comparison against NaN is false and `tuple_gt` therefore read NaN as "below epsilon". Two independent defects, fixed together: - Ingestion now rejects tied outcomes when the draw probability is zero, promoting the existing `debug_assert!` in `Game::ranked_with_arena` to a real `InferenceError::TieWithoutDrawProbability`. Validation sits in `add_events_with_prior`, the chokepoint every route reaches — including `record_draw`, which bypasses `Outcome` entirely. - `converge()` treats a non-finite step as failure and returns `InferenceError::NonFiniteResult` rather than claiming convergence. Also in this change: - `History::converge()` on an empty history returned a `usize` underflow panic from `0..len()-1`; it now short-circuits to a zero-iteration report. - `Outcome::scores_with_sigma` no longer panics on a non-positive sigma; the value is validated at ingestion so callers get an error instead. - `InferenceError` gains `WrongOutcomeKind`, replacing the misuse of `MismatchedShape` for variant mismatches (which rendered as the nonsense "expected length 0, got 0"), and is now `#[non_exhaustive]`. Note `Outcome::winner(w, n)` for n >= 3 ties every loser, so those events now require a positive `p_draw`. They previously returned NaN. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DnsaJg74eNSva3PJjK2eej |
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logaritmiskandClaude Opus 4.8
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e4ff46f45c |
fix(gaussian): treat non-positive precision as improper in mu()/sigma()
EP message cancellation can leave a Gaussian's precision (pi) a tiny negative value — round-off of exactly zero. mu()/sigma() only special-cased pi == 0, so sigma() computed 1/sqrt(pi) = NaN for pi < 0. That NaN flowed through the moment-space Sub in the game diff-chain and poisoned every skill in the slice once it grew past ~75 competitors, making converge() return all-NaN on real-scale histories (regression vs 0.1.0, which stored sigma directly). Guard pi <= 0.0 in both accessors (improper Gaussian: mu 0, sigma infinite), matching the existing pi == 0 handling. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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logaritmisk
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0705986929 |
feat(game): plumb ConvergenceOptions through to run_chain
Game and OwnedGame gain a convergence: ConvergenceOptions field set at
construction. Game::{ranked,scored} forward options.convergence into
OwnedGame::{new,new_scored} (previously dropped on the floor).
{ranked,scored}_with_arena take it as a parameter. run_chain reads
self.convergence.{epsilon, max_iter, alpha} instead of hardcoded
1e-6 / 10 / undamped. DiffFactor::propagate gains an alpha parameter
and dispatches into Trunc/MarginFactor::propagate_with_alpha.
In-tree callsites in src/time_slice.rs and src/history.rs pass
ConvergenceOptions::default(). Pre-existing T2 fallout in tests,
benches, and the atp example (struct literals missing the new alpha
field) is fixed by adding alpha: 1.0 so the workspace builds clean.
Default alpha is 1.0, so all 96 lib + 27 integration test goldens
remain bit-equal.
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logaritmiskandClaude Opus 4.7
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8b53cacd64 |
T4 (MarginFactor): scored outcomes via Gaussian-margin EP evidence
Adds soft Gaussian-observation evidence on the per-pair diff variable,
enabling continuous score margins as a richer alternative to ranks.
Public API:
- `Outcome::Scored([scores])` (non-breaking enum extension under
`#[non_exhaustive]`).
- `Game::scored(teams, outcome, options)` constructor parallel to
`Game::ranked`.
- `EventBuilder::scores([...])` fluent helper.
- `HistoryBuilder::score_sigma(σ)` knob (default 1.0, validated > 0).
- `GameOptions::score_sigma`.
- `EventKind` re-exported from `lib.rs` (annotated `#[non_exhaustive]`).
- New `InferenceError::InvalidParameter { name, value }` variant.
Internals:
- `MarginFactor` (`factor/margin.rs`): Gaussian observation factor that
closes in one EP step; cavity-cached log-evidence mirrors `TruncFactor`.
- `BuiltinFactor::Margin` dispatch arm.
- `DiffFactor` enum in `game.rs` lets `Game::likelihoods` and the new
`likelihoods_scored` share the per-pair link abstraction.
- Per-event `EventKind { Ranked, Scored { score_sigma } }` routed through
`TimeSlice::add_events`, `iteration_direct`, and `log_evidence`.
Tests: 88 lib + 27 integration (4 new in `tests/scored.rs`); existing
goldens byte-identical. Bench: `benches/scored.rs` baseline ~960µs for
60 events × 20-player pool with default convergence.
Plan: docs/superpowers/plans/2026-04-27-t4-margin-factor.md
Spec item marked Done.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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logaritmisk
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6bf3e7e294 |
T3: rayon-backed concurrency (opt-in) (#2)
Implements T3 of `docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md` Section 6. Plan: `docs/superpowers/plans/2026-04-24-t3-concurrency.md` (11 tasks).
## Summary
### Breaking
- `Send + Sync` bounds added to public traits: `Time`, `Drift<T>`, `Observer<T>`, `Factor`, `Schedule`. All built-in impls satisfy these via auto-derive; downstream custom impls will need the bounds.
### New
- Opt-in `rayon` cargo feature. When enabled:
- Within-slice event iteration runs color-group events in parallel via `par_iter_mut` (`TimeSlice::sweep_color_groups`).
- `History::learning_curves` computes per-slice posteriors in parallel; merges sequentially in slice order.
- `History::log_evidence` / `log_evidence_for` use per-slice parallel computation with deterministic sequential reduction (sum in slice order) — bit-identical to the sequential baseline.
- `ColorGroups` infrastructure (`src/color_group.rs`) with greedy graph coloring. Events sharing no `Index` go into the same color group; events in the same group can run concurrently without touching each other's skills.
- `tests/determinism.rs` asserts bit-identical posteriors across `RAYON_NUM_THREADS={1, 2, 4, 8}`.
- `benches/history_converge.rs` measures end-to-end convergence on three workload shapes.
## Performance
### Sequential (no rayon, default build)
| Metric | Before T3 | After T3 | Delta |
|---|---|---|---|
| `Batch::iteration` | 22.88 µs | 23.23 µs | **+1.5%** (noise) |
| `Gaussian::*` | ≈218–264 ps | ≈236 ps | within noise |
**No sequential regression.** Default build is as fast as T2.
### Parallel (`--features rayon`, Apple M5 Pro, auto thread count)
| Workload | Sequential | Parallel | Speedup |
|---|---:|---:|---:|
| 500 events / 100 competitors / 10 per slice | 4.03 ms | 4.24 ms | **1.0×** |
| 2000 events / 200 competitors / 20 per slice | 20.18 ms | 19.82 ms | **1.0×** |
| 5000 events / 50000 competitors / 1 slice | 11.88 ms | 9.10 ms | **1.3×** |
### ⚠️ The spec's >=2× target was not met on realistic workloads.
T3's within-slice color-group parallelism only shows material benefit when a slice holds many events AND the competitor pool is large enough to give the greedy coloring room to partition. Typical TrueSkill workloads (tens of events per slice) don't fit that profile — rayon's task-spawn overhead dominates.
**Cross-slice parallelism (dirty-bit slice skipping per spec Section 5) is the natural next step** for real-workload speedup and would deliver the spec's ~50–500× online-add speedup. Deferred to a future tier.
## Determinism
`tests/determinism.rs` runs a 200-event history at thread counts {1, 2, 4, 8} via `rayon::ThreadPoolBuilder::install` and asserts every `(time, posterior)` pair has bit-identical `mu` and `sigma` (compared via `f64::to_bits()`). Passes.
## Internals
- Parallel path uses an `unsafe` block to concurrently write to `SkillStore` from color-group-disjoint events. Soundness rests on the color-group invariant (events in the same color touch no shared `Index`), guaranteed by construction in `TimeSlice::recompute_color_groups`. Sequential path unchanged from T2.
- `RAYON_THRESHOLD = 64` — color groups smaller than this fall back to sequential inside `sweep_color_groups` to avoid task-spawn overhead.
- Thread-local `ScratchArena` per rayon worker thread.
## Test plan
- [x] `cargo test --features approx` — 96 tests pass (74 lib + 22 integration)
- [x] `cargo test --features approx,rayon` — 97 tests pass (+1 determinism)
- [x] `cargo clippy --all-targets --features approx -- -D warnings` — clean
- [x] `cargo clippy --all-targets --features approx,rayon -- -D warnings` — clean
- [x] `cargo +nightly fmt --check` — clean
- [x] `cargo bench --bench batch --features approx` — 23.23 µs (no regression vs T2)
- [x] `cargo bench --bench history_converge --features approx,rayon` — runs on all three workloads
- [x] Bit-identical posteriors across `RAYON_NUM_THREADS={1, 2, 4, 8}` — verified
## Commit history
13 commits on `t3-concurrency`. Each task is self-contained and bisectable. See `git log main..t3-concurrency` for the full list.
## Deferred
- **Cross-slice parallelism** (dirty-bit slice skipping) — the path that would actually speed up typical TrueSkill workloads.
- **Default-on `rayon` feature** — spec called for default-on; we keep it opt-in until the feature proves stable in production use.
- **Synchronous-EP schedule with barrier merge** — alternative parallel strategy per spec Section 6.
- **`MarginFactor` / `Outcome::Scored`** — T4.
- **`Damped` / `Residual` schedules** — T4.
- **N-team `predict_outcome`** — T4.
- **`Game::custom` full ergonomics** — T4.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Reviewed-on: #2
Co-authored-by: Anders Olsson <anders.e.olsson@gmail.com>
Co-committed-by: Anders Olsson <anders.e.olsson@gmail.com>
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logaritmisk
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d2aab82c1e |
T0 + T1 + T2: engine redesign through new API surface (#1)
Implements tiers T0, T1, T2 of `docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md`. All three tiers have landed together on this branch because they build on one another; this PR rolls them up for a single review pass. Per-tier plans: - T0: `docs/superpowers/plans/2026-04-23-t0-numerical-parity.md` - T1: `docs/superpowers/plans/2026-04-24-t1-factor-graph.md` - T2: `docs/superpowers/plans/2026-04-24-t2-new-api-surface.md` ## Summary ### T0 — Numerical parity (internal) - `Gaussian` switched to natural-parameter storage `(pi, tau)`; mul/div now ~7× faster (218 ps vs 1.57 ns). - `HashMap<Index, _>` → dense `Vec<_>` keyed by `Index.0` (via `AgentStore<D>`, `SkillStore`). - `ScratchArena` eliminates per-event allocations in `Game::likelihoods`. - `InferenceError` seed type added (1 variant). - 38 → 53 tests passing through T1. - Benchmark: `Batch::iteration` 29.84 → 21.25 µs. ### T1 — Factor graph machinery (internal) - `Factor` trait + `BuiltinFactor` enum (TeamSum / RankDiff / Trunc) driving within-game inference. - `VarStore` flat storage for variable marginals. - `Schedule` trait + `EpsilonOrMax` impl replacing the hand-rolled EP loop. - `Game::likelihoods` rebuilt on the factor-graph machinery; iteration counts and goldens preserved to within 1e-6. - 53 tests passing. - Benchmark: `Batch::iteration` 23.01 µs (slight regression absorbed in T2). ### T2 — New API surface (breaking) **Renames:** - `IndexMap → KeyTable`, `Player → Rating`, `Agent → Competitor`, `Batch → TimeSlice` **New types:** - `Time` trait with `Untimed` ZST and `i64` impls; `Drift<T>`, `Rating<T, D>`, `Competitor<T, D>`, `TimeSlice<T>`, `History<T, D, O, K>` all generic. - `Event<T, K>`, `Team<K>`, `Member<K>`, `Outcome` (`Ranked` variant; `#[non_exhaustive]`). - `Observer<T>` trait + `NullObserver`. - `ConvergenceOptions`, `ConvergenceReport`. - `GameOptions`, `OwnedGame<T, D>`. **Three-tier ingestion:** - `history.record_winner(&K, &K, T)` / `record_draw(&K, &K, T)` — 1v1 convenience. - `history.add_events(iter)` — typed bulk. - `history.event(T).team([...]).weights([...]).ranking([...]).commit()` — fluent. **Query API:** `current_skill`, `learning_curve`, `learning_curves` (keyed on `K`), `log_evidence`, `log_evidence_for`, `predict_quality`, `predict_outcome`. **Game constructors:** `ranked`, `one_v_one`, `free_for_all`, `custom` — all returning `Result<_, InferenceError>`. **`factors` module:** `Factor`, `Schedule`, `VarStore`, `VarId`, `BuiltinFactor`, `EpsilonOrMax`, `ScheduleReport`, `TeamSumFactor`, `RankDiffFactor`, `TruncFactor` now public. **Errors:** `InferenceError` gains `MismatchedShape`, `InvalidProbability`, `ConvergenceFailed`; boundary panics converted to `Result`. **Removed (breaking):** `History::convergence(iters, eps, verbose)`, `HistoryBuilder::gamma(f64)`, `HistoryBuilder::time(bool)`, `History.time: bool`, `learning_curves_by_index`, nested-Vec public `add_events`. ## Behavior change (documented in CHANGELOG) `Time = Untimed` has `elapsed_to → 0`, so no drift accumulates between slices. The old `time=false` mode implicitly forced `elapsed=1` on reappearance via an `i64::MAX` sentinel — that quirk is not reproducible under a typed time axis. Tests that depended on it now use `History::<i64, _>` with explicit `1..=n` timestamps. One test (`test_env_ttt`) had 3 Gaussian goldens updated to reflect the corrected semantics; documented in commit `33a7d90`. ## Final numbers | Metric | Before T0 | After T2 | Delta | |---|---|---|---| | `Batch::iteration` | 29.84 µs | 21.36 µs | **-28%** | | `Gaussian::mul` | 1.57 ns | 219 ps | **-86%** | | `Gaussian::div` | 1.57 ns | 219 ps | **-86%** | | Tests passing | 38 | 90 | +52 | All other Gaussian ops unchanged (~219 ps add/sub, ~264 ps pi/tau reads). ## Test plan - [x] `cargo test --features approx` — 90/90 pass (68 lib + 10 api_shape + 6 game + 4 record_winner + 2 equivalence) - [x] `cargo clippy --all-targets --features approx -- -D warnings` — clean - [x] `cargo +nightly fmt --check` — clean - [x] `cargo bench --bench batch` — 21.36 µs - [x] `cargo bench --bench gaussian` — unchanged from T1 - [x] `cargo run --example atp --features approx` — rewritten in new API, runs clean - [x] Historical Game-level goldens preserved in `tests/equivalence.rs` - [x] Public API matches spec Section 4 (verified by integration tests in `tests/api_shape.rs`) ## Commit history ~45 commits total across T0 + T1 + T2. Each task is self-contained and individually tested; the branch is bisectable. See `git log main..t2-new-api-surface` for the full list. ## Deferred to later tiers - `Outcome::Scored` + `MarginFactor` — T4 - `Damped` / `Residual` schedules — T4 - `Send + Sync` bounds + Rayon parallelism — T3 - N-team `predict_outcome` — T4 - `Game::custom` full ergonomics — T4 🤖 Generated with [Claude Code](https://claude.com/claude-code) Reviewed-on: #1 Co-authored-by: Anders Olsson <anders.e.olsson@gmail.com> Co-committed-by: Anders Olsson <anders.e.olsson@gmail.com> |