901f60972ed6110bdcbfeea468e807d320fdd305
25
Commits
| Author | SHA1 | Message | Date | |
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  logaritmiskandClaude Opus 5
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564969ee5d |
test: pin quality()'s N-group closed form, closing the README cross-check
The scan for precision defects found none in `quality()` — but it did find that N identical teams have an exact closed form, which is a much stronger regression net than the single two-team golden that was there. For two identical single-player teams quality is `sqrt(2b^2 / (2b^2 + s1^2 + s2^2))`. With the conventional parameters that ratio is exactly 1/5, and the N-group generalisation is `(1/5)^((n-1)/2)` — one factor per adjacent pair. Measured across n = 2..10 the implementation matches to 1e-9, so the determinant path that #9 rebuilt is correct over the whole range, not just at n = 2. The n=3 and n=5 values (0.200 and 0.040) are also what the `trueskill` Python package produces for the same configuration, which is the cross-implementation check the README Todo has been asking for since the redesign. Asserted separately as literals so a change to the closed-form reasoning cannot silently carry them along. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ |
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logaritmiskandClaude Opus 5
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d4f91fd221 |
fix: reject convergence options that silently disable inference
`Game::ranked` and `Game::scored` validated `p_draw` and `score_sigma`
but never `convergence`. `ConvergenceOptions` has public fields and
`GameOptions` carries one, so a caller could hand the engine a set that
`HistoryBuilder`'s eager asserts never saw. Past that, the only guard
was a `debug_assert!`, which is gone in the profile users ship.
An `alpha` of zero is the bad case, and it fails silently rather than
loudly. Measured in release before the fix:
likelihoods: [[Gaussian { pi: 0.0, tau: 0.0 }],
[Gaussian { pi: 0.0, tau: 0.0 }]]
Every EP update unapplied, every likelihood uninformative, inference
returning the priors it was given — and an `OwnedGame` that looks
entirely ordinary to the caller. `HistoryBuilder::convergence` already
documents exactly this hazard; the `Game` constructors just did not
share the check.
Adds `ConvergenceOptions::validate`, called by both constructors.
Rejects `alpha` outside `(0.0, 1.0]` and negative `epsilon`; NaN fails
both comparisons and is rejected too.
`tests/validation.rs` states the release-mode guarantee for the whole
public surface, not just this hole, and CI already runs the suite in
release. Probing the other conditions #18 lists found five of eight
already enforced — ties without a draw probability, per-event score
sigma, weight/team dimensions, draw-probability range, score-sigma
range — so this closes the remaining gap rather than the whole issue.
The engine keeps its `debug_assert!`s as invariant documentation.
Refs #18
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
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logaritmiskandClaude Opus 5
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8c087ad015 |
fix!: apply competitor configuration whenever it is supplied
`Member::with_prior` and `with_drift_scale` were consumed only on the branch that *creates* a competitor — `priors.remove` sat inside `if !self.agents.contains(..)`. Supplying either for a key the history already knew did nothing at all: no error, no warning, and output computed from the default prior. A prior applied on a competitor's very first event and was silently discarded ever after. Configuration now applies whenever supplied. Two details this forced: Configuration is tracked per *field* rather than as a merged `Rating`. A member setting only `drift_scale` must not also assert the default prior, or it would silently undo a prior seeded on an earlier event. Slice state has to be refreshed. `drift_scale` is re-derived on every forward pass, but a prior is written into the competitor's earliest slice once, at ingestion, and `iteration` refreshes only slices after the first. Without the refresh a late prior would reach the drift terms and nothing else — a subtler version of the drop being fixed. This was caught by a test, not by reading the code. Conflicting values for one competitor within a single batch are now `ConflictingCompetitorConfig` rather than resolved by iteration order. Events in a batch are unordered, so "last one wins" would make the result depend on traversal — and `tests/ingestion_equivalence.rs` exists to rule exactly that out. Repeating the same value stays inert, which is the shape callers get when configuration is a property of the domain. That invariant turned out to be tested only for *unconfigured* competitors: every helper in that file built members with `Member::new`. Extended to cover configured ones, including a check that configuration changes the fit at all, so the order tests cannot pass vacuously. `with_prior` had no coverage under `tests/` whatsoever, which is how this survived. Adds `tests/competitor_config.rs`. `drift_scale_is_ignored_after_first_appearance` asserted the old behaviour and now asserts the new one. It was written as a deliberate change-detector — "moving the capture would be a visible break, not a silent one" — so it inverted rather than being deleted. Also removes `InferenceError::ConvergenceFailed` and `NegativePrecision`, which no code path ever constructed: public variants advertising failure modes no caller could observe. Partial #20 — its other items were already resolved, except `Outcome::winner` still panicking. BREAKING CHANGE: `prior` and `drift_scale` now take effect for competitors the history already knows, where they were previously ignored; a batch supplying conflicting values for one competitor is now an error. `InferenceError::ConvergenceFailed` and `InferenceError::NegativePrecision` are removed. Closes #10. Refs #20. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ |
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logaritmiskandClaude Opus 5
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2fff745c3b |
feat: let observers be shared, boxed, or borrowed
`History` takes its observer by value and never hands it back, so a
caller who wanted to read what an observer recorded had no way to keep a
handle to it. The natural spelling did not compile:
let recorder = Arc::new(Recorder::default());
History::builder().observer(Arc::clone(&recorder))
// error[E0277]: `Arc<Recorder>: Observer<i64>` is not satisfied
The workaround was for every observer to wrap each of its own fields in
an `Arc` and derive `Clone` — one allocation and one lock per field, a
pattern each implementor had to rediscover, and nothing documenting it.
Adds blanket `Observer` impls for `Arc<O>`, `Box<O>` and `&O`. All are
`?Sized`, so `Arc<dyn Observer<T>>` and `Box<dyn Observer<T>>` work too
and an observer can be chosen at runtime. Also adds
`History::observer()` and `into_observer()`, so a non-shared observer's
state can be inspected in place or reclaimed after `converge` without
needing interior mutability at all.
`tests/observer.rs` is simplified to the shared spelling, so the
recommended pattern is the one demonstrated rather than the workaround.
Closes #40
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
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logaritmiskandClaude Opus 5
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3c2f9ac64c |
feat: add expected information gain for active matchup selection
`quality()` answers "is this matchup fair". Callers picking which
comparison to run next need "is this matchup informative", and the two
coincide only for two evenly matched competitors. Without a principled
alternative, downstream code was reaching for hand-rolled heuristics
like `quality * sigma_a^2 * sigma_b^2`, which double-counts uncertainty:
the two factors are not independent.
Adds `expected_information_gain`, the outcome-weighted divergence
between current beliefs and the beliefs each result would produce:
EIG = SUM P(outcome) * KL(posterior_after(outcome) || prior)
Available standalone over `Rating`s, and as
`History::expected_information_gain` using current skills and the
history's own beta, drift and p_draw — so the outcomes it weighs are the
ones that would actually be fitted.
This is the mutual information between the outcome and the skills, which
gives an analytic ceiling: gain cannot exceed the entropy of the thing
being observed, so at most `ln k` nats for k outcomes. That bound is the
sharpest test available, because an acquisition function is unusually
exposed to returning finite, plausible, monotone numbers while being
wrong — it would simply select slightly worse matchups forever. A
prototype of this returned 4.77 nats from a sign error while passing
every monotonicity check; `never_exceeds_the_entropy_of_the_outcome`
catches that class unconditionally.
Measured against the ceiling the values are meaningful rather than
vacuous: 0.382 nats for an even matchup between diffuse priors against
an 0.693 ceiling, falling to 0.013 for a lopsided one and 0.000 for a
hopeless one.
`disagrees_with_the_quality_times_variance_heuristic` pins down that
this is not a monotone transform of the heuristic it replaces — the two
rank a lopsided matchup and a confident even one in opposite orders — so
a later "simplification" cannot quietly revert to it.
Cost is one inference pass per possible outcome, documented on the
public API alongside the shortlist-then-score pattern, so callers do not
discover it in production.
Also folds the duplicated key-gathering in `predict_quality` and
`performances` into one validated `member_skills`.
Refs #39
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
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logaritmiskandClaude Opus 5
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507894dae7 |
refactor!: close the remaining API gaps from #21
Three unrelated small defects, all requiring signature changes: - `Game::one_v_one` hardcoded `GameOptions::default()`, so a 1v1 could never set `p_draw` or convergence options — and a drawn 1v1 was therefore unreachable through it, since the default `p_draw` is zero. It now takes `&GameOptions` like every other constructor. - `Observer::on_batch_processed` was declared on the trait and never called from anywhere: implementors wired up a callback that could not fire. It is now called after each slice sweep, and renamed `on_slice_processed` to match the vocabulary the codebase adopted in T2 — the unit of work is a `TimeSlice`, not a batch. A slice is swept once travelling backward and once forward, so a multi-slice history fires it twice per slice per iteration; the doc comment says so. - `pub mod factors` sat beside `pub(crate) mod factor`, two module paths differing by one character with only one of them importable. The public facade is now `graph`. Tests cover each as a behaviour rather than a compile check: a drawn 1v1 succeeds only when p_draw is supplied, and the observer tests fail if any callback stops firing. BREAKING CHANGE: `Game::one_v_one` takes a fourth `&GameOptions` argument; `Observer::on_batch_processed` is renamed `on_slice_processed`; the `factors` module is renamed `graph`. Closes #21 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ |
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logaritmiskandClaude Opus 5
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bb2a845882 |
feat!: N-team outcome prediction with draw mass, replacing the 2-team panic
`predict_outcome` asserted `teams.len() == 2` and returned `[p, 1 - p]`, allocating no probability to a draw even with `p_draw > 0`. For a draw-enabled model the numbers were simply wrong, at any team count. It now returns `Result<Prediction, InferenceError>` and supports N teams. Two algorithms, both deterministic: - Who finishes first. Performances are independent Gaussians, so this separates into a one-dimensional integral per team rather than a multivariate orthant probability. Adaptive Gauss-Kronrod evaluates it to ~1e-15, matching the exact two-team closed form. - A specific finishing order. The factor graph only constrains rank-adjacent teams, so a full order is a chain of local constraints, not a general orthant integral. That chain collapses into a sequential recursion over cumulative integrals: O(teams * grid) per order. Fixed-node Gauss-Hermite is the obvious tool for the first and is a trap: when a rival's sigma is small the CDF product becomes a step narrower than the node spacing, and the nodes step over it. Measured 4.4e-4 off the closed form on a mildly skewed matchup and 1.7e-2 on a small-sigma one, while still returning something that looks like a probability. Adaptive refinement is what makes that case safe, and `win_probabilities_survive_a_rival_with_a_tiny_sigma` pins it down. The acceptance test is an identity rather than a golden: the outcome space is exhaustive and disjoint, so the probabilities sum to one. Any drift is integration error and nothing else. Gauss-Hermite failed it at 4.4e-4; this holds to ~1e-9. Also from #21: unknown keys are now reported rather than dropped, so a team of strangers can no longer produce a confident-looking prediction. `predict_quality` returns `Result` for the same reason. BREAKING CHANGE: `predict_outcome` returns `Result<Prediction, _>` instead of `Vec<f64>`; `predict_quality` returns `Result<f64, _>`. Refs #21, #39 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ |
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logaritmiskandClaude Opus 5
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617bc07f6f |
feat: allow drift to vary per competitor via Member::with_drift_scale
Drift was a property of the History, so every competitor drifted at the same rate and a fixed reference point could not share a graph with moving competitors. A bot at a known strength, a rating floor, a course difficulty — all of them drifted along with the players. Member::with_drift_scale(s) multiplies the drift *variance* a competitor accumulates, so s is in the same units as gamma: ConstantDrift(g) at scale s behaves exactly as ConstantDrift(g * s) would for that competitor. A scalar rather than a per-competitor Drift keeps History's single D type parameter untouched and stays Copy. 0.0 pins a competitor still. The scale lives on Rating, beside the drift it scales, and is applied only through Rating::drift_variance_delta / drift_variance_for_elapsed. Making those the sole entry points means a caller cannot reach the raw drift and silently skip a competitor's scale — the filtered pass was exactly that bug during development, caught because its test was written before the wiring. Like with_prior, the scale is competitor configuration captured at first appearance rather than a per-event override; a competitor that is static is static, and a scale that changed between events would make the skill trajectory hard to interpret. Member's docs claimed prior was a per-event override, which the code has never done — corrected here. A negative scale is rejected rather than squared into its absolute value, and a non-finite one rejected outright, both as InvalidParameter. None means 1.0, so no existing call site changes and no existing fit moves. Adding a public field to Member does break struct-literal construction downstream. Closes #34 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014b6wy2q8rnFK8U8GPJVQNU |
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logaritmiskandClaude Opus 5
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1a88678384 |
refactor!: remove ConvergenceReport::slices_skipped
Closes #33. The field was public, hardcoded to 0 at both construction sites, and had no route to ever being non-zero. It was added in T3 as the reporting surface for dirty-bit slice skipping. That feature is #4, closed as unworkable — the ceiling measured at ~6% against a projected 5-50x, on top of three independent soundness blockers. #32, which reattributed the cost to ingestion, is closed too: the re-convergence is necessary work rather than waste, because appending one event genuinely moves the involved competitors ~1.2 sigma across their whole history. Nothing left would ever populate it. This is the same defect class as #19, where this arc started: a public surface that looks implemented, reports a plausible value, and is inert. A caller reading `slices_skipped: 0` reasonably concludes "no slices were skipped this run", not "this feature does not exist". Removed rather than documented as reserved. Its only value was as a hook for a plan that no longer exists, and keeping it preserves the shape of that plan. Breaking, but ConvergenceReport is returned rather than constructed by callers, so the only breakage is code reading a constant zero. Also added a test asserting every remaining field carries real information — iterations non-zero, final_step finite, log_evidence a finite negative log probability, and per_iteration_time holding one duration per iteration. Mutation-proved: pinning per_iteration_time to an empty SmallVec fails it. The next always-constant member now has to survive an assertion rather than just a reviewer's attention, which is the actual lesson of #19 and #33. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc |
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logaritmiskandClaude Opus 5
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2745fbb622 |
test: add property-based tests, a shared finiteness helper, and boundary inputs
Most of what remained on #26. **Property tests (`tests/properties.rs`, proptest as a dev-dependency).** Four invariants over generated 1v1 schedules rather than hand-written fixtures, which is where this crate's shipped defects actually hid — a linear evidence product that underflowed only past ~1000 teams, and a batching path no golden exercised because every golden ingests in one call: - converged posteriors are always finite with positive sigma - log-evidence, batch and filtered, is finite and never above zero - filtered evidence is invariant to whether `converge` has run - one-at-a-time ingestion reaches the same fixed point as batched The invariance property was mutation-proved: making `filtered_step` read `skill.forward` instead of the carried message fails it with `-1.1038430064192069 -> -1.1135747072822761`. **Shared finiteness helper (`tests/common/mod.rs`).** `assert_finite` was local to `degenerate_inputs.rs`. It now also rejects a non-positive sigma, which the old version let through — `Gaussian::sigma` reports a non-positive precision as improper rather than trapping, so a collapsed posterior would have passed a finite-only check. **Boundary inputs.** Zero and negative weights, out-of-order timestamps, and extreme beta/sigma combinations. Worth recording that zero weight reaches `(m - performance.exclude(..)) * (1.0 / w)` — a division by zero — and the posterior comes out finite anyway; the test pins that rather than asserting what ought to happen. The weight tests `expect()` the commit rather than returning early on error, because an early return would have made them vacuous the moment validation changed. I checked that specifically by turning the return into a failure and confirming it did not fire. Not done, and left on #26: benchmark regression gating. Nothing fails on a regression today; making it fail needs a threshold chosen against how noisy the shared runner is, which is a policy call rather than a mechanical one. 60 test binaries, up from 56. MSRV 1.85 verified with proptest in the graph. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc |
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logaritmiskandClaude Opus 5
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1ac3b21db5 |
fix: enforce EventBuilder weight/team length in release
Part of #18. `EventBuilder::weights` guarded the length match with a `debug_assert!`, so release builds accepted a mismatch, silently dropped the weights, and ingested the event anyway. That is the exact shape #18 is about: validation that exists only where it is least needed. The setters return `Self` to keep the chain fluent, so they cannot return a `Result`. The builder now records the first failure and `commit` returns it as `MismatchedShape`. The weights are not applied on mismatch either, so a partially-weighted team cannot reach the history by another route. Two tests in tests/degenerate_inputs.rs, whose CI job runs in release — which is the only place the old behaviour differed. The second test needed strengthening before it was worth anything. As first written it committed a ONE-team event, which ingestion rejects for an unrelated reason, so it passed under a mutation that disabled the whole check. It now uses two teams, so ingestion would otherwise succeed and the assertion is actually load-bearing. Both tests were then mutation-proved together: disabling the error path in `commit` fails both in release. #18 stays open. The remaining debug_asserts live in `ranked_with_arena` and `scored_with_arena`, and promoting those means threading `Result` up through `Event::compute`, `TimeSlice::iteration`, `log_evidence` and `filtered_step` — which lands on the public API as `log_evidence() -> Result<f64>` and `filtered_learning_curve() -> Result<...>`. That is a trade-off about what the query API should look like, not a mechanical change, so it is not mine to decide. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc |
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logaritmiskandClaude Opus 5
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eeb43e3be1 |
fix: close out four small issues and pin #27's repro
#29 — log_evidence and log_evidence_for took &mut self while mutating nothing. Loosening them to &self is not source-breaking for ordinary callers (a &mut reborrows as & transparently) and brings them in line with the filtered_* accessors added last week. Not the mechanical change it looked like: under the rayon feature the closure in log_evidence_internal captured all of &self rather than just the competitor store, which drags KeyTable<K> in and demands K: Sync from every caller. That compiled while the method took &mut self and stopped compiling the moment it did not. Binding `let agents = &self.agents;` before the closure narrows the capture; the comment there says why, because the next person to inline it will reintroduce the bound. #31 — TimeSlice::add_events constructed Skill with ..Default::default() while filtered_step spells every field out. The design relies on a new Skill field being a compile error at construction sites rather than a silent default, and that tripwire only fired at one of the two. Now both. #28 — log_evidence_internal's `forward` flag is a genuine forward-only quantity only on a history that has never been converged, because iteration alternates sweeps and the likelihood feeding the forward message absorbs backward information from the second iteration onward. Documented, with a pointer to filtered_log_evidence for the quantity that survives convergence. That trap is one function away from the one #19 was about. #23 — color_greedy carried #[allow(dead_code)] despite being called by recompute_color_groups: a mute button on a live function, which is the specific complaint in that issue. #27 was already fixed — the guard landed in |
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logaritmisk
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9c39d1e681 |
test: pin the invariants that make filtered estimates trustworthy
The bracket test proves the feature works on one fixture. These pin the bug class: - Invariance to converge(). This is the one that matters. Reading skill.forward instead of the carried message makes it fail immediately, because converge() alternates sweeps and contaminates skill.forward with backward information from the second iteration onward. That is the property a stored field cannot have, and the reason issue #19's proposed fix would not have worked. - Invariance to ingestion order, the crate's standing invariant. - One slice has no future to propagate back, so filtered equals smoothed. - Empty history yields zero and empty maps. Agreement is to 1e-8 under tight convergence rather than bit-identity: iteration recomputes the colour partition only when from == 0, so an incrementally built slice keeps insertion order until the first converge() reorders it, and the scratch clone inherits whichever order it finds. Same fixed point, different path to it. |
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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
|
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
|
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
|
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> |