Three issues from two downstream consumers, all small, all sharing a
theme: the crate had the information and would not hand it over.
#44 — `UnknownKey { team: 0, member: 0 }` did not say which key. A
consumer upgrading 0.1.2 -> 0.4.1 had every one of 5591 predictions
return this error, fell back to a neutral 0.5, and lost its entire
metadata model for a day. Nothing crashed and nothing logged; it was
found by sweeping an unrelated parameter and noticing the output did not
move. The 0.4.0 change that made unknown keys an error was right — the
error was just too anonymous to act on. It now carries the key's `Debug`
rendering, and its `Display` says what to do about it. The precondition
is documented on every prediction entry point, which the reporter said
would alone have saved the day.
#43 — `cdf` was `pub(crate)`, so a consumer asking "is this competitor
below the cutoff" approximated it with a `mu + z * sigma` band and had no
way to say what confidence any `z` bought. Adds
`Gaussian::probability_below` / `probability_above`. The second is
separate on purpose: `1 - cdf` collapses to exactly zero past ~8.3
sigma, and a stopping rule is evaluated precisely there. Both route
through the survival function added in 0.4.1, so this is visibility
rather than new numerics.
#50 — `ConvergenceReport` was not `#[must_use]`, so the one signal that
a fit stopped short was trivially discarded. It now is, and that
immediately found 78 sites doing exactly that — including this crate's
own ATP example, which was capped at 10 sweeps when the history needs
30. The example now reads the report and says so.
`ITERATIONS = 30` is documented as the floor it is, with the three
measurements to hand: 400 events over 100 competitors already stops
there at ~7e-3 against a 1e-6 tolerance, the ATP example needs 30 at a
much looser one, and a consumer's 2000-node model needs 76 to 161.
BREAKING CHANGE: `InferenceError::UnknownKey` gains a `key` field, and
the prediction methods now require `K: Debug` in order to fill it.
Closes#43, #50. Refs #44 — its third ask, an opt-in `UnknownKeys::Skip`
mode, is a live API question and deliberately not answered here.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
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.
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.
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.
logaritmiskandClaude Opus 5