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18b9a29ffa1fc2557302aefedbe30bda3526dd9b
5
Commits
| Author | SHA1 | Message | Date | |
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 kemal
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18b9a29ffa |
Earn the blend weights and the intervals from forecasts, not from refits
fit() called learn(), and learn() updated three things: the RLS, the conformal
calibrator and the Hedge weights. Only the first belongs to a refit. The comment
above that loop already said so, and was wrong about what the code did.
Measured on 8.2 days of the live station, the 15 minute head had taken 977,078
Hedge updates from 758 distinct supervised pairs, a factor of 1,289, and the
1 day head 296,715 from 12 pairs, a factor of 24,726. A refit is not an outcome.
It is the same week of weather being read again, once every seven minutes.
Hedge is multiplicative, so an edge far too small to be real compounds to
certainty: twelve of twelve temperature and humidity heads had collapsed onto
climatology at a weight of 0.991 or above, while their own member_mae said the
members were within a few percent of each other. The ACI integrator moves by
gamma per observation, so it had likewise pinned against its clips, leaving the
6 hour temperature band (1.571 C) narrower than the 3 hour one (2.258 C), and
pressure at 1 day covering 3 of 7 with alpha jammed at the 0.005 floor.
Three changes, because fixing only the first would freeze the weights forever:
- fit() calls refit_step(), which touches the regression and nothing else.
The climatology and setpoint members were evaluated in that loop purely to
feed the Hedge update, so fit() no longer needs a climatology or a
setpoint_fn at all.
- verify() feeds observe_outcome() with the member predictions the forecast
was actually blended from. These are now written to the forecasts table at
issue time, because the learned member cannot be recovered afterwards: the
RLS has moved on.
- a matured forecast teaches exactly once. It stays readable for an hour so
the scorecard can aggregate a rolling window, which meant verify() was
feeding the calibrator the same outcome about twelve times.
The Hedge weights additionally decline an outcome that overlaps the last one
they took, which is the stride rule from fit() applied on the scoring side.
Forecasts are issued every retrain tick, so at the 1 day horizon roughly two
hundred a day resolve against very nearly the same outcome. The conformal window
absorbs that, a quantile over duplicated scores being merely overconfident about
its sample size, but exponentiated gradient cannot.
Walk-forward over the full 8.2 day record, against the current code:
mean MAE 0.856 (0.938 over the second half alone)
heads improved 17/18
beats persistence 7/18 -> 12/18
coverage |dev from .90| 0.188 -> 0.060, second half 0.112 -> 0.041
Decimating the conformal feed as well was measured and rejected. It reads better
(coverage |dev| 0.023) and is not: three heads fall below MIN_SCORES, drop to
1.645*sigma, and "cover" with a median band of +/- 107% relative humidity. At
h/4 and h/8 it never starves and lands within noise of not decimating at all, so
the simpler rule wins. Honest regressions: humidity at 12 hours is 19% worse,
and pressure past 6 hours is still under-covered, because at 1 day the point
forecast is genuinely poor and ACI can only widen so far.
A state file from before this change has its weights, member_mae, n_scored and
alpha reset on load. They are products of the replay, they are not evidence, and
they do not decay on their own: Hedge needs about twenty independent outcomes to
climb off its 1e-4 floor and the 1 d head sees one a day. The conformal scores
are kept, being residuals of roughly the right size, and the window refreshes
within about two days.
Schema migration verified against a pristine copy of the live database: 1,437
forecast rows preserved, five columns added, idempotent across restarts.
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kemal
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3dd45f7ebf |
Joystick labelling, a clock guard, and throttle logging
Three things the hardware offers that the code ignored. The joystick has never had a line of code. Left records a dry label, right a wet one, middle cycles the LED scene, and a full-panel flash acknowledges the press because a headless box gives no other sign and a button you cannot tell worked gets pressed twice. Precipitation is the weakest head in the bank and strong labels are its binding constraint: this station has 80 of them against thousands of proxy ones, entirely because the only label control lives in a web page, and a web page is not where anyone is standing when it starts raining. The board has no RTC, so a power cut without a network gives a clock somewhere in 1970 on the next boot. Solar elevation, the diurnal harmonics and a sample's position on the 5-minute grid then all lie with complete confidence, and unlike a gap in the record the damage cannot be identified afterwards. train() now refuses a clock below 2025 or one that has stepped behind the newest stored row, and logs the refusal rather than training on fiction. Undervoltage and thermal capping both shift the SoC temperature, which is the regressor in the self-heating compensation, so a weak power supply presents as an unexplained temperature bias rather than as anything resembling a power problem. get_throttled is now sampled hourly and logged when set. Measured and deliberately not done: colour features. r, g and b are logged and 74% of rows carry usable colour, but adding blue/red, green/red and saturation made MAE 1.50% worse and helped in only 13 of 72 cases. Three more regressors on a 33-feature model whose longest horizon trains on 13 independent pairs is straightforwardly overfitting. That also prompted a sweep of the RLS prior and forgetting factor in both directions; delta = 100 with lambda = 0.9985 is a local optimum on both axes, so neither moved. |
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kemal
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bda42a0468 |
Log both Sense HAT thermometers, and migrate schemas that predate them
The board carries two independent thermometers and the code averaged them into temp_raw without ever recording either. Measured over 12 samples on a real station: HTS221 30.973 C at sd 0.060, LPS25HB 29.810 C at sd 0.443, a standing gradient of 1.163 C with the SoC at 44.55 C. Two things follow from that and neither is possible without the raw channels. A plain average of a quiet sensor and one seven times noisier lands at sd 0.223 where inverse-variance weighting reaches 0.060, and the gradient between two chips at different distances from the SoC is a second observation of self-heating that could identify the compensator's k with no reference thermometer. Both need history, and history cannot be backfilled, so the columns land on their own ahead of the work that consumes them. CREATE TABLE IF NOT EXISTS is a no-op against a table that already exists, so adding to COLUMNS would have reached a fresh install and silently missed every station already running, then surfaced as an OperationalError inside insert_telemetry. That sits on the sample loop, so it takes a station down rather than leaving a gap. Store now reconciles the table against COLUMNS on open, which makes every future column addition safe rather than just this one. The simulator gains the same two channels, with couplings solved so their forward models average to exactly the k = 0.55 the compensator is tuned against. Aggregate behaviour is unchanged; only the per-channel detail is new. Simulated temp_raw noise does rise from 0.05 to 0.223, which is not a regression but the end of an over-optimistic figure: it was modelling the quiet sensor and calling it the average. |
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kemal
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98210bff8f |
Six enhancements: recompute, markers, vendoring, tests, nerd stats, DS18B20
1. POST /api/recompute re-derives every compensated column from the untouched raw values, removing the step a calibration otherwise leaves through the history. Possible because temp_raw, cpu_temp and hum are never overwritten. Idempotent by construction and tested per row: 0 of 6051 rows change on a second run. 6069 rows in 0.25 s here, so a few seconds on the Pi. 2. Calibration now emits a 'discontinuity' event alongside the calibration log, so downstream views can find the boundary without parsing prose. 3. Vendored Tailwind, Chart.js, hammer, the zoom plugin, KaTeX with its 20 woff2 faces, and both Google fonts into ashvale/static, served by the station. 1.4 MB. Verified with every non-localhost request aborted in the browser: zero external requests, equations still render, fonts still load. The dashboard no longer needs internet. 4. 54 pytest cases over the pure numerics: physics closed forms and round trips, both compensator inverse properties, the Kalman covariance invariants and NIS consistency, the RLS trace cap under a deliberately unexcited regressor, conformal coverage, and the Zambretti ordering. Wired into CI after the seed step so the recompute cases have history. Writing them caught my own sign error on the conformal update: a hit raises alpha and narrows the band, which reads backwards until you follow it through. 5. Stats for Nerds gains the condition number of each head's covariance, a standardised innovation histogram per Kalman filter from a bounded 600 sample ring buffer, and a reliability strip of realised against nominal coverage. All arithmetic on data already in memory. 6. OutdoorProbe reads a DS18B20 over the kernel 1-Wire driver, no new dependency. Polled on its own slower cadence because the sensor blocks for up to 750 ms during conversion, which would eat a third of the 2 s sample budget. Rejects the 85000 power-on sentinel and out-of-range values, and reports age so a dead probe cannot masquerade as fresh. |
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kemal
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06ce53bc44 | Initial release: Ashvale Station 1.0.0 |