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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 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
|
3f41881c64 |
Take Kalman tuning from config, not from the saved state
q and r were written into station_state.json and restored over the configured values, so tuning was effectively immutable in the field. This was found the expensive way: the retune in the previous commit was deployed, the service restarted cleanly, and the filters carried on with q = 2e-6 because that is what the state file said. Measured median rate afterwards was 14.4 C/h against 12.4 before, which is to say nothing happened. Only the estimate is state. x, P and initialised are restored; q and r now come from config every time. P may be momentarily inconsistent with a changed q, which costs a few hundred samples of reconvergence and is far cheaper than a configuration change that appears to work and does not. load_dict also now skips filters this build no longer has, rather than resurrecting them from an older state file. |
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|
kemal
|
40f934901d |
Retune the Kalman process noise, and fuse the two thermometers
Two changes to the same signal path, one large and one small. The large one: all three filters were tuned to track one to three decades faster than their signals move. In a still room the temperature filter reported a median rate of 12.4 C/h while the air moved 0.4 C/h, and it overshot a real -36 C/h event by 77%. Sweeping q against the RMSE of the reported rate versus the true rate, using noise measured on the board (temperature 0.088 C, pressure 0.022 hPa, humidity 0.40 %): temperature 6.45 -> 0.37 C/h RMSE 2e-6 -> 1e-9 pressure 2.15 -> 0.24 hPa/h RMSE 1e-5 -> 1e-8 humidity 27.94 -> 3.55 %/h RMSE 5e-5 -> 2e-8 Tracking does not suffer. Lag against a genuine 2 C/h ramp is 0.003 C at both the old and new values, and the peak response to a five-minute event moves closer to the truth rather than further from it, because the overshoot goes away. What is given up is response to sub-minute transients, which for a station forecasting fifteen minutes to a day ahead is noise to reject. This matters most for pressure, whose tendency drives the precipitation forecast, and which was the worst tuned of the three. config.yaml shadowed kalman_q_temp, so editing the dataclass alone changed nothing. All six values are now listed there with that hazard spelled out, because a silent shadow cost real time here. The small one: temp_raw was the plain average of two thermometers whose white-noise sds differ by 7x (LPS25HB 0.007 C, HTS221 0.049 C), which throws the quiet one away. Inverse-variance weighting cuts the raw noise 3.5x. The trap is that the chips do not agree. They sit at different distances from the SoC and stand about 1.3 C apart, so weighting by variance alone drags temp_raw 0.48 C onto the LPS25HB, which after the 1.55x gain of the inverse compensator is 0.75 C of silent bias on every reading, since k was fitted against the mean of the two. The gradient is therefore tracked and removed before weighting and only the deviations are fused: measured mean shift 0.0001 C, noise still 3.5x lower. The tracked gradient is retained because it is a second observation of self-heating. Also corrected: the earlier claim that the HTS221 was the quieter channel was wrong, taken from twelve samples at a cadence slow enough that real drift dominated. At 0.5 s over 120 samples the LPS25HB is quieter by 7x and takes 98% of the weight. |
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kemal
|
4cca40388f |
Heated environment: a thermostat member in the forecast ensemble
A room held at a setpoint is a different process from one left to drift. It is
a closed loop, and persistence, the baseline everything here is scored against,
is the wrong statement about it: the truth is not that it stays where it is, it
is that it returns to the setpoint.
So site.heating adds a fourth ensemble member, first order because that is what
a controlled system is:
dT_set(h) = (T_set - T_now) * (1 - exp(-h / tau))
Humidity follows and is the part that is easy to get wrong. Heating adds no
moisture, so vapour pressure is conserved and not relative humidity:
RH(h) = RH_now * es(T_now) / es(T_now + dT_set(h))
Warm the air and RH falls although nothing was dried, which is why a heated
house in winter is dry. The test asserts the dew point is unchanged to 1e-6.
Pressure gets zero: a thermostat cannot move the synoptic field.
Offered, not imposed. Hedge scores this member on realised error like any
other, so a wrong tau or a stale setpoint costs accuracy and gets down-weighted
rather than quietly biasing every forecast. Verified: on history with no
heating the ensemble assigned it weight 0.000. With heating off it returns zero
and is identical to persistence.
Going from three members to four means old saved heads must migrate.
from_dict reinitialises weights and member_mae. I missed member_mae first time
and it did not fail on load, it failed later inside learn() on a broadcast
error, which is a much worse place to find out; the migration test now covers
both and calls learn() to prove it.
Settings tab gains the toggle, setpoint and time constant. Turning heating on
or off is treated as a regime change like a door: discontinuity marker plus a
queued retrain.
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kemal
|
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. |