main's 'ND' multiple_glazing_type mapper fix (361abc12) changes the mapped
ground-truth glazing for one fixture cert, so modal_glazing_type re-baselines
0.5833 -> 0.5556 (21/36 -> 20/36). A mapper change shifts the deterministic
fixture rates like a fixture change does — re-baseline, not a prediction
regression. All other component floors + residual ceilings unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Folds a haversine distance kernel into the categorical-mode weighting so a
nearer neighbour counts for more — applied ONLY to the components that showed
a clear distance signal in the corpus pre-check (age band, wall + floor
construction, glazing: homes built/retrofitted together cluster). Roof
construction showed no decay and is excluded; heating keeps its coherent
donor. Predictor stays pure: weights come from target.coordinates vs each
Comparable.coordinates (resolved at the boundary); geo is OFF when the target
has no coords, neutral for a neighbour with none.
Scale chosen on the harness: _GEO_SCALE_KM=0.1 is the gate-safe optimum
(0.05 lifts the corpus more but regresses fixture floor_construction).
Corpus (150pc/514, geo off->on): age 0.564->0.572, age_pm1 0.841->0.847,
wall 0.902->0.912, floor_con 0.786->0.796, glazing 0.667->0.673; roof
unchanged. Fixture: glazing 0.5278->0.5833 (floor ratcheted), all else held.
Refactored recency into a reusable _recency_weights vector composed via
_combine, so similarity/recency/geo factors multiply uniformly. Fixture ships
a committed _coordinates.json (OGL OS OpenData; build script carries it from
the corpus sidecar on rebuild) so the gate exercises geo without S3.
This is the per-component method applied to geography ([[feedback_per_component_best_method]]).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds coordinates: Optional[Coordinates] to Comparable and PredictionTarget
(data carriers — the pure predictor stays IO-free), and wires load_corpus to
read an optional _coordinates.json sidecar ({uprn: [lon, lat]}) and populate
each Comparable from its cert's uprn; iter_predictions threads the held-out
target's coordinates through. Absent sidecar -> geo-weighting stays off (no
behaviour change yet — weighting lands next slice). fetch_corpus_coordinates
now writes the sidecar into the corpus dir. load_corpus populates 99% of
corpus comparables.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds GeospatialRepository.coordinates_for_uprns(uprns) -> dict — a batch
coordinate lookup returning only covered UPRNs. The S3 adapter overrides it
to read the meta once, group UPRNs by their covering partition, and read each
partition once for all the UPRNs it covers; co-located (closely-numbered)
UPRNs share a partition, so an EPC Prediction cohort is typically one or two
reads instead of one per neighbour. Default port impl is a per-UPRN loop.
Feeds the EPC Prediction geo-proximity work: a cohort's UPRNs resolve to
coordinates in a couple of reads (validated at corpus scale: 170 partition
reads for 2683 UPRNs).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds roof_insulation_thickness_pm1 (mirrors construction_age_band_pm1, issue
#1222): adjacent RdSAP thickness buckets (0/NI,12mm..400mm+) carry near-
identical roof U-values, so an off-by-one bucket is a SAP-neutral hit. 'ND'
(no-data) is off the ordered scale, so only an exact match counts there.
Honest measurement of SAP-relevant roof-insulation quality.
Corpus (150pc/514): exact 49.3% -> +/-1 53.7% (the misses are often multiple
buckets or ND, so the band gain is smaller than age's). Fixture: exact ==
+/-1 (0.4118) — its misses are all >1 bucket; gate floor added at 0.4118.
Also fixes two pre-existing pyright errors in the touched test file
(_epc main_fuel_type/main_heating_control were Optional but the
MainHeatingDetail attributes are non-optional Union[int, str]).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three more pre-existing failures (present at 9ee38211, before this branch's
recent commits; same family as the orchestration multi-measure re-pin) —
golden-cert plan expectations that predate the ASHP generator (ADR-0025)
and the optimiser folding forced dependencies into candidate gain (ADR-0016):
- test_console: a multi-measure plan now leads with air_source_heat_pump,
not cavity_wall_insulation (which is dropped — its forced ventilation makes
the pair net-negative). Assert a measure actually in the package.
- test_report 0330: package is now {solid_floor_insulation, air_source_heat_
pump}; cavity_wall + forced mechanical_ventilation correctly excluded.
- test_report 0036: gain-maximising package is now {solid_floor_insulation,
low_energy_lighting}.
Same verified-correct optimiser evolution as 077e3a39 (cavity_wall +2.9 SAP
alone but its forced fabric→ventilation dep drags the pair net-negative).
Re-pin to the actual packages + their trigger fields; the forced wall→vent
edge stays covered by test_measure_dependency / test_optimiser.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Heating sub-fields can't be field-moded without breaking system coherence,
so the whole SapHeating cluster is now copied as a unit from a single
coherent donor rather than inherited from the structural template: the
neighbour matching the cohort's modal heating signature (main fuel +
category + cylinder presence), most recent among the matches (recent cert =
current system). Including cylinder presence in the signature is load-bearing
— it protects has_hot_water_cylinder + cylinder_insulation (a bare fuel+cat
signature regressed them).
Corpus (150pc/514): heating_main_control 66.3 -> 73.9% (+7.6, the target),
main_fuel 92.8 -> 96.9, category 90.7 -> 95.7, water_fuel 92.8 -> 96.3,
water_code 88.5 -> 95.3, has_cylinder 81.1 -> 89.7, secondary 36.2 -> 42.0.
SAP MAE vs lodged 7.08 -> 6.00 (calculator floor 1.57). cylinder_insulation
-13.6 corpus (tiny-n) but +33pp on the fixture; AC requires control up +
fuel/category hold + SAP not worsened, all met.
Gate (36-target fixture): zero regression; ratcheted main_category
0.8889->0.9444, main_control 0.7500->0.8056, water_fuel 0.9167->0.9722,
water_code 0.8889->0.9444, cylinder_insulation_type 0.1667->0.5000. This is
the per-component heating method ([[feedback_per_component_best_method]]):
coherent donor, never field-mode.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The optimiser-package expectation was stale: it predated the optimiser
folding a triggered measure's forced dependency into its candidate gain
(ADR-0016). The run considers ALL measures (considered_measures defaults
to None — no restriction), so once the ASHP bundle became SAP-beneficial
(ADR-0025) the gain-maximising package shifted.
Verified the new package is CORRECT, not a regression: on the test EPC,
cavity-wall insulation earns +2.9 SAP alone but its forced fabric→
ventilation dependency (ADR-0016) drags the wall+ventilation pair to a
NET −1.8 SAP (−0.9 on top of the ASHP package), so the gain-maximising
Optimiser correctly excludes the wall and its forced ventilation. Update
the expected set to {air_source_heat_pump, suspended_floor_insulation,
low_energy_lighting, secondary_heating_removal} and drop the wall/vent-
specific assertions — the forced wall→ventilation edge is covered by
test_measure_dependency / test_optimiser; this integration test keeps its
end-to-end optimise→persist→telescope coverage on the chosen package.
Pre-existing failure (present before this branch's recent commits), outside
the handover regression gate.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per-component method: glazing type is now the recency-weighted cohort mode
applied to every predicted window, rather than copied from the template.
Glazing is retrofitted over a dwelling's life (single -> double), so a
recent neighbour reflects the current state — same family as roof-insulation
thickness. Recency is the CORRECT weighting here: plain moding regressed the
fixture (-5.6pp) and was previously reverted; similarity weighting also
regressed it; recency improves BOTH (window geometry stays on the template,
only the glazing categorical moves).
modal_glazing_type: corpus (150pc/514) 60.7 -> 66.7% (+6.0pp); fixture
0.5000 -> 0.5278 (floor ratcheted up). Heating, geometry residuals and all
other components unchanged. Refactored _recency_weighted_mode to a reusable
_recency_weighted_choice(value_of) shared by roof insulation + glazing.
Closes the #1223 per-component approach: floor-area (median estimate) +
glazing (recency) shipped as distinct best-fit methods rather than a global
recency template, which would have disturbed the coherence-coupled heating
cluster.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The realistic re-generation of case 37 (code-117 gas boiler, control 2102,
+ a MAINS-GAS condensing gas-fire secondary code 611, vs case 37's biogas
605). The full extractor -> mapper -> calculator pipeline reproduces the
worksheet's SAP-rating block EXACTLY: continuous SAP 60.9152 (Δ 2e-5) and
(272) CO2 5801.0770 (Δ ~0). This confirms the boiler-efficiency /
control-2102 −5pp interlock / secondary-fuel handling are all correct, and
that case 37's +7 gap was purely the biogas sub-fuel the Summary export
cannot carry.
Summary mirrored into backend/documents_parser/tests/fixtures so the pin
runs without the unstaged workspace. PE not pinned — it is a separate
DPER block (different scope) already guarded by the corpus PE gauge.
Worksheet harness 47/47 unchanged; pyright net-zero.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per-component method, not a global template change: the predicted floor
area is now the cohort median (the MAD-minimising point estimate of the
target's size) rather than whichever structural template's own area. The
calculator derives heat loss from building-part geometry, not this scalar,
so decoupling them is safe and the scalar becomes a better size estimate.
floor_area mean|.|: corpus (150pc/514 targets) 10.62 -> 10.48; fixture
12.2175 -> 11.8983 (ceiling ratcheted down). No other component moves.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The HHR-storage HeatingOverlay (ADR-0024) added an off-peak electric
immersion cylinder but never set `immersion_heating_type`, so the overlaid
cert left it None. The calculator then could not resolve `immersion_single`
for the SAP 10.2 Table 13 HW high-rate split and billed hot water 100% at
the off-peak low rate — £127.41 vs the relodged after-cert's £169.39,
overstating the overlay's SAP by +1.26 (CO2/PE matched, isolating it to the
HW cost path).
Add `immersion_heating_type` to HeatingOverlay, route it through
`_fold_heating` (it lives on `sap_heating`), and set it to 1 (single
off-peak immersion) on the HHR overlay to match the relodged reference.
Closes both `test_hhr_storage_overlay_reproduces_the_relodged_after_*`
cascade pins (electric-storage and no-system befores share the after).
Pre-existing failure (present before this branch's recent commits), outside
the handover regression gate. Full modelling suite 220 pass, pyright net-
zero.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ADR-0029 decision 5: survivors were treated equally; now each neighbour's
vote in the cohort mode decays with its distance from the cohort's physical
centre (floor area from the median, age band from the modal band), so the
mode leans on the most representative neighbours instead of being swayed by
size/era outliers. Scales (size 20 m^2, age weight 0.5) chosen on the
validation corpus; the tight size kernel is load-bearing (looser scales
regress floor_insulation on the fixture).
Corpus (181 SAP-10.2 targets): wall_insulation 83.4->86.2%,
roof_construction 86.2->87.3%, floor_construction 78.8->81.2%,
floor_insulation 92.9->94.1%; net +7.5pp gained vs -1.1pp (two 1-cert dips,
both held on the fixture). Geometry/residuals untouched (template unchanged).
Gate (36-target fixture): zero regression across all 24 floors/ceilings;
ratcheted wall_insulation_type 0.7778->0.8333, floor_construction
0.7500->0.8125, floor_insulation 0.9062->0.9375. Dead _mode/_int_mode
removed (superseded by the weighted variants).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds PredictionConfidence (cohort size + per-component agreement = the
modal value's share among neighbours that lodge one) and
EpcPrediction.confidence(), a compute-only signal so downstream can flag
low-confidence components (ADR-0029 open item: 'confidence signal').
Sanity check on the 40-postcode corpus (1068 component predictions):
agreement is strongly predictive of correctness — pooled hit-rate 21.9%
(<0.5) / 46.7% (0.5-0.7) / 73.6% (0.7-0.9) / 95.5% (>=0.9); point-biserial
corr(agreement, correct) = 0.582. Cohort size tracks too (<6 -> 68.4%,
>=20 -> 96.0%). Surfacing / persistence is a separate HITL follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Measurement honesty so we optimise SAP-relevant accuracy, not SAP-neutral
misses (ADR-0030 Component Accuracy):
- Add construction_age_band_pm1: an exact-or-adjacent-band hit. Adjacent
RdSAP age bands carry near-identical U-values, so an off-by-one is
~SAP-neutral. Full corpus: exact 78.5% but ±1-band 91.7% (fixture
63.9% -> 83.3%) — most age misses are adjacent.
- Drop window_count from the gate's residual ceilings (cosmetic): the
predicted picture clusters at a mapper-default 4 windows vs actuals 1-21,
but total_window_area (the SAP-relevant signal) stays tight at ~3.4 m2.
Gate: + construction_age_band_pm1 floor 0.8333; window_count no longer gated.
Closes#1222
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
End-to-end API-path regression pin for the battery behaviour validated by
the user-simulated Elmhurst worksheet pair (cert 001431 "simulated case
35/36", 5 kWh, export-capable, mains-gas, standard tariff). The official
SAP rating ("10a. Fuel costs - using Table 12 prices") values PV used-in-
dwelling and PV exported identically at 13.19 p/kWh (export code 60 ==
import code 30, ADR-0010), so a battery only redistributes PV between two
equally-priced lines: worksheet PV credit (252) = -455.6458 and SAP (258)
= 88.0859 are IDENTICAL with/without the battery (ΔSAP = 0).
Two tests over the committed RdSAP-21.0.1 corpus:
- standard tariff (meter 2): toggling the battery holds continuous SAP
EXACTLY constant, while at least one cert's primary energy DOES respond
(proving the App-M1 §3c β-split is wired, not a dropped battery).
- off-peak tariff (meter != 2): the battery STRICTLY raises SAP, because
self-consumed PV displaces high-rate import (15.29) above the 13.19
export credit — confirming the standard-tariff neutrality is a price
coincidence, not a no-op.
Guards table_32 export price (code 60) and the battery β-split against
silent regression. Complements the unit-level β tests in
test_photovoltaic.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Investigated recency-weighting (weight cohort votes by an exponential decay
in cert age). Key finding: it must be SELECTIVE. On the validation corpus it
HURTS permanent categoricals (wall 91.2->89.5, age 78.5->75.7 — discards
still-valid data) but clearly HELPS time-varying ones, where a recent
neighbour reflects the current physical state:
roof_insulation_thickness 56.7 -> 60.7% corpus (+4pp)
29.4 -> 41.2% fixture (+12pp)
So apply a recency-weighted mode only to roof_insulation_thickness (loft
top-ups happen over time); keep the plain mode for permanent categoricals.
tau = 4yr (~2.8yr half-life); falls back to plain mode when no registration
dates are lodged. Gate floor ratcheted 0.2941 -> 0.4118.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
These independent fabric categoricals were template-copied; mode them like
the construction categoricals. Verified mode beats template before applying.
Big fixture win on roof insulation thickness (doubled), floor insulation
neutral-to-positive:
roof_insulation_thickness 14.7% -> 29.4% (gate floor ratcheted up)
floor_insulation 90.6% (unchanged on the fixture)
Glazing type was tried too (+1.6pp on the 40-postcode corpus) but REGRESSED
the 36-target fixture (0.50 -> 0.44) — the gate caught it. Glazing moding is
marginal/noisy, so it's left on the template; revisit with a larger corpus.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The committed CI gate: run the calculator-free leave-one-out scorer over the
frozen anonymised fixture (36 SAP-10.2 targets) and assert each per-component
classification rate / geometry residual is no worse than a committed baseline.
Prediction is deterministic + the fixture frozen, so the numbers reproduce
exactly — a failure is a real regression, never sample noise.
- 19 rate floors + 5 residual ceilings, seeded at the currently-measured
values; they only ever tighten (no-widening ethos on an aggregate).
- Calculator-FREE — component floors are the real gate; the end-to-end
SAP/carbon/PE guards stay out (their floor is the separate API-path
calculator workstream).
- Skips with a message when the fixture is absent.
25 parametrized assertions, all green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The committed gate needs frozen, reproducible data without dumping real UK
addresses into the repo. Add:
- harness anonymise_payload + stable_hash: hash street address + cert number
into opaque, dedup-stable tokens; blank secondary address lines + post_town;
keep postcode + all component/lodged fields (gov data is OGL). Unit-tested.
- scripts/build_epc_prediction_fixture.py: curate qualifying postcodes (>=1
SAP 10.2 target + >=2 distinct addresses) from the local scratch corpus,
anonymise, freeze under tests/fixtures/epc_prediction/.
- The frozen fixture: 15 postcodes / 280 certs / 36 SAP-10.2 targets.
Verified no plaintext address_line_1 and post_town all blank.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A "No system present: electric heaters assumed" lodging carries SAP
Table 4a code 699 (electric room heaters) but RdSAP main_heating_category
1, NOT 10. `_table_12a_system_for_main` keyed the direct-acting-electric
routing on category==10 only, so the category-1 form fell through to None
and `_space_heating_fuel_cost_gbp_per_kwh` billed space heating 100% at
the off-peak LOW rate — as if direct-acting room heaters charged overnight
like storage.
Per RdSAP 10 §12 Rule 3 (PDF p.62) electric room heaters (691-694, 699)
route to the 10-hour tariff, and SAP 10.2 Table 12a Grid 1 (PDF p.191)
gives the "other direct-acting electric" row a 0.50 high-rate fraction at
10-hour (1.00 at 7-hour). Route those SAP codes — the same set §12 Rule 3
already uses — to OTHER_DIRECT_ACTING_ELECTRIC alongside the category-10
gate.
Found via the PE/CO2-vs-cost split on the worst over-rater in the /tmp
sample: cert 2958 PE +0% / CO2 -1% (energy correct) but SAP +32.2 — a
pure cost-side bug. Space rate 7.50 -> 11.09 p/kWh; cert 2958 +32.2 ->
+14.7. The committed corpus gauge is unchanged (its 3 non-category-10
code-699 certs are all on Single meters -> STANDARD tariff, so this split
never applies to them); the win is on the unbiased /tmp population's
single worst cert.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
"One scorer, two harnesses" (ADR-0030): the committed gate, the local script,
and the future battle-test must run the *same* scoring. Extract it:
- domain/epc_prediction/validation.py — `iter_predictions` (the single
leave-one-out orchestration: latest-per-address hold-out, SAP-10.2 target
filter, all-vintage source) + `evaluate_component_accuracy` (calculator-free
ComponentAccuracy aggregation, the primary signal). Unit-tested.
- harness/epc_prediction_corpus.py — `load_corpus(dir)` IO: corpus dir ->
Comparable cohorts (maps payloads, carries address + registration_date).
validate_epc_prediction.py now just loads + calls the scorer for the component
section and iterates iter_predictions for the calculator-floored end-to-end.
Identical numbers (181 targets, SAP MAE 6.34) — behaviour-preserving.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Heating is the dominant SAP lever (ablating it to actual cut the SAP error
~7 -> ~4.5) yet was entirely unscored. Add the heating group to
compare_prediction's categorical_hits: main fuel / category / control (off
the primary MainHeatingDetail), water-heating fuel / code, has-cylinder,
cylinder insulation, secondary heating (off SapHeating).
Template-copied baseline on the 40-postcode corpus (no predictor change
yet — this just makes the signal visible):
heating_main_fuel 93.4%
heating_main_category 92.7%
water_heating_fuel/code 91.7% / 92.4%
heating_main_control 62.1% <- weak
has_hot_water_cylinder 78.5%
cylinder_insulation_type 35.8% (n=120) <- weak
secondary_heating_type 16.8% (n=125) <- weak
Fuel/category predict well from the template; controls, cylinder, and
secondary heating are poor and now drive the next predictor slices.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ADR-0030 commits Component Accuracy to ~19 categorical components (5 today
+ 8 heating + glazing/renewables). Flat *_correct dataclass fields don't
scale — each needs manual runner wiring. Collapse them into a single
`categorical_hits: dict[str, Optional[bool]]` keyed by component name, which
also matches the runner's name-keyed aggregation (now generic: it tallies
whatever components the comparison reports). No behaviour change; the
classification rates are identical (wall n 578->575 is the 3 certs whose
actual wall is None, now correctly counted as not-applicable via _classify).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
SAP 10.2 Appendix M1 (PDF p.94): "EPV,ex,m = 0 if the PV system is not
connected to an export-capable meter." The cascade computed the β-split
export stream regardless of `is_dwelling_export_capable`, so a non-export-
capable dwelling was credited the full PV export — in the §10a COST it
credits at the Table 32 import rate (13.19 p/kWh), which dominates the rating.
On 7 Wybourn Terrace S2 5BJ the PE (144 vs lodged 151) and CO2 (27 vs 29)
already matched, yet the phantom export cost credit pulled SAP from ~73 to
92.1 (+19). Zero `epv_exported_monthly_kwh` after the Appendix-G4 diverter
adjustment when not export-capable; the onsite (EPV,dw) consumption and the
diverter HW reduction are unchanged.
Not-export-capable PV cohort (corpus, 4 certs): 7 Wybourn +19.1 -> +6.5,
4 Lime Ave +11.1 -> +0.4, 8 Hatherleigh +7.6 -> -0.2, Flat 5 ~-0.4. Gauge
66.1% -> 66.9%, MAE 1.124 -> 1.039. Floor 0.64 -> 0.65 / ceiling 1.18 -> 1.08.
Worksheet harness 47/47 0 diverge (Summary certs carry export-capable meters).
1 AAA test, pyright net-zero. Found by auditing the worst over-rater without a
worksheet: PE/CO2-match + cost-miss localised it to the PV export credit.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
RdSAP 10 §10.5 (PDF p.55): "If the actual size is not determined, the size of
a hot-water cylinder is taken as according to Table 28." When a cylinder is
present (has_hot_water_cylinder) but no size descriptor resolves — the gov API
lodges cylinder_size=0, or Exact with no measured volume — `_hot_water_
cylinder_volume_l` returned None, silently dropping BOTH the cylinder's
storage loss and the Table 13 electric-DHW high-rate fraction, under-costing
and over-rating the dwelling. Default such cylinders to the Table 28 baseline
"Normal" 110 L (the value §10.7 also instantiates as the first-row default).
The context-dependent Inaccessible 210/160 values are deliberately NOT applied
here — they are tied to the explicit "Inaccessible" descriptor (code 5) the
assessor lodges, not to an unpopulated size field.
Scope: 7 of 301 cylinder certs in the corpus (2%). Correctness fix — closes a
real spec gap; marginal on the headline (within-0.5 66.1% unchanged, MAE
1.128 -> 1.124) because these certs' residual is dominated by a separate HW-
demand gap, not the cylinder. Worksheet harness 47/47 0 diverge (Summary certs
lodge a real size, so the fallback never fires). 1 AAA test, pyright net-zero.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
SAP 10.2 Table 12a Grid 1 (PDF p.191): electric storage heater SAP code 408
is an "Integrated (storage + direct-acting) system" with a 0.20 space-heating
high-rate fraction on a 7-hour tariff — NOT the 0.00 of "other storage
heaters". `_table_12a_system_for_main` returned None for all storage codes (an
explicit TODO), so code 408 fell back to the 100%-low-rate path and billed
space heating at the bare 7-hour low rate (5.50 p/kWh) — under-costing →
over-rating. Mapped cat-7 storage: 408 -> INTEGRATED_STORAGE_DIRECT (0.20),
others -> OTHER_STORAGE_HEATERS (0.00, unchanged behaviour). The enum +
fraction rows already existed; this only wires the dispatch, so the split
flows self-consistently to both the §10a cost and the Appendix-M1 D_PV
high-rate fraction.
Corpus: sap408 over-raters +14.6/+12.9/+12.7 -> +7.1/+5.1/+3.4 (two crossed
into within-0.5). Gauge 65.9% -> 66.1%, MAE 1.160 -> 1.128. Floor 0.63 -> 0.64
/ MAE ceiling 1.22 -> 1.18. Worksheet harness 47/47 0 diverge. The residual
+3..+7 is the "all other uses" 0.90 high-rate fraction (lighting/pumps/HW
still billed 100%-low on the off-peak legacy path) — the next slice.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
SAP 10.2 Table 4c(3) (PDF p.169) "Factor for controls and charging method"
multiplies a heat network's heat requirement by 1.05-1.10 for FLAT-RATE
charging (note d: household pays a fixed amount regardless of heat used, so
no incentive to economise), and by 1.0 for charging linked to use. The
worksheet folds it into the heat-network requirement alongside the Table 12c
distribution loss factor:
(307) space = (98c) x (302) x (305) x (306)
(310) DHW = (64) x (305a) x (306)
Our cascade applied (306) DLF but never (305)/(305a), so every flat-rate
community-heating cert under-counted demand -> over-rated SAP.
Folded the factor into the 1/DLF efficiency override at the space-heating
(206) and DHW (water-inherits-from-main) sites. Space column adds +0.05 for
no thermostatic control (2301/2302); DHW column is 1.05 flat-rate / 1.0
linked-to-use.
Corpus (RdSAP-21.0.1, 1000 certs): community cluster median +0.32 -> -0.19,
within-0.5 38% -> 62% (control 2307 +0.83 -> -0.19; 2306 unchanged at factor
1.0 as spec requires). Overall gauge 65.0% -> 65.9%, MAE 1.174 -> 1.160.
Ratcheted the corpus-test floor 0.62 -> 0.63 / MAE ceiling 1.25 -> 1.22.
Also records (corpus-test comment + scripts/decompose_co2_pe_error.py) the
disproof of the prior "CO2/PE +5% is a factor/scope bug" lead: factors are
spec-exact, scope identical, and the bias is per-cert demand fidelity
(corr(SAP-err, PE-diff) = -0.54), not a one-slice factor fix.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The register lists every historical lodgement, so a postcode cohort
contains the same physical address many times (LS61AA: 15 certs / 11
addresses; NG71AA: 15 / 9 — "FLAT 3" appears 3x in each). Two
consequences:
- Production: a re-lodged neighbour was counting up to 3x towards the
cohort mode. select_comparables now dedupes candidates to the latest
cert per address (one comparable per real neighbour) — Comparable
gains address + registration_date (the register metadata its docstring
already anticipated, read straight off the cached payload).
- Validation: leave-one-out leaked — predicting a flat from a near-
identical re-lodgement of itself. The harness now holds out a whole
address (excludes every sibling cert) and evaluates on the latest cert
per address (the best ground truth).
Removing the leak gives the honest numbers (19 distinct addresses):
wall_construction 93.1% -> 89.5%
construction_age_band 65.5% -> 52.6%
roof_construction 79.3% -> 68.4%
floor_area mean|.| 37.9 -> 52.6 m2
The earlier figures were inflated by self-leakage; these are the real
accuracy to beat.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Only main wall_construction was set to the cohort mode; the other
homogeneous categoricals (wall insulation, construction age band, roof
construction, floor construction) were left as template-copied, so one
median-size template's quirks set them. Apply the same cohort-mode
mechanism to all of them per ADR-0029 decision 4 — the template still
supplies geometry, only the categorical codes move to the mode.
Verified mode beats (or ties) template-copy per categorical before
applying. Smoke corpus (29 leave-one-out) classification rates:
construction_age_band 55.2% -> 65.5%
roof_construction 72.4% -> 79.3%
floor_construction 46.2% -> 84.6%
wall_insulation_type 93.1% (tie — already template-strong)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The comparison only scored main wall_construction; everything else the
predictor produces (by template-copy) went unmeasured. Extend
compare_prediction to the rest of the ADR-0029 homogeneous categoricals —
wall insulation type, construction age band, roof construction, floor
construction — and aggregate per-categorical classification rates in the
runner. A categorical hit is "not applicable" (None, excluded from the
denominator) when the actual lodges no value, so absent-roof flats don't
score free wins.
Smoke corpus (29 leave-one-out, all but wall are template-copied today):
wall_construction 93.1%
wall_insulation_type 93.1%
construction_age_band 55.2% <- loud; candidate for cohort-mode
roof_construction 72.4%
floor_construction 46.2% (n=13)
These numbers drive the next slice (extend cohort-mode coverage).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Template (the comparable whose structure/geometry is copied wholesale)
was members[0] — an arbitrary draw from the API search order. With floor
area varying widely within a property_type cohort (NG71AA houses span
51-340 m2), this made the copied geometry noisy and systematically large.
Pick the member whose floor area is closest to the cohort median instead,
implementing ADR-0029 decision 4's unimplemented "closest on size"
criterion while keeping the structure coherent (it is still one real
property, so floor dims / windows / parts stay internally consistent for
the calculator).
Smoke corpus (29 leave-one-out predictions):
floor_area mean|.| 68.0 -> 37.9 m2 (bias +46.8 -> -3.9)
window_area mean|.| 11.1 -> 7.3 m2
parts mean|.| 1.00 -> 0.38
SAP |pred-calc - calc(actual)| MAE 7.19 -> 4.86
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pure compare_prediction (TDD): wall-construction classification hit + signed
residuals on floor area, window count, total window area, building-parts count.
Plus validate_epc_prediction.py (IO plumbing): drops each cert from its postcode
cohort, predicts from the rest on guaranteed inputs only, aggregates the metrics,
and reports SAP three ways (pred-calc vs lodged / vs calc-on-actual / vs the
neighbour-mean baseline). Smoke run: wall 90.9%, floor-area mean|·| 42.6 m2 (a
real signal — template-copied floor area is noisy), SAP pred-calc edges baseline.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
predict() copies a representative template comparable's structure (coherent for
the calculator), overrides the homogeneous categorical with the cohort mode
(robust to an atypical template), then applies known Landlord Overrides on top
(a known value wins over the estimate). Proven on wall construction; roof/floor/
insulation/age extend on the same mode+override mechanism, driven next by the
validation harness metrics.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pure-domain select_comparables: property type is an always-hard filter; built
form and known Landlord Overrides (e.g. solid brick) are conditioning filters on
the filter-then-relax ladder — applied while >= minimum_cohort survive, relaxed
otherwise (the mixed-street border case degrades gracefully). PredictionTarget
(known inputs) + Comparable (epc + register metadata) + ComparableProperties
(selected cohort). Weighting (recency x similarity) follows in the synthesis slice.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds a committed integration test driving the full API path — raw gov-EPC
response → from_api_response → cert_to_inputs → calculate_sap_from_inputs —
across all 1000 certs in the in-repo RdSAP-21.0.1 corpus, and pins the
aggregate accuracy of our continuous SAP (plus CO2 and primary energy)
against each cert's lodged figures. Mirrors scripts/eval_api_sap_accuracy.py
but runs in CI off the committed corpus (~2s, no /tmp sample needed).
Scoped to RdSAP-21.0.1 — the SAP 10.2-era schema whose lodged rating uses the
same methodology we compute (a fair target). Pre-SAP10 schemas (17.x-20.0.0)
lodge SAP 2012 ratings and are out of scope (guarded for mapping only by
test_mapper_corpus.py).
Current: SAP within-0.5 = 65.0%, MAE = 1.174 (tight floor/ceiling — the
optimised gauge). CO2 MAE = 0.27 t/yr (bias +0.17) and PE MAE = 14.6
kWh/m2/yr (bias +8.9) are reported + loosely guarded: cost is well-calibrated
but CO2/PE both run ~+5-10% high (uniform across fuels — a systematic
CO2/PE-factor or scope gap, not yet investigated). Thresholds ratchet as
slices tighten each metric.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The mixer-shower hot-water demand (worksheet 42a) divided N_shower by the
count of MIXER outlets only. But SAP 10.2 Appendix J step 1a is explicit:
"Establish how many shower outlets are present in the dwelling, Noutlets
(including in the count any instantaneous electric showers)" — and the
electric-shower step (64a) uses that same Noutlets from step 1a. So a
dwelling with both a mixer and an electric shower assigned the FULL N_shower
to the mixer system AND billed the electric shower on top of it, double-
counting shower demand → over-counted main HW → under-rated the dwelling.
Fix: thread the electric-shower count into the mixer demand so the
denominator is the total outlet count (mixer + electric), iterating the
warm-water draw over the mixer outlets only (per step 1e).
shower_types=1,2 cohort: -0.37 median -> +0.28 (crossed zero); API gauge
68.4% -> 69.0% within-0.5. Golden cert 0300-2747 (1 mixer + 1 electric)
re-pinned: PE +0.93 -> -0.10, CO2 +0.25 -> +0.15 (both toward zero,
confirming the double-count). Worksheet harness 47/47, 0 divergers (the
Elmhurst fixtures have no electric showers).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The no-PCDB MEV fan-electricity path fed the SAP 10.2 Table 4g default SFP
(0.8 W/(l/s)) directly as SFPav. But Table 4g note 3 (PDF p.176) is explicit:
the default SFP values "are to be multiplied by the appropriate in-use factor
for default data from the PCDB" — PCDB Table 329 system_type 10 ("default
data, used when SFP is taken from Table 4g rather than the PCDB"), IUF 2.5
(duct-agnostic per note 2). Table 4h, which previously held these factors, is
retired ("no longer used – data now stored in the PCDB").
Omitting the IUF under-billed the index-less MEV fan electricity by 2.5x
(SFPav 0.8 instead of 0.8 x 2.5 = 2.0), so cost was too low and the cohort
over-rated. This is distinct from the with-index path, which already applies
the tested-product system_type-2 "no scheme" IUF (~1.45) per fan.
Index-less gas-house MEV cohort: +1.37 median -> -0.18 (12% -> 92% within 0.5),
no overshoot — the missing IUF was exactly the over-rate. API gauge 67.7% ->
68.4% within-0.5 (mean|err| 0.992 -> 0.986, signed +0.031 -> +0.006).
Worksheet harness 47/47, 0 divergers (Summary-path MEV certs carry a PCDB
index or are natural, so unaffected).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
