mirror of
https://github.com/Hestia-Homes/Model.git
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68401c517a
Sibling migration to the sap10_calculator move — `domain.ml` now lives
at the root-level layout (`domain/sap10_ml/`) matching the pattern
already used by `domain.addresses`, `domain.tasks`, `domain.postcode`,
and `domain.sap10_calculator`.
Changes:
- `git mv packages/domain/src/domain/ml → domain/sap10_ml` (19 files;
history preserved).
- Subpackage rename: `domain.ml` → `domain.sap10_ml`. 32 references
rewritten across .py and .md files: 11 internal + 21 external
(datatypes/epc/domain/mapper.py, 14 files in domain/sap10_calculator,
2 backend tests, 2 ADRs, 1 README, 1 design doc).
- Path-string updates: `pytest.ini` testpath
`packages/domain/src/domain/ml/tests` → `domain/sap10_ml/tests` so
ML tests stay in the default auto-discovered sweep. `CONTEXT.md`
also updated.
`packages/domain/src/domain/` is now empty — the workspace `domain/`
tree has been fully migrated. Together with the `domain/__init__.py`
deletions from the sap10_calculator commit (29ac35cc), `domain` is
now a single root-level namespace package with subpackages
{addresses, sap10_calculator, sap10_ml, tasks} + the standalone
`postcode.py` module.
Verified:
- Focused sweep (backend mapper-chain + sap10_calculator worksheet
e2e + golden fixtures): 99 passed / 19 failed — identical baseline.
- Wider sweep (all sap10_calculator + sap10_ml): 1654 passed / 20
failed (same pre-existing failures).
- domain/sap10_ml/tests: 210/210 PASSED at new path.
- Pyright net-zero: heat_transmission.py 13, cert_to_inputs.py 35,
mapper.py 33, rdsap_uvalues.py 1 (all unchanged from baseline).
Note: `packages/domain/pyproject.toml` still declares
`packages = ["src/domain"]` for the hatchling wheel — that target
directory is now empty and the wheel build is effectively a no-op.
Retiring the workspace package or repointing the wheel is a follow-up.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
158 lines
5.6 KiB
Python
158 lines
5.6 KiB
Python
"""Tests for the predicted_total_cost / predicted_ecf / predicted_log10_ecf
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features (slice 16e, ADR-0008)."""
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from math import log10
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import pytest
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from domain.sap10_ml.ecf import (
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predicted_ecf,
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predicted_log10_ecf,
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predicted_pv_generation_kwh,
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predicted_total_fuel_cost_gbp,
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)
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def test_predicted_pv_generation_kwh_scales_linearly_with_peak_power() -> None:
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# Arrange — UK average yield ~ 850 kWh/kWp/yr; 4 kWp -> ~3400 kWh.
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# Act
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a = predicted_pv_generation_kwh(pv_total_peak_power_kw=4.0, region_code="1")
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b = predicted_pv_generation_kwh(pv_total_peak_power_kw=8.0, region_code="1")
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# Assert
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assert b == pytest.approx(2.0 * a, abs=0.01)
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def test_predicted_pv_generation_kwh_returns_zero_for_no_pv() -> None:
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# Arrange / Act / Assert
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assert predicted_pv_generation_kwh(pv_total_peak_power_kw=0.0, region_code="1") == 0.0
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assert predicted_pv_generation_kwh(pv_total_peak_power_kw=None, region_code="1") == 0.0
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def test_predicted_total_fuel_cost_subtracts_pv_credit_at_electricity_price() -> None:
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# Arrange — gas heat + DHW + lighting, with 3000 kWh PV generation.
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# Base cost: (10000*3.48 + 2500*3.48 + 600*13.19) / 100 = 514.14
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# PV credit: 3000 * 13.19 / 100 = 395.70
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# Net: 514.14 - 395.70 = 118.44
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# Act
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with_pv = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=1, water_heating_fuel_code=1,
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predicted_pv_kwh=3000.0,
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)
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no_pv = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=1, water_heating_fuel_code=1,
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predicted_pv_kwh=0.0,
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)
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# Assert
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assert no_pv == pytest.approx(514.14, abs=0.05)
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assert with_pv == pytest.approx(118.44, abs=0.05)
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def test_predicted_total_fuel_cost_pv_kwh_defaults_to_zero_for_backwards_compatibility() -> None:
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# Arrange / Act — existing callers pre-17a omit predicted_pv_kwh entirely.
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# Act
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result = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=1, water_heating_fuel_code=1,
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)
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# Assert — same as predicted_pv_kwh=0.
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assert result == pytest.approx(514.14, abs=0.05)
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def test_predicted_pv_generation_kwh_scotland_lower_than_southern_england() -> None:
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# Arrange — Thames (region 1) vs Highland (region 17); same kWp.
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# Act
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thames = predicted_pv_generation_kwh(pv_total_peak_power_kw=4.0, region_code="1")
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highland = predicted_pv_generation_kwh(pv_total_peak_power_kw=4.0, region_code="17")
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# Assert
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assert highland < thames
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def test_predicted_total_fuel_cost_gas_heated_returns_expected_gbp() -> None:
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# Arrange — 12,000 kWh gas heat, 3,000 kWh gas DHW, 800 kWh lighting.
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# Gas (code 1) 3.48 p/kWh, electricity (30) 13.19 p/kWh.
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# Expected total: (12000*3.48 + 3000*3.48 + 800*13.19) / 100 = (41760 + 10440 + 10552) / 100 = 627.52
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# Act
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result = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=12000.0,
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predicted_hot_water_kwh=3000.0,
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predicted_lighting_kwh=800.0,
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main_fuel_code=1,
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water_heating_fuel_code=1,
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)
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# Assert
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assert result == pytest.approx(627.52, abs=0.05)
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def test_predicted_total_fuel_cost_electric_heated_higher_than_gas() -> None:
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# Arrange — same kWh demand on electricity vs gas.
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# Act
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gas = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=1, water_heating_fuel_code=1,
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)
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elec = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=30, water_heating_fuel_code=30,
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)
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# Assert
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assert elec > gas * 2.0
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def test_predicted_ecf_uses_sap_deflator_and_tfa_plus_45() -> None:
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# Arrange — total cost 627.52, TFA 100.
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# ECF = 0.42 * 627.52 / (100 + 45) = 263.56 / 145 = 1.817
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# Act
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result = predicted_ecf(predicted_total_cost_gbp=627.52, total_floor_area_m2=100.0)
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# Assert
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assert result == pytest.approx(1.817, abs=0.005)
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def test_predicted_ecf_returns_zero_for_unspecified_floor_area() -> None:
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# Arrange / Act / Assert
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assert predicted_ecf(predicted_total_cost_gbp=627.52, total_floor_area_m2=None) == 0.0
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def test_predicted_log10_ecf_matches_log10_for_positive_input() -> None:
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# Arrange / Act / Assert
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assert predicted_log10_ecf(1.817) == pytest.approx(log10(1.817), abs=0.0001)
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def test_predicted_log10_ecf_returns_zero_for_nonpositive_input() -> None:
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# Arrange / Act / Assert
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assert predicted_log10_ecf(0.0) == 0.0
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assert predicted_log10_ecf(-1.5) == 0.0
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def test_predicted_ecf_grows_when_more_expensive_fuel() -> None:
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# Arrange — same kWh, different fuel; electricity ECF >> gas ECF.
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# Act
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gas_cost = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=1, water_heating_fuel_code=1,
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)
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elec_cost = predicted_total_fuel_cost_gbp(
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predicted_space_heating_kwh=10000.0, predicted_hot_water_kwh=2500.0,
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predicted_lighting_kwh=600.0, main_fuel_code=30, water_heating_fuel_code=30,
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)
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gas_ecf = predicted_ecf(gas_cost, total_floor_area_m2=100.0)
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elec_ecf = predicted_ecf(elec_cost, total_floor_area_m2=100.0)
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# Assert — higher ECF -> worse SAP, matches intuition for resistive-electric heating.
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assert elec_ecf > gas_ecf
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