junte/Model
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/Model.git synced 2026-06-08 11:17:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

add more type hint

Browse source
This commit is contained in:
Jun-te Kim 2026-05-12 09:48:21 +00:00
parent b72d5fbf42
commit e06ead55d0
2 changed files with 150 additions and 64 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
backend/address2UPRN/main.py
View file

@ -24,6 +24,7 @@ from datatypes.epc.domain.historic_epc_matching import (
match_addresses_for_postcode,
)
from backend.epc_client.client import EpcClientService
from datatypes.epc.domain.historic_epc_matching import ScoredHistoricEpc
logger = setup_logger()
@ -64,7 +65,7 @@ def get_uprn_from_historic_epc(
if not uprn or uprn == "nan":
return None
top = result.top()
top: Optional[ScoredHistoricEpc] = result.top()
if top is None:
return None

211
datatypes/epc/domain/tests/test_historic_epc_matching.py
View file

@ -1,3 +1,4 @@
from typing import Optional
from unittest.mock import patch
import numpy as np
@ -13,40 +14,103 @@ from datatypes.epc.domain.historic_epc_matching import (
match_addresses_for_postcode,
)
# Columns required by the HistoricEpc dataclass (lower-cased CSV columns).
# The matcher only reads ADDRESS + UPRN to score; everything else is filled
# with "" but must be present for HistoricEpc(**kwargs) to construct.
_FULL_COLUMN_FIELDS = [
"LMK_KEY", "ADDRESS1", "ADDRESS2", "ADDRESS3", "POSTCODE",
"BUILDING_REFERENCE_NUMBER", "CURRENT_ENERGY_RATING", "POTENTIAL_ENERGY_RATING",
"CURRENT_ENERGY_EFFICIENCY", "POTENTIAL_ENERGY_EFFICIENCY", "PROPERTY_TYPE",
"BUILT_FORM", "INSPECTION_DATE", "LOCAL_AUTHORITY", "CONSTITUENCY", "COUNTY",
"LODGEMENT_DATE", "TRANSACTION_TYPE", "ENVIRONMENT_IMPACT_CURRENT",
"ENVIRONMENT_IMPACT_POTENTIAL", "ENERGY_CONSUMPTION_CURRENT",
"ENERGY_CONSUMPTION_POTENTIAL", "CO2_EMISSIONS_CURRENT",
"CO2_EMISS_CURR_PER_FLOOR_AREA", "CO2_EMISSIONS_POTENTIAL",
"LIGHTING_COST_CURRENT", "LIGHTING_COST_POTENTIAL", "HEATING_COST_CURRENT",
"HEATING_COST_POTENTIAL", "HOT_WATER_COST_CURRENT", "HOT_WATER_COST_POTENTIAL",
"TOTAL_FLOOR_AREA", "ENERGY_TARIFF", "MAINS_GAS_FLAG", "FLOOR_LEVEL",
"FLAT_TOP_STOREY", "FLAT_STOREY_COUNT", "MAIN_HEATING_CONTROLS",
"MULTI_GLAZE_PROPORTION", "GLAZED_TYPE", "GLAZED_AREA", "EXTENSION_COUNT",
"NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS", "LOW_ENERGY_LIGHTING",
"NUMBER_OPEN_FIREPLACES", "HOTWATER_DESCRIPTION", "HOT_WATER_ENERGY_EFF",
"HOT_WATER_ENV_EFF", "FLOOR_DESCRIPTION", "FLOOR_ENERGY_EFF", "FLOOR_ENV_EFF",
"WINDOWS_DESCRIPTION", "WINDOWS_ENERGY_EFF", "WINDOWS_ENV_EFF",
"WALLS_DESCRIPTION", "WALLS_ENERGY_EFF", "WALLS_ENV_EFF",
"SECONDHEAT_DESCRIPTION", "SHEATING_ENERGY_EFF", "SHEATING_ENV_EFF",
"ROOF_DESCRIPTION", "ROOF_ENERGY_EFF", "ROOF_ENV_EFF", "MAINHEAT_DESCRIPTION",
"MAINHEAT_ENERGY_EFF", "MAINHEAT_ENV_EFF", "MAINHEATCONT_DESCRIPTION",
"MAINHEATC_ENERGY_EFF", "MAINHEATC_ENV_EFF", "LIGHTING_DESCRIPTION",
"LIGHTING_ENERGY_EFF", "LIGHTING_ENV_EFF", "MAIN_FUEL", "WIND_TURBINE_COUNT",
"HEAT_LOSS_CORRIDOR", "UNHEATED_CORRIDOR_LENGTH", "FLOOR_HEIGHT",
"PHOTO_SUPPLY", "SOLAR_WATER_HEATING_FLAG", "MECHANICAL_VENTILATION",
"ADDRESS", "LOCAL_AUTHORITY_LABEL", "CONSTITUENCY_LABEL", "POSTTOWN",
"CONSTRUCTION_AGE_BAND", "LODGEMENT_DATETIME", "TENURE",
"FIXED_LIGHTING_OUTLETS_COUNT", "LOW_ENERGY_FIXED_LIGHT_COUNT", "UPRN",
"UPRN_SOURCE", "REPORT_TYPE",
"LMK_KEY",
"ADDRESS1",
"ADDRESS2",
"ADDRESS3",
"POSTCODE",
"BUILDING_REFERENCE_NUMBER",
"CURRENT_ENERGY_RATING",
"POTENTIAL_ENERGY_RATING",
"CURRENT_ENERGY_EFFICIENCY",
"POTENTIAL_ENERGY_EFFICIENCY",
"PROPERTY_TYPE",
"BUILT_FORM",
"INSPECTION_DATE",
"LOCAL_AUTHORITY",
"CONSTITUENCY",
"COUNTY",
"LODGEMENT_DATE",
"TRANSACTION_TYPE",
"ENVIRONMENT_IMPACT_CURRENT",
"ENVIRONMENT_IMPACT_POTENTIAL",
"ENERGY_CONSUMPTION_CURRENT",
"ENERGY_CONSUMPTION_POTENTIAL",
"CO2_EMISSIONS_CURRENT",
"CO2_EMISS_CURR_PER_FLOOR_AREA",
"CO2_EMISSIONS_POTENTIAL",
"LIGHTING_COST_CURRENT",
"LIGHTING_COST_POTENTIAL",
"HEATING_COST_CURRENT",
"HEATING_COST_POTENTIAL",
"HOT_WATER_COST_CURRENT",
"HOT_WATER_COST_POTENTIAL",
"TOTAL_FLOOR_AREA",
"ENERGY_TARIFF",
"MAINS_GAS_FLAG",
"FLOOR_LEVEL",
"FLAT_TOP_STOREY",
"FLAT_STOREY_COUNT",
"MAIN_HEATING_CONTROLS",
"MULTI_GLAZE_PROPORTION",
"GLAZED_TYPE",
"GLAZED_AREA",
"EXTENSION_COUNT",
"NUMBER_HABITABLE_ROOMS",
"NUMBER_HEATED_ROOMS",
"LOW_ENERGY_LIGHTING",
"NUMBER_OPEN_FIREPLACES",
"HOTWATER_DESCRIPTION",
"HOT_WATER_ENERGY_EFF",
"HOT_WATER_ENV_EFF",
"FLOOR_DESCRIPTION",
"FLOOR_ENERGY_EFF",
"FLOOR_ENV_EFF",
"WINDOWS_DESCRIPTION",
"WINDOWS_ENERGY_EFF",
"WINDOWS_ENV_EFF",
"WALLS_DESCRIPTION",
"WALLS_ENERGY_EFF",
"WALLS_ENV_EFF",
"SECONDHEAT_DESCRIPTION",
"SHEATING_ENERGY_EFF",
"SHEATING_ENV_EFF",
"ROOF_DESCRIPTION",
"ROOF_ENERGY_EFF",
"ROOF_ENV_EFF",
"MAINHEAT_DESCRIPTION",
"MAINHEAT_ENERGY_EFF",
"MAINHEAT_ENV_EFF",
"MAINHEATCONT_DESCRIPTION",
"MAINHEATC_ENERGY_EFF",
"MAINHEATC_ENV_EFF",
"LIGHTING_DESCRIPTION",
"LIGHTING_ENERGY_EFF",
"LIGHTING_ENV_EFF",
"MAIN_FUEL",
"WIND_TURBINE_COUNT",
"HEAT_LOSS_CORRIDOR",
"UNHEATED_CORRIDOR_LENGTH",
"FLOOR_HEIGHT",
"PHOTO_SUPPLY",
"SOLAR_WATER_HEATING_FLAG",
"MECHANICAL_VENTILATION",
"ADDRESS",
"LOCAL_AUTHORITY_LABEL",
"CONSTITUENCY_LABEL",
"POSTTOWN",
"CONSTRUCTION_AGE_BAND",
"LODGEMENT_DATETIME",
"TENURE",
"FIXED_LIGHTING_OUTLETS_COUNT",
"LOW_ENERGY_FIXED_LIGHT_COUNT",
"UPRN",
"UPRN_SOURCE",
"REPORT_TYPE",
]
@ -63,7 +127,9 @@ def _build_df(rows: list[dict]) -> pd.DataFrame:
@pytest.fixture
def patch_postcode_valid():
with patch.object(matcher_mod.AddressMatch, "is_valid_postcode", return_value=True) as m:
with patch.object(
matcher_mod.AddressMatch, "is_valid_postcode", return_value=True
) as m:
yield m
@ -106,10 +172,12 @@ class TestMatchAddressesForPostcode:
self, patch_read, patch_postcode_valid
):
# Disjoint number sets => hard zero. Still kept in matches.
patch_read.return_value = _build_df([
_row("47 GORDON ROAD", "100"),
_row("999 SOMEWHERE ELSE", "200"),
])
patch_read.return_value = _build_df(
[
_row("47 GORDON ROAD", "100"),
_row("999 SOMEWHERE ELSE", "200"),
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
assert isinstance(result, HistoricEpcMatches)
assert len(result.matches) == 2
@ -117,10 +185,12 @@ class TestMatchAddressesForPostcode:
def test_top_has_lexirank_one_and_lexiscore_monotone(
self, patch_read, patch_postcode_valid
):
patch_read.return_value = _build_df([
_row("48 GORDON ROAD", "200"), # near miss
_row("47 GORDON ROAD", "100"), # exact (after normalisation)
])
patch_read.return_value = _build_df(
[
_row("48 GORDON ROAD", "200"), # near miss
_row("47 GORDON ROAD", "100"), # exact (after normalisation)
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
assert result.top().lexirank == 1
scores = [m.lexiscore for m in result.matches]
@ -173,19 +243,23 @@ class TestMatchAddressesForPostcode:
class TestUnambiguousUprn:
def test_exact_match_returns_uprn(self, patch_read, patch_postcode_valid):
patch_read.return_value = _build_df([
_row("47 GORDON ROAD", "100"),
_row("48 GORDON ROAD", "200"),
])
patch_read.return_value = _build_df(
[
_row("47 GORDON ROAD", "100"),
_row("48 GORDON ROAD", "200"),
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
assert result.unambiguous_uprn() == "100"
def test_ambiguous_tie_returns_none(self, patch_read, patch_postcode_valid):
# Two duplicate addresses with different UPRNs share rank-1.
patch_read.return_value = _build_df([
_row("47 GORDON ROAD", "100"),
_row("47 GORDON ROAD", "200"),
])
patch_read.return_value = _build_df(
[
_row("47 GORDON ROAD", "100"),
_row("47 GORDON ROAD", "200"),
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
assert result.unambiguous_uprn() is None
@ -193,10 +267,12 @@ class TestUnambiguousUprn:
self, patch_read, patch_postcode_valid
):
# User address has building number 47; no row has 47 -> all hard-zero.
patch_read.return_value = _build_df([
_row("999 ELSEWHERE", "100"),
_row("888 ELSEWHERE", "200"),
])
patch_read.return_value = _build_df(
[
_row("999 ELSEWHERE", "100"),
_row("888 ELSEWHERE", "200"),
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
assert all(m.lexiscore == 0.0 for m in result.matches)
assert result.unambiguous_uprn() is None
@ -205,15 +281,22 @@ class TestUnambiguousUprn:
self, patch_read, patch_postcode_valid
):
# Use a real NaN in the UPRN cell.
patch_read.return_value = _build_df([
_row("47 GORDON ROAD", np.nan),
_row("48 GORDON ROAD", "200"),
])
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
top = result.top()
patch_read.return_value = _build_df(
[
_row("47 GORDON ROAD", np.nan),
_row("48 GORDON ROAD", "200"),
]
)
result: HistoricEpcMatches = match_addresses_for_postcode(
"47 Gordon Road", "AB33 8AL"
)
top: Optional[ScoredHistoricEpc] = result.top()
# pandas_cell_to_str must turn NaN/"nan" into "" (not the literal string "nan"),
# so unambiguous_uprn's truthiness check correctly drops the row.
assert top.record.uprn == ""
if top:
assert top.record.uprn == ""
else:
pytest.fail("should have an epc score, no results found :(")
# ---------- top / top_n ----------
@ -222,11 +305,13 @@ class TestUnambiguousUprn:
class TestTopHelpers:
def test_top_n_returns_first_k(self, patch_read, patch_postcode_valid):
patch_read.return_value = _build_df([
_row("47 GORDON ROAD", "100"),
_row("48 GORDON ROAD", "200"),
_row("49 GORDON ROAD", "300"),
])
patch_read.return_value = _build_df(
[
_row("47 GORDON ROAD", "100"),
_row("48 GORDON ROAD", "200"),
_row("49 GORDON ROAD", "300"),
]
)
result = match_addresses_for_postcode("47 Gordon Road", "AB33 8AL")
top2 = result.top_n(2)
assert len(top2) == 2