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7ca1f815f6
Two review points from @dancafc: 1) Rename the `Comparable` dataclass → `ComparableProperty` (it models one comparable *property*; the collection stays `ComparableProperties`). Applied across domain, repositories, orchestration, harness, scripts, and tests with a word-boundary rename so `ComparableProperties` is untouched. 2) Move `PredictionTarget` out of comparable_properties.py into prediction_target.py (where `PredictionTargetAttributes` + `build_prediction_target` already live). comparable_properties.py now imports it; no import cycle (prediction_target no longer depends on comparable_properties). Importers updated. 92 tests pass across the touched suites; pyright strict clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
"""Assemble an EPC-less Property's PredictionTarget, with the eligibility gate
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(ADR-0031 slice-5d).
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A `PredictionTarget` needs the target's own known inputs: its postcode (to find
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the cohort), coordinates (to distance-weight it), and the Landlord-Override
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attributes that condition selection — `property_type` (the HARD cohort filter),
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plus optional `built_form` / `wall_construction`. `property_type` is required: a
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Property whose type is unknown is gated out (never sized from a mixed-type
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cohort), so the builder returns None and the caller skips prediction.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Optional, Union
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from domain.geospatial.coordinates import Coordinates
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from domain.property.property import PropertyIdentity
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@dataclass(frozen=True)
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class PredictionTarget:
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"""The known inputs for the Property whose EPC we are predicting — the fields
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guaranteed at ingestion (plus any Landlord Overrides, added as they're used).
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`built_form` is often but not always known.
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"""
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postcode: str
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property_type: str
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built_form: Optional[str] = None
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# A known Landlord Override (e.g. solid brick) conditions cohort selection —
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# matching comparables are emphasised while enough remain (ADR-0029).
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wall_construction: Optional[Union[int, str]] = None
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# The target Property's own coordinates (resolved from its UPRN), against
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# which neighbours are distance-weighted. None disables geo-weighting.
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coordinates: Optional[Coordinates] = None
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@dataclass(frozen=True)
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class PredictionTargetAttributes:
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"""The target Property's own attributes resolved from Landlord Overrides,
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needed to find and condition its cohort. `property_type` is the code-space
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value the cohort EPCs carry (e.g. "2"); None means it could not be resolved,
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which gates the Property out of prediction."""
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property_type: Optional[str]
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built_form: Optional[str] = None
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wall_construction: Optional[Union[int, str]] = None
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def build_prediction_target(
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identity: PropertyIdentity,
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coordinates: Optional[Coordinates],
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attributes: PredictionTargetAttributes,
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) -> Optional[PredictionTarget]:
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"""The PredictionTarget for an EPC-less Property, or None when ineligible —
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`property_type` is the hard cohort filter, so a Property whose type is unknown
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is gated out of prediction (ADR-0031) rather than sized from a mixed-type
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cohort."""
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if attributes.property_type is None:
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return None
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return PredictionTarget(
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postcode=identity.postcode,
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property_type=attributes.property_type,
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built_form=attributes.built_form,
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wall_construction=attributes.wall_construction,
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coordinates=coordinates,
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)
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