Model/domain/epc_prediction/comparable_properties.py

"""Comparable Properties selection for EPC Prediction (ADR-0029).

Given a `PredictionTarget` (the known inputs for an EPC-less Property) and the
raw postcode cohort of candidate `ComparableProperty` objects, `select_comparables`
chooses the reference cohort EPC Prediction synthesises from. Pure domain logic —
the cohort IO (postcode search → per-cert fetch) lives behind a repository port.
"""

from __future__ import annotations

from dataclasses import dataclass
from datetime import date
from typing import Callable, Optional

from datatypes.epc.domain.epc_property_data import EpcPropertyData
from domain.epc_prediction.prediction_target import PredictionTarget
from domain.geospatial.coordinates import Coordinates

# Default floor on the cohort: a conditioning filter (built form, a known
# override) is applied only while at least this many comparables survive it,
# else it is relaxed (ADR-0029 filter-then-relax ladder).
_DEFAULT_MINIMUM_COHORT = 5


@dataclass(frozen=True)
class ComparableProperty:
    """One candidate neighbour: its structured `EpcPropertyData` picture plus the
    register metadata not carried on the cert (identity for leave-one-out
    exclusion; recency + address for weighting + re-lodgement dedup)."""

    epc: EpcPropertyData
    certificate_number: str
    address: Optional[str] = None
    registration_date: Optional[date] = None
    # Resolved from the neighbour's UPRN at the boundary (the harness / modelling
    # orchestrator), so the pure predictor can weight by physical distance to the
    # target without an IO dependency. None when no UPRN/coordinate is available.
    coordinates: Optional[Coordinates] = None


@dataclass(frozen=True)
class ComparableProperties:
    """The selected reference cohort for a `PredictionTarget`."""

    members: tuple[ComparableProperty, ...]


def _maybe_filter(
    cohort: list[ComparableProperty],
    predicate: Callable[[ComparableProperty], bool],
    *,
    active: bool,
    minimum_cohort: int,
) -> list[ComparableProperty]:
    """Apply a conditioning filter only while it leaves at least
    `minimum_cohort` comparables; otherwise relax it (keep the pre-filter
    cohort) — the filter-then-relax ladder (ADR-0029)."""
    if not active:
        return cohort
    filtered = [c for c in cohort if predicate(c)]
    return filtered if len(filtered) >= minimum_cohort else cohort


def select_comparables(
    target: PredictionTarget,
    candidates: list[ComparableProperty],
    *,
    minimum_cohort: int = _DEFAULT_MINIMUM_COHORT,
) -> ComparableProperties:
    """Select the ComparableProperty Properties for `target` from the raw postcode
    cohort. The register lists every historical lodgement, so first dedupe each
    address to its latest cert (one comparable per real neighbour); then property
    type is an always-hard filter (a flat is never a comparable for a house) and
    built form is a conditioning filter on the relax ladder."""
    cohort = _dedupe_to_latest_per_address(candidates)
    cohort = [
        c for c in cohort if c.epc.property_type == target.property_type
    ]
    cohort = _maybe_filter(
        cohort,
        lambda c: c.epc.built_form == target.built_form,
        active=target.built_form is not None,
        minimum_cohort=minimum_cohort,
    )
    cohort = _maybe_filter(
        cohort,
        lambda c: _main_wall_construction(c) == target.wall_construction,
        active=target.wall_construction is not None,
        minimum_cohort=minimum_cohort,
    )
    return ComparableProperties(members=tuple(cohort))


def _dedupe_to_latest_per_address(
    candidates: list[ComparableProperty],
) -> list[ComparableProperty]:
    """Collapse the register's re-lodgements: keep one comparable per address —
    the latest by registration date (ties broken by certificate number, for
    determinism) — so a re-lodged neighbour does not count more than once.
    Candidates with no address are passed through untouched (each is its own
    neighbour). Input order is otherwise preserved."""
    latest: dict[str, ComparableProperty] = {}
    passthrough: list[ComparableProperty] = []
    for c in candidates:
        if c.address is None:
            passthrough.append(c)
            continue
        incumbent = latest.get(c.address)
        if incumbent is None or _recency_key(c) > _recency_key(incumbent):
            latest[c.address] = c
    return list(latest.values()) + passthrough


def _recency_key(comparable: ComparableProperty) -> tuple[date, str]:
    """Sort key making the most recent (then highest cert number) win. A missing
    registration date sorts oldest."""
    return (
        comparable.registration_date or date.min,
        comparable.certificate_number,
    )


def _main_wall_construction(comparable: ComparableProperty) -> object:
    """The main building part's wall construction, or None when no part lodged."""
    parts = comparable.epc.sap_building_parts
    return parts[0].wall_construction if parts else None