1214 lines
46 KiB
Python
1214 lines
46 KiB
Python
import polars as pl
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import pyarrow as pa
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import pytest
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from shapely import box, to_wkb
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from shapely.geometry import Point
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from pipeline.transform.merge import (
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_AREA_COLUMNS,
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CONSERVATION_AREA_FEATURE,
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LISTED_BUILDING_FEATURE,
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TREE_DENSITY_FEATURE,
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_LISTING_OVERLAY_SOURCES,
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_active_english_postcode_area,
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_build_unmatched_listing_seed_rows,
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_canonical_postcode_expr,
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_coalesce_direct_epc_columns,
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_filter_to_active_english_postcodes,
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_join_area_side_tables,
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_finalize_listings,
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_integrate_listings,
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_match_direct_epc,
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_match_listing_properties,
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_normalize_uprn,
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_is_dynamic_poi_metric_column,
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_less_deprived_percentile_expr,
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_load_conservation_area_geometries,
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_load_listings_for_merge,
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_matched_listed_building_flags,
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_postcode_conservation_area_flags,
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_postcode_listed_building_candidates,
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_remap_terminated_postcodes,
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_split_normal_outputs,
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_tree_density_by_postcode,
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_validate_lad_source_coverage,
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_validate_postcode_feature_output,
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_validate_property_postcodes,
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)
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def test_less_deprived_percentile_expr_preserves_direction_and_nulls() -> None:
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df = pl.DataFrame({"Income Score (rate)": [1.0, 2.0, 3.0, None]})
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result = (
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df.lazy()
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.with_columns(_less_deprived_percentile_expr("Income Score (rate)"))
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.collect()
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)
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assert result["Income Score (rate)"].to_list() == [100.0, 50.0, 0.0, None]
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def test_less_deprived_percentile_expr_uses_exact_scale_endpoints() -> None:
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df = pl.DataFrame({"Income Score (rate)": [1.0, 1.0, 2.0, 3.0, 3.0]})
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result = (
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df.lazy()
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.with_columns(_less_deprived_percentile_expr("Income Score (rate)"))
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.collect()
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)
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assert result["Income Score (rate)"].to_list() == [100.0, 100.0, 50.0, 0.0, 0.0]
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def test_dynamic_poi_metric_columns_are_area_level() -> None:
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assert _is_dynamic_poi_metric_column("Distance to nearest amenity (Cafe) (km)")
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assert _is_dynamic_poi_metric_column("Distance to nearest amenity (Park) (km)")
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assert _is_dynamic_poi_metric_column("Number of amenities (Cafe) within 2km")
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assert _is_dynamic_poi_metric_column("Number of amenities (Cafe) within 5km")
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assert not _is_dynamic_poi_metric_column("Number of restaurants within 2km")
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def test_country_code_is_kept_in_postcode_area_columns() -> None:
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assert "ctry25cd" in _AREA_COLUMNS
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def test_conservation_area_feature_is_area_level() -> None:
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assert CONSERVATION_AREA_FEATURE in _AREA_COLUMNS
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def test_crime_columns_are_spatial_counts_not_per_capita() -> None:
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# Crime is now a raw spatial count per postcode; the per-1k-residents
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# variants were dropped along with the LSOA population denominator.
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assert "Serious crime (avg/yr)" in _AREA_COLUMNS
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assert "Minor crime (avg/yr)" in _AREA_COLUMNS
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assert "Serious crime per 1k residents (avg/yr)" not in _AREA_COLUMNS
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assert "Minor crime per 1k residents (avg/yr)" not in _AREA_COLUMNS
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def test_active_english_postcode_area_filters_to_active_england() -> None:
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arcgis = pl.DataFrame(
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{
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"pcds": ["AA1 1AA", "AA1 1AB", "CF1 1AA"],
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"ctry25cd": ["E92000001", "E92000001", "W92000004"],
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"doterm": [None, "2020-01-01", None],
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"lat": [51.0, 51.1, 52.0],
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"long": [-0.1, -0.2, -3.0],
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"lsoa21cd": ["L1", "L2", "L3"],
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"oa21cd": ["O1", "O2", "O3"],
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"pcon24cd": ["P1", "P2", "P3"],
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}
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)
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result = _active_english_postcode_area(arcgis.lazy()).collect()
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assert result.to_dicts() == [
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{
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"postcode": "AA1 1AA",
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"lat": 51.0,
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"lon": -0.1,
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"ctry25cd": "E92000001",
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"lsoa21": "L1",
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"oa21": "O1",
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"pcon": "P1",
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}
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]
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def test_remap_then_active_filter_keeps_terminated_english_properties() -> None:
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wide = pl.DataFrame(
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{
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"postcode": ["OLD 1AA", "NEW 1AA", "CF1 1AA"],
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"row_id": [1, 2, 3],
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}
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).lazy()
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mapping = pl.DataFrame(
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{"old_postcode": ["OLD 1AA"], "new_postcode": ["NEW 1AA"]}
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).lazy()
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active_postcodes = pl.DataFrame({"postcode": ["NEW 1AA"]}).lazy()
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result = (
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_filter_to_active_english_postcodes(
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_remap_terminated_postcodes(wide, mapping), active_postcodes
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)
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.collect()
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.sort("row_id")
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)
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assert result.to_dicts() == [
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{"postcode": "NEW 1AA", "row_id": 1},
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{"postcode": "NEW 1AA", "row_id": 2},
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]
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def test_split_normal_outputs_uses_postcode_feature_universe() -> None:
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df = pl.DataFrame(
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{
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"Postcode": ["AA1 1AA"],
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"Address per Property Register": ["1 Example Road"],
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"Last known price": [250_000],
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"lat": [51.0],
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"lon": [-0.1],
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"ctry25cd": ["E92000001"],
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"lsoa21": ["L1"],
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}
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)
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postcode_features = pl.DataFrame(
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{
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"Postcode": ["AA1 1AA", "BB1 1BB"],
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"lat": [51.0, 52.0],
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"lon": [-0.1, -0.2],
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"ctry25cd": ["E92000001", "E92000001"],
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"lsoa21": ["L1", "L2"],
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"Distance to nearest amenity (Park) (km)": [0.3, 0.8],
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}
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)
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postcode_df, properties_df = _split_normal_outputs(
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df, postcode_features, expected_postcode_count=2
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)
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assert postcode_df["Postcode"].to_list() == ["AA1 1AA", "BB1 1BB"]
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assert "Distance to nearest amenity (Park) (km)" in postcode_df.columns
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assert properties_df.to_dicts() == [
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{
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"Postcode": "AA1 1AA",
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"Address per Property Register": "1 Example Road",
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"Last known price": 250_000,
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}
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]
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def test_postcode_feature_validation_rejects_unsupported_or_ungeocoded_rows() -> None:
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postcode_df = pl.DataFrame(
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{
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"Postcode": ["AA1 1AA", "CF1 1AA"],
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"lat": [51.0, None],
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"lon": [-0.1, None],
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"ctry25cd": ["E92000001", "W92000004"],
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}
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)
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with pytest.raises(ValueError, match="unsupported or ungeocoded"):
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_validate_postcode_feature_output(postcode_df, expected_postcode_count=2)
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def test_listed_building_feature_is_property_level() -> None:
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assert LISTED_BUILDING_FEATURE not in _AREA_COLUMNS
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def test_postcode_conservation_area_flags_marks_point_membership() -> None:
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postcodes = pl.DataFrame(
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{
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"postcode": ["AA1 1AA", "BB1 1BB", "CC1 1CC"],
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"lat": [0.5, 2.0, None],
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"lon": [0.5, 2.0, 0.5],
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}
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)
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result = _postcode_conservation_area_flags(
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postcodes, [box(0, 0, 1, 1)], "EPSG:4326", batch_size=2
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).sort("postcode")
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assert result.to_dicts() == [
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{"postcode": "AA1 1AA", CONSERVATION_AREA_FEATURE: "Yes"},
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{"postcode": "BB1 1BB", CONSERVATION_AREA_FEATURE: "No"},
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{"postcode": "CC1 1CC", CONSERVATION_AREA_FEATURE: "No"},
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]
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def test_load_conservation_area_geometries_uses_current_planning_data_records(
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monkeypatch: pytest.MonkeyPatch,
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tmp_path,
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) -> None:
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real_area = box(0, 0, 1, 1)
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ended_area = box(2, 2, 3, 3)
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other_dataset_area = box(4, 4, 5, 5)
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point = Point(0.5, 0.5)
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def fake_read_arrow(path):
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assert path == tmp_path / "conservation_areas.geojson"
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table = pa.table(
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{
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"dataset": [
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"conservation-area",
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"conservation-area",
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"listed-building",
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"conservation-area",
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],
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"end-date": ["", "2025-01-01", "", ""],
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"name": ["Central Village", "Old Boundary", "Other", "Point Record"],
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"SHAPE": to_wkb([real_area, ended_area, other_dataset_area, point]),
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}
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)
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return {"geometry_name": "SHAPE", "crs": "EPSG:4326"}, table
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monkeypatch.setattr("pipeline.transform.merge.pyogrio.read_arrow", fake_read_arrow)
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geometries, crs = _load_conservation_area_geometries(
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tmp_path / "conservation_areas.geojson"
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)
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assert crs == "EPSG:4326"
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assert geometries == [real_area]
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def test_postcode_listed_building_candidates_uses_nearby_postcodes() -> None:
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listed_points = pl.DataFrame(
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{
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"ListEntry": [1234, 5678],
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"Name": ["1 and 2 High Street", "Distant Hall"],
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"Grade": ["II", "I"],
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"Easting": [100.0, 1000.0],
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"Northing": [100.0, 1000.0],
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}
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).with_columns(
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pl.col("Name")
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.str.to_uppercase()
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.str.replace_all(r"[^0-9A-Z]+", " ")
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.str.replace_all(r"\s+", " ")
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.str.strip_chars()
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.alias("_listed_match_name")
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)
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active_postcodes = pl.DataFrame(
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{
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"postcode": ["AA1 1AA", "BB1 1BB"],
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"east1m": [105.0, 5000.0],
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"north1m": [105.0, 5000.0],
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}
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)
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result = _postcode_listed_building_candidates(
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listed_points,
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active_postcodes,
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nearest_postcodes=1,
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max_distance_m=25,
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)
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assert result.select("postcode", "_listed_match_name").to_dicts() == [
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{"postcode": "AA1 1AA", "_listed_match_name": "1 AND 2 HIGH STREET"}
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]
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def test_matched_listed_building_flags_requires_address_match() -> None:
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properties = pl.DataFrame(
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{
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"postcode": ["AA1 1AA", "AA1 1AA", "BB1 1BB"],
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"pp_address": ["1 HIGH STREET", "99 HIGH STREET", "THE OLD RECTORY"],
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"epc_address": ["1, High Street", "99, High Street", "Old Rectory"],
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}
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)
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listed_candidates = pl.DataFrame(
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{
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"postcode": ["AA1 1AA", "BB1 1BB"],
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"_listed_match_name": ["1 AND 2 HIGH STREET", "OLD RECTORY"],
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"_listed_grade": ["II", "II*"],
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"_listed_entry": [1234, 5678],
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}
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)
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result = _matched_listed_building_flags(
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properties.lazy(), listed_candidates, min_score=95
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).sort("postcode", "pp_address")
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assert result.to_dicts() == [
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{
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"postcode": "AA1 1AA",
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"pp_address": "1 HIGH STREET",
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LISTED_BUILDING_FEATURE: "Yes",
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},
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{
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"postcode": "BB1 1BB",
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"pp_address": "THE OLD RECTORY",
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LISTED_BUILDING_FEATURE: "Yes",
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},
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]
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def test_validate_property_postcodes_rejects_blank_rows() -> None:
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df = pl.DataFrame(
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{
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"Postcode": ["AA1 1AA", ""],
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"Address per Property Register": ["1 Example Street", "2 Example Street"],
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"Last known price": [100_000, 200_000],
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}
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)
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with pytest.raises(ValueError, match="Property rows missing a postcode"):
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_validate_property_postcodes(df)
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def test_validate_lad_source_coverage_allows_only_known_rent_no_data_lads(
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tmp_path,
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) -> None:
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iod_path = tmp_path / "iod.parquet"
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ethnicity_path = tmp_path / "ethnicity.parquet"
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rental_path = tmp_path / "rental.parquet"
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pl.DataFrame(
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{
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"Local Authority District code (2024)": [
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"E08000016",
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"E06000053",
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"E09000001",
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],
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"Local Authority District name (2024)": [
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"Barnsley",
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"Isles of Scilly",
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"City of London",
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],
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}
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).write_parquet(iod_path)
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pl.DataFrame(
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{"Geography_code": ["E08000016", "E06000053", "E09000001"]}
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).write_parquet(ethnicity_path)
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pl.DataFrame({"area_code": ["E08000016"], "bedrooms": [1]}).write_parquet(
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rental_path
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)
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_validate_lad_source_coverage(iod_path, ethnicity_path, rental_path)
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def test_validate_lad_source_coverage_rejects_unexpected_rent_holes(tmp_path) -> None:
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iod_path = tmp_path / "iod.parquet"
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ethnicity_path = tmp_path / "ethnicity.parquet"
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rental_path = tmp_path / "rental.parquet"
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pl.DataFrame(
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{
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"Local Authority District code (2024)": ["E08000016"],
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"Local Authority District name (2024)": ["Barnsley"],
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}
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).write_parquet(iod_path)
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pl.DataFrame({"Geography_code": ["E08000016"]}).write_parquet(ethnicity_path)
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pl.DataFrame({"area_code": ["E08000019"], "bedrooms": [1]}).write_parquet(
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rental_path
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)
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with pytest.raises(ValueError, match="Rental data is missing"):
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_validate_lad_source_coverage(iod_path, ethnicity_path, rental_path)
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def test_tree_density_by_postcode_aliases_radius_percentile(tmp_path) -> None:
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path = tmp_path / "tree_density_by_postcode.parquet"
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pl.DataFrame(
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{
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"postcode": ["AB1 2CD", "EF3 4GH"],
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"Tree canopy density percentile within 50m": [12.5, 99.0],
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}
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).write_parquet(path)
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result = _tree_density_by_postcode(path).collect().sort("postcode")
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assert result.columns == ["postcode", TREE_DENSITY_FEATURE]
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assert result[TREE_DENSITY_FEATURE].to_list() == [12.5, 99.0]
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assert result.schema[TREE_DENSITY_FEATURE] == pl.Float32
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def test_tree_density_by_postcode_requires_postcode_and_density_columns(
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tmp_path,
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||
) -> None:
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path = tmp_path / "tree_density_by_postcode.parquet"
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||
pl.DataFrame({"postcode": ["AB1 2CD"], "unrelated": [1.0]}).write_parquet(path)
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with pytest.raises(ValueError, match="must contain column"):
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_tree_density_by_postcode(path)
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missing_postcode_path = tmp_path / "missing_postcode.parquet"
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pl.DataFrame({"Tree canopy density percentile within 50m": [12.5]}).write_parquet(
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missing_postcode_path
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)
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with pytest.raises(ValueError, match="missing required column: postcode"):
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_tree_density_by_postcode(missing_postcode_path)
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||
|
||
|
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def _sample_listings_frame() -> pl.DataFrame:
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return pl.DataFrame(
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{
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"Bedrooms": [3],
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"Bathrooms": [2],
|
||
"Number of bedrooms & living rooms": [4],
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"lon": [-0.1],
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"lat": [51.5],
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"Postcode": ["sw1a1aa"],
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"Address per Property Register": ["1 Example Road"],
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||
"Leasehold/Freehold": ["Freehold"],
|
||
"Property type": ["Terraced"],
|
||
"Property sub-type": ["Mid-Terrace"],
|
||
"Price qualifier": [""],
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||
"Total floor area (sqm)": [120.0],
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||
"Listing URL": ["https://example.test/abc"],
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"Listing features": [["Garden", "Off-street parking"]],
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||
"Listing date": [None],
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"Listing status": ["For sale"],
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"Asking price": [750_000],
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"Asking price per sqm": [6_250],
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||
},
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schema={
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||
"Bedrooms": pl.Int32,
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||
"Bathrooms": pl.Int32,
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||
"Number of bedrooms & living rooms": pl.Int32,
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||
"lon": pl.Float64,
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||
"lat": pl.Float64,
|
||
"Postcode": pl.Utf8,
|
||
"Address per Property Register": pl.Utf8,
|
||
"Leasehold/Freehold": pl.Utf8,
|
||
"Property type": pl.Utf8,
|
||
"Property sub-type": pl.Utf8,
|
||
"Price qualifier": pl.Utf8,
|
||
"Total floor area (sqm)": pl.Float64,
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||
"Listing URL": pl.Utf8,
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||
"Listing features": pl.List(pl.Utf8),
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||
"Listing date": pl.Datetime("us"),
|
||
"Listing status": pl.Utf8,
|
||
"Asking price": pl.Int64,
|
||
"Asking price per sqm": pl.Int32,
|
||
},
|
||
)
|
||
|
||
|
||
def _stub_arcgis(path) -> None:
|
||
pl.DataFrame(
|
||
{
|
||
"pcds": ["SW1A 1AA"],
|
||
"ctry25cd": ["E92000001"],
|
||
"doterm": [None],
|
||
"east1m": [530000.0],
|
||
"north1m": [180000.0],
|
||
},
|
||
schema={
|
||
"pcds": pl.Utf8,
|
||
"ctry25cd": pl.Utf8,
|
||
"doterm": pl.Utf8,
|
||
"east1m": pl.Float64,
|
||
"north1m": pl.Float64,
|
||
},
|
||
).write_parquet(path)
|
||
|
||
|
||
def test_canonical_postcode_expr_formats_compact_postcodes() -> None:
|
||
df = pl.DataFrame({"Postcode": ["sw1a1aa", "SW1A 1AA", "bad", None]})
|
||
result = df.with_columns(_canonical_postcode_expr("Postcode").alias("canonical"))
|
||
assert result["canonical"].to_list() == ["SW1A 1AA", "SW1A 1AA", None, None]
|
||
|
||
|
||
def test_load_listings_for_merge_canonicalises_and_exposes_overlay_columns(
|
||
tmp_path,
|
||
) -> None:
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
|
||
loaded = _load_listings_for_merge(listings_path, arcgis_path)
|
||
|
||
assert loaded["postcode"].to_list() == ["SW1A 1AA"]
|
||
assert loaded["pp_address"].to_list() == ["1 Example Road"]
|
||
assert loaded["_actual_listing_url"].to_list() == ["https://example.test/abc"]
|
||
assert loaded["_actual_asking_price"].to_list() == [750_000]
|
||
assert loaded["_actual_lat"].to_list() == [51.5]
|
||
|
||
|
||
def test_load_listings_for_merge_uprn_key_matches_normalize_uprn(tmp_path) -> None:
|
||
# A Float UPRN (e.g. read from a NaN-bearing parquet column) must produce
|
||
# the same digits-only key as `_normalize_uprn` on the candidate side, so
|
||
# the exact UPRN match is not lost. Naively stringifying "100023336956.0"
|
||
# and stripping non-digits would yield "1000233369560" (a bogus trailing
|
||
# zero) which never collides with the candidate key "100023336956".
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().with_columns(
|
||
pl.lit(100023336956.0, dtype=pl.Float64).alias("UPRN")
|
||
).write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
|
||
loaded = _load_listings_for_merge(listings_path, arcgis_path)
|
||
|
||
assert loaded["_listing_uprn"].to_list() == [_normalize_uprn(100023336956.0)]
|
||
assert loaded["_listing_uprn"].to_list() == ["100023336956"]
|
||
|
||
|
||
def test_build_unmatched_listing_seed_rows_fills_property_shape_fields(
|
||
tmp_path,
|
||
) -> None:
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
|
||
listings = _load_listings_for_merge(listings_path, arcgis_path)
|
||
template_schema = pl.Schema(
|
||
{
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"pp_property_type": pl.Utf8,
|
||
"duration": pl.Utf8,
|
||
"total_floor_area": pl.Float64,
|
||
"number_habitable_rooms": pl.Int16,
|
||
"latest_price": pl.Int64,
|
||
"epc_address": pl.Utf8,
|
||
**{dst: dtype for _src, dst, dtype in _LISTING_OVERLAY_SOURCES},
|
||
}
|
||
)
|
||
unmatched_idxs = listings.select("_listing_idx")
|
||
|
||
seed = _build_unmatched_listing_seed_rows(unmatched_idxs, listings, template_schema)
|
||
|
||
assert seed.height == 1
|
||
assert seed["postcode"].to_list() == ["SW1A 1AA"]
|
||
assert seed["pp_address"].to_list() == ["1 Example Road"]
|
||
assert seed["pp_property_type"].to_list() == ["Terraced"]
|
||
assert seed["duration"].to_list() == ["Freehold"]
|
||
assert seed["total_floor_area"].to_list() == [120.0]
|
||
assert seed["number_habitable_rooms"].to_list() == [4]
|
||
assert seed["latest_price"].to_list() == [750_000]
|
||
# Columns not populated from the listing default to null.
|
||
assert seed["epc_address"].to_list() == [None]
|
||
# Overlay columns flow through 1:1.
|
||
assert seed["_actual_listing_url"].to_list() == ["https://example.test/abc"]
|
||
|
||
|
||
def test_build_unmatched_listing_seed_rows_uses_direct_epc_fallbacks(
|
||
tmp_path,
|
||
) -> None:
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().with_columns(
|
||
pl.lit(None, dtype=pl.Float64).alias("Total floor area (sqm)"),
|
||
pl.lit(None, dtype=pl.Int32).alias("Number of bedrooms & living rooms"),
|
||
).write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
|
||
listings = _load_listings_for_merge(listings_path, arcgis_path).with_columns(
|
||
pl.lit("1 Example Road").alias("_direct_epc_address"),
|
||
pl.lit("C").alias("_direct_current_energy_rating"),
|
||
pl.lit("B").alias("_direct_potential_energy_rating"),
|
||
pl.lit(98.0).alias("_direct_total_floor_area"),
|
||
pl.lit(4, dtype=pl.Int16).alias("_direct_number_habitable_rooms"),
|
||
pl.lit(2.4).alias("_direct_floor_height"),
|
||
pl.lit(1930, dtype=pl.UInt16).alias("_direct_construction_age_band"),
|
||
pl.lit(1, dtype=pl.UInt8).alias("_direct_is_construction_date_approximate"),
|
||
pl.lit("No").alias("_direct_was_council_house"),
|
||
)
|
||
template_schema = pl.Schema(
|
||
{
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"total_floor_area": pl.Float64,
|
||
"number_habitable_rooms": pl.Int16,
|
||
"epc_address": pl.Utf8,
|
||
"current_energy_rating": pl.Utf8,
|
||
"was_council_house": pl.Utf8,
|
||
**{dst: dtype for _src, dst, dtype in _LISTING_OVERLAY_SOURCES},
|
||
}
|
||
)
|
||
|
||
seed = _build_unmatched_listing_seed_rows(
|
||
listings.select("_listing_idx"), listings, template_schema
|
||
)
|
||
|
||
assert seed["total_floor_area"].to_list() == [98.0]
|
||
assert seed["number_habitable_rooms"].to_list() == [4]
|
||
assert seed["epc_address"].to_list() == ["1 Example Road"]
|
||
assert seed["current_energy_rating"].to_list() == ["C"]
|
||
assert seed["was_council_house"].to_list() == ["No"]
|
||
|
||
|
||
_DIRECT_EPC_CANDIDATE_SCHEMA = {
|
||
"_direct_epc_row": pl.UInt32,
|
||
"_direct_epc_match_address": pl.Utf8,
|
||
"_direct_epc_match_postcode": pl.Utf8,
|
||
"_direct_epc_outcode": pl.Utf8,
|
||
"_direct_epc_canonical_property_type": pl.Utf8,
|
||
"_direct_epc_uprn": pl.Utf8,
|
||
"_direct_epc_address": pl.Utf8,
|
||
"_direct_current_energy_rating": pl.Utf8,
|
||
"_direct_potential_energy_rating": pl.Utf8,
|
||
"_direct_total_floor_area": pl.Float64,
|
||
"_direct_number_habitable_rooms": pl.Int16,
|
||
"_direct_floor_height": pl.Float64,
|
||
"_direct_construction_age_band": pl.UInt16,
|
||
"_direct_is_construction_date_approximate": pl.UInt8,
|
||
"_direct_was_council_house": pl.Utf8,
|
||
}
|
||
|
||
_LISTING_MATCH_SCHEMA = {
|
||
"_listing_idx": pl.UInt32,
|
||
"_listing_match_address": pl.Utf8,
|
||
"_listing_match_postcode": pl.Utf8,
|
||
"_listing_uprn": pl.Utf8,
|
||
}
|
||
|
||
|
||
def _direct_epc_candidates(rows: list[dict]) -> pl.DataFrame:
|
||
base = {
|
||
"_direct_epc_row": 0,
|
||
"_direct_epc_match_address": "1 EXAMPLE ROAD",
|
||
"_direct_epc_match_postcode": "AA11AA",
|
||
"_direct_epc_outcode": "AA1",
|
||
"_direct_epc_canonical_property_type": "Terraced",
|
||
"_direct_epc_uprn": None,
|
||
"_direct_epc_address": "1, Example Road",
|
||
"_direct_current_energy_rating": "C",
|
||
"_direct_potential_energy_rating": "B",
|
||
"_direct_total_floor_area": 101.0,
|
||
"_direct_number_habitable_rooms": 4,
|
||
"_direct_floor_height": 2.5,
|
||
"_direct_construction_age_band": 1930,
|
||
"_direct_is_construction_date_approximate": 1,
|
||
"_direct_was_council_house": "No",
|
||
}
|
||
return pl.DataFrame(
|
||
[{**base, **row} for row in rows], schema=_DIRECT_EPC_CANDIDATE_SCHEMA
|
||
)
|
||
|
||
|
||
def _listing_matches(rows: list[dict]) -> pl.DataFrame:
|
||
base = {
|
||
"_listing_idx": 0,
|
||
"_listing_match_address": "1 EXAMPLE ROAD",
|
||
"_listing_match_postcode": "AA11AA",
|
||
"_listing_uprn": None,
|
||
}
|
||
return pl.DataFrame([{**base, **row} for row in rows], schema=_LISTING_MATCH_SCHEMA)
|
||
|
||
|
||
def test_match_direct_epc_matches_by_uprn_across_postcodes() -> None:
|
||
# UPRN is matched globally (not within a postcode bucket), so a listing
|
||
# whose detail-page postcode is slightly off still resolves to the right
|
||
# EPC certificate by its UPRN.
|
||
matches = _match_direct_epc(
|
||
_listing_matches(
|
||
[{"_listing_uprn": "100000000001", "_listing_match_postcode": "ZZ99ZZ"}]
|
||
),
|
||
_direct_epc_candidates(
|
||
[
|
||
{
|
||
"_direct_epc_uprn": "100000000001",
|
||
"_direct_epc_match_postcode": "AA11AA",
|
||
}
|
||
]
|
||
),
|
||
)
|
||
|
||
assert matches.height == 1
|
||
assert matches["_direct_epc_address"].to_list() == ["1, Example Road"]
|
||
assert matches["_direct_epc_match_method"].to_list() == ["uprn"]
|
||
|
||
|
||
def test_match_direct_epc_matches_by_address_in_same_postcode() -> None:
|
||
matches = _match_direct_epc(
|
||
_listing_matches([{"_listing_match_address": "1 EXAMPLE ROAD"}]),
|
||
_direct_epc_candidates([{"_direct_epc_match_address": "1 EXAMPLE ROAD"}]),
|
||
)
|
||
|
||
assert matches.height == 1
|
||
assert matches["_direct_epc_address"].to_list() == ["1, Example Road"]
|
||
assert matches["_direct_epc_match_method"].to_list() == ["address"]
|
||
|
||
|
||
def test_normalize_uprn_handles_types_and_floats() -> None:
|
||
assert _normalize_uprn(None) is None
|
||
assert _normalize_uprn("") is None
|
||
assert _normalize_uprn(" 100012345678 ") == "100012345678"
|
||
assert _normalize_uprn(100012345678) == "100012345678"
|
||
# An integral float normalises to its digits, NOT "1230".
|
||
assert _normalize_uprn(123.0) == "123"
|
||
# Non-integral / NaN floats are rejected rather than mangled.
|
||
assert _normalize_uprn(1.5) is None
|
||
assert _normalize_uprn(float("nan")) is None
|
||
|
||
|
||
def test_coalesce_direct_epc_was_council_house_prefers_yes() -> None:
|
||
# The raw property value is fill_null("No") upstream, so a plain coalesce
|
||
# would let a non-null "No" override a directly-matched listing "Yes".
|
||
# "Former council house" should fire if EITHER side says "Yes".
|
||
none_col = [None] * 5
|
||
wide = pl.LazyFrame(
|
||
{
|
||
"was_council_house": ["No", "Yes", "No", None, None],
|
||
"_direct_was_council_house": ["Yes", "No", None, "Yes", None],
|
||
# An unrelated direct-EPC column keeps the plain-coalesce behaviour.
|
||
"current_energy_rating": [None, "C", "D", None, None],
|
||
"_direct_current_energy_rating": ["B", "A", None, "E", None],
|
||
# _coalesce_direct_epc_columns coalesces every pair in
|
||
# _DIRECT_EPC_RAW_COLUMN_MAP, so the rest must be present too.
|
||
"epc_address": none_col,
|
||
"_direct_epc_address": none_col,
|
||
"potential_energy_rating": none_col,
|
||
"_direct_potential_energy_rating": none_col,
|
||
"total_floor_area": none_col,
|
||
"_direct_total_floor_area": none_col,
|
||
"number_habitable_rooms": none_col,
|
||
"_direct_number_habitable_rooms": none_col,
|
||
"floor_height": none_col,
|
||
"_direct_floor_height": none_col,
|
||
"construction_age_band": none_col,
|
||
"_direct_construction_age_band": none_col,
|
||
"is_construction_date_approximate": none_col,
|
||
"_direct_is_construction_date_approximate": none_col,
|
||
}
|
||
)
|
||
|
||
result = _coalesce_direct_epc_columns(wide).collect()
|
||
|
||
assert result["was_council_house"].to_list() == ["Yes", "Yes", "No", "Yes", None]
|
||
# Plain coalesce (raw wins when non-null) is untouched for other columns.
|
||
assert result["current_energy_rating"].to_list() == ["B", "C", "D", "E", None]
|
||
|
||
|
||
def test_join_area_side_tables_preserves_missing_crime_as_null() -> None:
|
||
# The crime table is LEFT-joined per postcode; a postcode absent from it
|
||
# must NOT be fabricated as "zero crime" (the safest value). When every
|
||
# per-type column is null the Serious/Minor rollups must stay null.
|
||
base = pl.LazyFrame(
|
||
{
|
||
"postcode": ["AA1 1AA", "BB2 2BB"],
|
||
"lsoa21": ["E01000001", "E01000002"],
|
||
"Local Authority District code (2024)": ["E09000001", "E09000002"],
|
||
"pcon": ["E14000001", "E14000002"],
|
||
}
|
||
)
|
||
|
||
def _by_postcode(extra: dict) -> pl.LazyFrame:
|
||
return pl.LazyFrame({"postcode": ["AA1 1AA", "BB2 2BB"], **extra})
|
||
|
||
# Crime is present only for AA1 1AA; BB2 2BB is absent from the table.
|
||
crime = pl.LazyFrame(
|
||
{
|
||
"postcode": ["AA1 1AA"],
|
||
"Violence and sexual offences (avg/yr)": [1.0],
|
||
"Robbery (avg/yr)": [2.0],
|
||
"Burglary (avg/yr)": [3.0],
|
||
"Possession of weapons (avg/yr)": [4.0],
|
||
"Anti-social behaviour (avg/yr)": [1.0],
|
||
"Criminal damage and arson (avg/yr)": [1.0],
|
||
"Shoplifting (avg/yr)": [1.0],
|
||
"Bicycle theft (avg/yr)": [1.0],
|
||
"Theft from the person (avg/yr)": [1.0],
|
||
"Other theft (avg/yr)": [1.0],
|
||
"Vehicle crime (avg/yr)": [1.0],
|
||
"Public order (avg/yr)": [1.0],
|
||
"Drugs (avg/yr)": [1.0],
|
||
"Other crime (avg/yr)": [1.0],
|
||
}
|
||
)
|
||
|
||
joined = _join_area_side_tables(
|
||
base,
|
||
iod=pl.LazyFrame({"LSOA code (2021)": ["E01000001", "E01000002"]}),
|
||
ethnicity=pl.LazyFrame({"Geography_code": ["E09000001", "E09000002"]}),
|
||
crime=crime,
|
||
median_age=pl.LazyFrame({"lsoa21": ["E01000001", "E01000002"]}),
|
||
election=pl.LazyFrame({"pcon": ["E14000001", "E14000002"]}),
|
||
poi_counts=_by_postcode({}),
|
||
noise=_by_postcode({}),
|
||
school_proximity=_by_postcode({}),
|
||
conservation_areas=_by_postcode({CONSERVATION_AREA_FEATURE: ["Yes", "No"]}),
|
||
tree_density=None,
|
||
broadband=pl.LazyFrame({"bb_postcode": ["AA1 1AA", "BB2 2BB"]}),
|
||
).collect()
|
||
|
||
by_postcode = {
|
||
row["postcode"]: row
|
||
for row in joined.select(
|
||
"postcode", "serious_crime_avg_yr", "minor_crime_avg_yr"
|
||
).iter_rows(named=True)
|
||
}
|
||
# Present postcode: rollups are the component sums (1+2+3+4, 10×1).
|
||
assert by_postcode["AA1 1AA"]["serious_crime_avg_yr"] == 10.0
|
||
assert by_postcode["AA1 1AA"]["minor_crime_avg_yr"] == 10.0
|
||
# Missing postcode: rollups stay null rather than fabricating 0.0.
|
||
assert by_postcode["BB2 2BB"]["serious_crime_avg_yr"] is None
|
||
assert by_postcode["BB2 2BB"]["minor_crime_avg_yr"] is None
|
||
|
||
|
||
def _property_candidates(rows: list[dict]) -> pl.DataFrame:
|
||
base = {
|
||
"postcode": "AA1 1AA",
|
||
"pp_address": "1 Example Road",
|
||
"_property_match_postcode": "AA11AA",
|
||
"_property_match_address": "1 EXAMPLE ROAD",
|
||
"_property_epc_match_address": "1 EXAMPLE ROAD",
|
||
"uprn": None,
|
||
}
|
||
return pl.DataFrame(
|
||
[{**base, **row} for row in rows],
|
||
schema={
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"_property_match_postcode": pl.Utf8,
|
||
"_property_match_address": pl.Utf8,
|
||
"_property_epc_match_address": pl.Utf8,
|
||
"uprn": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
|
||
def test_match_listing_properties_uprn_wins_dedup_tie() -> None:
|
||
# Two listings claim the same property: one by UPRN, one by exact address
|
||
# (both score 100). The UPRN match must win even though it has the higher
|
||
# _listing_idx (which would otherwise break the tie the wrong way).
|
||
listings = _listing_matches(
|
||
[
|
||
{
|
||
"_listing_idx": 5,
|
||
"_listing_uprn": "100000000001",
|
||
"_listing_match_address": "SOMETHING ELSE",
|
||
},
|
||
{
|
||
"_listing_idx": 1,
|
||
"_listing_uprn": None,
|
||
"_listing_match_address": "1 EXAMPLE ROAD",
|
||
},
|
||
]
|
||
)
|
||
matches = _match_listing_properties(
|
||
listings, _property_candidates([{"uprn": "100000000001"}])
|
||
)
|
||
|
||
assert matches.height == 1
|
||
assert matches["_listing_idx"].to_list() == [5]
|
||
assert matches["_property_match_method"].to_list() == ["uprn"]
|
||
|
||
|
||
def test_match_direct_epc_does_not_match_other_postcode_without_uprn() -> None:
|
||
# Matching is by postcode/UPRN/street — never by coordinate proximity — so a
|
||
# same-street EPC in a different postcode with no shared UPRN is skipped.
|
||
matches = _match_direct_epc(
|
||
_listing_matches([{"_listing_match_postcode": "AA11AA"}]),
|
||
_direct_epc_candidates(
|
||
[{"_direct_epc_match_postcode": "BB22BB", "_direct_epc_uprn": None}]
|
||
),
|
||
)
|
||
|
||
assert matches.height == 0
|
||
|
||
|
||
def test_integrate_listings_attaches_overlay_by_matched_property_key(tmp_path) -> None:
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
wide = pl.DataFrame(
|
||
{
|
||
"postcode": ["SW1A 1AA", "SW1A 1AA"],
|
||
"pp_address": ["9 Other Road", "1 Example Road"],
|
||
"pp_property_type": ["Detached", "Terraced"],
|
||
"duration": ["Freehold", "Freehold"],
|
||
"total_floor_area": [80.0, 90.0],
|
||
"number_habitable_rooms": [3, 4],
|
||
"latest_price": [500_000, 600_000],
|
||
"epc_address": [None, "1 Example Road"],
|
||
"current_energy_rating": [None, "C"],
|
||
"potential_energy_rating": [None, "B"],
|
||
"floor_height": [None, 2.4],
|
||
"construction_age_band": [None, 1930],
|
||
"is_construction_date_approximate": [None, 1],
|
||
"was_council_house": [None, "No"],
|
||
},
|
||
schema={
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"pp_property_type": pl.Utf8,
|
||
"duration": pl.Utf8,
|
||
"total_floor_area": pl.Float64,
|
||
"number_habitable_rooms": pl.Int16,
|
||
"latest_price": pl.Int64,
|
||
"epc_address": pl.Utf8,
|
||
"current_energy_rating": pl.Utf8,
|
||
"potential_energy_rating": pl.Utf8,
|
||
"floor_height": pl.Float64,
|
||
"construction_age_band": pl.UInt16,
|
||
"is_construction_date_approximate": pl.UInt8,
|
||
"was_council_house": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
integrated = _integrate_listings(
|
||
wide.lazy(), listings_path, arcgis_path, epc_path=None
|
||
).collect()
|
||
|
||
matched = integrated.filter(pl.col("pp_address") == "1 Example Road")
|
||
other = integrated.filter(pl.col("pp_address") == "9 Other Road")
|
||
assert matched["_actual_listing_url"].to_list() == ["https://example.test/abc"]
|
||
assert other["_actual_listing_url"].to_list() == [None]
|
||
|
||
|
||
def test_integrate_listings_matches_by_uprn_over_address(tmp_path) -> None:
|
||
# The listing's address deliberately does not match the property's, but the
|
||
# shared UPRN drives an exact match anyway (UPRN beats fuzzy street).
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().with_columns(
|
||
pl.lit("Totally Different Road").alias("Address per Property Register"),
|
||
pl.lit("100000000009").alias("UPRN"),
|
||
).write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
wide = pl.DataFrame(
|
||
{
|
||
"postcode": ["SW1A 1AA"],
|
||
"pp_address": ["1 Example Road"],
|
||
"uprn": ["100000000009"],
|
||
"pp_property_type": ["Terraced"],
|
||
"duration": ["Freehold"],
|
||
"total_floor_area": [90.0],
|
||
"number_habitable_rooms": [4],
|
||
"latest_price": [600_000],
|
||
"epc_address": ["1 Example Road"],
|
||
"current_energy_rating": ["C"],
|
||
"potential_energy_rating": ["B"],
|
||
"floor_height": [2.4],
|
||
"construction_age_band": [1930],
|
||
"is_construction_date_approximate": [1],
|
||
"was_council_house": ["No"],
|
||
},
|
||
schema={
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"uprn": pl.Utf8,
|
||
"pp_property_type": pl.Utf8,
|
||
"duration": pl.Utf8,
|
||
"total_floor_area": pl.Float64,
|
||
"number_habitable_rooms": pl.Int16,
|
||
"latest_price": pl.Int64,
|
||
"epc_address": pl.Utf8,
|
||
"current_energy_rating": pl.Utf8,
|
||
"potential_energy_rating": pl.Utf8,
|
||
"floor_height": pl.Float64,
|
||
"construction_age_band": pl.UInt16,
|
||
"is_construction_date_approximate": pl.UInt8,
|
||
"was_council_house": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
integrated = _integrate_listings(
|
||
wide.lazy(), listings_path, arcgis_path, epc_path=None
|
||
).collect()
|
||
|
||
matched = integrated.filter(pl.col("pp_address") == "1 Example Road")
|
||
# The listing overlay attached to the UPRN-matched property row.
|
||
assert matched["_actual_listing_url"].to_list() == ["https://example.test/abc"]
|
||
# No spurious seed row for the listing's (non-matching) address.
|
||
assert "Totally Different Road" not in integrated["pp_address"].to_list()
|
||
|
||
|
||
def test_integrate_listings_seeds_listing_with_unmatched_street(tmp_path) -> None:
|
||
# A number-less listing whose street is not the property's street (and which
|
||
# shares no UPRN) must not be force-matched onto it; it becomes its own seed
|
||
# row instead of stamping the wrong property's overlay.
|
||
listings_path = tmp_path / "listings.parquet"
|
||
arcgis_path = tmp_path / "arcgis.parquet"
|
||
_sample_listings_frame().with_columns(
|
||
pl.lit("Juniper Crescent").alias("Address per Property Register"),
|
||
).write_parquet(listings_path)
|
||
_stub_arcgis(arcgis_path)
|
||
wide = pl.DataFrame(
|
||
{
|
||
"postcode": ["SW1A 1AA"],
|
||
"pp_address": ["Old Cottage High Street"],
|
||
"pp_property_type": ["Terraced"],
|
||
"duration": ["Freehold"],
|
||
"total_floor_area": [120.0],
|
||
"number_habitable_rooms": [4],
|
||
"latest_price": [750_000],
|
||
"epc_address": ["Old Cottage High Street"],
|
||
"current_energy_rating": ["C"],
|
||
"potential_energy_rating": ["B"],
|
||
"floor_height": [2.4],
|
||
"construction_age_band": [1930],
|
||
"is_construction_date_approximate": [1],
|
||
"was_council_house": ["No"],
|
||
},
|
||
schema={
|
||
"postcode": pl.Utf8,
|
||
"pp_address": pl.Utf8,
|
||
"pp_property_type": pl.Utf8,
|
||
"duration": pl.Utf8,
|
||
"total_floor_area": pl.Float64,
|
||
"number_habitable_rooms": pl.Int16,
|
||
"latest_price": pl.Int64,
|
||
"epc_address": pl.Utf8,
|
||
"current_energy_rating": pl.Utf8,
|
||
"potential_energy_rating": pl.Utf8,
|
||
"floor_height": pl.Float64,
|
||
"construction_age_band": pl.UInt16,
|
||
"is_construction_date_approximate": pl.UInt8,
|
||
"was_council_house": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
integrated = _integrate_listings(
|
||
wide.lazy(), listings_path, arcgis_path, epc_path=None
|
||
).collect()
|
||
|
||
existing = integrated.filter(pl.col("pp_address") == "Old Cottage High Street")
|
||
seed = integrated.filter(pl.col("pp_address") == "Juniper Crescent")
|
||
assert existing["_actual_listing_url"].to_list() == [None]
|
||
assert seed["_actual_listing_url"].to_list() == ["https://example.test/abc"]
|
||
|
||
|
||
def test_finalize_listings_promotes_overlay_columns_and_filters_to_listing_rows() -> (
|
||
None
|
||
):
|
||
df = pl.DataFrame(
|
||
{
|
||
"Postcode": ["SW1A 1AA", "SW1A 1AA"],
|
||
"Address per Property Register": ["1 Example Road", "2 Example Road"],
|
||
"Address per EPC": ["1 Example Road", None],
|
||
"Date of last transaction": [1990.0, None],
|
||
"lat": [51.5, 51.5],
|
||
"lon": [-0.1, -0.1],
|
||
"Total floor area (sqm)": [100.0, 95.0],
|
||
"Number of bedrooms & living rooms": [3, None],
|
||
"Property type": ["Terraced", None],
|
||
"Leasehold/Freehold": ["Leasehold", None],
|
||
"Last known price": [500_000, None],
|
||
"Street tree density percentile": [42.0, 42.0],
|
||
# Overlay columns: row 0 is a matched listing, row 1 is unmatched, row none.
|
||
"_actual_listing_url": ["url0", "url1"],
|
||
"_actual_asking_price": [600_000, 700_000],
|
||
"_actual_asking_price_per_sqm": [5_000, None],
|
||
"_actual_listing_date": [None, None],
|
||
"_actual_listing_status": ["For sale", "For sale"],
|
||
"_actual_listing_features": [["Garden"], ["Parking"]],
|
||
"_actual_bedrooms": [3, 4],
|
||
"_actual_bathrooms": [1, 2],
|
||
"_actual_price_qualifier": ["", ""],
|
||
"_actual_property_sub_type": ["Mid-Terrace", "End-Terrace"],
|
||
"_actual_lat": [51.51, 51.52],
|
||
"_actual_lon": [-0.11, -0.12],
|
||
"_actual_total_floor_area": [110.0, None],
|
||
"_actual_number_habitable_rooms": [4, 3],
|
||
"_actual_property_type": ["Terraced", "Flats/Maisonettes"],
|
||
"_actual_leasehold_freehold": ["Freehold", "Leasehold"],
|
||
},
|
||
schema={
|
||
"Postcode": pl.Utf8,
|
||
"Address per Property Register": pl.Utf8,
|
||
"Address per EPC": pl.Utf8,
|
||
"Date of last transaction": pl.Float64,
|
||
"lat": pl.Float64,
|
||
"lon": pl.Float64,
|
||
"Total floor area (sqm)": pl.Float64,
|
||
"Number of bedrooms & living rooms": pl.Int16,
|
||
"Property type": pl.Utf8,
|
||
"Leasehold/Freehold": pl.Utf8,
|
||
"Last known price": pl.Int64,
|
||
"Street tree density percentile": pl.Float32,
|
||
"_actual_listing_url": pl.Utf8,
|
||
"_actual_asking_price": pl.Int64,
|
||
"_actual_asking_price_per_sqm": pl.Int32,
|
||
"_actual_listing_date": pl.Datetime("us"),
|
||
"_actual_listing_status": pl.Utf8,
|
||
"_actual_listing_features": pl.List(pl.Utf8),
|
||
"_actual_bedrooms": pl.Int32,
|
||
"_actual_bathrooms": pl.Int32,
|
||
"_actual_price_qualifier": pl.Utf8,
|
||
"_actual_property_sub_type": pl.Utf8,
|
||
"_actual_lat": pl.Float64,
|
||
"_actual_lon": pl.Float64,
|
||
"_actual_total_floor_area": pl.Float64,
|
||
"_actual_number_habitable_rooms": pl.Int16,
|
||
"_actual_property_type": pl.Utf8,
|
||
"_actual_leasehold_freehold": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
finalized = _finalize_listings(df).sort("Address per Property Register")
|
||
|
||
assert finalized.height == 2
|
||
assert finalized["Listing URL"].to_list() == ["url0", "url1"]
|
||
assert finalized["Asking price"].to_list() == [600_000, 700_000]
|
||
assert finalized["Asking price per sqm"].to_list() == [5_000, 7_368]
|
||
assert finalized["Est. price per sqm"].to_list() == [5_000, 7_368]
|
||
assert finalized["Estimated current price"].to_list() == [600_000, 700_000]
|
||
assert finalized["Last known price"].to_list() == [500_000, 700_000]
|
||
# Listing's preferred floor area / rooms / property type / tenure.
|
||
assert finalized["Total floor area (sqm)"].to_list() == [110.0, 95.0]
|
||
assert finalized["Number of bedrooms & living rooms"].to_list() == [4, 3]
|
||
assert finalized["Property type"].to_list() == ["Terraced", "Flats/Maisonettes"]
|
||
assert finalized["Leasehold/Freehold"].to_list() == ["Freehold", "Leasehold"]
|
||
# Postcode-level feature carried through to both matched and unmatched rows.
|
||
assert finalized["Street tree density percentile"].to_list() == [42.0, 42.0]
|
||
# Match status reflects historical context availability.
|
||
assert finalized["Historical property match status"].to_list() == [
|
||
"matched",
|
||
"unmatched",
|
||
]
|
||
# Overlay scaffolding is dropped.
|
||
for src, dst, _dt in _LISTING_OVERLAY_SOURCES:
|
||
assert dst not in finalized.columns, src
|
||
|
||
|
||
def test_finalize_listings_dedupes_fanned_out_listing_rows() -> None:
|
||
# The terminated-postcode remap can collapse two distinct wide rows onto the same
|
||
# (postcode, pp_address), so a single matched listing attaches to both. Finalize
|
||
# must emit one row per listing URL, not one per collapsed wide row.
|
||
df = pl.DataFrame(
|
||
{
|
||
"Postcode": ["SW1A 1AA", "SW1A 1AA"],
|
||
"Address per Property Register": ["1 Example Road", "1 Example Road"],
|
||
"Address per EPC": ["1 Example Road", "1 Example Road"],
|
||
"Date of last transaction": [1990.0, 1995.0],
|
||
"lat": [51.5, 51.5],
|
||
"lon": [-0.1, -0.1],
|
||
"Total floor area (sqm)": [100.0, 95.0],
|
||
"Number of bedrooms & living rooms": [3, 3],
|
||
"Property type": ["Terraced", "Terraced"],
|
||
"Leasehold/Freehold": ["Leasehold", "Leasehold"],
|
||
"Last known price": [500_000, 480_000],
|
||
"Street tree density percentile": [42.0, 42.0],
|
||
# Same listing URL on both collapsed rows — the fan-out to fix.
|
||
"_actual_listing_url": ["url0", "url0"],
|
||
"_actual_asking_price": [600_000, 600_000],
|
||
"_actual_asking_price_per_sqm": [5_000, 5_000],
|
||
"_actual_listing_date": [None, None],
|
||
"_actual_listing_status": ["For sale", "For sale"],
|
||
"_actual_listing_features": [["Garden"], ["Garden"]],
|
||
"_actual_bedrooms": [3, 3],
|
||
"_actual_bathrooms": [1, 1],
|
||
"_actual_price_qualifier": ["", ""],
|
||
"_actual_property_sub_type": ["Mid-Terrace", "Mid-Terrace"],
|
||
"_actual_lat": [51.51, 51.51],
|
||
"_actual_lon": [-0.11, -0.11],
|
||
"_actual_total_floor_area": [110.0, 110.0],
|
||
"_actual_number_habitable_rooms": [4, 4],
|
||
"_actual_property_type": ["Terraced", "Terraced"],
|
||
"_actual_leasehold_freehold": ["Freehold", "Freehold"],
|
||
},
|
||
schema={
|
||
"Postcode": pl.Utf8,
|
||
"Address per Property Register": pl.Utf8,
|
||
"Address per EPC": pl.Utf8,
|
||
"Date of last transaction": pl.Float64,
|
||
"lat": pl.Float64,
|
||
"lon": pl.Float64,
|
||
"Total floor area (sqm)": pl.Float64,
|
||
"Number of bedrooms & living rooms": pl.Int16,
|
||
"Property type": pl.Utf8,
|
||
"Leasehold/Freehold": pl.Utf8,
|
||
"Last known price": pl.Int64,
|
||
"Street tree density percentile": pl.Float32,
|
||
"_actual_listing_url": pl.Utf8,
|
||
"_actual_asking_price": pl.Int64,
|
||
"_actual_asking_price_per_sqm": pl.Int32,
|
||
"_actual_listing_date": pl.Datetime("us"),
|
||
"_actual_listing_status": pl.Utf8,
|
||
"_actual_listing_features": pl.List(pl.Utf8),
|
||
"_actual_bedrooms": pl.Int32,
|
||
"_actual_bathrooms": pl.Int32,
|
||
"_actual_price_qualifier": pl.Utf8,
|
||
"_actual_property_sub_type": pl.Utf8,
|
||
"_actual_lat": pl.Float64,
|
||
"_actual_lon": pl.Float64,
|
||
"_actual_total_floor_area": pl.Float64,
|
||
"_actual_number_habitable_rooms": pl.Int16,
|
||
"_actual_property_type": pl.Utf8,
|
||
"_actual_leasehold_freehold": pl.Utf8,
|
||
},
|
||
)
|
||
|
||
finalized = _finalize_listings(df)
|
||
|
||
assert finalized.height == 1
|
||
assert finalized["Listing URL"].to_list() == ["url0"]
|