perfect-postcode/pipeline/transform/test_poi_proximity.py

import polars as pl

from pipeline.transform.poi_proximity import (
    GREENSPACE_PARK_FUNCTIONS,
    POI_GROUPS_2KM,
    _build_poi_category_groups,
    _dynamic_poi_metric_renames,
    _greenspace_count_frame,
    _groceries_categories,
)
from pipeline.utils.poi_counts import count_pois_per_postcode


def test_groceries_2km_counts_geolytix_brand_categories() -> None:
    """The static groceries 2km count must include GEOLYTIX brand POIs.

    GEOLYTIX stores the brand (e.g. "Tesco") in `category` with group
    "Groceries" and never emits the literal "Supermarket"; matching only the
    OSM strings counts the supermarket but drops the brand store.
    """
    postcodes = pl.DataFrame(
        {
            "postcode": ["SW1A 1AA"],
            "lat": [51.5010],
            "lon": [-0.1416],
        }
    )
    pois = pl.DataFrame(
        {
            "category": ["Tesco", "Supermarket"],
            "group": ["Groceries", "Groceries"],
            "lat": [51.5011, 51.5012],
            "lng": [-0.1417, -0.1418],
        }
    )

    groups_2km = {**POI_GROUPS_2KM, "groceries": _groceries_categories(pois)}
    result = count_pois_per_postcode(postcodes, pois, groups=groups_2km, radius_km=2)

    # Both the GEOLYTIX brand ("Tesco") and the OSM "Supermarket" must count.
    # Pre-fix the static list was ["Greengrocer", "Supermarket", "Convenience
    # Store"], so "Tesco" was dropped and this was 1.
    assert result["groceries_2km"][0] == 2


def test_dynamic_poi_groups_include_requested_categories_only() -> None:
    pois = pl.DataFrame(
        {
            "group": (
                ["Public Transport"] * 2
                + ["Leisure"] * 2
                + ["Groceries"] * 101
                + ["Groceries"] * 100
                + ["Leisure"] * 10
                + ["Education"] * 200
                + ["Health"] * 200
            ),
            "category": (
                ["Rail station", "Bus stop"]
                + ["Café", "Restaurant"]
                + ["Tesco"] * 101
                + ["Waitrose"] * 100
                + ["Park"] * 10
                + ["School"] * 200
                + ["Pharmacy"] * 200
            ),
            "lat": [51.5] * 615,
            "lng": [-0.1] * 615,
        }
    )

    groups, display_names = _build_poi_category_groups(pois)

    assert set(display_names.values()) == {
        "Bus stop",
        "Café",
        "Pharmacy",
        "Rail station",
        "Restaurant",
        "Tesco",
    }
    assert "poi_waitrose" not in groups
    assert "poi_park" not in groups
    assert "poi_school" not in groups


def test_dynamic_poi_metric_renames_support_park_count_options() -> None:
    assert _dynamic_poi_metric_renames({"parks": "Park"}) == {
        "parks_nearest_km": "Distance to nearest amenity (Park) (km)",
        "parks_2km": "Number of amenities (Park) within 2km",
        "parks_5km": "Number of amenities (Park) within 5km",
    }


def test_groceries_categories_exclude_speciality_food_retail() -> None:
    """The static groceries metric must not count bakeries/butchers/delis/
    off-licences (speciality retail, ~a third of the group), while keeping
    Supermarket, Convenience Store, Greengrocer and GEOLYTIX brands."""
    pois = pl.DataFrame(
        {
            "category": [
                "Tesco",
                "Supermarket",
                "Convenience Store",
                "Greengrocer",
                "Bakery",
                "Butcher & Fishmonger",
                "Deli & Specialty",
                "Off-Licence",
                "Café",
            ],
            "group": ["Groceries"] * 8 + ["Leisure"],
            "lat": [51.5] * 9,
            "lng": [-0.1] * 9,
        }
    )

    assert _groceries_categories(pois) == [
        "Convenience Store",
        "Greengrocer",
        "Supermarket",
        "Tesco",
    ]


def test_park_group_excludes_playgrounds_and_play_space() -> None:
    # "Play Space" (playgrounds) must not count as a Park; Public Park Or
    # Garden and Playing Field (open recreation grounds) are in scope.
    assert GREENSPACE_PARK_FUNCTIONS == {
        "parks": ["Public Park Or Garden", "Playing Field"]
    }


def test_greenspace_count_frame_collapses_to_one_row_per_site() -> None:
    # Three gates of one park (with a site centroid), one gate of another park
    # without a centroid, and one centroid-fallback row with a null site_id.
    greenspace = pl.DataFrame(
        {
            "lat": [51.50, 51.51, 51.52, 53.0, 54.0],
            "lng": [-0.10, -0.11, -0.12, -2.0, -3.0],
            "category": ["Public Park Or Garden"] * 3
            + ["Playing Field", "Public Park Or Garden"],
            "site_id": ["site-a", "site-a", "site-a", "site-b", None],
            "site_lat": [51.505, 51.505, 51.505, None, None],
            "site_lng": [-0.105, -0.105, -0.105, None, None],
        }
    )

    result = _greenspace_count_frame(greenspace).sort("lat")

    # One row per site (site-a collapses 3 → 1), null-site rows preserved.
    assert result.height == 3
    site_a = result.filter(pl.col("site_id") == "site-a")
    # The representative point is the site centroid…
    assert site_a["lat"].to_list() == [51.505]
    assert site_a["lng"].to_list() == [-0.105]
    # …or the first access point when no centroid is available.
    site_b = result.filter(pl.col("site_id") == "site-b")
    assert site_b["lat"].to_list() == [53.0]


def test_greenspace_count_frame_passes_legacy_parquet_through() -> None:
    # The shipped parquet predates the site_id column; counting must not crash
    # (it keeps the old access-point grain until regenerated).
    legacy = pl.DataFrame(
        {
            "lat": [51.50, 51.51],
            "lng": [-0.10, -0.11],
            "category": ["Public Park Or Garden", "Play Space"],
        }
    )

    assert _greenspace_count_frame(legacy).equals(legacy)