school_compare/backend/tests/test_similar_schools.py

"""Selection rules for the "similar schools nearby" section.

The hard filters encode claims the section is not allowed to make — that a
selective school is an alternative to a non-selective one, that a special
school is comparable to a mainstream one, or that a Girls school is an option
for a Boys school's reader. They never relax. The soft preferences describe
how close the intake is, and they do — but only far enough to reach a usable
set, never far enough to fill the last of the six slots.
"""

import numpy as np
import pandas as pd

from backend.similar_schools import select_similar

# Roughly 0.7 miles apart in latitude at this longitude.
BASE_LAT, BASE_LON = 51.5000, -0.1000


def _row(urn, name, **overrides):
    base = {
        "urn": urn,
        "school_name": name,
        "local_authority": "Testshire",
        "school_type": "Community school",
        "phase": "Primary",
        "age_range": "4-11",
        "status": "Open",
        "gender": "Mixed",
        "religious_denomination": "None",
        "admissions_policy": "Not applicable",
        "latitude": BASE_LAT,
        "longitude": BASE_LON,
        "year": 202425,
        "rwm_expected_pct": 70.0,
        "attainment_8_score": np.nan,
    }
    base.update(overrides)
    return base


def _frame(*rows):
    return pd.DataFrame(list(rows))


def _at(miles):
    """A latitude `miles` north of BASE_LAT."""
    return BASE_LAT + miles / 69.0


def test_returns_nearest_same_phase_schools():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "Near", latitude=_at(0.5)),
        _row(100003, "Mid", latitude=_at(1.0)),
        _row(100004, "Far", latitude=_at(2.0)),
    )
    result = select_similar(frame, 100001, is_secondary=False)
    assert [s["urn"] for s in result] == [100002, 100003, 100004]
    assert result[0]["distance_miles"] == 0.5


def test_excludes_the_subject_school():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "A", latitude=_at(0.5)),
        _row(100003, "B", latitude=_at(0.6)),
    )
    assert 100001 not in {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}


def test_selective_never_meets_non_selective():
    frame = _frame(
        _row(100001, "Grammar", phase="Secondary", admissions_policy="Selective"),
        _row(100002, "Comp A", phase="Secondary", admissions_policy="Non-selective", latitude=_at(0.5)),
        _row(100003, "Comp B", phase="Secondary", admissions_policy="Non-selective", latitude=_at(0.6)),
    )
    assert select_similar(frame, 100001, is_secondary=True) == []

    reverse = select_similar(frame, 100002, is_secondary=True)
    assert 100001 not in {s["urn"] for s in reverse}


def test_special_schools_match_only_each_other():
    frame = _frame(
        _row(100001, "Special", school_type="Community special school"),
        _row(100002, "Mainstream A", latitude=_at(0.5)),
        _row(100003, "Mainstream B", latitude=_at(0.6)),
    )
    assert select_similar(frame, 100001, is_secondary=False) == []
    assert select_similar(frame, 100002, is_secondary=False) == []


def test_boys_never_meets_girls():
    frame = _frame(
        _row(100001, "Boys School", gender="Boys"),
        _row(100002, "Girls School", gender="Girls", latitude=_at(0.5)),
        _row(100003, "Mixed School", gender="Mixed", latitude=_at(0.6)),
        _row(100004, "Another Mixed", gender="Mixed", latitude=_at(0.7)),
    )
    urns = {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}
    assert 100002 not in urns
    assert urns == {100003, 100004}


def test_closed_schools_and_missing_coordinates_are_dropped():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "Closed", status="Closed", latitude=_at(0.5)),
        _row(100003, "No coords", latitude=np.nan, longitude=np.nan),
        _row(100004, "Good A", latitude=_at(0.6)),
        _row(100005, "Good B", latitude=_at(0.7)),
    )
    assert {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)} == {100004, 100005}


def test_tiers_relax_faith_before_gender():
    frame = _frame(
        _row(100001, "Subject", gender="Boys", religious_denomination="Roman Catholic"),
        # Tier 1: same gender and same faith.
        _row(100002, "Tier one", gender="Boys", religious_denomination="Roman Catholic", latitude=_at(2.0)),
        # Tier 2: same gender, different faith — closer, but a weaker match.
        _row(100003, "Tier two", gender="Boys", religious_denomination="None", latitude=_at(0.5)),
        # Tier 3: mixed gender, different faith.
        _row(100004, "Tier three", gender="Mixed", religious_denomination="None", latitude=_at(0.6)),
    )
    result = select_similar(frame, 100001, is_secondary=False)
    tier_by_urn = {s["urn"]: s["tier"] for s in result}
    assert tier_by_urn == {100002: 1, 100003: 2, 100004: 3}
    # Selected by tier, displayed by distance.
    assert [s["urn"] for s in result] == [100003, 100004, 100002]


def test_caps_at_six_taking_the_nearest():
    frame = _frame(
        _row(100001, "Subject"),
        *[_row(100010 + n, f"Peer {n}", latitude=_at(0.1 * (n + 1))) for n in range(7)],
    )
    result = select_similar(frame, 100001, is_secondary=False)
    assert len(result) == 6
    # The seventh-nearest is the one dropped, not an arbitrary one.
    assert 100016 not in {s["urn"] for s in result}


def test_tiers_stop_once_enough_are_found():
    """Four tier-1 matches are a usable set, so tier 2 is never opened — even
    though it holds a school that is closer than any of them."""
    frame = _frame(
        _row(100001, "Subject", religious_denomination="Roman Catholic"),
        _row(100002, "RC one", religious_denomination="Roman Catholic", latitude=_at(0.5)),
        _row(100003, "RC two", religious_denomination="Roman Catholic", latitude=_at(0.6)),
        _row(100004, "RC three", religious_denomination="Roman Catholic", latitude=_at(0.7)),
        _row(100005, "RC four", religious_denomination="Roman Catholic", latitude=_at(0.8)),
        # Closer than every one of them, but only a tier-2 match.
        _row(100006, "Secular and nearer", religious_denomination="None", latitude=_at(0.2)),
    )
    result = select_similar(frame, 100001, is_secondary=False)
    assert 100006 not in {s["urn"] for s in result}
    assert len(result) == 4
    assert all(s["tier"] == 1 for s in result)


def test_a_school_is_never_taken_twice():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "A", latitude=_at(0.5)),
        _row(100003, "B", latitude=_at(0.6)),
    )
    result = select_similar(frame, 100001, is_secondary=False)
    assert len(result) == len({s["urn"] for s in result})


def test_fewer_than_two_matches_returns_empty():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "Only neighbour", latitude=_at(0.5)),
    )
    assert select_similar(frame, 100001, is_secondary=False) == []


def test_beyond_the_widest_radius_is_not_offered():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "A", latitude=_at(11.0)),
        _row(100003, "B", latitude=_at(12.0)),
    )
    assert select_similar(frame, 100001, is_secondary=False) == []


def test_all_through_is_offered_on_both_phase_sides():
    frame = _frame(
        _row(100001, "Primary subject", phase="Primary"),
        _row(100002, "All through", phase="All-through", latitude=_at(0.5)),
        _row(100003, "Primary peer", phase="Primary", latitude=_at(0.6)),
    )
    assert 100002 in {s["urn"] for s in select_similar(frame, 100001, is_secondary=False)}

    secondary = _frame(
        _row(100010, "Secondary subject", phase="Secondary"),
        _row(100002, "All through", phase="All-through", latitude=_at(0.5)),
        _row(100011, "Secondary peer", phase="Secondary", latitude=_at(0.6)),
    )
    assert 100002 in {s["urn"] for s in select_similar(secondary, 100010, is_secondary=True)}


def test_chips_state_only_what_the_tier_earned():
    frame = _frame(
        _row(100001, "Subject", phase="Secondary", gender="Mixed",
             religious_denomination="None", admissions_policy="Non-selective"),
        _row(100002, "Full match", phase="Secondary", gender="Mixed",
             religious_denomination="None", admissions_policy="Non-selective", latitude=_at(0.5)),
        _row(100003, "Faith differs", phase="Secondary", gender="Mixed",
             religious_denomination="Church of England", admissions_policy="Non-selective", latitude=_at(0.6)),
    )
    by_urn = {s["urn"]: s for s in select_similar(frame, 100001, is_secondary=True)}
    assert by_urn[100002]["shared"] == ["Mixed", "Non-selective", "No religious character"]
    assert by_urn[100003]["shared"] == ["Mixed", "Non-selective"]


def test_tier_three_chip_is_the_plain_phase():
    frame = _frame(
        _row(100001, "Subject", gender="Boys"),
        _row(100002, "A", gender="Mixed", latitude=_at(0.5)),
        _row(100003, "B", gender="Mixed", latitude=_at(0.6)),
    )
    result = select_similar(frame, 100001, is_secondary=False)
    assert all(s["shared"] == ["Primary school"] for s in result)


def test_metric_follows_the_template_not_the_neighbour():
    frame = _frame(
        _row(100001, "Subject", phase="Secondary", attainment_8_score=50.0),
        _row(100002, "A", phase="Secondary", attainment_8_score=52.8, latitude=_at(0.5)),
        _row(100003, "B", phase="Secondary", attainment_8_score=np.nan, latitude=_at(0.6)),
    )
    by_urn = {s["urn"]: s for s in select_similar(frame, 100001, is_secondary=True)}
    assert by_urn[100002]["metric_key"] == "attainment_8_score"
    assert by_urn[100002]["metric_value"] == 52.8
    assert by_urn[100002]["metric_year"] == 202425
    assert by_urn[100003]["metric_value"] is None


def test_values_are_json_safe_native_types():
    frame = _frame(
        _row(100001, "Subject"),
        _row(100002, "A", latitude=_at(0.5)),
        _row(100003, "B", latitude=_at(0.6)),
    )
    for school in select_similar(frame, 100001, is_secondary=False):
        assert isinstance(school["urn"], int)
        assert isinstance(school["distance_miles"], float)
        assert not isinstance(school["metric_value"], np.generic)


# ---------------------------------------------------------------------------
# The endpoint
# ---------------------------------------------------------------------------

import pytest
from fastapi.testclient import TestClient


def _endpoint_frame():
    return _frame(
        _row(100001, "Subject Primary"),
        _row(100002, "Neighbour A", latitude=_at(0.5)),
        _row(100003, "Neighbour B", latitude=_at(0.6)),
    )


@pytest.fixture()
def client(monkeypatch):
    from backend import app as app_module

    monkeypatch.setattr(app_module, "load_latest_school_data", _endpoint_frame)
    monkeypatch.setattr(app_module, "load_school_data", _endpoint_frame)
    monkeypatch.setattr(app_module, "get_supplementary_data", lambda db, urn: {})
    return TestClient(app_module.app, raise_server_exceptions=False)


def test_detail_payload_carries_similar_schools(client):
    resp = client.get("/api/schools/100001")
    assert resp.status_code == 200, resp.text
    similar = resp.json()["similar_schools"]
    assert [s["school_name"] for s in similar] == ["Neighbour A", "Neighbour B"]
    assert similar[0]["metric_key"] == "rwm_expected_pct"


def test_a_failure_in_selection_does_not_break_the_page(client, monkeypatch):
    from backend import app as app_module

    def _explode(*args, **kwargs):
        raise ValueError("selection blew up")

    monkeypatch.setattr(app_module, "select_similar", _explode)
    resp = client.get("/api/schools/100001")
    assert resp.status_code == 200, resp.text
    assert resp.json()["similar_schools"] == []