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| """Tests for the reference table builder, nearest lookup, and three-tier estimator.""" | |
| from __future__ import annotations | |
| from pathlib import Path | |
| import pytest | |
| pd = pytest.importorskip("pandas") | |
| from ccdp.costing import build_seed_catalog | |
| from ccdp.identification import fallback_estimator as fb | |
| from ccdp.identification import reference_table as reftab | |
| from ccdp.identification.car_identifier import IdentificationResult | |
| def _sample_rows(): | |
| return [ | |
| {"make": "honda", "model": "civic", "year": 2018, "body_type": "sedan", | |
| "segment": "mid", "cost_usd": 2800, "dataset": "iaai"}, | |
| {"make": "honda", "model": "civic", "year": 2018, "body_type": "sedan", | |
| "segment": "mid", "cost_usd": 3000, "dataset": "iaai"}, | |
| {"make": "honda", "model": "civic", "year": 2019, "body_type": "sedan", | |
| "segment": "mid", "cost_usd": 3200, "dataset": "iaai"}, | |
| {"make": "toyota", "model": "camry", "year": 2019, "body_type": "sedan", | |
| "segment": "mid", "cost_usd": 3400, "dataset": "iaai"}, | |
| {"make": "bmw", "model": "3-series", "year": 2020, "body_type": "sedan", | |
| "segment": "luxury", "cost_usd": 7200, "dataset": "iaai"}, | |
| ] | |
| def test_build_and_lookup_exact(tmp_path: Path): | |
| out = tmp_path / "ref.parquet" | |
| reftab.build(_sample_rows(), out_path=out) | |
| r = reftab.nearest(make="honda", model="civic", year=2018, path=out) | |
| assert r is not None | |
| assert r["match_how"] == "exact" | |
| assert r["avg_cost_usd"] == pytest.approx(2900.0) | |
| assert r["n_samples"] == 2 | |
| def test_lookup_make_model_any_year(tmp_path: Path): | |
| out = tmp_path / "ref.parquet" | |
| reftab.build(_sample_rows(), out_path=out) | |
| r = reftab.nearest(make="honda", model="civic", year=2099, path=out) | |
| assert r["match_how"] == "make_model_any_year" | |
| def test_lookup_segment_body_type_fallback(tmp_path: Path): | |
| out = tmp_path / "ref.parquet" | |
| reftab.build(_sample_rows(), out_path=out) | |
| r = reftab.nearest(make="lamborghini", model="huracan", year=2022, | |
| body_type="sedan", segment="luxury", path=out) | |
| assert r["match_how"] in {"segment_body_type", "body_type", "segment"} | |
| def test_lookup_returns_none_on_empty(tmp_path: Path): | |
| out = tmp_path / "ref.parquet" | |
| reftab.build([], out_path=out) | |
| assert reftab.nearest(make="x", model="y", year=2000, path=out) is None | |
| def test_three_tier_estimator(tmp_path: Path, monkeypatch): | |
| out = tmp_path / "ref.parquet" | |
| reftab.build(_sample_rows(), out_path=out) | |
| monkeypatch.setattr(reftab, "DEFAULT_PATH", out) | |
| monkeypatch.setattr(fb.reftab, "DEFAULT_PATH", out) | |
| cat = build_seed_catalog() | |
| # Tier 1: identified + reference match | |
| ident = IdentificationResult( | |
| image_path=Path("/x.jpg"), make="honda", model="civic", year=2018, | |
| body_type="sedan", segment="mid", confidence=0.9, source="filename", | |
| ) | |
| est = fb.estimate({"front_bumper": "moderate"}, identification=ident, catalog=cat) | |
| assert est.tier == "exact" | |
| assert est.cost_usd > 0 | |
| assert "honda" in est.provenance | |
| # Tier 3: no identification | |
| est3 = fb.estimate({"front_bumper": "moderate"}, identification=None, catalog=cat) | |
| assert est3.tier == "category_only" | |
| assert est3.warning is not None | |