goodgo-platform/libs/ai-services/tests/test_avm_v2.py

"""Tests for AVM v2 ensemble endpoints."""

from fastapi.testclient import TestClient

from app.main import app

client = TestClient(app)

# ── Minimal valid request payload ───────────────────────────────

_PREDICT_PAYLOAD = {
    "district": "Cầu Giấy",
    "city": "Hà Nội",
    "property_type": "apartment",
    "area_m2": 80.0,
    "rooms": 2,
    "month": 3,
    "quarter": 1,
}


def test_predict_v2_heuristic():
    """Predict using heuristic fallback (no trained models)."""
    resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
    assert resp.status_code == 200
    data = resp.json()

    assert data["estimated_price_vnd"] > 0
    assert 0 <= data["confidence"] <= 1
    assert data["price_per_m2_vnd"] > 0
    assert data["price_range_low_vnd"] < data["estimated_price_vnd"]
    assert data["price_range_high_vnd"] > data["estimated_price_vnd"]
    assert data["ensemble_method"] == "weighted_average"
    assert data["model_version"] == "ensemble-v2-heuristic"


def test_predict_v2_returns_model_predictions():
    """Heuristic should return 3 simulated model predictions."""
    resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
    data = resp.json()

    preds = data["model_predictions"]
    assert len(preds) == 3

    names = {p["model_name"] for p in preds}
    assert names == {"xgboost", "lightgbm", "catboost"}

    for p in preds:
        assert p["weight"] > 0
        assert p["predicted_price_vnd"] > 0
        assert p["predicted_price_per_m2_vnd"] > 0


def test_predict_v2_returns_drivers():
    """Heuristic should return feature importance drivers."""
    resp = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD)
    data = resp.json()

    drivers = data["drivers"]
    assert len(drivers) > 0
    assert all(0 <= d["importance"] <= 1 for d in drivers)
    # Most important feature should be area or district price
    top_feature = drivers[0]["feature"]
    assert top_feature in ("area_m2", "avg_price_district_3m_vnd_m2")


def test_predict_v2_with_full_features():
    """Predict with all features populated."""
    payload = {
        **_PREDICT_PAYLOAD,
        "distance_to_cbd_km": 5.0,
        "distance_to_metro_km": 0.8,
        "distance_to_school_km": 0.5,
        "distance_to_hospital_km": 2.0,
        "distance_to_park_km": 0.3,
        "distance_to_mall_km": 1.0,
        "flood_zone_risk": 0.1,
        "floor_ratio": 1.2,
        "building_age_years": 5,
        "has_elevator": True,
        "has_parking": True,
        "has_pool": False,
        "avg_price_district_3m_vnd_m2": 85_000_000,
        "listing_density": 12.5,
        "absorption_rate": 0.3,
        "dom_avg": 45.0,
        "price_momentum_30d": 0.02,
        "yoy_change": 0.05,
        "renovation_score": 0.8,
        "view_quality": 0.7,
        "interior_quality": 0.75,
        "noise_level": 0.3,
        "natural_light": 0.8,
        "is_year_end": False,
    }
    resp = client.post("/avm/v2/predict", json=payload)
    assert resp.status_code == 200
    data = resp.json()
    assert data["estimated_price_vnd"] > 0
    assert data["confidence"] > 0


def test_predict_v2_villa_premium():
    """Villas should be priced higher than apartments (same area)."""
    apt = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD).json()
    villa_payload = {**_PREDICT_PAYLOAD, "property_type": "villa"}
    villa = client.post("/avm/v2/predict", json=villa_payload).json()

    assert villa["price_per_m2_vnd"] > apt["price_per_m2_vnd"]


def test_predict_v2_year_end_premium():
    """Q4/Tết season should add a premium."""
    normal = client.post(
        "/avm/v2/predict",
        json={**_PREDICT_PAYLOAD, "is_year_end": False, "month": 6, "quarter": 2},
    ).json()
    year_end = client.post(
        "/avm/v2/predict",
        json={**_PREDICT_PAYLOAD, "is_year_end": True, "month": 12, "quarter": 4},
    ).json()

    assert year_end["estimated_price_vnd"] > normal["estimated_price_vnd"]


def test_predict_v2_no_legal_paper_discount():
    """Properties without legal papers should be discounted."""
    with_paper = client.post("/avm/v2/predict", json=_PREDICT_PAYLOAD).json()
    without_paper = client.post(
        "/avm/v2/predict",
        json={**_PREDICT_PAYLOAD, "has_legal_paper": False},
    ).json()

    assert without_paper["estimated_price_vnd"] < with_paper["estimated_price_vnd"]


def test_predict_v2_validation_error():
    """Missing required fields should return 422."""
    resp = client.post("/avm/v2/predict", json={"area_m2": 80})
    assert resp.status_code == 422


def test_predict_v2_invalid_area():
    """Zero or negative area should be rejected."""
    resp = client.post(
        "/avm/v2/predict",
        json={**_PREDICT_PAYLOAD, "area_m2": 0},
    )
    assert resp.status_code == 422


def test_train_v2_scaffold():
    """Training endpoint should return scaffold response."""
    resp = client.post(
        "/avm/v2/train",
        json={"optuna_trials": 10},
    )
    assert resp.status_code == 200
    data = resp.json()
    assert "model_version" in data
    assert "ensemble-v2-" in data["model_version"]
    assert data["metrics"]["mae"] == 0.0  # scaffold returns zeros
    assert "xgboost" in data["best_params"]
    assert "lightgbm" in data["best_params"]
    assert "catboost" in data["best_params"]


def test_model_info_v2():
    """Model info endpoint should return current model version."""
    resp = client.get("/avm/v2/model-info")
    assert resp.status_code == 200
    data = resp.json()
    assert "model_version" in data
    assert data["is_active"] is True