from __future__ import annotations

from k1link.compute.e38_perception_baseline import (
    _dimension_metrics,
    _predict_tree,
    _train_tree,
)


def test_e38_cart_is_deterministic_and_respects_minimum_leaf() -> None:
    rows = [
        ({"score": 0.1, "support": 0.0}, "background"),
        ({"score": 0.2, "support": 1.0}, "background"),
        ({"score": 0.7, "support": 3.0}, "object"),
        ({"score": 0.8, "support": 4.0}, "object"),
        ({"score": 0.9, "support": 5.0}, "object"),
        ({"score": 1.0, "support": 6.0}, "object"),
    ]
    first = _train_tree(
        rows,
        feature_names=["score", "support"],
        max_depth=3,
        min_leaf=2,
    )
    second = _train_tree(
        rows,
        feature_names=["score", "support"],
        max_depth=3,
        min_leaf=2,
    )
    assert first == second
    assert _predict_tree(first, {"score": 0.15, "support": 1.0}) == "background"
    assert _predict_tree(first, {"score": 0.85, "support": 4.0}) == "object"


def test_e38_dimension_metrics_do_not_blend_strata() -> None:
    rows = [
        {
            "source_stratum": "agree",
            "prediction": {"presence": "object-present"},
            "reference": {"presence": "object-present"},
        },
        {
            "source_stratum": "agree",
            "prediction": {"presence": "object-present"},
            "reference": {"presence": "background-or-noise"},
        },
        {
            "source_stratum": "geometry-only",
            "prediction": {"presence": "occupied-environment"},
            "reference": {"presence": "occupied-environment"},
        },
    ]
    metrics = _dimension_metrics(rows, dimension="presence", target=0.9)
    assert metrics["correct"] == 2
    assert metrics["incorrect"] == 1
    assert metrics["accuracy"] == 0.666667
    assert metrics["passed"] is False
    assert metrics["by_stratum"]["agree"]["accuracy"] == 0.5
    assert metrics["by_stratum"]["geometry-only"]["accuracy"] == 1.0