"""Tests para suggest_reexpression."""

from suggest_reexpression import suggest_reexpression


def test_aproximadamente_simetrica_recomienda_none():
    # |skew| < 0.5 -> no hace falta re-expresar.
    out = suggest_reexpression({"skew": 0.1, "min": 5.0, "zero_pct": 0.0, "negative_pct": 0.0})
    assert out["recommended"] == "none"
    assert out["ladder_power"] == 1.0
    assert out["alternatives"] == []
    assert out["note"] == ""


def test_positiva_fuerte_todo_positivo_recomienda_log():
    # Cola derecha larga sobre datos estrictamente positivos -> log.
    out = suggest_reexpression({"skew": 2.3, "min": 1.0, "zero_pct": 0.0, "negative_pct": 0.0})
    assert out["recommended"] == "log"
    assert out["ladder_power"] == 0.0
    transforms = [a["transform"] for a in out["alternatives"]]
    assert "box-cox" in transforms


def test_positiva_moderada_todo_positivo_recomienda_sqrt():
    out = suggest_reexpression({"skew": 0.7, "min": 2.0, "zero_pct": 0.0, "negative_pct": 0.0})
    assert out["recommended"] == "sqrt"
    assert out["ladder_power"] == 0.5


def test_positiva_con_ceros_fuerte_recomienda_log1p():
    # log(0) indefinido -> log1p en presencia de ceros.
    out = suggest_reexpression({"skew": 1.5, "min": 0.0, "zero_pct": 12.0, "negative_pct": 0.0})
    assert out["recommended"] == "log1p"
    assert out["ladder_power"] == 0.0


def test_positiva_con_negativos_recomienda_yeo_johnson():
    # log/Box-Cox no admiten negativos -> Yeo-Johnson.
    out = suggest_reexpression({"skew": 1.8, "min": -4.0, "zero_pct": 0.0, "negative_pct": 20.0})
    assert out["recommended"] == "yeo-johnson"
    assert out["ladder_power"] is None  # data-driven


def test_negativa_fuerte_todo_positivo_recomienda_cube():
    # Cola izquierda -> subir por la escalera de Tukey.
    out = suggest_reexpression({"skew": -1.6, "min": 3.0, "zero_pct": 0.0, "negative_pct": 0.0})
    assert out["recommended"] == "cube"
    assert out["ladder_power"] == 3.0


def test_negativa_moderada_todo_positivo_recomienda_square():
    out = suggest_reexpression({"skew": -0.8, "min": 3.0, "zero_pct": 0.0, "negative_pct": 0.0})
    assert out["recommended"] == "square"
    assert out["ladder_power"] == 2.0


def test_dominio_desconocido_recomienda_yeo_johnson_con_nota():
    # Solo skew, sin min/zero_pct/negative_pct -> opción segura + nota.
    out = suggest_reexpression({"skew": 1.4})
    assert out["recommended"] == "yeo-johnson"
    assert "dominio desconocido" in out["note"]


def test_acepta_columnprofile_completo_con_numeric_anidado():
    # Si llega un ColumnProfile entero, baja a su sub-bloque numeric.
    profile = {
        "name": "precio",
        "inferred_type": "numeric",
        "numeric": {"skew": 2.0, "min": 1.0, "zero_pct": 0.0, "negative_pct": 0.0},
    }
    out = suggest_reexpression(profile)
    assert out["recommended"] == "log"


def test_skew_ausente_devuelve_nota():
    out = suggest_reexpression({"min": 1.0, "max": 9.0})
    assert out["recommended"] is None
    assert "skew ausente" in out["note"]


def test_stats_vacio_devuelve_nota():
    out = suggest_reexpression({})
    assert out["recommended"] is None
    assert out["alternatives"] == []
    assert out["note"]


def test_no_dict_no_lanza():
    out = suggest_reexpression(None)
    assert out["recommended"] is None
    assert out["note"]


def test_skew_no_numerico_devuelve_nota():
    out = suggest_reexpression({"skew": "mucho"})
    assert out["recommended"] is None
    assert out["skew"] is None