"""Tests para select_groupby_keys (grupo eda, dominio datascience)."""

import os
import sys

sys.path.insert(0, os.path.dirname(__file__))

from select_groupby_keys import select_groupby_keys


def _cat_col(name, card, *, imbalance=2.0, flags=None, null_pct=0.0):
    """ColumnProfile categorico minimo con bloque categorical."""
    return {
        "name": name,
        "inferred_type": "categorical",
        "distinct_count": card,
        "unique_pct": card / 1000.0,
        "null_pct": null_pct,
        "flags": flags or [],
        "numeric": None,
        "categorical": {"imbalance": imbalance, "mode_pct": 0.5, "n_distinct": card},
    }


def _num_col(name, *, std, cv, flags=None, unique_pct=0.1):
    """ColumnProfile numerico minimo con bloque numeric."""
    return {
        "name": name,
        "inferred_type": "numeric",
        "distinct_count": 200,
        "unique_pct": unique_pct,
        "null_pct": 0.0,
        "flags": flags or [],
        "numeric": {"std": std, "cv": cv},
        "categorical": None,
    }


def _titanic_like_profile() -> dict:
    """Perfil estilo titanic: 2 categoricas buenas, 2 numericas, 1 id, 1 constante."""
    return {
        "n_rows": 891,
        "key_candidates": ["passenger_id"],
        "columns": [
            _cat_col("sex", 2, imbalance=1.8),
            _cat_col("pclass", 3, imbalance=2.5),
            _num_col("age", std=14.5, cv=0.49),
            _num_col("fare", std=49.7, cv=1.54),
            # id secuencial: flag possible_id + unique_pct alto.
            {
                "name": "passenger_id",
                "inferred_type": "numeric",
                "distinct_count": 891,
                "unique_pct": 1.0,
                "null_pct": 0.0,
                "flags": ["possible_id"],
                "numeric": {"std": 257.4, "cv": 0.58},
                "categorical": None,
            },
            # columna constante: flag constant + std 0.
            {
                "name": "embarked_const",
                "inferred_type": "categorical",
                "distinct_count": 1,
                "unique_pct": 0.001,
                "null_pct": 0.0,
                "flags": ["constant"],
                "numeric": None,
                "categorical": {"imbalance": 1.0},
            },
        ],
    }


def test_titanic_picks_good_cats_excludes_id_and_constant():
    out = select_groupby_keys(_titanic_like_profile())

    # Elige las dos categoricas buenas.
    chosen_cols = {g["col"] for g in out["group_keys"]}
    assert chosen_cols == {"sex", "pclass"}

    # Excluye la constante y el key_candidate.
    assert "embarked_const" not in chosen_cols
    assert "passenger_id" not in chosen_cols

    # Cada group key trae col, cardinality y score.
    for g in out["group_keys"]:
        assert set(g.keys()) == {"col", "cardinality", "score"}
        assert isinstance(g["score"], float)
    by_col = {g["col"]: g for g in out["group_keys"]}
    assert by_col["sex"]["cardinality"] == 2
    assert by_col["pclass"]["cardinality"] == 3

    # Ordenadas por score descendente.
    scores = [g["score"] for g in out["group_keys"]]
    assert scores == sorted(scores, reverse=True)


def test_titanic_measures_exclude_id_constant_and_keep_numerics():
    out = select_groupby_keys(_titanic_like_profile())

    # Solo nombres (strings) de numericas informativas, sin el id secuencial.
    assert all(isinstance(m, str) for m in out["measures"])
    assert "passenger_id" not in out["measures"]
    assert set(out["measures"]) == {"age", "fare"}

    # fare tiene mayor cv (1.54 > 0.49) -> primero.
    assert out["measures"][0] == "fare"


def test_titanic_generates_one_pivot():
    out = select_groupby_keys(_titanic_like_profile())

    # Con 2 group keys -> exactamente 1 pivot.
    assert len(out["pivots"]) == 1
    pivot = out["pivots"][0]
    assert set(pivot.keys()) == {"index", "columns", "value"}
    assert {pivot["index"], pivot["columns"]} == {"sex", "pclass"}
    # El valor es la primera measure (fare).
    assert pivot["value"] == "fare"


def test_empty_profile_returns_all_empty_and_does_not_crash():
    out = select_groupby_keys({})
    assert out["group_keys"] == []
    assert out["measures"] == []
    assert out["pivots"] == []
    assert isinstance(out["note"], str)


def test_none_profile_does_not_crash():
    out = select_groupby_keys(None)
    assert out == {
        "group_keys": [],
        "measures": [],
        "pivots": [],
        "note": out["note"],
    }
    assert isinstance(out["note"], str)


def test_only_numerics_yields_empty_group_keys_and_no_pivots():
    profile = {
        "n_rows": 500,
        "key_candidates": [],
        "columns": [
            _num_col("price", std=12.0, cv=0.6),
            _num_col("weight", std=3.0, cv=0.2),
        ],
    }
    out = select_groupby_keys(profile)
    assert out["group_keys"] == []
    assert out["pivots"] == []
    # Las numericas si se eligen como measures.
    assert set(out["measures"]) == {"price", "weight"}
    assert out["measures"][0] == "price"  # mayor cv.


def test_high_cardinality_and_max_card_are_excluded():
    profile = {
        "n_rows": 1000,
        "key_candidates": [],
        "columns": [
            _cat_col("city", 50, flags=["high_cardinality"]),  # flag -> fuera.
            _cat_col("zone", 35),  # card 35 > max_card 20 -> fuera.
            _cat_col("region", 5),  # valida.
        ],
    }
    out = select_groupby_keys(profile, max_card=20)
    assert {g["col"] for g in out["group_keys"]} == {"region"}


def test_max_keys_limits_group_keys():
    profile = {
        "n_rows": 1000,
        "key_candidates": [],
        "columns": [
            _cat_col("a", 4, imbalance=1.0),
            _cat_col("b", 5, imbalance=1.2),
            _cat_col("c", 6, imbalance=1.5),
            _cat_col("d", 7, imbalance=2.0),
        ],
    }
    out = select_groupby_keys(profile, max_keys=2)
    assert len(out["group_keys"]) == 2
    # Hasta 2 pivots con >=2 keys (aqui exactamente 1 par posible entre 2 keys).
    assert len(out["pivots"]) == 1


def test_three_keys_cap_pivots_to_two():
    profile = {
        "n_rows": 1000,
        "key_candidates": [],
        "columns": [
            _cat_col("a", 4, imbalance=1.0),
            _cat_col("b", 5, imbalance=1.1),
            _cat_col("c", 6, imbalance=1.2),
            _num_col("m", std=10.0, cv=0.5),
        ],
    }
    out = select_groupby_keys(profile, max_keys=3)
    assert len(out["group_keys"]) == 3
    # 3 keys -> 3 pares posibles, capado a 2.
    assert len(out["pivots"]) == 2
    for p in out["pivots"]:
        assert p["value"] == "m"


def test_does_not_mutate_input():
    profile = _titanic_like_profile()
    before = repr(profile)
    select_groupby_keys(profile)
    assert repr(profile) == before