fn_registry/python/functions/pipelines/profile_table_test.py

"""Tests para profile_table — pipeline EDA one-shot del grupo `eda`.

Crea una DuckDB temporal con tres columnas representativas:
  - id_str: enteros guardados como VARCHAR ('10','20',...) -> debe promocionarse
    a inferred_type "numeric" y recibir un bloque col["numeric"].
  - precio: numerica nativa (DOUBLE).
  - categoria: categorica textual.
Luego corre profile_table(write_report=False) y verifica el contrato.
"""

import os
import tempfile

import duckdb

from pipelines.profile_table import (
    _is_continuous_for_reexpr,
    _looks_financial,
    profile_table,
)


# --- H12: re-expresión solo para columnas continuas -------------------------

def test_is_continuous_for_reexpr_baja_cardinalidad():
    # Binaria (2 niveles) y ordinal de baja cardinalidad (3 niveles): NO continuas.
    binaria = {"distinct_count": 2, "flags": []}
    ordinal = {"distinct_count": 3, "flags": []}
    assert _is_continuous_for_reexpr(binaria, [0.0, 1.0, 0.0, 1.0]) is False
    assert _is_continuous_for_reexpr(ordinal, [1.0, 2.0, 3.0, 2.0]) is False


def test_is_continuous_for_reexpr_id_entero():
    # Identificador entero (possible_id + todos enteros): NO continua.
    idcol = {"distinct_count": 200, "flags": ["possible_id"]}
    vals = [float(i) for i in range(1, 201)]
    assert _is_continuous_for_reexpr(idcol, vals) is False


def test_is_continuous_for_reexpr_float_continuo():
    # Float continuo de alta cardinalidad, aunque lleve possible_id, SÍ es continuo
    # (tiene parte decimal, no es un id entero).
    precio = {"distinct_count": 200, "flags": ["possible_id"]}
    vals = [i * 1.7 for i in range(200)]
    assert _is_continuous_for_reexpr(precio, vals) is True


def test_reexpression_solo_para_columnas_continuas():
    # En una tabla con binaria/ordinal/id/continua, solo la continua trae el bloque
    # reexpression en su ColumnProfile.
    tmp_dir = tempfile.mkdtemp(prefix="reexpr_test_")
    db_path = os.path.join(tmp_dir, "t.duckdb")
    con = duckdb.connect(db_path)
    con.execute(
        "CREATE TABLE t (pid INTEGER, surv INTEGER, pclass INTEGER, fare DOUBLE)"
    )
    con.execute(
        "INSERT INTO t SELECT i, i%2, (i%3)+1, ((i*1.7)%50)+0.3 "
        "FROM range(300) tbl(i)"
    )
    con.close()

    r = profile_table(db_path, "t", write_report=False)
    assert r["status"] == "ok", r
    prof = r["profile"]

    assert _col(prof, "pid").get("reexpression") is None       # id entero
    assert _col(prof, "surv").get("reexpression") is None       # binaria
    assert _col(prof, "pclass").get("reexpression") is None     # ordinal baja card
    assert _col(prof, "fare").get("reexpression") is not None   # continua


# --- H13: retornos (financiera) vs diferencias (física) ---------------------

def test_looks_financial_por_nombre_y_semantic():
    assert _looks_financial({"name": "Close"}) is True
    assert _looks_financial({"name": "Adj Close"}) is True
    assert _looks_financial({"name": "Volume"}) is True
    assert _looks_financial({"name": "precio_cierre"}) is True
    assert _looks_financial({"name": "temp_max"}) is False
    assert _looks_financial({"name": "precipitation"}) is False
    assert _looks_financial({"name": "caudal", "semantic_type": "currency"}) is True


def _make_series_db(value_col: str) -> str:
    """DuckDB con una serie de niveles no estacionaria (random walk creciente)."""
    tmp_dir = tempfile.mkdtemp(prefix="series_test_")
    db_path = os.path.join(tmp_dir, "s.duckdb")
    con = duckdb.connect(db_path)
    con.execute(f'CREATE TABLE s (ts INTEGER, "{value_col}" DOUBLE)')
    # Niveles estrictamente positivos con tendencia creciente (no estacionaria).
    level = 100.0
    rows = []
    for t in range(80):
        level += 1.0 + (t % 7) * 0.3   # incrementos positivos deterministas
        rows.append((t, level))
    con.executemany(f'INSERT INTO s VALUES (?, ?)', rows)
    con.close()
    return db_path


def test_series_financiera_sugiere_retornos():
    db_path = _make_series_db("close")
    r = profile_table(db_path, "s", run_series=True, write_report=False)
    assert r["status"] == "ok", r
    s = _col(r["profile"], "close").get("series")
    assert s is not None
    if s.get("levels_suggested"):
        assert s.get("levels_kind") == "returns"


def test_series_no_financiera_sugiere_diferencias():
    db_path = _make_series_db("temp_max")
    r = profile_table(db_path, "s", run_series=True, write_report=False)
    assert r["status"] == "ok", r
    s = _col(r["profile"], "temp_max").get("series")
    assert s is not None
    if s.get("levels_suggested"):
        assert s.get("levels_kind") == "differences"
        # Para diferencias no se computa el bloque de retornos.
        assert "to_returns" not in s


def _make_db() -> str:
    """Crea una DuckDB temporal con la tabla de prueba y devuelve su path."""
    tmp_dir = tempfile.mkdtemp(prefix="profile_table_test_")
    db_path = os.path.join(tmp_dir, "t.duckdb")
    con = duckdb.connect(db_path)
    con.execute(
        "CREATE TABLE items ("
        "  id_str VARCHAR,"   # enteros guardados como texto
        "  precio DOUBLE,"    # numerica nativa
        "  categoria VARCHAR" # categorica
        ")"
    )
    rows = [
        ("10", 9.5, "alfa"),
        ("20", 12.0, "beta"),
        ("30", 7.25, "alfa"),
        ("40", 15.75, "gamma"),
        ("50", 3.0, "beta"),
        ("60", 22.4, "alfa"),
    ]
    con.executemany("INSERT INTO items VALUES (?, ?, ?)", rows)
    con.close()
    return db_path


def _col(profile: dict, name: str) -> dict:
    return next(c for c in profile["columns"] if c["name"] == name)


def test_varchar_integer_promotes_to_numeric():
    db_path = _make_db()
    r = profile_table(db_path, "items", sample=5000, write_report=False)

    # status ok y sin tocar disco.
    assert r["status"] == "ok", r
    assert r["report_md_path"] is None
    assert r["report_json_path"] is None

    prof = r["profile"]

    # La columna VARCHAR-entera se promociono a numeric con bloque numeric.
    id_col = _col(prof, "id_str")
    assert id_col["inferred_type"] == "numeric", id_col["inferred_type"]
    assert id_col["numeric"] is not None
    assert id_col["numeric"]["min"] == 10.0
    assert id_col["numeric"]["max"] == 60.0

    # La numerica nativa sigue siendo numeric con su bloque.
    precio_col = _col(prof, "precio")
    assert precio_col["inferred_type"] == "numeric"
    assert precio_col["numeric"] is not None

    # La categorica recibe su bloque categorical.
    cat_col = _col(prof, "categoria")
    assert cat_col["inferred_type"] in ("categorical", "text")
    assert cat_col["categorical"] is not None
    assert cat_col["categorical"]["mode"] == "alfa"

    # key_candidates es una lista; quality_score existe (tabla y columnas).
    assert isinstance(prof["key_candidates"], list)
    assert prof["quality_score"] is not None
    assert id_col["quality_score"] is not None

    # type_breakdown recalculado refleja la promocion (>=2 numeric).
    assert prof["type_breakdown"]["numeric"] >= 2