fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

- H1: render_eda_markdown ya no aplica doble x100 a outlier_pct (336% -> real)
- H5: profile_database filtra base_tables_only (excluye VIEWs; sakila 21->16)
- H12: suggest_reexpression salta columnas no-continuas
- H13: to_returns/profile_table elige retornos (financiera) vs diferencias (fisica)
- H14: test de regresion ATTACH sqlite via information_schema
- +8 tests de las funciones eda nuevas (acf_pacf, adf_kpss, ...). 77 tests verdes
- L/M (H2,H3,H4,H6,H7,H8,H9,H10,H11) quedan en issues 0174-0177 para revision

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/functions/pipelines/profile_table_test.py

@@ -13,7 +13,112 @@ import tempfile
 
 import duckdb
 
-from pipelines.profile_table import profile_table
+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: