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/datascience/adf_kpss_stationarity_test.py

@@ -0,0 +1,76 @@
+"""Tests para adf_kpss_stationarity."""
+
+import numpy as np
+
+from adf_kpss_stationarity import adf_kpss_stationarity
+
+
+def test_random_walk_es_no_estacionario():
+    # Random walk = suma acumulada de ruido: tiene raiz unitaria.
+    rng = np.random.default_rng(123)
+    paseo = np.cumsum(rng.normal(0.0, 1.0, 400)).tolist()
+    res = adf_kpss_stationarity(paseo)
+    assert res["verdict"] == "non_stationary"
+    assert res["adf"]["stationary"] is False
+    assert res["kpss"]["stationary"] is False
+
+
+def test_ruido_blanco_es_estacionario():
+    # Ruido blanco gaussiano: estacionario por construccion.
+    rng = np.random.default_rng(42)
+    ruido = rng.normal(0.0, 1.0, 400).tolist()
+    res = adf_kpss_stationarity(ruido)
+    assert res["verdict"] == "stationary"
+    assert res["adf"]["stationary"] is True
+    assert res["kpss"]["stationary"] is True
+    assert res["warning"] is None
+
+
+def test_serie_con_tendencia_no_es_estacionaria():
+    # Tendencia lineal determinista + ruido pequeno: KPSS la marca no estacionaria.
+    rng = np.random.default_rng(7)
+    serie = [0.1 * i + rng.normal(0, 0.5) for i in range(300)]
+    res = adf_kpss_stationarity(serie)
+    assert res["verdict"] != "stationary"
+    assert res["warning"] is not None
+
+
+def test_muestra_insuficiente_devuelve_nota():
+    res = adf_kpss_stationarity([1, 2, 3, 4, 5])
+    assert res["n"] == 5
+    assert res["note"] == "datos insuficientes"
+    assert res["verdict"] is None
+
+
+def test_descarta_none_y_nan():
+    rng = np.random.default_rng(1)
+    base = rng.normal(0, 1, 200).tolist()
+    sucio = []
+    for i, v in enumerate(base):
+        sucio.append(v)
+        if i % 20 == 0:
+            sucio.append(None)
+            sucio.append(float("nan"))
+    res = adf_kpss_stationarity(sucio)
+    assert res["n"] == 200  # las None/NaN no cuentan
+
+
+def test_warning_presente_si_no_estacionaria():
+    # Tendencia lineal fuerte: garantiza no estacionariedad (verdict != stationary).
+    rng = np.random.default_rng(99)
+    serie = [0.5 * i + rng.normal(0, 0.3) for i in range(300)]
+    res = adf_kpss_stationarity(serie)
+    assert res["verdict"] != "stationary"
+    assert res["warning"] is not None
+    assert "espuria" in res["warning"].lower()
+
+
+def test_estructura_basica_del_dict():
+    rng = np.random.default_rng(5)
+    ruido = rng.normal(0, 1, 100).tolist()
+    res = adf_kpss_stationarity(ruido)
+    for key in ("n", "alpha", "adf", "kpss", "verdict"):
+        assert key in res
+    for sub in ("stat", "p_value", "lags", "stationary", "conclusion"):
+        assert sub in res["adf"]
+        assert sub in res["kpss"]