Egutierrez
caf8c25d99
- 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>
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""Tests para adf_kpss_stationarity."""
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import numpy as np
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from adf_kpss_stationarity import adf_kpss_stationarity
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def test_random_walk_es_no_estacionario():
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# Random walk = suma acumulada de ruido: tiene raiz unitaria.
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rng = np.random.default_rng(123)
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paseo = np.cumsum(rng.normal(0.0, 1.0, 400)).tolist()
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res = adf_kpss_stationarity(paseo)
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assert res["verdict"] == "non_stationary"
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assert res["adf"]["stationary"] is False
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assert res["kpss"]["stationary"] is False
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def test_ruido_blanco_es_estacionario():
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# Ruido blanco gaussiano: estacionario por construccion.
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rng = np.random.default_rng(42)
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ruido = rng.normal(0.0, 1.0, 400).tolist()
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res = adf_kpss_stationarity(ruido)
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assert res["verdict"] == "stationary"
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assert res["adf"]["stationary"] is True
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assert res["kpss"]["stationary"] is True
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assert res["warning"] is None
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def test_serie_con_tendencia_no_es_estacionaria():
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# Tendencia lineal determinista + ruido pequeno: KPSS la marca no estacionaria.
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rng = np.random.default_rng(7)
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serie = [0.1 * i + rng.normal(0, 0.5) for i in range(300)]
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res = adf_kpss_stationarity(serie)
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assert res["verdict"] != "stationary"
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assert res["warning"] is not None
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def test_muestra_insuficiente_devuelve_nota():
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res = adf_kpss_stationarity([1, 2, 3, 4, 5])
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assert res["n"] == 5
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assert res["note"] == "datos insuficientes"
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assert res["verdict"] is None
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def test_descarta_none_y_nan():
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rng = np.random.default_rng(1)
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base = rng.normal(0, 1, 200).tolist()
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sucio = []
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for i, v in enumerate(base):
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sucio.append(v)
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if i % 20 == 0:
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sucio.append(None)
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sucio.append(float("nan"))
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res = adf_kpss_stationarity(sucio)
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assert res["n"] == 200 # las None/NaN no cuentan
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def test_warning_presente_si_no_estacionaria():
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# Tendencia lineal fuerte: garantiza no estacionariedad (verdict != stationary).
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rng = np.random.default_rng(99)
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serie = [0.5 * i + rng.normal(0, 0.3) for i in range(300)]
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res = adf_kpss_stationarity(serie)
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assert res["verdict"] != "stationary"
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assert res["warning"] is not None
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assert "espuria" in res["warning"].lower()
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def test_estructura_basica_del_dict():
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rng = np.random.default_rng(5)
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ruido = rng.normal(0, 1, 100).tolist()
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res = adf_kpss_stationarity(ruido)
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for key in ("n", "alpha", "adf", "kpss", "verdict"):
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assert key in res
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for sub in ("stat", "p_value", "lags", "stationary", "conclusion"):
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assert sub in res["adf"]
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assert sub in res["kpss"]
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