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>
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
"""Tests para acf_pacf."""
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import numpy as np
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from acf_pacf import acf_pacf
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def _ar1(phi: float, n: int, seed: int) -> list:
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rng = np.random.default_rng(seed)
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series = [0.0]
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for _ in range(n):
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series.append(phi * series[-1] + rng.normal(0, 1))
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return series
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def test_ruido_blanco_no_autocorrelado():
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rng = np.random.default_rng(0)
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ruido = rng.normal(0, 1, 500).tolist()
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res = acf_pacf(ruido)
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assert res["is_autocorrelated"] is False
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def test_ar1_es_autocorrelado():
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ar = _ar1(0.8, 500, seed=1)
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res = acf_pacf(ar)
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assert res["is_autocorrelated"] is True
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def test_lag1_significativo_en_ar1():
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# En un AR(1) la PACF corta tras el lag 1: lag 1 debe ser significativo.
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ar = _ar1(0.8, 500, seed=2)
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res = acf_pacf(ar)
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assert 1 in res["significant_pacf_lags"]
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assert 1 in res["significant_acf_lags"]
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def test_muestra_insuficiente_devuelve_nota():
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res = acf_pacf([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["is_autocorrelated"] is None
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def test_descarta_none_y_nan():
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rng = np.random.default_rng(3)
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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 % 25 == 0:
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sucio.append(None)
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sucio.append(float("nan"))
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res = acf_pacf(sucio)
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assert res["n"] == 200
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def test_recorta_nlags_a_limites():
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# Serie de 20 puntos con nlags=40: debe recortar a < n/2.
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rng = np.random.default_rng(4)
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serie = rng.normal(0, 1, 20).tolist()
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res = acf_pacf(serie, nlags=40)
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assert res["nlags"] < 20 // 2
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assert len(res["acf"]) == res["nlags"] + 1
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def test_acf_lag0_es_uno():
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rng = np.random.default_rng(5)
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serie = rng.normal(0, 1, 100).tolist()
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res = acf_pacf(serie)
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assert abs(res["acf"][0] - 1.0) < 1e-9
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assert abs(res["pacf"][0] - 1.0) < 1e-9
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