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>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
"""Tests para stl_decompose."""
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import numpy as np
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from stl_decompose import stl_decompose
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def _serie_estacional(n: int, period: int, trend: float, amp: float, seed: int) -> list:
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rng = np.random.default_rng(seed)
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return [
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trend * i + amp * np.sin(2 * np.pi * i / period) + rng.normal(0, 1)
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for i in range(n)
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]
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def test_serie_con_tendencia_y_estacionalidad():
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serie = _serie_estacional(n=120, period=12, trend=0.3, amp=10.0, seed=0)
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res = stl_decompose(serie, period=12)
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assert res["period"] == 12
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assert res["trend_strength"] > 0.5
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assert res["seasonal_strength"] > 0.5
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assert len(res["trend"]["values"]) == 120
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def test_fuerza_estacional_alta_con_estacionalidad_fuerte():
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# Amplitud estacional grande, ruido pequeno => seasonal_strength cercano a 1.
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serie = _serie_estacional(n=120, period=12, trend=0.05, amp=20.0, seed=1)
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res = stl_decompose(serie, period=12)
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assert res["seasonal_strength"] > 0.9
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def test_infiere_periodo_si_none():
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serie = _serie_estacional(n=120, period=12, trend=0.1, amp=10.0, seed=2)
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res = stl_decompose(serie) # period=None
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assert res.get("period_inferred") is True
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assert res["period"] is not None
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def test_serie_corta_devuelve_nota():
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# period=12 pero solo 20 puntos (< 2*period=24): nota, no descompone.
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serie = _serie_estacional(n=20, period=12, trend=0.1, amp=5.0, seed=3)
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res = stl_decompose(serie, period=12)
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assert "note" in res
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assert res["trend_strength"] is None
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def test_muestra_insuficiente_devuelve_nota():
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res = stl_decompose([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["seasonal_strength"] is None
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def test_descarta_none_y_nan():
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serie = _serie_estacional(n=120, period=12, trend=0.2, amp=8.0, seed=4)
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sucio = []
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for i, v in enumerate(serie):
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sucio.append(v)
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if i % 30 == 0:
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sucio.append(None)
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sucio.append(float("nan"))
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res = stl_decompose(sucio, period=12)
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assert res["n"] == 120
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def test_serie_larga_resume_sin_values():
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# >200 puntos: las componentes vienen resumidas sin 'values'.
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serie = _serie_estacional(n=300, period=12, trend=0.1, amp=10.0, seed=5)
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res = stl_decompose(serie, period=12)
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assert res["trend"]["values"] is None
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assert "mean" in res["trend"]
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assert "note" in res["trend"]
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