109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
"""Tests para build_boxplot_stats."""
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import os
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import sys
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sys.path.insert(0, os.path.dirname(__file__))
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from build_boxplot_stats import build_boxplot_stats
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# Keys that a non-empty result dict must always contain.
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_EXPECTED_KEYS = {
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"q1", "median", "q3", "iqr", "lower_fence", "upper_fence",
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"whisker_lo", "whisker_hi", "min", "max",
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"has_low_outliers", "has_high_outliers", "n_outliers",
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}
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def test_boxplot_tukey_basico():
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"""Golden: bloque numeric con outlier alto claro -> fences IQR de Tukey."""
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numeric = {
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"min": 1.0, "max": 100.0,
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"p25": 10.0, "median": 25.0, "p75": 40.0,
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"iqr": 30.0, "n_outliers": 3,
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}
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box = build_boxplot_stats(numeric)
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assert set(box.keys()) == _EXPECTED_KEYS
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assert box["q1"] == 10.0
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assert box["median"] == 25.0
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assert box["q3"] == 40.0
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# iqr recomputado desde los cuartiles.
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assert box["iqr"] == 30.0
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# lower = 10 - 1.5*30 = -35 ; upper = 40 + 1.5*30 = 85.
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assert box["lower_fence"] == -35.0
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assert box["upper_fence"] == 85.0
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# whisker_lo = max(min=1, -35) = 1 ; whisker_hi = min(max=100, 85) = 85.
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assert box["whisker_lo"] == 1.0
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assert box["whisker_hi"] == 85.0
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assert box["min"] == 1.0
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assert box["max"] == 100.0
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# Solo hay outliers altos (100 > 85), no bajos (1 no < -35).
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assert box["has_low_outliers"] is False
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assert box["has_high_outliers"] is True
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# n_outliers se propaga del bloque z-score (informativo).
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assert box["n_outliers"] == 3
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def test_percentiles_faltan_devuelve_vacio():
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"""Si falta p25/median/p75 -> {} (caller omite el boxplot)."""
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# Falta p25.
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assert build_boxplot_stats({"median": 25.0, "p75": 40.0}) == {}
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# Falta p75.
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assert build_boxplot_stats({"p25": 10.0, "median": 25.0}) == {}
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# Falta median y p50.
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assert build_boxplot_stats({"p25": 10.0, "p75": 40.0}) == {}
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# numeric None / no dict tambien es vacio, nunca lanza.
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assert build_boxplot_stats(None) == {}
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assert build_boxplot_stats({}) == {}
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def test_median_cae_a_p50():
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"""median ausente cae a p50."""
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numeric = {"min": 0.0, "max": 10.0, "p25": 2.0, "p50": 5.0, "p75": 8.0}
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box = build_boxplot_stats(numeric)
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assert box["median"] == 5.0
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assert box["q1"] == 2.0
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assert box["q3"] == 8.0
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def test_whiskers_usan_fence_si_falta_min_max():
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"""Sin min/max los bigotes caen a las fences y no hay outliers marcados."""
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numeric = {"p25": 10.0, "median": 25.0, "p75": 40.0} # sin min ni max
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box = build_boxplot_stats(numeric)
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assert box["min"] is None
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assert box["max"] is None
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# iqr = 30, fences -35 / 85; los bigotes caen a las fences.
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assert box["whisker_lo"] == box["lower_fence"] == -35.0
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assert box["whisker_hi"] == box["upper_fence"] == 85.0
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# Sin extremos reales, no se afirma que haya outliers.
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assert box["has_low_outliers"] is False
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assert box["has_high_outliers"] is False
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# n_outliers ausente -> 0.
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assert box["n_outliers"] == 0
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def test_tipos_salida_float_bool_int():
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"""Numericos en float, flags bool nativos, n_outliers int."""
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numeric = {
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"min": -50.0, "max": 200.0,
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"p25": 10.0, "median": 25.0, "p75": 40.0,
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"n_outliers": 7,
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}
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box = build_boxplot_stats(numeric)
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for key in ("q1", "median", "q3", "iqr", "lower_fence", "upper_fence",
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"whisker_lo", "whisker_hi", "min", "max"):
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assert isinstance(box[key], float), f"{key} debe ser float"
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assert isinstance(box["has_low_outliers"], bool)
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assert isinstance(box["has_high_outliers"], bool)
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assert isinstance(box["n_outliers"], int) and not isinstance(box["n_outliers"], bool)
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# min=-50 < lower_fence=-35 -> outlier bajo ; max=200 > upper_fence=85 -> alto.
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assert box["has_low_outliers"] is True
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assert box["has_high_outliers"] is True
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assert box["n_outliers"] == 7
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