Gahow Wang
0701f84c00
- tests/test_metrics.py asserts the new linear-interp _percentile against hand-computed expected values (single value, two-value interpolation, endpoints, numpy-equivalent linear default, on-integer rank). - tests/test_proxy_pick.py exercises InstanceState LRU eviction and move-to-end on hit, plus session-affinity stickiness, the overload fallback, the active_p_offloads penalty, and lmetric scoring. The proxy is loaded by file path with stub fastapi/uvicorn/httpx modules so the suite runs without the FastAPI server deps installed. - pyproject.toml gets a hatchling wheel target and a [tool.pytest] section so `uv run --extra dev pytest` works out of the box.
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
"""Tests for replayer.metrics percentile + summary helpers (B5)."""
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from __future__ import annotations
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import math
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from replayer.metrics import _percentile
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def test_percentile_single_value():
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assert _percentile([42.0], 0.50) == 42.0
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assert _percentile([42.0], 0.99) == 42.0
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def test_percentile_two_values_interpolates():
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# For [0, 10] linear interpolation gives p50=5.0, p90=9.0.
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assert math.isclose(_percentile([0.0, 10.0], 0.50), 5.0)
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assert math.isclose(_percentile([0.0, 10.0], 0.90), 9.0)
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def test_percentile_endpoints():
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vals = [1.0, 2.0, 3.0, 4.0, 5.0]
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assert _percentile(vals, 0.0) == 1.0
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assert _percentile(vals, 1.0) == 5.0
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def test_percentile_matches_numpy_linear_default():
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# Independently computed using numpy's default linear interpolation;
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# we hardcode the expectations so the test does not depend on numpy.
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vals = [1.0, 2.0, 4.0, 8.0, 16.0, 32.0]
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# rank for p50 = 0.5 * 5 = 2.5 -> 0.5 * 4 + 0.5 * 8 = 6.0
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assert math.isclose(_percentile(vals, 0.50), 6.0)
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# rank for p90 = 0.9 * 5 = 4.5 -> 0.5 * 16 + 0.5 * 32 = 24.0
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assert math.isclose(_percentile(vals, 0.90), 24.0)
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# rank for p99 = 0.99 * 5 = 4.95 -> 0.05 * 16 + 0.95 * 32 = 31.2
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assert math.isclose(_percentile(vals, 0.99), 31.2)
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def test_percentile_no_off_by_one_at_boundary():
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# Regression: previous round-based implementation returned the wrong
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# element when rank fell exactly on an integer.
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vals = [10.0, 20.0, 30.0]
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# rank for p50 = 0.5 * 2 = 1.0 -> exactly element 1 -> 20.0
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assert _percentile(vals, 0.50) == 20.0
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