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Pringled merged 2 commits into from
Jan 30, 2026
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tests: Add regression test #25

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c31053f
Added integration test
Pringled Jan 30, 2026
09f7543
Added regression test
Pringled Jan 30, 2026
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367 changes: 367 additions & 0 deletions tests/test_regression.py
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# Golden-value regression tests. Regenerate with: python tests/test_regression.py
import numpy as np
import pytest
from pyversity import Metric, Strategy, diversify

# Dataset 1: 20 items, 8 dims, k=5
_RNG1 = np.random.default_rng(42)
_RAW1 = _RNG1.standard_normal((20, 8)).astype(np.float32)
EMBEDDINGS_1 = _RAW1 / np.maximum(np.linalg.norm(_RAW1, axis=1, keepdims=True), 1e-7)
SCORES_1 = _RNG1.uniform(0.1, 1.0, size=20).astype(np.float32)
K_1 = 5

_RAW1_RECENT = _RNG1.standard_normal((3, 8)).astype(np.float32)
RECENT_1 = _RAW1_RECENT / np.maximum(np.linalg.norm(_RAW1_RECENT, axis=1, keepdims=True), 1e-7)

# Dataset 2: 50 items (45 + 5 near-duplicates), 32 dims, k=10
_RNG2 = np.random.default_rng(123)
_RAW2_BASE = _RNG2.standard_normal((45, 32)).astype(np.float32)
_RAW2_DUPES = _RAW2_BASE[:5] + _RNG2.normal(0, 0.01, (5, 32)).astype(np.float32)
_RAW2 = np.vstack([_RAW2_BASE, _RAW2_DUPES])
EMBEDDINGS_2 = _RAW2 / np.maximum(np.linalg.norm(_RAW2, axis=1, keepdims=True), 1e-7)
SCORES_2 = _RNG2.uniform(0.1, 1.0, size=50).astype(np.float32)
K_2 = 10

_RAW2_RECENT = _RNG2.standard_normal((4, 32)).astype(np.float32)
RECENT_2 = _RAW2_RECENT / np.maximum(np.linalg.norm(_RAW2_RECENT, axis=1, keepdims=True), 1e-7)

_DATASETS = {
1: (EMBEDDINGS_1, SCORES_1, K_1),
2: (EMBEDDINGS_2, SCORES_2, K_2),
}

# Golden test cases
CASES: list[dict] = [
# Dataset 1, default parameters
dict(
id="d1-mmr-0.0",
strategy=Strategy.MMR,
diversity=0.0,
dataset=1,
indices=[14, 6, 10, 18, 15],
scores=[0.993138, 0.9576095, 0.9424392, 0.904102, 0.9025095],
),
dict(
id="d1-mmr-0.5",
strategy=Strategy.MMR,
diversity=0.5,
dataset=1,
indices=[14, 6, 16, 4, 15],
scores=[0.496569, 0.4788047, 0.3501282, 0.3043389, 0.2549736],
),
dict(
id="d1-mmr-1.0",
strategy=Strategy.MMR,
diversity=1.0,
dataset=1,
indices=[14, 4, 5, 8, 10],
scores=[0.0, 0.0, 0.0, 0.0, 0.0],
),
dict(
id="d1-msd-0.0",
strategy=Strategy.MSD,
diversity=0.0,
dataset=1,
indices=[14, 6, 10, 18, 15],
scores=[0.993138, 0.9576095, 0.9424392, 0.904102, 0.9025095],
),
dict(
id="d1-msd-0.5",
strategy=Strategy.MSD,
diversity=0.5,
dataset=1,
indices=[14, 17, 10, 18, 5],
scores=[0.496569, 1.2563956, 1.6436498, 2.0642159, 2.5259635],
),
dict(
id="d1-msd-1.0",
strategy=Strategy.MSD,
diversity=1.0,
dataset=1,
indices=[14, 17, 8, 7, 12],
scores=[0.0, 1.6110779, 2.372129, 3.538434, 4.6836829],
),
dict(
id="d1-dpp-0.0",
strategy=Strategy.DPP,
diversity=0.0,
dataset=1,
indices=[14, 6, 10, 18, 15],
scores=[0.993138, 0.9576095, 0.9424392, 0.904102, 0.9025095],
),
dict(
id="d1-dpp-0.5",
strategy=Strategy.DPP,
diversity=0.5,
dataset=1,
indices=[14, 10, 18, 4, 6],
scores=[3.8320513, 3.1946325, 2.278928, 2.0403609, 1.7162278],
),
dict(
id="d1-dpp-1.0",
strategy=Strategy.DPP,
diversity=1.0,
dataset=1,
indices=[0, 3, 13, 19, 12],
scores=[1.0000001, 0.9999955, 0.9922758, 0.9399173, 0.8916375],
),
dict(
id="d1-cover-0.0",
strategy=Strategy.COVER,
diversity=0.0,
dataset=1,
indices=[14, 6, 10, 18, 15],
scores=[0.993138, 0.9576095, 0.9424392, 0.904102, 0.9025095],
),
dict(
id="d1-cover-0.5",
strategy=Strategy.COVER,
diversity=0.5,
dataset=1,
indices=[1, 6, 18, 3, 15],
scores=[4.2265797, 2.5809641, 2.2656803, 1.8373549, 1.4752011],
),
dict(
id="d1-cover-1.0",
strategy=Strategy.COVER,
diversity=1.0,
dataset=1,
indices=[1, 6, 18, 3, 0],
scores=[7.8537917, 4.2043185, 3.6272588, 2.827899, 2.24547],
),
dict(
id="d1-ssd-0.0",
strategy=Strategy.SSD,
diversity=0.0,
dataset=1,
indices=[14, 6, 10, 18, 15],
scores=[0.993138, 0.9576095, 0.9424392, 0.904102, 0.9025095],
),
dict(
id="d1-ssd-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=1,
indices=[14, 6, 18, 10, 17],
scores=[1.3788071, 1.281781, 1.0446954, 1.0005223, 0.9349319],
),
dict(
id="d1-ssd-1.0",
strategy=Strategy.SSD,
diversity=1.0,
dataset=1,
indices=[14, 17, 10, 5, 8],
scores=[1.4142135, 1.3872166, 1.1854348, 1.1495396, 1.0392091],
),
dict(
id="d1-ssd-recent-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=1,
kwargs={"recent_embeddings": RECENT_1},
indices=[14, 6, 17, 18, 15],
scores=[1.2624965, 1.1179161, 0.9618138, 0.8816395, 0.6654232],
),
# Dataset 1, non-default parameters
dict(
id="d1-mmr-dot-0.5",
strategy=Strategy.MMR,
diversity=0.5,
dataset=1,
kwargs={"metric": Metric.DOT, "normalize": False},
indices=[14, 6, 16, 4, 15],
scores=[0.496569, 0.4788047, 0.3501282, 0.3043389, 0.2549736],
),
dict(
id="d1-msd-dot-0.5",
strategy=Strategy.MSD,
diversity=0.5,
dataset=1,
kwargs={"metric": Metric.DOT, "normalize": False},
indices=[14, 17, 10, 18, 5],
scores=[0.496569, 0.7563956, 0.6436498, 0.564216, 0.5259635],
),
dict(
id="d1-cover-gamma0.8-0.5",
strategy=Strategy.COVER,
diversity=0.5,
dataset=1,
kwargs={"gamma": 0.8},
indices=[15, 6, 14, 3, 18],
scores=[3.339906, 2.8881586, 2.5098512, 2.31229, 2.2262282],
),
dict(
id="d1-dpp-scale2-0.5",
strategy=Strategy.DPP,
diversity=0.5,
dataset=1,
kwargs={"scale": 2.0},
indices=[14, 10, 6, 18, 4],
scores=[14.684617, 10.2230501, 6.7176108, 5.8371487, 4.0808001],
),
dict(
id="d1-ssd-raw-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=1,
kwargs={"normalize": False, "append_bias": False, "normalize_scores": False},
indices=[14, 10, 4, 18, 6],
scores=[0.996569, 0.9707946, 0.9087705, 0.89896, 0.835229],
),
dict(
id="d1-ssd-window3-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=1,
kwargs={"window": 3},
indices=[14, 6, 18, 10, 3],
scores=[1.3788071, 1.281781, 1.0446954, 1.0005223, 1.0854802],
),
dict(
id="d2-mmr-0.5",
strategy=Strategy.MMR,
diversity=0.5,
dataset=2,
indices=[34, 44, 0, 8, 11, 2, 33, 21, 35, 49],
scores=[
0.4982942,
0.4752265,
0.4423259,
0.4213459,
0.4192513,
0.4143822,
0.3673249,
0.3629451,
0.3548062,
0.3130703,
],
),
dict(
id="d2-msd-0.5",
strategy=Strategy.MSD,
diversity=0.5,
dataset=2,
indices=[34, 44, 8, 26, 0, 35, 24, 33, 48, 2],
scores=[
0.4982942,
1.0318062,
1.5670512,
2.1662273,
2.6055198,
3.1134424,
3.6113653,
4.1183109,
4.5576282,
5.0157132,
],
),
dict(
id="d2-dpp-0.5",
strategy=Strategy.DPP,
diversity=0.5,
dataset=2,
indices=[34, 11, 44, 41, 0, 14, 21, 2, 35, 8],
scores=[
5.1772084,
4.6469154,
4.3583627,
4.0794702,
3.3010585,
3.0233638,
2.8123801,
2.6220398,
2.5165148,
2.2487965,
],
),
dict(
id="d2-cover-0.5",
strategy=Strategy.COVER,
diversity=0.5,
dataset=2,
indices=[21, 7, 28, 1, 23, 48, 2, 41, 15, 49],
scores=[
6.4557028,
4.119772,
3.1374972,
2.4369617,
2.2468584,
2.0424407,
1.9487213,
1.7973837,
1.756186,
1.6591004,
],
),
dict(
id="d2-ssd-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=2,
indices=[34, 44, 11, 41, 0, 2, 14, 21, 8, 35],
scores=[
1.52924,
1.376298,
1.3285697,
1.2422681,
1.2112706,
1.1682143,
1.1040297,
1.0666059,
1.0444114,
1.0130265,
],
),
dict(
id="d2-ssd-recent-0.5",
strategy=Strategy.SSD,
diversity=0.5,
dataset=2,
kwargs={"recent_embeddings": RECENT_2},
indices=[34, 11, 44, 41, 2, 0, 21, 14, 15, 8],
scores=[
1.3352783,
1.2505096,
1.2227125,
1.1950345,
1.1083739,
1.0967314,
1.052088,
1.0184829,
0.9867558,
0.975732,
],
),
]


@pytest.mark.parametrize("case", CASES, ids=lambda c: c["id"])
def test_golden_values(case: dict) -> None:
"""Assert that strategy outputs match recorded golden values."""
embeddings, scores, k = _DATASETS[case["dataset"]]
kwargs = case.get("kwargs", {})

result = diversify(embeddings, scores, k, strategy=case["strategy"], diversity=case["diversity"], **kwargs)

assert np.array_equal(
result.indices,
np.array(case["indices"]),
), f"Indices mismatch for {case['id']}"

assert np.allclose(
result.selection_scores,
np.array(case["scores"], dtype=np.float32),
atol=1e-6,
), f"Scores mismatch for {case['id']}"


if __name__ == "__main__":
for case in CASES:
embeddings, scores, k = _DATASETS[case["dataset"]]
kwargs = case.get("kwargs", {})
r = diversify(embeddings, scores, k, strategy=case["strategy"], diversity=case["diversity"], **kwargs)
print( # noqa: T201
f"{case['id']}:\n"
f" indices={r.indices.tolist()},\n"
f" scores={[round(float(s), 7) for s in r.selection_scores]},"
)