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ffdd9aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | #!/usr/bin/env python3
"""Evaluate mixed BF16/quantized embedding-index compatibility.
The similarity threshold is selected from BF16 scores only, then frozen and
applied unchanged to both mixed directions. This avoids tuning the decision
boundary on the candidate representation.
"""
from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
LABELS = ("q4", "oq4", "oq4e", "q6", "oq6", "oq6e", "q8", "oq8", "oq8e")
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def rank_metrics(scores: np.ndarray) -> dict:
order = np.argsort(-scores, axis=1)
ranks = np.array([
int(np.where(order[index] == index)[0][0]) + 1
for index in range(scores.shape[0])
])
return {
"top1": float(np.mean(ranks == 1)),
"recall_at_5": float(np.mean(ranks <= 5)),
"mrr": float(np.mean(1.0 / ranks)),
"rank_changes_from_identity": int(np.count_nonzero(ranks != 1)),
"worst_rank": int(ranks.max()),
}
def threshold_metrics(scores: np.ndarray, threshold: float) -> dict:
identity = np.eye(scores.shape[0], dtype=bool)
predicted = scores >= threshold
tp = int(np.count_nonzero(predicted & identity))
fn = int(np.count_nonzero(~predicted & identity))
fp = int(np.count_nonzero(predicted & ~identity))
tn = int(np.count_nonzero(~predicted & ~identity))
tpr = tp / (tp + fn) if tp + fn else 0.0
tnr = tn / (tn + fp) if tn + fp else 0.0
return {
"threshold": threshold,
"true_positive": tp,
"false_negative": fn,
"false_positive": fp,
"true_negative": tn,
"balanced_accuracy": (tpr + tnr) / 2.0,
}
def calibrate_threshold(scores: np.ndarray) -> tuple[float, dict]:
identity = np.eye(scores.shape[0], dtype=bool)
values = np.unique(scores)
candidates = np.concatenate((
[np.nextafter(values[0], -np.inf)],
(values[:-1] + values[1:]) / 2.0,
[np.nextafter(values[-1], np.inf)],
))
evaluated = [(float(value), threshold_metrics(scores, float(value))) for value in candidates]
# Prefer the most accurate threshold, then the stricter threshold on ties.
threshold, metrics = max(
evaluated,
key=lambda item: (item[1]["balanced_accuracy"], item[0]),
)
positives = scores[identity]
negatives = scores[~identity]
metrics.update({
"minimum_positive_score": float(positives.min()),
"maximum_negative_score": float(negatives.max()),
"separation_gap": float(positives.min() - negatives.max()),
})
return threshold, metrics
def load_vectors(path: Path) -> tuple[np.ndarray, np.ndarray]:
with np.load(path) as artifact:
return artifact["queries"], artifact["documents"]
def evaluate_family(quality_dir: Path) -> dict:
bf16_path = quality_dir / "bf16.npz"
bf16_queries, bf16_documents = load_vectors(bf16_path)
bf16_scores = bf16_queries @ bf16_documents.T
threshold, calibration = calibrate_threshold(bf16_scores)
candidates = {}
for label in LABELS:
path = quality_dir / f"{label}.npz"
candidate_queries, candidate_documents = load_vectors(path)
directions = {
"bf16_query_candidate_index": bf16_queries @ candidate_documents.T,
"candidate_query_bf16_index": candidate_queries @ bf16_documents.T,
}
candidates[label] = {
"artifact_sha256": sha256(path),
"directions": {
name: {
"retrieval": rank_metrics(scores),
"fixed_threshold": threshold_metrics(scores, threshold),
}
for name, scores in directions.items()
},
}
return {
"bf16_artifact_sha256": sha256(bf16_path),
"bf16_threshold_calibration": calibration,
"candidates": candidates,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--results-root", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
families = {}
for quality_dir in sorted(args.results_root.glob("*/quality")):
families[quality_dir.parent.name] = evaluate_family(quality_dir)
result = {
"method": "BF16-calibrated fixed threshold applied without retuning",
"families": families,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(result, indent=2) + "\n")
print(json.dumps({
"output": str(args.output),
"families": list(families),
}))
if __name__ == "__main__":
main()
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