scripts/frozen-original/mixed_index_compatibility.py

#!/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()