Datasets:
File size: 2,896 Bytes
07b266f | 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 | #!/usr/bin/env python
"""Export the index tables as CSV, including a human-readable join.
llm_rerank.parquet is only ids + rank + verdict, which is unreadable on its own:
you cannot tell whether 'Y' was right without seeing the two narrations. The
readable export joins the narrations and the matcher's component scores so a
row can be judged by eye.
python 04_export_csv.py
"""
import argparse
from pathlib import Path
import pandas as pd
DATA = Path(__file__).resolve().parent.parent
IDX = DATA / "index"
def main():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--only-y", action="store_true", help="readable export keeps Y rows only")
a = p.parse_args()
seg = pd.read_parquet(IDX / "segments.parquet")
r = pd.read_parquet(IDX / "llm_rerank.parquet")
r.to_csv(IDX / "llm_rerank.csv", index=False)
print(f"[write] {len(r):,} rows -> llm_rerank.csv (exact twin of the parquet)")
# narrations + the matcher scores the verdict is meant to correct
cols = ["annotation_id", "video_uid", "scene", "narration_en", "start_sec", "end_sec"]
ego = seg[cols].add_prefix("ego_").rename(columns={"ego_annotation_id": "ego_annotation_id"})
exo = seg[cols].add_prefix("exo_").rename(columns={"exo_annotation_id": "exo_annotation_id"})
m = pd.read_parquet(IDX / "ego2exo.parquet")[
["ego_annotation_id", "exo_annotation_id", "score", "verb_j", "noun_j", "text_sim",
"hand_match", "object_kind", "coverage_ratio"]]
out = (r.merge(ego, on="ego_annotation_id", how="left")
.merge(exo, on="exo_annotation_id", how="left")
.merge(m, on=["ego_annotation_id", "exo_annotation_id"], how="left"))
if a.only_y:
out = out[out.llm_verdict == "Y"]
order = ["llm_verdict", "hand_match", "object_kind", "coverage_ratio",
"matcher_rank", "score", "verb_j", "noun_j", "text_sim",
"ego_scene", "ego_narration_en", "exo_narration_en",
"ego_annotation_id", "exo_annotation_id", "ego_video_uid", "exo_video_uid",
"ego_start_sec", "ego_end_sec", "exo_start_sec", "exo_end_sec"]
out = out[[c for c in order if c in out.columns]].sort_values(
["ego_annotation_id", "matcher_rank"])
f = IDX / ("llm_rerank_readable_Y.csv" if a.only_y else "llm_rerank_readable.csv")
out.to_csv(f, index=False)
print(f"[write] {len(out):,} rows -> {f.name} (narrations + matcher scores joined)")
# a small file that opens instantly in any editor
s = out.sample(min(300, len(out)), random_state=0).sort_values(
["llm_verdict", "matcher_rank"])
s.to_csv(IDX / "llm_rerank_sample300.csv", index=False)
print(f"[write] {len(s):,} rows -> llm_rerank_sample300.csv (spot-check file)")
if __name__ == "__main__":
main()
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