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Running on Zero
Running on Zero
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from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
def summarize_false_alarm(path, label):
df = pd.read_csv(path)
return {
"model_or_eval": label,
"rows": len(df),
"false_fake_rate_065_percent": round(df["is_false_fake_original_065"].mean() * 100, 2) if len(df) else None,
"false_fake_rate_085_percent": round(df["is_false_fake_strict_085"].mean() * 100, 2) if len(df) else None,
"review_rate_percent": round(df["review_required"].mean() * 100, 2) if "review_required" in df.columns and len(df) else None,
}
def main():
p = argparse.ArgumentParser(description="Build final comparison table from false-alarm CSVs.")
p.add_argument("--false-alarm-csvs", nargs="+", required=True)
p.add_argument("--labels", nargs="+", required=True)
p.add_argument("--out-csv", required=True)
p.add_argument("--out-md", required=True)
args = p.parse_args()
if len(args.false_alarm_csvs) != len(args.labels):
raise ValueError("Number of CSVs must match number of labels.")
rows = [summarize_false_alarm(path, label) for path, label in zip(args.false_alarm_csvs, args.labels)]
out = pd.DataFrame(rows)
Path(args.out_csv).parent.mkdir(parents=True, exist_ok=True)
out.to_csv(args.out_csv, index=False)
md = out.to_markdown(index=False)
Path(args.out_md).write_text(md, encoding="utf-8")
print(md)
print("Saved:", args.out_csv)
print("Saved:", args.out_md)
if __name__ == "__main__":
main()
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