| """ |
| Exact-substring scoring. |
| |
| A response is correct if the whole golden answer appears in it as one |
| contiguous, case-insensitive substring. No tokenisation, no word boundaries, |
| no stemming. |
| |
| is_correct = answer.lower() in response.lower() |
| |
| See scripts/README.md for what this measures, where it gives false positives |
| and false negatives, and how it compares with word_overlap.py. |
| |
| Usage: |
| python exact_substring.py --responses my_model_english.csv |
| python exact_substring.py --responses my_model_english.csv --out scored.csv |
| |
| Input CSV: the language file from this dataset (columns `question`, `answer`, |
| `Domain`) with a `response` column added holding the model's raw output. |
| """ |
|
|
| import argparse |
| import sys |
|
|
| import pandas as pd |
|
|
| REQUIRED = ["question", "answer", "Domain", "response"] |
|
|
|
|
| def is_correct(answer, response): |
| """The golden answer must appear as one contiguous run of characters.""" |
| return str(answer).lower() in str(response).lower() |
|
|
|
|
| def report(df, label): |
| """Print per-domain and combined accuracy.""" |
| per_domain = df.groupby("Domain")["is_correct"].agg(["sum", "size"]) |
|
|
| print(f"\n{label}\n") |
| print(f"{'Domain':<20} {'Correct':>8} {'Total':>7} {'Accuracy':>10}") |
| print("-" * 48) |
| for domain, row in per_domain.iterrows(): |
| acc = row["sum"] / row["size"] * 100 |
| print(f"{domain:<20} {int(row['sum']):>8} {int(row['size']):>7} {acc:>9.2f}%") |
|
|
| correct, total = int(df["is_correct"].sum()), len(df) |
| blank = int((df["response"].fillna("").astype(str).str.strip() == "").sum()) |
| print("-" * 48) |
| print(f"{'COMBINED':<20} {correct:>8} {total:>7} {correct / total * 100:>9.2f}%") |
| print("\nCombined is the micro-average over all pooled questions, which is") |
| print("identical to weighting each domain by its size.") |
| if blank: |
| print(f"Blank responses: {blank} (scored incorrect)") |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser(description=__doc__, |
| formatter_class=argparse.RawDescriptionHelpFormatter) |
| ap.add_argument("--responses", required=True, |
| help="CSV with columns: question, answer, Domain, response") |
| ap.add_argument("--out", help="Optional path to write per-question verdicts") |
| args = ap.parse_args() |
|
|
| df = pd.read_csv(args.responses) |
|
|
| missing = [c for c in REQUIRED if c not in df.columns] |
| if missing: |
| sys.exit(f"Error: {args.responses} is missing column(s): {', '.join(missing)}\n" |
| f"Found: {', '.join(df.columns)}") |
|
|
| |
| |
| blank_answers = int((df["answer"].fillna("").astype(str).str.strip() == "").sum()) |
| if blank_answers: |
| print(f"Warning: {blank_answers} row(s) have an empty golden answer. " |
| f"These match any response and will score correct.", file=sys.stderr) |
|
|
| df["is_correct"] = [is_correct(a, r) for a, r in zip(df["answer"], df["response"])] |
|
|
| report(df, f"Exact substring — {args.responses}") |
|
|
| if args.out: |
| df.to_csv(args.out, index=False) |
| print(f"\nPer-question verdicts written to {args.out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
