"""Create GenomeQA mcq_carbon by replacing species classification questions.""" import argparse import json import random from collections import defaultdict from pathlib import Path from datasets import Dataset, concatenate_datasets, load_dataset from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download DATASET_ID = "HuggingFaceBio/genomeqa" SEED = 42 PARQUET_PATH = "mcq_carbon/train-00000-of-00001.parquet" QUOTAS = {"basic_class": 300, "reverse_search": 100, "odd_one_out": 100} LABEL_KEYS = {"basic_class": "true", "reverse_search": "target", "odd_one_out": "minority"} def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--source-revision", required=True) parser.add_argument("--output-dataset", default=DATASET_ID) parser.add_argument("--output-dir", type=Path, default=Path.cwd()) parser.add_argument("--publish", action="store_true") args = parser.parse_args() mcq = load_dataset(DATASET_ID, "mcq", split="train", revision=args.source_revision) eukaryote = load_dataset(DATASET_ID, "eukaryote_cls", split="train", revision=args.source_revision) if mcq.features != eukaryote.features: raise ValueError("The source configurations must have matching features") strata = defaultdict(lambda: defaultdict(list)) for index, metadata_text in enumerate(eukaryote["metadata"]): metadata = json.loads(metadata_text) dimension = metadata["dim"] strata[dimension][metadata[LABEL_KEYS[dimension]]].append(index) rng = random.Random(SEED) selected = [] for dimension, quota in QUOTAS.items(): groups = strata[dimension] if sum(len(indices) for indices in groups.values()) != 2 * quota: raise ValueError(f"Expected {2 * quota} {dimension} source rows") take = {label: len(indices) // 2 for label, indices in groups.items()} odd_labels = sorted(label for label, indices in groups.items() if len(indices) % 2) rng.shuffle(odd_labels) for label in odd_labels[: quota - sum(take.values())]: take[label] += 1 for label in sorted(groups): selected.extend(rng.sample(groups[label], take[label])) rng.shuffle(selected) replacement = eukaryote.select(selected) species_positions = [index for index, subtask in enumerate(mcq["subtask"]) if subtask == "3_species_cls"] if len(species_positions) != 500 or species_positions != list(range(species_positions[0], species_positions[-1] + 1)): raise ValueError("Expected one contiguous 500-row 3_species_cls block") start = species_positions[0] combined = concatenate_datasets([mcq.select(range(start)), replacement, mcq.select(range(start + 500, len(mcq)))]) parquet_path = args.output_dir / PARQUET_PATH parquet_path.parent.mkdir(parents=True, exist_ok=True) combined.to_parquet(parquet_path) materialized = Dataset.from_parquet(str(parquet_path)) api = HfApi() head = api.repo_info(args.output_dataset, repo_type="dataset").sha card = Path(hf_hub_download(args.output_dataset, "README.md", repo_type="dataset", revision=head)).read_text() info = f"""- config_name: mcq_carbon features: - name: id dtype: int64 - name: subtask dtype: string - name: question dtype: string - name: options list: string - name: answer_index dtype: int64 - name: answer_text dtype: string - name: metadata dtype: string splits: - name: train num_bytes: {materialized.info.splits['train'].num_bytes} num_examples: {len(materialized)} download_size: {parquet_path.stat().st_size} dataset_size: {materialized.info.dataset_size} """ marker = "- config_name: eukaryote_cls\n features:" existing = "- config_name: mcq_carbon\n features:" if existing in card: start = card.index(existing) end = card.index(marker, start) card = card[:start] + info + card[end:] commit_message = "Correct mcq_carbon size metadata" else: card = card.replace(marker, info + marker, 1) commit_message = "Add stratified mcq_carbon configuration" config = """- config_name: mcq_carbon data_files: - split: train path: mcq_carbon/train-* """ card = card.replace("- config_name: eukaryote_cls\n data_files:", config + "- config_name: eukaryote_cls\n data_files:", 1) row = "| `mcq` | 2,600 | Full benchmark in four-option multiple-choice format. |\n" new_row = "| `mcq_carbon` | 2,600 | MCQ benchmark with the 500 `3_species_cls` rows replaced by 500 stratified `eukaryote_cls` rows. |\n" card = card.replace(row, row + new_row, 1) note = f"""The `mcq_carbon` configuration preserves the other `mcq` rows and inserts 500 `eukaryote_cls` questions at the original species-classification position. From source revision `{args.source_revision}`, seed {SEED} selects 300 `basic_class`, 100 `reverse_search`, and 100 `odd_one_out` rows, sampling approximately half of each taxon label within each question type. See [`scripts/create_mcq_carbon.py`](scripts/create_mcq_carbon.py). """ card = card.replace("The `eukaryote_cls` configuration uses the `mcq` feature schema", note + "The `eukaryote_cls` configuration uses the `mcq` feature schema", 1) if "### mcq_carbon" not in card: card += f""" ### mcq_carbon To rebuild and publish `mcq_carbon` from the pinned source revision, run: ```bash python scripts/create_mcq_carbon.py --source-revision {args.source_revision} --output-dataset {args.output_dataset} --publish ``` The script publishes the Parquet file, this card, and the builder script together. """ card_path = args.output_dir / "README.md" card_path.write_text(card) print(f"Prepared {len(combined)} rows ({len(replacement)} replacement rows) at {parquet_path}") if args.publish: commit = api.create_commit( repo_id=args.output_dataset, repo_type="dataset", parent_commit=head, commit_message=commit_message, operations=[ CommitOperationAdd(path_in_repo=PARQUET_PATH, path_or_fileobj=parquet_path), CommitOperationAdd(path_in_repo="README.md", path_or_fileobj=card_path), CommitOperationAdd(path_in_repo="scripts/create_mcq_carbon.py", path_or_fileobj=__file__), ], ) print(f"Published {commit.commit_url}") if __name__ == "__main__": main()