#!/usr/bin/env python3 """Convert Random Promoter DREAM 2022 files to Hugging Face datasets. The script keeps raw downloads under scratch/ by default and publishes several configs into one dataset repo: - supervised: train/validation/test sequence-to-activity regression splits - challenge_test_sequences: unlabeled challenge test sequences - test_subset_membership: IDs from test_subset_ids.tar.gz - public_leaderboard_ids: IDs from public_leaderboard_ids.tar.gz """ from __future__ import annotations import argparse import hashlib import io import logging import shutil import tarfile import urllib.request from collections.abc import Iterator from dataclasses import dataclass from pathlib import Path from typing import Any from datasets import Dataset from datasets import DatasetDict from datasets import Features from datasets import Value from datasets import load_dataset from huggingface_hub import HfApi ZENODO_RECORD = "10633252" DEFAULT_HUB_REPO_ID = "HuggingFaceBio/random-promoter-dream-2022" DEFAULT_RAW_DIR = Path("scratch/promoter_activity_dream/raw") DEFAULT_OUTPUT_DIR = Path("scratch/promoter_activity_dream/hf_dataset") logger = logging.getLogger(__name__) @dataclass(frozen=True) class SourceFile: filename: str md5: str url: str SOURCE_FILES = { "train": SourceFile( filename="train.txt", md5="b387d6e053f797d80abc8b972d16c355", url=f"https://zenodo.org/records/{ZENODO_RECORD}/files/train.txt?download=1", ), "validation": SourceFile( filename="val.txt", md5="c0f8d2fc873f194e540586ad51cb5ae6", url=f"https://zenodo.org/records/{ZENODO_RECORD}/files/val.txt?download=1", ), "test": SourceFile( filename="filtered_test_data_with_MAUDE_expression.txt", md5="d0880059150ddebc900450a9d2a6418d", url=( f"https://zenodo.org/records/{ZENODO_RECORD}/files/" "filtered_test_data_with_MAUDE_expression.txt?download=1" ), ), "challenge_test_sequences": SourceFile( filename="test_sequences.txt", md5="0173024c6f468cc1409e80a1b339609c", url=( f"https://zenodo.org/records/{ZENODO_RECORD}/files/" "test_sequences.txt?download=1" ), ), "test_subset_ids": SourceFile( filename="test_subset_ids.tar.gz", md5="40b56d200273990560b5b1c2def2185a", url=( f"https://zenodo.org/records/{ZENODO_RECORD}/files/" "test_subset_ids.tar.gz?download=1" ), ), "public_leaderboard_ids": SourceFile( filename="public_leaderboard_ids.tar.gz", md5="49ce1bc5118665e6c4382c6b9c470efa", url=( f"https://zenodo.org/records/{ZENODO_RECORD}/files/" "public_leaderboard_ids.tar.gz?download=1" ), ), } SUPERVISED_FEATURES = Features( { "sequence": Value("string"), "activity": Value("float32"), "sequence_length": Value("int32"), "source_file": Value("string"), "row_id": Value("int64"), } ) SEQUENCE_FEATURES = Features( { "sequence": Value("string"), "sequence_length": Value("int32"), "source_file": Value("string"), "row_id": Value("int64"), } ) ID_FEATURES = Features( { "subset": Value("string"), "item_id": Value("string"), "row_id": Value("int64"), "source_member": Value("string"), "line_number": Value("int64"), "raw_line": Value("string"), } ) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Convert Random Promoter DREAM 2022 data to HF datasets." ) parser.add_argument("--hub-repo-id", default=DEFAULT_HUB_REPO_ID) parser.add_argument( "--private", action=argparse.BooleanOptionalAction, default=True ) parser.add_argument("--push-to-hub", action="store_true") parser.add_argument("--raw-dir", type=Path, default=DEFAULT_RAW_DIR) parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR) parser.add_argument( "--dataset-cache-dir", type=Path, default=None, help="Optional datasets cache directory. Defaults to the HF cache.", ) parser.add_argument("--num-proc", type=int, default=8) parser.add_argument("--max-rows-per-split", type=int, default=None) parser.add_argument( "--no-download", action="store_true", help="Use existing files in --raw-dir and fail if any are missing.", ) parser.add_argument("--train-file", type=Path, default=None) parser.add_argument("--validation-file", type=Path, default=None) parser.add_argument("--test-file", type=Path, default=None) parser.add_argument("--test-sequences-file", type=Path, default=None) parser.add_argument("--test-subset-ids-tar", type=Path, default=None) parser.add_argument("--public-leaderboard-ids-tar", type=Path, default=None) return parser.parse_args() def file_md5(path: Path, chunk_size: int = 1024 * 1024) -> str: digest = hashlib.md5() with path.open("rb") as handle: for chunk in iter(lambda: handle.read(chunk_size), b""): digest.update(chunk) return digest.hexdigest() def download_file(source: SourceFile, raw_dir: Path, no_download: bool) -> Path: raw_dir.mkdir(parents=True, exist_ok=True) path = raw_dir / source.filename if path.exists(): actual_md5 = file_md5(path) if actual_md5 != source.md5: raise ValueError( f"{path} exists but md5 is {actual_md5}; expected {source.md5}" ) logger.info("Using cached %s", path) return path if no_download: raise FileNotFoundError(f"Missing {path}; rerun without --no-download") tmp_path = path.with_suffix(path.suffix + ".tmp") logger.info("Downloading %s", source.url) with urllib.request.urlopen(source.url) as response, tmp_path.open("wb") as out: shutil.copyfileobj(response, out) actual_md5 = file_md5(tmp_path) if actual_md5 != source.md5: tmp_path.unlink(missing_ok=True) raise ValueError( f"Downloaded {source.filename} md5 {actual_md5}; expected {source.md5}" ) tmp_path.replace(path) return path def resolve_paths(args: argparse.Namespace) -> dict[str, Path]: explicit = { "train": args.train_file, "validation": args.validation_file, "test": args.test_file, "challenge_test_sequences": args.test_sequences_file, "test_subset_ids": args.test_subset_ids_tar, "public_leaderboard_ids": args.public_leaderboard_ids_tar, } if any(path is not None for path in explicit.values()): missing = [ name for name in ("train", "validation", "test") if explicit[name] is None ] if missing: raise ValueError( "When using explicit local files, provide train, validation, " f"and test files. Missing: {missing}" ) return {name: path for name, path in explicit.items() if path is not None} return { name: download_file(source, args.raw_dir, args.no_download) for name, source in SOURCE_FILES.items() } def add_supervised_metadata( batch: dict[str, list[Any]], indices: list[int], source_file: str, ) -> dict[str, list[Any]]: sequences = [str(sequence).strip().upper() for sequence in batch["sequence"]] return { "sequence": sequences, "activity": [float(value) for value in batch["activity"]], "sequence_length": [len(sequence) for sequence in sequences], "source_file": [source_file] * len(sequences), "row_id": list(indices), } def limit_dataset(dataset: Dataset, max_rows: int | None) -> Dataset: if max_rows is None or max_rows >= len(dataset): return dataset return dataset.select(range(max_rows)) def build_supervised_dataset( paths: dict[str, Path], *, max_rows_per_split: int | None, num_proc: int, cache_dir: Path | None = None, ) -> DatasetDict: raw = load_dataset( "csv", data_files={ "train": str(paths["train"]), "validation": str(paths["validation"]), "test": str(paths["test"]), }, delimiter="\t", column_names=["sequence", "activity"], features=Features({"sequence": Value("string"), "activity": Value("float32")}), cache_dir=str(cache_dir) if cache_dir is not None else None, ) processed = {} for split, dataset in raw.items(): dataset = limit_dataset(dataset, max_rows_per_split) map_kwargs: dict[str, Any] = { "with_indices": True, "fn_kwargs": {"source_file": paths[split].name}, "features": SUPERVISED_FEATURES, "desc": f"Adding DREAM metadata to {split}", } if num_proc and num_proc > 1: map_kwargs["num_proc"] = num_proc processed[split] = dataset.map( add_supervised_metadata, batched=True, **map_kwargs ) return DatasetDict(processed) def iter_sequence_file( path: Path, max_rows: int | None = None ) -> Iterator[dict[str, Any]]: with path.open("r", encoding="utf-8") as handle: for row_id, line in enumerate(handle): if max_rows is not None and row_id >= max_rows: break line = line.strip() if not line: continue sequence = line.split("\t", 1)[0].strip().upper() yield { "sequence": sequence, "sequence_length": len(sequence), "source_file": path.name, "row_id": row_id, } def build_sequence_dataset( path: Path, max_rows: int | None, cache_dir: Path | None = None ) -> Dataset: return Dataset.from_generator( iter_sequence_file, gen_kwargs={"path": path, "max_rows": max_rows}, features=SEQUENCE_FEATURES, cache_dir=str(cache_dir) if cache_dir is not None else None, ) def subset_name_from_member(member_name: str) -> str: path = Path(member_name) name = path.name for suffix in (".txt", ".tsv", ".csv", ".ids"): if name.endswith(suffix): name = name[: -len(suffix)] return name.replace(" ", "_") def parse_id_line(line: str) -> tuple[str, int]: item_id = line.split("\t", 1)[0].split(",", 1)[0].strip() row_id = int(item_id) if item_id.isdigit() else -1 return item_id, row_id def iter_tar_ids(path: Path, max_rows: int | None = None) -> Iterator[dict[str, Any]]: emitted = 0 with tarfile.open(path, "r:gz") as tar: for member in tar: if not member.isfile(): continue extracted = tar.extractfile(member) if extracted is None: continue subset = subset_name_from_member(member.name) with io.TextIOWrapper(extracted, encoding="utf-8") as handle: for line_number, line in enumerate(handle): raw_line = line.strip() if not raw_line: continue if max_rows is not None and emitted >= max_rows: return item_id, row_id = parse_id_line(raw_line) yield { "subset": subset, "item_id": item_id, "row_id": row_id, "source_member": member.name, "line_number": line_number, "raw_line": raw_line, } emitted += 1 def build_id_dataset( path: Path, max_rows: int | None, cache_dir: Path | None = None ) -> Dataset: return Dataset.from_generator( iter_tar_ids, gen_kwargs={"path": path, "max_rows": max_rows}, features=ID_FEATURES, cache_dir=str(cache_dir) if cache_dir is not None else None, ) def render_dataset_card(hub_repo_id: str) -> str: return f"""--- license: cc-by-4.0 task_categories: - text-regression language: - en tags: - biology - dna - genomics - promoter - gene-expression - carbon --- # Random Promoter DREAM Challenge 2022 This dataset repackages the processed Random Promoter DREAM Challenge 2022 files from Zenodo record `{ZENODO_RECORD}` for use with `datasets`. The task is sequence-to-expression regression on synthetic yeast promoter sequences. The canonical supervised config contains random promoter training examples, validation examples, and labeled designed test promoters. ## Configs - `supervised`: `train`, `validation`, and `test` splits with promoter sequences and measured activity. - `challenge_test_sequences`: unlabeled test sequences for submission-style prediction workflows. - `test_subset_membership`: normalized IDs from `test_subset_ids.tar.gz`. - `public_leaderboard_ids`: normalized IDs from `public_leaderboard_ids.tar.gz`. ## Schema `supervised`: - `sequence`: DNA sequence. - `activity`: measured promoter activity. - `sequence_length`: sequence length in base pairs. - `source_file`: source filename. - `row_id`: zero-based row index within the source split. ID metadata configs: - `subset`: subset name inferred from the archive member. - `item_id`: raw ID token from the source line. - `row_id`: integer row ID when `item_id` is numeric; otherwise `-1`. - `source_member`: archive member path. - `line_number`: line number within the member. - `raw_line`: unmodified stripped source line. ## Usage ```py from datasets import load_dataset ds = load_dataset("{hub_repo_id}", "supervised") train = ds["train"] validation = ds["validation"] test = ds["test"] subsets = load_dataset("{hub_repo_id}", "test_subset_membership", split="train") ``` ## Source Source: Random Promoter DREAM Challenge 2022, Zenodo DOI `10.5281/zenodo.10633252`. The source record is licensed CC BY 4.0. Cite the original DREAM Challenge data and paper when using this dataset. ## Reproduction This dataset repo includes `create_dataset.py`, the script used to download, convert, and upload the configs. """ def save_or_push( *, dataset: Dataset | DatasetDict, config_name: str, args: argparse.Namespace, api: HfApi | None, ) -> None: if args.push_to_hub: dataset.push_to_hub( args.hub_repo_id, config_name=config_name, commit_message=f"Upload {config_name} config", ) logger.info("Pushed %s to %s", config_name, args.hub_repo_id) return output_path = args.output_dir / config_name dataset.save_to_disk(str(output_path)) logger.info("Saved %s to %s", config_name, output_path) def upload_dataset_metadata(args: argparse.Namespace, api: HfApi) -> None: readme_bytes = render_dataset_card(args.hub_repo_id).encode("utf-8") api.upload_file( path_or_fileobj=readme_bytes, path_in_repo="README.md", repo_id=args.hub_repo_id, repo_type="dataset", commit_message="Upload dataset card", ) api.upload_file( path_or_fileobj=Path(__file__).read_bytes(), path_in_repo="create_dataset.py", repo_id=args.hub_repo_id, repo_type="dataset", commit_message="Upload create_dataset.py", ) def run(args: argparse.Namespace) -> None: paths = resolve_paths(args) args.output_dir.mkdir(parents=True, exist_ok=True) api = None if args.push_to_hub: api = HfApi() api.whoami() api.create_repo( repo_id=args.hub_repo_id, repo_type="dataset", exist_ok=True, private=args.private, ) supervised = build_supervised_dataset( paths, max_rows_per_split=args.max_rows_per_split, num_proc=args.num_proc, cache_dir=args.dataset_cache_dir, ) save_or_push(dataset=supervised, config_name="supervised", args=args, api=api) if "challenge_test_sequences" in paths: challenge_test = build_sequence_dataset( paths["challenge_test_sequences"], args.max_rows_per_split, cache_dir=args.dataset_cache_dir, ) save_or_push( dataset=challenge_test, config_name="challenge_test_sequences", args=args, api=api, ) if "test_subset_ids" in paths: subset_ids = build_id_dataset( paths["test_subset_ids"], args.max_rows_per_split, cache_dir=args.dataset_cache_dir, ) save_or_push( dataset=subset_ids, config_name="test_subset_membership", args=args, api=api, ) if "public_leaderboard_ids" in paths: leaderboard_ids = build_id_dataset( paths["public_leaderboard_ids"], args.max_rows_per_split, cache_dir=args.dataset_cache_dir, ) save_or_push( dataset=leaderboard_ids, config_name="public_leaderboard_ids", args=args, api=api, ) if args.push_to_hub and api is not None: upload_dataset_metadata(args, api) logger.info("Uploaded dataset card and create_dataset.py") else: (args.output_dir / "README.md").write_text( render_dataset_card(args.hub_repo_id), encoding="utf-8", ) def main() -> None: logging.basicConfig( level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s", ) run(parse_args()) if __name__ == "__main__": main()