preprocess.py

import argparse
import json
import random
from pathlib import Path
from typing import Optional, Sequence, TypedDict

import requests
from datasets import Dataset, DatasetDict

OUT_DIR = Path(__file__).parent / "data"
METADATA_PATH = OUT_DIR / "metadata.json"

SEED = 42
VAL_RATIO = 0.1

URLS = {
    "train": "https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label",
    "test": "https://cogcomp.seas.upenn.edu/Data/QA/QC/TREC_10.label",
}

COARSE_DESC = {
    "ABBR": "abbreviation", "ENTY": "entities", "DESC": "description and abstract concepts",
    "HUM": "human beings", "LOC": "locations", "NUM": "numeric values"
}

FINE_DESC = {
    "ABBR:abb":"abbreviation","ABBR:exp":"expression abbreviated",
    "ENTY:animal":"animals","ENTY:body":"organs of body","ENTY:color":"colors","ENTY:cremat":"creative works",
    "ENTY:currency":"currency names","ENTY:dismed":"diseases and medicine","ENTY:event":"events","ENTY:food":"food",
    "ENTY:instru":"musical instrument","ENTY:lang":"languages","ENTY:letter":"letters like a-z","ENTY:other":"other entities",
    "ENTY:plant":"plants","ENTY:product":"products","ENTY:religion":"religions","ENTY:sport":"sports",
    "ENTY:substance":"elements and substances","ENTY:symbol":"symbols and signs","ENTY:techmeth":"techniques and methods",
    "ENTY:termeq":"equivalent terms","ENTY:veh":"vehicles","ENTY:word":"words with a special property",
    "DESC:def":"definition of something","DESC:desc":"description of something","DESC:manner":"manner of an action","DESC:reason":"reasons",
    "HUM:gr":"a group/organization","HUM:ind":"an individual","HUM:title":"title of a person","HUM:desc":"description of a person",
    "LOC:city":"cities","LOC:country":"countries","LOC:mount":"mountains","LOC:other":"other locations","LOC:state":"states",
    "NUM:code":"codes","NUM:count":"counts","NUM:date":"dates","NUM:dist":"distances","NUM:money":"prices","NUM:ord":"ranks",
    "NUM:other":"other numbers","NUM:period":"duration","NUM:perc":"percentages","NUM:speed":"speed","NUM:temp":"temperature",
    "NUM:volsize":"size/area/volume","NUM:weight":"weight",
}


class TrecExample(TypedDict):
    text: str
    coarse_label: str
    coarse_description: Optional[str]
    fine_label: str
    fine_description: Optional[str]


def fetch(url: str) -> list[bytes]:
    r = requests.get(url, timeout=30)
    r.raise_for_status()
    return r.content.splitlines()


def parse(lines: Sequence[bytes]) -> list[TrecExample]:
    rows: list[TrecExample] = []
    for b in lines:
        line = b.decode("utf-8", errors="replace").strip()
        if not line or " " not in line:
            continue
        fine, text = line.split(" ", 1)
        coarse = fine.split(":", 1)[0]
        rows.append(
            {
                "text": text.strip(),
                "coarse_label": coarse,
                "coarse_description": COARSE_DESC.get(coarse, ""),
                "fine_label": fine,
                "fine_description": FINE_DESC.get(fine, ""),
            }
        )
    return rows


def extract_metadata(ds: DatasetDict) -> dict:
    num_rows = {name: len(split) for name, split in ds.items()}
    first_split = next(iter(ds.values()))
    features = {name: repr(feat) for name, feat in first_split.features.items()}

    coarse_labels = {label for split in ds.values() for label in split["coarse_label"]}
    fine_labels = {label for split in ds.values() for label in split["fine_label"]}

    label_maps = {
        "coarse_label": sorted(coarse_labels),
        "fine_label": sorted(fine_labels),
    }

    return {
        "num_rows": num_rows,
        "features": features,
        "label_maps": label_maps}


if __name__ == "__main__":
    """Fetch TREC from source, split it, save as Parquet and add metadata.

    Run: python preprocess_trec.py --val-ratio 0.1 --seed 42 --out-dir data
    """
    ap = argparse.ArgumentParser()
    ap.add_argument("--val-ratio", type=float, default=VAL_RATIO, help="Fraction of training set for validation")
    ap.add_argument("--seed", type=int, default=SEED, help="Random seed for shuffling")
    ap.add_argument("--out-dir", type=Path, help="Output directory for Parquet files")
    ap.add_argument("--metadata-path", type=Path, help="Path for metadata.json")
    args = ap.parse_args()

    out_dir = args.out_dir or OUT_DIR
    metadata_path = args.metadata_path or METADATA_PATH

    train = parse(fetch(URLS["train"]))
    test = parse(fetch(URLS["test"]))

    rng = random.Random(args.seed)
    rng.shuffle(train)
    n_val = int(len(train) * args.val_ratio)
    validation = train[:n_val]
    train = train[n_val:]

    data = DatasetDict(
        {
            "train": Dataset.from_list(train),
            "validation": Dataset.from_list(validation),
            "test": Dataset.from_list(test),
        }
    )

    out_dir.mkdir(exist_ok=True, parents=True)
    for name, split in data.items():
        split.to_parquet(str(out_dir / f"{name}.parquet"))

    metadata_path.write_text(json.dumps(extract_metadata(data), indent=2))