Datasets:

chiron-group
/

Blog_Authorship_Corpus

#!/usr/bin/env python3
"""Build the per-individual QA dataset (Parquet shards) for the Blog domain.
Input
-----
``data_*.zst``: zstd-compressed JSON arrays. Each element is one individual
with fields ``observations`` (list[str]), ``predictions`` (list of
``{question, ground_truth}``), ``data_source`` (str), ``split`` (str), and
``extra_info`` (dict). The upstream split label is ignored — this script
re-derives train / val / test deterministically.
Output
------
``<output-dir>/<split>-00000-of-00001.parquet`` for split in {train, val, test}.
Per-row schema
~~~~~~~~~~~~~~
::
    observations: list[str]
    predictions:  list[{question, ground_truth}]
    data_source:  str ("Blog Authorship Corpus")
    split:        str ("train" | "val" | "test")
    held_out_predictions: list[{question, ground_truth}]   # train only; absent on val/test
    extra_info:   {interview_id, n_observations, n_predictions, [n_held_out_predictions]}
Selection / split policy
~~~~~~~~~~~~~~~~~~~~~~~~
* Drop individuals with fewer than ``MIN_OBSERVATIONS`` observations.
* If observations exceed ``MAX_OBSERVATIONS``, deterministically subsample.
* Per individual, shuffle predictions with ``SEED + interview_id`` and split
  ~2/3 -> ``predictions`` and ~1/3 -> ``held_out``.
* Drop individuals with fewer than ``MIN_PREDICTIONS`` predictions or fewer
  than ``MIN_HELD_OUT`` held-out items.
* Globally shuffle qualified individuals with ``SEED`` and assign 90/5/5
  to train/val/test.
* For val/test, the held-out slice is folded into ``predictions`` so the eval
  pool is the union; the ``held_out_predictions`` field is dropped.
Self-contained: depends only on ``pyarrow`` (+ ``zstd`` binary on PATH).
No chiron imports.
"""

from __future__ import annotations

import argparse
import json
import random
import re
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterator

import pyarrow as pa
import pyarrow.parquet as pq

# --- fixed policy -----------------------------------------------------------
SEED = 42

MIN_OBSERVATIONS = 20
MAX_OBSERVATIONS = 200

HELD_OUT_FRACTION = 1.0 / 3.0   # 2/3 -> predictions, 1/3 -> held-out

MIN_PREDICTIONS = 5
MIN_HELD_OUT = 3

TRAIN_RATIO = 0.9
VAL_RATIO = 0.05
TEST_RATIO = 0.05

DATA_SOURCE = "Blog Authorship Corpus"


# --- helpers -----------------------------------------------------------------

@dataclass
class _Row:
    interview_id: str
    observations: list[str]
    predictions: list[dict[str, str]]
    held_out: list[dict[str, str]]


def _decompress_zst(path: Path) -> bytes:
    return subprocess.check_output(["zstd", "-dcf", str(path)])


def _iter_records(input_dir: Path) -> Iterator[dict]:
    files = sorted(input_dir.glob("data_*.zst"))
    if not files:
        raise SystemExit(f"no data_*.zst files in {input_dir}")
    for p in files:
        arr = json.loads(_decompress_zst(p))
        for r in arr:
            yield r


def _interview_seed(interview_id: str) -> int:
    try:
        return SEED + int(interview_id)
    except (TypeError, ValueError):
        return SEED + (hash(str(interview_id)) & 0x7FFFFFFF)


def _normalize_ground_truth(value: str) -> str:
    """Return number-only labels; accepts legacy values like 'Option 2'."""
    m = re.search(r"(?:option\s*)?(\d+)", value.strip(), re.IGNORECASE)
    if not m:
        raise ValueError(f"cannot parse ground_truth: {value!r}")
    return m.group(1)


def _record_to_row(rec: dict) -> _Row | None:
    extra = rec.get("extra_info") or {}
    interview_id = str(extra.get("interview_id") or "")
    if not interview_id:
        return None

    observations = list(rec.get("observations") or [])
    predictions = list(rec.get("predictions") or [])
    if len(observations) < MIN_OBSERVATIONS:
        return None
    if not predictions:
        return None

    rng = random.Random(_interview_seed(interview_id))

    # Cap observations by deterministic subsample (preserve order after sample).
    if len(observations) > MAX_OBSERVATIONS:
        idxs = sorted(rng.sample(range(len(observations)), MAX_OBSERVATIONS))
        observations = [observations[i] for i in idxs]

    # Per-individual split predictions -> (predictions, held_out).
    pred_pool = list(predictions)
    rng.shuffle(pred_pool)
    n_held = max(1, int(round(len(pred_pool) * HELD_OUT_FRACTION)))
    n_held = min(n_held, len(pred_pool) - 1)  # ensure at least one prediction left
    if n_held < 1:
        return None
    n_pred = len(pred_pool) - n_held
    if n_pred < MIN_PREDICTIONS or n_held < MIN_HELD_OUT:
        return None

    pred_slice = pred_pool[:n_pred]
    held_slice = pred_pool[n_pred:]

    def _clean(items: list[dict]) -> list[dict[str, str]]:
        out = []
        for it in items:
            q = it.get("question")
            gt = it.get("ground_truth")
            if not isinstance(q, str) or not isinstance(gt, str):
                continue
            out.append({"question": q, "ground_truth": _normalize_ground_truth(gt)})
        return out

    pred_clean = _clean(pred_slice)
    held_clean = _clean(held_slice)
    if len(pred_clean) < MIN_PREDICTIONS or len(held_clean) < MIN_HELD_OUT:
        return None

    return _Row(
        interview_id=interview_id,
        observations=observations,
        predictions=pred_clean,
        held_out=held_clean,
    )


def _partition_split_counts(n: int) -> tuple[int, int, int]:
    n_val = round(n * VAL_RATIO)
    n_test = round(n * TEST_RATIO)
    n_train = n - n_val - n_test
    if n_train < 1 and n > 0:
        half = max(0, n - 1) // 2
        n_train, n_val, n_test = 1, half, n - 1 - half
    return n_train, n_val, n_test


def _record_to_dict(rec: _Row, split: str) -> dict[str, Any]:
    extra: dict[str, Any] = {
        "interview_id": rec.interview_id,
        "n_observations": len(rec.observations),
    }
    if split == "train":
        out = {
            "observations": rec.observations,
            "predictions": rec.predictions,
            "data_source": DATA_SOURCE,
            "split": "train",
            "held_out_predictions": rec.held_out,
        }
        extra["n_predictions"] = len(rec.predictions)
        extra["n_held_out_predictions"] = len(rec.held_out)
    else:
        merged = list(rec.predictions) + list(rec.held_out)
        out = {
            "observations": rec.observations,
            "predictions": merged,
            "data_source": DATA_SOURCE,
            "split": split,
        }
        extra["n_predictions"] = len(merged)
    out["extra_info"] = extra
    return out


# --- arrow schemas (explicit so train and val/test are stable) ---------------

_PRED_TYPE = pa.struct([
    pa.field("question", pa.string()),
    pa.field("ground_truth", pa.string()),
])

_TRAIN_SCHEMA = pa.schema([
    pa.field("observations", pa.list_(pa.string())),
    pa.field("predictions", pa.list_(_PRED_TYPE)),
    pa.field("data_source", pa.string()),
    pa.field("split", pa.string()),
    pa.field("held_out_predictions", pa.list_(_PRED_TYPE)),
    pa.field("extra_info", pa.struct([
        pa.field("interview_id", pa.string()),
        pa.field("n_observations", pa.int32()),
        pa.field("n_predictions", pa.int32()),
        pa.field("n_held_out_predictions", pa.int32()),
    ])),
])

_EVAL_SCHEMA = pa.schema([
    pa.field("observations", pa.list_(pa.string())),
    pa.field("predictions", pa.list_(_PRED_TYPE)),
    pa.field("data_source", pa.string()),
    pa.field("split", pa.string()),
    pa.field("extra_info", pa.struct([
        pa.field("interview_id", pa.string()),
        pa.field("n_observations", pa.int32()),
        pa.field("n_predictions", pa.int32()),
    ])),
])


def _write_split(records: list[dict], split: str, output_dir: Path, *,
                 compression: str | None, compression_level: int | None) -> Path:
    schema = _TRAIN_SCHEMA if split == "train" else _EVAL_SCHEMA
    table = pa.Table.from_pylist(records, schema=schema)
    out_path = output_dir / f"{split}-00000-of-00001.parquet"
    pq.write_table(
        table, out_path,
        compression=compression,
        compression_level=compression_level,
        use_dictionary=True,
    )
    return out_path


def parse_args() -> argparse.Namespace:
    domain_dir = Path(__file__).resolve().parent.parent
    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    p.add_argument(
        "--input-dir",
        type=Path,
        default=domain_dir / "upstream",
        help="Directory containing the upstream data_*.zst shards.",
    )
    p.add_argument("--output-dir", type=Path, default=domain_dir / "qa_parquet")
    p.add_argument("--max-rows", type=int, default=0,
                   help="Debug: stop after this many input rows (0 = all).")
    p.add_argument("--compression", default="zstd",
                   choices=["zstd", "gzip", "snappy", "brotli", "lz4", "none"])
    p.add_argument("--compression-level", type=int, default=None)
    return p.parse_args()


def main() -> None:
    args = parse_args()
    args.output_dir.mkdir(parents=True, exist_ok=True)

    pool: list[_Row] = []
    raw_seen = 0
    skipped = 0

    for rec in _iter_records(args.input_dir):
        raw_seen += 1
        if args.max_rows and raw_seen > args.max_rows:
            break
        row = _record_to_row(rec)
        if row is None:
            skipped += 1
            continue
        pool.append(row)

    rng = random.Random(SEED)
    rng.shuffle(pool)
    n = len(pool)
    n_train, n_val, _n_test = _partition_split_counts(n)

    splits = {
        "train": pool[:n_train],
        "val": pool[n_train : n_train + n_val],
        "test": pool[n_train + n_val :],
    }

    # Preserve the published IID test policy: exclude the historical training
    # prefix, then retain only the first test row for each adapter identity.
    train_ids = {str(rec.interview_id) for rec in splits["train"][:4366]}
    seen_test_ids: set[str] = set()
    clean_test: list[_Row] = []
    for rec in splits["test"]:
        sample_id = str(rec.interview_id)
        if sample_id in train_ids or sample_id in seen_test_ids:
            continue
        seen_test_ids.add(sample_id)
        clean_test.append(rec)
    splits["test"] = clean_test

    comp = None if args.compression == "none" else args.compression
    written: dict[str, dict] = {}
    for split_name, recs in splits.items():
        if not recs:
            continue
        records = [_record_to_dict(r, split_name) for r in recs]
        path = _write_split(records, split_name, args.output_dir,
                            compression=comp, compression_level=args.compression_level)
        written[split_name] = {"rows": len(records), "path": str(path)}

    print(json.dumps({
        "raw_lines_seen": raw_seen,
        "samples_built": n,
        "skipped_insufficient_data": skipped,
        "split_counts": {k: len(v) for k, v in splits.items()},
        "written": written,
    }, indent=2))


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""Build the per-individual QA dataset (Parquet shards) for the Blog domain.

	Input
	-----
	``data_*.zst``: zstd-compressed JSON arrays. Each element is one individual
	with fields ``observations`` (list[str]), ``predictions`` (list of
	``{question, ground_truth}``), ``data_source`` (str), ``split`` (str), and
	``extra_info`` (dict). The upstream split label is ignored — this script
	re-derives train / val / test deterministically.

	Output
	------
	``<output-dir>/<split>-00000-of-00001.parquet`` for split in {train, val, test}.

	Per-row schema
	~~~~~~~~~~~~~~
	::

	observations: list[str]
	predictions: list[{question, ground_truth}]
	data_source: str ("Blog Authorship Corpus")
	split: str ("train" \| "val" \| "test")
	held_out_predictions: list[{question, ground_truth}] # train only; absent on val/test
	extra_info: {interview_id, n_observations, n_predictions, [n_held_out_predictions]}

	Selection / split policy
	~~~~~~~~~~~~~~~~~~~~~~~~
	* Drop individuals with fewer than ``MIN_OBSERVATIONS`` observations.
	* If observations exceed ``MAX_OBSERVATIONS``, deterministically subsample.
	* Per individual, shuffle predictions with ``SEED + interview_id`` and split
	~2/3 -> ``predictions`` and ~1/3 -> ``held_out``.
	* Drop individuals with fewer than ``MIN_PREDICTIONS`` predictions or fewer
	than ``MIN_HELD_OUT`` held-out items.
	* Globally shuffle qualified individuals with ``SEED`` and assign 90/5/5
	to train/val/test.
	* For val/test, the held-out slice is folded into ``predictions`` so the eval
	pool is the union; the ``held_out_predictions`` field is dropped.

	Self-contained: depends only on ``pyarrow`` (+ ``zstd`` binary on PATH).
	No chiron imports.
	"""

	from __future__ import annotations

	import argparse
	import json
	import random
	import re
	import subprocess
	from dataclasses import dataclass
	from pathlib import Path
	from typing import Any, Iterator

	import pyarrow as pa
	import pyarrow.parquet as pq

	# --- fixed policy -----------------------------------------------------------
	SEED = 42

	MIN_OBSERVATIONS = 20
	MAX_OBSERVATIONS = 200

	HELD_OUT_FRACTION = 1.0 / 3.0 # 2/3 -> predictions, 1/3 -> held-out

	MIN_PREDICTIONS = 5
	MIN_HELD_OUT = 3

	TRAIN_RATIO = 0.9
	VAL_RATIO = 0.05
	TEST_RATIO = 0.05

	DATA_SOURCE = "Blog Authorship Corpus"


	# --- helpers -----------------------------------------------------------------

	@dataclass
	class _Row:
	interview_id: str
	observations: list[str]
	predictions: list[dict[str, str]]
	held_out: list[dict[str, str]]


	def _decompress_zst(path: Path) -> bytes:
	return subprocess.check_output(["zstd", "-dcf", str(path)])


	def _iter_records(input_dir: Path) -> Iterator[dict]:
	files = sorted(input_dir.glob("data_*.zst"))
	if not files:
	raise SystemExit(f"no data_*.zst files in {input_dir}")
	for p in files:
	arr = json.loads(_decompress_zst(p))
	for r in arr:
	yield r


	def _interview_seed(interview_id: str) -> int:
	try:
	return SEED + int(interview_id)
	except (TypeError, ValueError):
	return SEED + (hash(str(interview_id)) & 0x7FFFFFFF)


	def _normalize_ground_truth(value: str) -> str:
	"""Return number-only labels; accepts legacy values like 'Option 2'."""
	m = re.search(r"(?:option\s*)?(\d+)", value.strip(), re.IGNORECASE)
	if not m:
	raise ValueError(f"cannot parse ground_truth: {value!r}")
	return m.group(1)


	def _record_to_row(rec: dict) -> _Row \| None:
	extra = rec.get("extra_info") or {}
	interview_id = str(extra.get("interview_id") or "")
	if not interview_id:
	return None

	observations = list(rec.get("observations") or [])
	predictions = list(rec.get("predictions") or [])
	if len(observations) < MIN_OBSERVATIONS:
	return None
	if not predictions:
	return None

	rng = random.Random(_interview_seed(interview_id))

	# Cap observations by deterministic subsample (preserve order after sample).
	if len(observations) > MAX_OBSERVATIONS:
	idxs = sorted(rng.sample(range(len(observations)), MAX_OBSERVATIONS))
	observations = [observations[i] for i in idxs]

	# Per-individual split predictions -> (predictions, held_out).
	pred_pool = list(predictions)
	rng.shuffle(pred_pool)
	n_held = max(1, int(round(len(pred_pool) * HELD_OUT_FRACTION)))
	n_held = min(n_held, len(pred_pool) - 1) # ensure at least one prediction left
	if n_held < 1:
	return None
	n_pred = len(pred_pool) - n_held
	if n_pred < MIN_PREDICTIONS or n_held < MIN_HELD_OUT:
	return None

	pred_slice = pred_pool[:n_pred]
	held_slice = pred_pool[n_pred:]

	def _clean(items: list[dict]) -> list[dict[str, str]]:
	out = []
	for it in items:
	q = it.get("question")
	gt = it.get("ground_truth")
	if not isinstance(q, str) or not isinstance(gt, str):
	continue
	out.append({"question": q, "ground_truth": _normalize_ground_truth(gt)})
	return out

	pred_clean = _clean(pred_slice)
	held_clean = _clean(held_slice)
	if len(pred_clean) < MIN_PREDICTIONS or len(held_clean) < MIN_HELD_OUT:
	return None

	return _Row(
	interview_id=interview_id,
	observations=observations,
	predictions=pred_clean,
	held_out=held_clean,
	)


	def _partition_split_counts(n: int) -> tuple[int, int, int]:
	n_val = round(n * VAL_RATIO)
	n_test = round(n * TEST_RATIO)
	n_train = n - n_val - n_test
	if n_train < 1 and n > 0:
	half = max(0, n - 1) // 2
	n_train, n_val, n_test = 1, half, n - 1 - half
	return n_train, n_val, n_test


	def _record_to_dict(rec: _Row, split: str) -> dict[str, Any]:
	extra: dict[str, Any] = {
	"interview_id": rec.interview_id,
	"n_observations": len(rec.observations),
	}
	if split == "train":
	out = {
	"observations": rec.observations,
	"predictions": rec.predictions,
	"data_source": DATA_SOURCE,
	"split": "train",
	"held_out_predictions": rec.held_out,
	}
	extra["n_predictions"] = len(rec.predictions)
	extra["n_held_out_predictions"] = len(rec.held_out)
	else:
	merged = list(rec.predictions) + list(rec.held_out)
	out = {
	"observations": rec.observations,
	"predictions": merged,
	"data_source": DATA_SOURCE,
	"split": split,
	}
	extra["n_predictions"] = len(merged)
	out["extra_info"] = extra
	return out


	# --- arrow schemas (explicit so train and val/test are stable) ---------------

	_PRED_TYPE = pa.struct([
	pa.field("question", pa.string()),
	pa.field("ground_truth", pa.string()),
	])

	_TRAIN_SCHEMA = pa.schema([
	pa.field("observations", pa.list_(pa.string())),
	pa.field("predictions", pa.list_(_PRED_TYPE)),
	pa.field("data_source", pa.string()),
	pa.field("split", pa.string()),
	pa.field("held_out_predictions", pa.list_(_PRED_TYPE)),
	pa.field("extra_info", pa.struct([
	pa.field("interview_id", pa.string()),
	pa.field("n_observations", pa.int32()),
	pa.field("n_predictions", pa.int32()),
	pa.field("n_held_out_predictions", pa.int32()),
	])),
	])

	_EVAL_SCHEMA = pa.schema([
	pa.field("observations", pa.list_(pa.string())),
	pa.field("predictions", pa.list_(_PRED_TYPE)),
	pa.field("data_source", pa.string()),
	pa.field("split", pa.string()),
	pa.field("extra_info", pa.struct([
	pa.field("interview_id", pa.string()),
	pa.field("n_observations", pa.int32()),
	pa.field("n_predictions", pa.int32()),
	])),
	])


	def _write_split(records: list[dict], split: str, output_dir: Path, *,
	compression: str \| None, compression_level: int \| None) -> Path:
	schema = _TRAIN_SCHEMA if split == "train" else _EVAL_SCHEMA
	table = pa.Table.from_pylist(records, schema=schema)
	out_path = output_dir / f"{split}-00000-of-00001.parquet"
	pq.write_table(
	table, out_path,
	compression=compression,
	compression_level=compression_level,
	use_dictionary=True,
	)
	return out_path


	def parse_args() -> argparse.Namespace:
	domain_dir = Path(__file__).resolve().parent.parent
	p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
	p.add_argument(
	"--input-dir",
	type=Path,
	default=domain_dir / "upstream",
	help="Directory containing the upstream data_*.zst shards.",
	)
	p.add_argument("--output-dir", type=Path, default=domain_dir / "qa_parquet")
	p.add_argument("--max-rows", type=int, default=0,
	help="Debug: stop after this many input rows (0 = all).")
	p.add_argument("--compression", default="zstd",
	choices=["zstd", "gzip", "snappy", "brotli", "lz4", "none"])
	p.add_argument("--compression-level", type=int, default=None)
	return p.parse_args()


	def main() -> None:
	args = parse_args()
	args.output_dir.mkdir(parents=True, exist_ok=True)

	pool: list[_Row] = []
	raw_seen = 0
	skipped = 0

	for rec in _iter_records(args.input_dir):
	raw_seen += 1
	if args.max_rows and raw_seen > args.max_rows:
	break
	row = _record_to_row(rec)
	if row is None:
	skipped += 1
	continue
	pool.append(row)

	rng = random.Random(SEED)
	rng.shuffle(pool)
	n = len(pool)
	n_train, n_val, _n_test = _partition_split_counts(n)

	splits = {
	"train": pool[:n_train],
	"val": pool[n_train : n_train + n_val],
	"test": pool[n_train + n_val :],
	}

	# Preserve the published IID test policy: exclude the historical training
	# prefix, then retain only the first test row for each adapter identity.
	train_ids = {str(rec.interview_id) for rec in splits["train"][:4366]}
	seen_test_ids: set[str] = set()
	clean_test: list[_Row] = []
	for rec in splits["test"]:
	sample_id = str(rec.interview_id)
	if sample_id in train_ids or sample_id in seen_test_ids:
	continue
	seen_test_ids.add(sample_id)
	clean_test.append(rec)
	splits["test"] = clean_test

	comp = None if args.compression == "none" else args.compression
	written: dict[str, dict] = {}
	for split_name, recs in splits.items():
	if not recs:
	continue
	records = [_record_to_dict(r, split_name) for r in recs]
	path = _write_split(records, split_name, args.output_dir,
	compression=comp, compression_level=args.compression_level)
	written[split_name] = {"rows": len(records), "path": str(path)}

	print(json.dumps({
	"raw_lines_seen": raw_seen,
	"samples_built": n,
	"skipped_insufficient_data": skipped,
	"split_counts": {k: len(v) for k, v in splits.items()},
	"written": written,
	}, indent=2))


	if __name__ == "__main__":
	main()