File size: 3,543 Bytes
32030e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | """Load EJBench tasks from a release folder.
Each split is one file, ``<split>.csv`` or ``<split>-*.parquet``, and the ``family`` column says
which task family a row belongs to. Both formats hold the same columns.
"""
from __future__ import annotations
import csv
import json
import sys
from pathlib import Path
from typing import Iterable
JSON_COLUMNS = ("output_schema", "inputs", "environment", "answer", "scoring", "tags", "provenance")
OPTIONAL_TEXT = ("difficulty",)
FAMILIES = (
"case_ranking", "regime_recognition", "estimation", "operating_point_transfer",
"limit_check", "approval_choice", "evidence_budget_transfer", "result_review",
"campaign_reconstruction", "evidence_signoff", "operating_envelope",
"tool_campaign", "tool_signoff", "tool_envelope",
"duty_signoff", # training sets only (tools/build_training.py)
)
def decode_record(row: dict) -> dict:
"""Nested fields are stored as JSON text so every family shares one flat schema."""
task = dict(row)
for column in JSON_COLUMNS:
value = task.get(column)
if isinstance(value, str):
task[column] = json.loads(value) if value else None
for column in OPTIONAL_TEXT:
if task.get(column) == "":
task[column] = None
return task
def task_files(root: Path, split: str) -> list[Path]:
root = Path(root)
csvs = sorted(root.glob(f"{split}.csv")) + sorted(root.glob(f"{split}-*.csv"))
parquets = sorted(root.glob(f"{split}.parquet")) + sorted(root.glob(f"{split}-*.parquet"))
if csvs and parquets:
raise ValueError(f"{root} has both CSV and parquet files for {split}; keep one format")
return csvs or parquets
def read_rows(path: Path) -> list[dict]:
if path.suffix == ".csv":
csv.field_size_limit(min(sys.maxsize, 2**31 - 1))
with path.open(newline="", encoding="utf-8-sig") as handle:
return list(csv.DictReader(handle))
try:
import pandas as pd
except ImportError as exc: # pragma: no cover
raise ImportError("pandas and pyarrow are needed to read parquet files") from exc
return pd.read_parquet(path).to_dict("records")
def load_tasks(dataset_dir: str | Path, split: str, families: Iterable[str] | None = None) -> list[dict]:
root = Path(dataset_dir)
wanted = set(families or FAMILIES)
tasks: list[dict] = []
for path in task_files(root, split):
tasks.extend(decode_record(row) for row in read_rows(path) if row["family"] in wanted)
if not tasks:
raise FileNotFoundError(f"No {split} tasks under {root} for {sorted(wanted)}")
return tasks
def select_task_set(tasks: list[dict], set_path: str | Path, split: str,
families: Iterable[str] | None = None) -> list[dict]:
"""Keep the tasks a fixed task set lists for ``split``; every listed task must be present."""
wanted_families = set(families or FAMILIES)
with Path(set_path).open(newline="", encoding="utf-8") as handle:
rows = [row for row in csv.DictReader(handle) if row["split"] == split]
if not rows:
raise ValueError(f"{set_path} lists no {split} tasks")
listed = {row["task_id"] for row in rows if row["family"] in wanted_families}
missing = listed - {task["task_id"] for task in tasks}
if missing:
raise ValueError(f"{len(missing)} tasks in {set_path} are not in the loaded tasks; "
"the set was made from a different build")
return [task for task in tasks if task["task_id"] in listed]
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