Datasets:
File size: 2,615 Bytes
e1ced61 | 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 | """ChartQA-H importer — human-authored questions only, table artifact hashed.
Of ChartQA's 28,299 train rows, only the 7,398 ``type=human`` rows are kept
(``docs/01`` §3.4); augmented questions are excluded from the core
human-question-preservation evidence. Non-human rows are *dropped* (the importer
returns ``None``), never silently relabeled, and the driver counts them so the
"zero non-human rows in output" invariant is verifiable.
The underlying table is the ground truth for recomputing an ``A_CHANGED``
answer, so its canonical-JSON SHA-256 is recorded in
``provenance.table_sha256`` and linked separately from the raster image.
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any
from ..hashing import canonical_json, sha256_bytes
from .base import (
ImageResolver,
ImageStore,
IngestError,
NormalizedItem,
Policy,
infer_open_answer_type,
make_item,
)
NAME = "chartqa"
POLICY: Policy = "c2_train_candidate"
ALLOWED_SPLIT = "train"
HUMAN_TYPE = "human"
def is_human(row: Mapping[str, Any]) -> bool:
"""True when a ChartQA row is a human-authored question."""
return str(row.get("type", "")).strip() == HUMAN_TYPE
def normalize(
row: Mapping[str, Any],
images: ImageStore,
resolve: ImageResolver,
*,
revision: str,
split: str,
config: str = "default",
) -> NormalizedItem | None:
"""Normalize one ChartQA row; return ``None`` for non-human (augmented) rows."""
if split != ALLOWED_SPLIT:
raise IngestError(f"chartqa: only split {ALLOWED_SPLIT!r} may be ingested, got {split!r}")
if not is_human(row):
return None # augmented row — dropped, counted by the driver
question = str(row["query"])
answer_raw = str(row["label"])
answer_type = infer_open_answer_type(answer_raw)
image_ref = str(row["img"])
rel, digest = images.store(resolve(image_ref))
table = row.get("table")
extra: dict[str, Any] = {}
if table is not None:
extra["table_sha256"] = sha256_bytes(canonical_json(table).encode("utf-8"))
native_id = str(row.get("id", row.get("index")))
return make_item(
source=NAME,
source_revision=revision,
source_config=config,
source_split=split,
source_native_id=native_id,
question=question,
choices=(),
answer_raw=answer_raw,
answer_canonical=answer_raw,
answer_type=answer_type,
image_paths=(rel,),
image_sha256=(digest,),
policy=POLICY,
native_row=row,
extra_provenance=extra,
)
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