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1db7196 | 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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | #!/usr/bin/env python3
import argparse
import json
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
EXPECTED_LABELS = (
"low_health_literacy",
"intermediate_health_literacy",
"proficient_health_literacy",
)
@dataclass
class MergeStats:
total_rows: int = 0
total_doc_ids: int = 0
missing_label_rows: int = 0
unexpected_labels: int = 0
doc_ids_missing_some_labels: int = 0
doc_ids_fulltext_mismatch: int = 0
doc_ids_summary_mismatch: int = 0
doc_ids_fulltext_subclaims_mismatch: int = 0
doc_ids_summary_subclaims_mismatch: int = 0
def _pick_first_non_empty(values: List[Optional[str]]) -> Optional[str]:
for value in values:
if isinstance(value, str) and value.strip():
return value
for value in values:
if value is not None:
return value
return None
def _normalize_text(value: Any) -> Optional[str]:
if value is None:
return None
if not isinstance(value, str):
return str(value)
return value
def _normalize_string_list(value: Any) -> Optional[Tuple[str, ...]]:
if value is None:
return None
if not isinstance(value, list):
return (str(value),)
normalized: List[str] = []
for item in value:
if item is None:
continue
if isinstance(item, str):
normalized.append(item.strip())
else:
normalized.append(str(item).strip())
return tuple(normalized)
def combine_by_doc_id(rows: List[Dict[str, Any]], keep_all_fields_per_label: bool = True) -> Tuple[List[Dict[str, Any]], MergeStats]:
stats = MergeStats(total_rows=len(rows))
grouped: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
for row in rows:
if not isinstance(row, dict):
continue
doc_id = row.get("doc_id")
if doc_id is None:
continue
grouped[int(doc_id)].append(row)
stats.total_doc_ids = len(grouped)
combined: List[Dict[str, Any]] = []
for doc_id in sorted(grouped.keys()):
bucket = grouped[doc_id]
labels_map: Dict[str, Dict[str, Any]] = {}
fulltexts: List[Optional[str]] = []
summaries: List[Optional[str]] = []
fulltext_subclaims_sets: List[Optional[Tuple[str, ...]]] = []
summary_subclaims_sets: List[Optional[Tuple[str, ...]]] = []
for row in bucket:
label = row.get("label")
if not label:
stats.missing_label_rows += 1
continue
if label not in EXPECTED_LABELS:
stats.unexpected_labels += 1
fulltexts.append(_normalize_text(row.get("fulltext")))
summaries.append(_normalize_text(row.get("summary")))
fulltext_subclaims_sets.append(_normalize_string_list(row.get("fulltext_subclaims")))
summary_subclaims_sets.append(_normalize_string_list(row.get("summary_subclaims")))
label_payload: Dict[str, Any]
if keep_all_fields_per_label:
# Shared within a doc_id; keep them only once at top-level
label_payload = {
k: v
for k, v in row.items()
if k
not in (
"doc_id",
"label",
"fulltext",
"summary",
"fulltext_subclaims",
"summary_subclaims",
)
}
else:
label_payload = {
"diff_label_texts": row.get("diff_label_texts"),
"diff_label_subclaims": row.get("diff_label_subclaims"),
}
labels_map[str(label)] = label_payload
chosen_fulltext = _pick_first_non_empty(fulltexts)
chosen_summary = _pick_first_non_empty(summaries)
chosen_fulltext_subclaims: Optional[List[str]] = None
for items in fulltext_subclaims_sets:
if items:
chosen_fulltext_subclaims = list(items)
break
chosen_summary_subclaims: Optional[List[str]] = None
for items in summary_subclaims_sets:
if items:
chosen_summary_subclaims = list(items)
break
distinct_fulltexts = {t.strip() for t in fulltexts if isinstance(t, str) and t.strip()}
distinct_summaries = {t.strip() for t in summaries if isinstance(t, str) and t.strip()}
if len(distinct_fulltexts) > 1:
stats.doc_ids_fulltext_mismatch += 1
if len(distinct_summaries) > 1:
stats.doc_ids_summary_mismatch += 1
distinct_fulltext_subclaims = {t for t in fulltext_subclaims_sets if t}
distinct_summary_subclaims = {t for t in summary_subclaims_sets if t}
if len(distinct_fulltext_subclaims) > 1:
stats.doc_ids_fulltext_subclaims_mismatch += 1
if len(distinct_summary_subclaims) > 1:
stats.doc_ids_summary_subclaims_mismatch += 1
missing_some = any(lbl not in labels_map for lbl in EXPECTED_LABELS)
if missing_some:
stats.doc_ids_missing_some_labels += 1
combined.append(
{
"doc_id": doc_id,
"fulltext": chosen_fulltext,
"fulltext_subclaims": chosen_fulltext_subclaims,
"summary": chosen_summary,
"summary_subclaims": chosen_summary_subclaims,
"labels": labels_map,
}
)
return combined, stats
def main() -> None:
parser = argparse.ArgumentParser(
description=(
"Combine per-label rows into a single object per doc_id. "
"Input is a JSON array with repeated doc_id for different labels."
)
)
parser.add_argument(
"--input",
required=True,
help="Path to input JSON file (list of rows)",
)
parser.add_argument(
"--output",
default=None,
help="Path to output JSON file. Default: same folder with *_by_docid.json suffix",
)
parser.add_argument(
"--minimal",
action="store_true",
help="Only keep diff_label_texts/diff_label_subclaims/fulltext_subclaims/summary_subclaims per label.",
)
args = parser.parse_args()
input_path = Path(args.input)
output_path = Path(args.output) if args.output else input_path.with_name(input_path.stem + "_by_docid.json")
rows = json.loads(input_path.read_text(encoding="utf-8"))
if not isinstance(rows, list):
raise SystemExit("Input JSON must be a list")
combined, stats = combine_by_doc_id(rows, keep_all_fields_per_label=not args.minimal)
output_path.write_text(
json.dumps(combined, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)
print("Wrote:", str(output_path))
print(
"Stats:",
json.dumps(
{
"total_rows": stats.total_rows,
"total_doc_ids": stats.total_doc_ids,
"missing_label_rows": stats.missing_label_rows,
"unexpected_labels": stats.unexpected_labels,
"doc_ids_missing_some_labels": stats.doc_ids_missing_some_labels,
"doc_ids_fulltext_mismatch": stats.doc_ids_fulltext_mismatch,
"doc_ids_summary_mismatch": stats.doc_ids_summary_mismatch,
"doc_ids_fulltext_subclaims_mismatch": stats.doc_ids_fulltext_subclaims_mismatch,
"doc_ids_summary_subclaims_mismatch": stats.doc_ids_summary_subclaims_mismatch,
},
indent=2,
),
)
if __name__ == "__main__":
main()
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