Datasets:
File size: 5,453 Bytes
fb17822 c0a9877 fb17822 c0a9877 fb17822 c0a9877 fb17822 c0a9877 fb17822 e0c9361 cb02b42 fb17822 cb02b42 e0c9361 fb17822 cb02b42 fb17822 e0c9361 fb17822 c0a9877 fb17822 c0a9877 | 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 | #!/usr/bin/env python3
"""
Compute comprehensive dataset statistics for UnsolvedAstronomy v1.1.0 and write statistics.json.
"""
import json
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
PROBLEMS_FILE = ROOT / "problems.json"
ARCHIVED_FILE = ROOT / "archived_problems.json"
RELATIONS_FILE = ROOT / "relations.jsonl"
SOURCES_FILE = ROOT / "source_registry.json"
STATS_ROOT = ROOT / "statistics.json"
def load_jsonl(path: Path) -> list[dict]:
if not path.exists():
return []
return [json.loads(line) for line in path.open("r", encoding="utf-8") if line.strip()]
def load_json_array(path: Path) -> list[dict]:
if not path.exists():
return []
return json.loads(path.read_text(encoding="utf-8"))
def compute_stats() -> dict:
published = load_json_array(PROBLEMS_FILE)
archived = load_json_array(ARCHIVED_FILE)
all_problems = published + archived
sources = load_json_array(SOURCES_FILE)
relations = load_jsonl(RELATIONS_FILE)
gold = [p for p in published if p.get("is_gold_seed")]
cat_counts = Counter()
gold_cat_counts = Counter()
subcat_counts = Counter()
qtype_counts = Counter()
status_counts = Counter(p["status"] for p in all_problems)
pub_status_counts = Counter(p["status"] for p in published)
ev_counts = Counter(p["evidence_state"] for p in published)
cons_counts = Counter(p["consensus_state"] for p in published)
vmode_counts = Counter(p["verification_mode"] for p in published)
vhor_counts = Counter(p["verification_horizon"] for p in published)
diff_counts = Counter(p["difficulty_level"] for p in published)
obj_counts = Counter()
scale_counts = Counter()
wave_counts = Counter()
source_usage = Counter()
cross_domain_counts = Counter()
for p in published:
key = f"{p['category_number']:02d}_{p['category']}"
cat_counts[key] += 1
if p.get("is_gold_seed"):
gold_cat_counts[key] += 1
subcat_counts[p["subcategory"]] += 1
qtype_counts[p["question_type"]] += 1
for o in p.get("object_classes", []):
obj_counts[o] += 1
for sc in p.get("scales", []):
scale_counts[sc] += 1
for w in p.get("wavelengths_and_messengers", []):
wave_counts[w] += 1
if p.get("sources"):
source_usage[p["sources"][0]["source_id"]] += 1
for cd in p.get("cross_domain_links", []):
cross_domain_counts[cd["dataset"]] += 1
max_src_id, max_src_cnt = source_usage.most_common(1)[0] if source_usage else ("NONE", 0)
src_len_counts = Counter(len({s["source_id"] for s in p.get("sources", [])}) for p in published)
total_src_links = sum(len({s["source_id"] for s in p.get("sources", [])}) for p in published)
unique_titles = len({(p.get("title") or "").strip().lower() for p in published})
stats = {
"dataset_name": "UnsolvedAstronomy",
"version": "1.1.0",
"release_date": "2026-10-16",
"total_records_all_statuses": len(all_problems),
"published_problems_count": len(published),
"unique_titles_published_count": unique_titles,
"gold_seed_problems_count": len(gold),
"archived_and_excluded_count": len(archived),
"distinct_sources_count": len(sources),
"relations_count": len(relations),
"min_sources_per_published_problem": min(src_len_counts.keys()) if src_len_counts else 0,
"max_sources_per_published_problem": max(src_len_counts.keys()) if src_len_counts else 0,
"average_sources_per_published_problem": round(total_src_links / max(1, len(published)), 3),
"sources_count_distribution_published": dict(sorted(src_len_counts.items())),
"max_single_source_id": max_src_id,
"max_single_source_published_count": max_src_cnt,
"max_single_source_share_percent": round(100.0 * max_src_cnt / max(1, len(published)), 3),
"status_distribution_all": dict(status_counts),
"status_distribution_published": dict(pub_status_counts),
"category_distribution_published": dict(sorted(cat_counts.items())),
"gold_seed_category_distribution": dict(sorted(gold_cat_counts.items())),
"subcategory_distribution_published": dict(sorted(subcat_counts.items())),
"question_type_distribution": dict(qtype_counts.most_common()),
"evidence_state_distribution": dict(ev_counts.most_common()),
"consensus_state_distribution": dict(cons_counts.most_common()),
"object_class_distribution": dict(obj_counts.most_common()),
"scale_distribution": dict(scale_counts.most_common()),
"wavelength_and_messenger_distribution": dict(wave_counts.most_common()),
"verification_mode_distribution": dict(vmode_counts.most_common()),
"verification_horizon_distribution": dict(vhor_counts.most_common()),
"difficulty_level_distribution": dict(sorted(diff_counts.items())),
"source_tier_distribution": dict(Counter(s.get("tier") for s in sources)),
"source_type_distribution": dict(Counter(s.get("source_type") for s in sources)),
"cross_domain_links_distribution": dict(cross_domain_counts),
}
STATS_ROOT.write_text(json.dumps(stats, indent=2) + "\n", encoding="utf-8")
print(f"Wrote statistics to {STATS_ROOT}")
return stats
if __name__ == "__main__":
compute_stats()
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