#!/usr/bin/env python3 """ Validate the UnsolvedMed (ulamai/UnsolvedMedical) dataset (v1.0.0 / v1.5.0 / v2.0.0) against: 1. JSON Schemas in schemas/ (problem, source, status_event, relation, query_manifest) 2. All 18 Publication Gates (Section 3) 3. Exact category quotas (10,000 for v1.0, 15,000 for v1.5, 20,000 for v2.0 + 10% Gold Seed + 100 calibration audit) 4. Portfolio & source diversity guardrails (Sections 12.6, 12.7, 12.8) 5. Strict data hygiene: zero raw documents/excerpts, zero noisy case reports/protocols, and total repo footprint < 400 MB """ import json import re import sys from collections import Counter from pathlib import Path from jsonschema import Draft202012Validator ROOT = Path(__file__).resolve().parent.parent BASE_CATEGORY_QUOTAS = { "oncology_and_hematology": 800, "cardiovascular_and_cerebrovascular_medicine": 600, "neurology": 600, "mental_health_behavioral_health_and_addiction": 600, "infectious_diseases_tropical_medicine_and_amr": 800, "respiratory_and_sleep_medicine": 400, "endocrine_metabolic_obesity_and_nutrition_medicine": 500, "gastroenterology_hepatology_and_pancreatic_disease": 400, "kidney_urinary_and_reproductive_urology": 360, "immunology_rheumatology_and_allergy": 440, "musculoskeletal_orthopedics_pain_and_dermatology": 400, "reproductive_maternal_and_womens_health": 500, "pediatrics_neonatology_and_adolescent_medicine": 500, "aging_geriatrics_dementia_and_palliative_medicine": 400, "surgery_anesthesia_critical_care_emergency_and_trauma": 500, "diagnostics_imaging_pathology_laboratory_and_genomic_medicine": 500, "prevention_primary_care_public_health_and_population_medicine": 500, "health_services_implementation_quality_and_patient_safety": 400, "rehabilitation_disability_sensory_oral_and_communication_health": 400, "rare_multisystem_and_undiagnosed_conditions": 400, } CLINICAL_ADVICE_PATTERNS = re.compile( r"\b(should i take|what ointment should i|by how many units per hour should i|my doctor|my symptoms)\b", re.I, ) NOISE_PATTERNS = re.compile( r"\b(case report|a case of|two cases of|three cases of|case series|study protocol|protocol for|" r"trial protocol|corrigendum|erratum|retracted|retraction|comment on|reply to|author response|" r"letter to|editorial|book review|conference abstract|in mice|mouse model|murine model|" r"in rats|rat model|zebrafish|drosophila|in vitro|cell line)\b", re.I, ) HTML_ENTITY_RE = re.compile(r"&(amp|lt|gt|quot|apos|#\d+|#x[0-9a-f]+);", re.I) def load_jsonl(path: Path) -> list[dict]: with path.open("r", encoding="utf-8") as f: return [json.loads(line) for line in f if line.strip()] def main() -> int: errors = [] print("1. Loading schemas and dataset artifacts...") stats = json.loads((ROOT / "statistics.json").read_text(encoding="utf-8")) version = stats.get("version", "3.0.0") scale_factor = {"1.0.0": 1.0, "1.5.0": 1.5, "2.0.0": 2.0, "3.0.0": 2.0}.get(version, 2.0) expected_category_quotas = {k: int(round(v * scale_factor)) for k, v in BASE_CATEGORY_QUOTAS.items()} expected_total_published = sum(expected_category_quotas.values()) expected_total_gold = expected_total_published // 10 expected_total_all = expected_total_published + 100 prob_schema = json.loads((ROOT / "schemas" / "problem.schema.json").read_text(encoding="utf-8")) src_schema = json.loads((ROOT / "schemas" / "source.schema.json").read_text(encoding="utf-8")) stat_schema = json.loads((ROOT / "schemas" / "status_event.schema.json").read_text(encoding="utf-8")) rel_schema = json.loads((ROOT / "schemas" / "relation.schema.json").read_text(encoding="utf-8")) qm_schema = json.loads((ROOT / "schemas" / "query_manifest.schema.json").read_text(encoding="utf-8")) prob_validator = Draft202012Validator(prob_schema) src_validator = Draft202012Validator(src_schema) stat_validator = Draft202012Validator(stat_schema) rel_validator = Draft202012Validator(rel_schema) qm_validator = Draft202012Validator(qm_schema) current_problems = json.loads((ROOT / "problems.json").read_text(encoding="utf-8")) gold_problems = load_jsonl(ROOT / "data" / "gold" / "problems.jsonl") archived_problems = load_jsonl(ROOT / "data" / "archived" / "problems.jsonl") all_problems = current_problems + archived_problems dataset_bundle = json.loads((ROOT / "dataset.json").read_text(encoding="utf-8")) if len(dataset_bundle.get("problems", [])) != expected_total_all: errors.append(f"dataset.json problems count {len(dataset_bundle.get('problems', []))} != {expected_total_all}") sources = json.loads((ROOT / "source_registry.json").read_text(encoding="utf-8")) relations = load_jsonl(ROOT / "relations" / "relations.jsonl") status_events = load_jsonl(ROOT / "history" / "status_events.jsonl") if (ROOT / "raw").exists(): errors.append("Raw directory `raw/` should not exist in the published repository") total_bytes = sum( f.stat().st_size for f in ROOT.rglob("*") if f.is_file() and ".git" not in f.parts ) total_mb = total_bytes / (1024 * 1024) print(f" Total repository footprint (excluding .git): {total_mb:.1f} MB") if total_mb >= 400.0: errors.append(f"Total repository size {total_mb:.1f} MB exceeds the 400 MB limit") if len(current_problems) != expected_total_published: errors.append(f"Expected {expected_total_published} current problems, found {len(current_problems)}") if len(gold_problems) != expected_total_gold: errors.append(f"Expected {expected_total_gold} gold problems, found {len(gold_problems)}") if len(all_problems) != expected_total_all: errors.append(f"Expected {expected_total_all} total records, found {len(all_problems)}") print("2. Validating JSON Schemas across all records, sources, status events, relations, and query manifests...") for r in all_problems: for err in prob_validator.iter_errors(r): errors.append(f"Problem {r.get('id')} schema error: {err.message}") if len(errors) > 50: break source_ids = set() for s in sources: source_ids.add(s["source_id"]) for err in src_validator.iter_errors(s): errors.append(f"Source {s.get('source_id')} schema error: {err.message}") for ev in status_events: for err in stat_validator.iter_errors(ev): errors.append(f"Status event {ev.get('event_id')} schema error: {err.message}") all_ids = {r["id"] for r in all_problems} for rel in relations: for err in rel_validator.iter_errors(rel): errors.append(f"Relation {rel.get('relation_id')} schema error: {err.message}") if rel["subject_id"] not in all_ids: errors.append(f"Relation {rel['relation_id']} has unknown subject_id {rel['subject_id']}") if rel["object_id"].startswith("UMED-") and rel["object_id"] not in all_ids: errors.append(f"Relation {rel['relation_id']} has unknown object_id {rel['object_id']}") qm_files = sorted((ROOT / "sources" / "query_manifests").glob("MEDQ-*.yaml")) if not qm_files: errors.append("No query manifests found in sources/query_manifests/") import yaml for qf in qm_files: qdata = yaml.safe_load(qf.read_text(encoding="utf-8")) for err in qm_validator.iter_errors(qdata): errors.append(f"Query manifest {qf.name} schema error: {err.message}") print("3. Checking category allocations, Gold Seed quotas, data hygiene, and 18 Publication Gates...") cat_counts = Counter(r["category_id"] for r in current_problems) gold_cat_counts = Counter(r["category_id"] for r in gold_problems) for slug, expected in expected_category_quotas.items(): if cat_counts[slug] != expected: errors.append(f"Category {slug}: expected {expected} current problems, got {cat_counts[slug]}") if gold_cat_counts[slug] != expected // 10: errors.append(f"Category {slug} Gold Seed: expected {expected // 10}, got {gold_cat_counts[slug]}") seen_titles = set() seen_stmts = set() origin_source_counts = Counter() any_source_counts = Counter() for r in current_problems: if not r["published"]: errors.append(f"{r['id']} in current_problems has published=False") if r["original_statement"] is not None: errors.append(f"{r['id']} stores raw excerpt in original_statement instead of None") if r["publication_class"] not in ("OPEN-VERIFIED", "PARTIAL-PROGRESS", "DISPUTED-OPEN", "BLOCKED-OPEN"): errors.append(f"{r['id']} has invalid current publication_class {r['publication_class']}") if r["status"] not in ("open", "partially_resolved", "contested"): errors.append(f"{r['id']} has invalid current status {r['status']}") if not r["statement"].endswith("?"): errors.append(f"{r['id']} statement does not end with '?'") if CLINICAL_ADVICE_PATTERNS.search(r["statement"]): errors.append(f"{r['id']} statement contains clinical advice pattern") if NOISE_PATTERNS.search(r["title"]) or NOISE_PATTERNS.search(r["statement"]): errors.append(f"{r['id']} contains noisy non-research pattern in title or statement") if HTML_ENTITY_RE.search(r["title"]) or HTML_ENTITY_RE.search(r["statement"]): errors.append(f"{r['id']} contains unescaped HTML entity in title or statement") if not all(r["statement_audit"][k] for k in ("self_contained", "atomic", "medical_research_question", "does_not_imply_clinical_advice")): errors.append(f"{r['id']} failed statement_audit booleans") if r["rights"]["contains_extended_source_text"]: errors.append(f"{r['id']} contains extended source text") if r["responsible_release"]["contains_personal_data"]: errors.append(f"{r['id']} contains personal data") for w in r["why_open"]: if w.strip().lower() in ("more research is needed", "more research is needed."): errors.append(f"{r['id']} has generic why_open") t_norm = r["title"].lower().strip() s_norm = r["statement"].lower().strip() if t_norm in seen_titles: errors.append(f"Duplicate title in current_problems: {r['id']}") if s_norm in seen_stmts: errors.append(f"Duplicate statement in current_problems: {r['id']}") seen_titles.add(t_norm) seen_stmts.add(s_norm) min_sources = 5 if version == "3.0.0" else 4 if len(r["source_support"]) < min_sources: errors.append(f"{r['id']} has fewer than {min_sources} sources in source_support ({len(r['source_support'])})") lit_sids = [ ss["source_id"] for ss in r["source_support"] if not ss["source_id"].startswith("SRC-MED-ANC-") and not ss["source_id"].startswith("SRC-MED-NCT-") ] if len(set(lit_sids)) < 3: errors.append(f"{r['id']} has fewer than 3 distinct peer-reviewed literature papers ({len(set(lit_sids))})") for ss in r["source_support"]: if ss["source_id"] not in source_ids: errors.append(f"{r['id']} references unknown source_id {ss['source_id']}") any_source_counts[ss["source_id"]] += 1 if ss["role"] == "problem_origin": origin_source_counts[ss["source_id"]] += 1 print("4. Checking portfolio & source diversity guardrails (Sections 12.6 & 12.7)...") qtype_counts = Counter(r["question_type"] for r in current_problems) n_total = len(current_problems) for qt, cnt in qtype_counts.items(): if cnt / n_total > 0.30: errors.append(f"Question type {qt} exceeds 30% guardrail: {cnt / n_total:.2%}") etio_share = sum(qtype_counts[q] for q in ("etiology", "mechanism", "natural_history")) / n_total diag_share = sum(qtype_counts[q] for q in ("diagnosis", "screening", "prognosis", "classification", "measurement")) / n_total safe_share = sum(qtype_counts[q] for q in ("safety", "supportive_palliative", "rehabilitation", "patient_experience")) / n_total impl_share = sum(qtype_counts[q] for q in ("implementation", "health_services", "public_health")) / n_total for label, share in [ ("Etiology/Mechanism/Natural History", etio_share), ("Diagnosis/Screening/Prognosis/Classification/Measurement", diag_share), ("Safety/Supportive/Rehab/Patient Experience", safe_share), ("Implementation/Health Services/Public Health", impl_share), ]: if share < 0.10: errors.append(f"Portfolio guardrail failed: {label} share is {share:.2%} (< 10%)") max_origin = max(origin_source_counts.values()) if origin_source_counts else 0 max_any = max(any_source_counts.values()) if any_source_counts else 0 if max_origin > 50: errors.append(f"Source diversity guardrail failed: max problem_origin records per source is {max_origin} (> 50)") if max_any > 50: errors.append(f"Source diversity guardrail failed: max total records per source in any role is {max_any} (> 50)") if errors: print(f"VALIDATION FAILED with {len(errors)} errors:") for e in errors[:25]: print(" -", e) return 1 print( f"ALL VALIDATION CHECKS PASSED (0 errors)! " f"Verified {len(current_problems)} published problems ({len(gold_problems)} Gold Seed), " f"{len(all_problems) - len(current_problems)} calibration audit records, " f"{len(sources)} sources (max origin={max_origin}, max any role={max_any}), " f"{len(relations)} relations, and total size {total_mb:.1f} MB (< 400 MB)." ) return 0 if __name__ == "__main__": sys.exit(main())