| """Phase 4: Duplicate Audit — exact, near, formula, cross-source.""" |
| import json, time |
| from pathlib import Path |
| from collections import Counter, defaultdict |
| import numpy as np |
|
|
| OUT = Path("scripts/audit_reports") |
| DATASET = "dataset/entries_final_v3.json" |
| AUDIT_DIR = Path.cwd() if Path.cwd().name == "Scandium-Dataset" else Path("/home/shamique/Scandium Labs SSB/Scandium-Dataset") |
|
|
| severity_counts = {"CRITICAL": 0, "HIGH": 0, "MEDIUM": 0, "LOW": 0, "PASS": 0} |
| findings = [] |
|
|
| def finding(severity, phase, check, status, detail): |
| severity_counts[severity] += 1 |
| findings.append({"severity": severity, "phase": phase, "check": check, "status": status, "detail": str(detail)[:200]}) |
| s = "🔴" if severity == "CRITICAL" else "🟠" if severity == "HIGH" else "🟡" if severity == "MEDIUM" else "🔵" if severity == "LOW" else "✅" |
| print(f" {s} [{severity:8s}] {check}: {str(detail)[:120]}") |
|
|
| def main(): |
| print("=" * 60) |
| print(" PHASE 4: DUPLICATE AUDIT") |
| print("=" * 60) |
|
|
| with open(AUDIT_DIR / DATASET) as f: |
| entries = json.load(f) |
| N = len(entries) |
| print(f" Loaded {N:,} entries") |
|
|
| |
| dup_groups = Counter() |
| cross_source_dup = 0 |
| intra_source_dup = 0 |
| kept_entries = 0 |
| duplicate_entries = 0 |
| dup_group_details = defaultdict(list) |
|
|
| for e in entries: |
| dg = e.get("duplicate_group") |
| if dg is not None: |
| dup_groups[dg] += 1 |
| dup_group_details[dg].append(e.get("source", "?")) |
|
|
| duplicate_entries = sum(dup_groups.values()) |
| kept_entries = N - duplicate_entries |
| n_groups = len(dup_groups) |
|
|
| print(f"\n--- Duplicate Summary ---") |
| print(f" Duplicate groups: {n_groups:,}") |
| print(f" Duplicate entries: {duplicate_entries:,} (these were kept as representatives)") |
| print(f" Original total before dedup: {N + 31997:,} (assuming 31,997 removed)") |
|
|
| |
| cross_source_groups = 0 |
| for gid, srcs in dup_group_details.items(): |
| unique_srcs = set(srcs) |
| if len(unique_srcs) > 1: |
| cross_source_groups += 1 |
| intra_source_dup += len(srcs) - 1 |
|
|
| intra_source_groups = n_groups - cross_source_groups |
|
|
| finding("PASS", "duplicates", "total_duplicate_groups", |
| f"{n_groups:,} groups ({duplicate_entries:,} entries kept)", "") |
| finding("PASS", "duplicates", "cross_source_groups", |
| f"{cross_source_groups:,} groups span multiple sources", "") |
| finding("PASS", "duplicates", "intra_source_groups", |
| f"{intra_source_groups:,} groups within single source", "") |
|
|
| |
| group_sizes = sorted(dup_groups.values(), reverse=True) |
| print(f"\n--- Group Size Distribution ---") |
| print(f" Max group size: {max(group_sizes) if group_sizes else 0}") |
| print(f" Mean group size: {np.mean(group_sizes):.2f}" if group_sizes else " No groups") |
| |
| size_bins = Counter() |
| for sz in group_sizes: |
| if sz == 2: size_bins["2"] += 1 |
| elif sz <= 5: size_bins["3-5"] += 1 |
| elif sz <= 10: size_bins["6-10"] += 1 |
| else: size_bins[">10"] += 1 |
| |
| for label, cnt in size_bins.most_common(): |
| print(f" size {label}: {cnt:,} groups") |
|
|
| |
| print(f"\n--- Cross-Source Formula Overlap ---") |
| by_formula = defaultdict(set) |
| for e in entries: |
| f = e.get("formula", "").strip() |
| if f: |
| by_formula[f].add(e.get("source", "")) |
|
|
| cross_formula = {f: s for f, s in by_formula.items() if len(s) > 1} |
| print(f" Formulas with multiple sources: {len(cross_formula):,}") |
|
|
| overlap_pairs = Counter() |
| for f, srcs in cross_formula.items(): |
| for s1 in srcs: |
| for s2 in srcs: |
| if s1 < s2: |
| overlap_pairs[(s1, s2)] += 1 |
|
|
| for (s1, s2), cnt in overlap_pairs.most_common(): |
| finding("PASS", "cross_source_overlap", f"{s1}_{s2}_formulas", |
| f"{cnt:,} formulas shared", "") |
|
|
| |
| triple = {f: s for f, s in cross_formula.items() if len(s) >= 3} |
| if triple: |
| finding("MEDIUM", "duplicates", "triple_source_formulas", |
| f"{len(triple):,} formulas appear in all 3 sources", "") |
| else: |
| finding("PASS", "duplicates", "triple_source_formulas", "0", "") |
|
|
| print(f"\n{'=' * 60}") |
| print(f" PHASE 4 SUMMARY") |
| print(f" CRITICAL: {severity_counts['CRITICAL']}") |
| print(f" HIGH: {severity_counts['HIGH']}") |
| print(f" MEDIUM: {severity_counts['MEDIUM']}") |
| print(f" LOW: {severity_counts['LOW']}") |
| print(f" PASS: {severity_counts['PASS']}") |
| print(f"{'=' * 60}") |
|
|
| report = { |
| "phase": "Phase 4: Duplicate Audit", |
| "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), |
| "total_entries": N, |
| "duplicate_groups": n_groups, |
| "duplicate_entries_kept": duplicate_entries, |
| "estimated_removed": 31997, |
| "cross_source_groups": cross_source_groups, |
| "intra_source_groups": intra_source_groups, |
| "cross_source_formulas": len(cross_formula), |
| "overlap_pairs": {f"{s1}_{s2}": c for (s1, s2), c in overlap_pairs.most_common()}, |
| "group_size_distribution": dict(size_bins), |
| "findings": findings, |
| "summary": dict(severity_counts), |
| } |
| with open(OUT / "phase4_duplicate_audit.json", "w") as f: |
| json.dump(report, f, indent=2) |
| print(f"\n Report: {OUT / 'phase4_duplicate_audit.json'}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
