File size: 5,624 Bytes
df32294 | 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 | """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")
# Get dedup info
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 # these are the ones kept
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 analysis
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 size distribution
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")
# Cross-source overlap by formula
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", "")
# Check if any formulas have >2 sources (potential triple duplicates)
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()
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