110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""
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Mass Quality Measure — Measure theta on every organ of every model
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Find the organs closest to theta=90 (pure signal)
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CSCI v1.0 — Cross-Scale Coherence Index
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"""
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import subprocess, os, json, sys
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sys.path.insert(0, "/root/organ-architecture")
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from organ_measure import measure_directory, compute_z_measure, read_organ_data_f32
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ORGANS_DIR = "/root/organ-architecture/organs"
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all_results = {}
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models = sorted(os.listdir(ORGANS_DIR))
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for model_name in models:
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model_path = os.path.join(ORGANS_DIR, model_name)
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manifest_path = os.path.join(model_path, "manifest.json")
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if not os.path.isdir(model_path) or not os.path.exists(manifest_path):
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continue
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print(f"\n[QUALITY-MEASURE] {model_name}")
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print(f" Measuring organs...")
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results = measure_directory(model_path)
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if not results:
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print(f" [SKIP] No measurable organs")
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continue
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# Group by type
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groups = {}
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for r in results:
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dirname = os.path.dirname(r['file']).split('/')[-1]
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if dirname not in groups:
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groups[dirname] = []
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groups[dirname].append(r)
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model_summary = {
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"model": model_name,
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"total_tensors": len(results),
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"avg_theta": sum(r['theta_deg'] for r in results) / len(results),
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"avg_signal": sum(r['signal_ratio'] for r in results) / len(results),
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"groups": {}
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}
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for gname in ['skeleton', 'organs', 'embed', 'norm']:
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if gname in groups:
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g = groups[gname]
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avg_t = sum(r['theta_deg'] for r in g) / len(g)
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avg_s = sum(r['signal_ratio'] for r in g) / len(g)
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best = max(g, key=lambda r: r['theta_deg'])
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worst = min(g, key=lambda r: r['theta_deg'])
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model_summary["groups"][gname] = {
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"count": len(g),
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"avg_theta": round(avg_t, 1),
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"avg_signal": round(avg_s, 3),
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"best_theta": round(best['theta_deg'], 1),
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"best_name": best['name'],
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"worst_theta": round(worst['theta_deg'], 1),
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"worst_name": worst['name']
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}
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print(f" {gname:12s}: {len(g):3d} tensors | avg theta={avg_t:5.1f} | signal={avg_s:.3f} | best={best['theta_deg']:.1f}")
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print(f" GLOBAL: theta={model_summary['avg_theta']:.1f} | signal={model_summary['avg_signal']:.3f}")
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all_results[model_name] = model_summary
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# Rank models by signal quality
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print(f"\n{'='*70}")
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print(f" QUALITY RANKING — ALL MODELS")
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print(f"{'='*70}")
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ranked = sorted(all_results.values(), key=lambda m: m['avg_theta'], reverse=True)
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for i, m in enumerate(ranked, 1):
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print(f" {i:2d}. theta={m['avg_theta']:5.1f} signal={m['avg_signal']:.3f} {m['model']}")
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# Find best organs across ALL models for each type
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print(f"\n{'='*70}")
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print(f" BEST ORGANS ACROSS ALL MODELS (theta closest to 90)")
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print(f"{'='*70}")
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for organ_type in ['skeleton', 'organs', 'embed']:
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print(f"\n [{organ_type.upper()}]")
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candidates = []
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for model_name, summary in all_results.items():
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if organ_type in summary['groups']:
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g = summary['groups'][organ_type]
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candidates.append((model_name, g['best_theta'], g['best_name'], g['avg_theta']))
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candidates.sort(key=lambda c: c[1], reverse=True)
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for c in candidates[:5]:
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print(f" theta={c[1]:5.1f} avg={c[3]:5.1f} {c[0]:30s} {c[2][:40]}")
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print(f"\n Signature: 935")
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print(f"{'='*70}")
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# Save full report
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with open("/root/organ-architecture/z_measure_report.json", "w") as f:
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json.dump(all_results, f, indent=2)
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print(f"\nReport: /root/organ-architecture/z_measure_report.json")
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# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
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# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
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# Licensed under Business Source License 1.1 — https://inference-x.com
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# ─────────────────────────────────────────────────────────
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# SHA256: 711671a1721bae194388cb363ad0bfcb2ed874f007a45e45ea6ed5d917cbf060
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# SIG-ED25519: Jd0hVyr5epgPlpNjtioVeKfPaOeYgRiAnAEnxINh51WsfwGFLJouBDdYribxqY0JOmOnDwjGnOK5I9qeJJTRDg==
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# VERIFY: python3 verify_authorship.py mass_z_measure.py
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