"""Phase 3: Property Audit — distributions, outliers, physical constraints.""" import json, time from pathlib import Path from collections import Counter import numpy as np OUT = Path("scripts/audit_reports") OUT.mkdir(parents=True, exist_ok=True) 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 analyze_prop(values, name, unit, phys_min=None, phys_max=None): vals = np.array([v for v in values if v is not None]) n_missing = sum(1 for v in values if v is None) print(f"\n {name} ({unit}):") print(f" n={len(vals):,}, missing={n_missing:,}, mean={np.mean(vals):.4f}, " f"median={np.median(vals):.4f}, std={np.std(vals):.4f}") print(f" min={np.min(vals):.4f}, max={np.max(vals):.4f}") # Percentiles for p in [1, 5, 25, 50, 75, 95, 99]: print(f" P{p:2d}={np.percentile(vals, p):.4f}", end="") print() # Skewness and kurtosis from scipy import stats print(f" skewness={stats.skew(vals):.4f}, kurtosis={stats.kurtosis(vals):.4f}") return vals, n_missing def main(): print("=" * 60) print(" PHASE 3: PROPERTY AUDIT") print("=" * 60) with open(AUDIT_DIR / DATASET) as f: entries = json.load(f) N = len(entries) print(f" Loaded {N:,} entries") FE = [e.get("formation_energy_per_atom") for e in entries] EaH = [e.get("energy_above_hull") for e in entries] BG = [e.get("band_gap") for e in entries] # --- Distribution analysis --- print("\n--- Distribution Analysis ---") fe_vals, fe_missing = analyze_prop(FE, "Formation Energy", "eV/atom") eah_vals, eah_missing = analyze_prop(EaH, "Energy Above Hull", "eV/atom") bg_vals, bg_missing = analyze_prop(BG, "Band Gap", "eV") # --- Physical constraints --- print("\n--- Physical Constraints ---") # BG >= 0 neg_bg = np.sum(bg_vals < -0.01) if neg_bg: finding("CRITICAL", "physical", "negative_band_gap", f"{neg_bg:,} entries with BG < 0", "") else: finding("PASS", "physical", "band_gap_non_negative", f"{np.sum(bg_vals < 0)} negative", "") # EaH >= 0 neg_eah = np.sum(eah_vals < -0.01) if neg_eah: finding("CRITICAL", "physical", "negative_hull_energy", f"{neg_eah:,} entries with EaH < 0", "") else: finding("PASS", "physical", "hull_non_negative", f"{neg_eah} negative", "") # FE within physical range (-5 to +3 eV/atom) extreme_fe = np.sum((fe_vals < -6) | (fe_vals > 4)) if extreme_fe: finding("HIGH", "physical", "extreme_formation_energy", f"{extreme_fe:,} entries with FE outside [-6, 4]", "") else: finding("PASS", "physical", "fe_reasonable_range", "all within [-6, 4]", "") # --- Outliers --- print("\n--- Outliers ---") # IQR-based outlier detection def iqr_outliers(vals, name, n_iqr=3): q1, q3 = np.percentile(vals, [25, 75]) iqr = q3 - q1 low = q1 - n_iqr * iqr high = q3 + n_iqr * iqr outliers = vals[(vals < low) | (vals > high)] return outliers, low, high fe_out, fe_low, fe_high = iqr_outliers(fe_vals, "FE") eah_out, eah_low, eah_high = iqr_outliers(eah_vals, "EaH") bg_out, bg_low, bg_high = iqr_outliers(bg_vals, "BG") finding("LOW", "outliers", "fe_iqr_outliers", f"{len(fe_out):,} (thresholds: {fe_low:.3f}, {fe_high:.3f})", "") finding("LOW", "outliers", "eah_iqr_outliers", f"{len(eah_out):,} (thresholds: {eah_low:.3f}, {eah_high:.3f})", "") finding("LOW", "outliers", "bg_iqr_outliers", f"{len(bg_out):,} (thresholds: {bg_low:.3f}, {bg_high:.3f})", "") # Extreme values (hard thresholds) extreme_fe_hard = fe_vals[(fe_vals < -5) | (fe_vals > 5)] max_fe = float(np.max(fe_vals)) min_fe = float(np.min(fe_vals)) severity = "CRITICAL" if max_fe > 10 or min_fe < -10 else "HIGH" finding(severity, "outliers", "extreme_fe_hard", f"{len(extreme_fe_hard):,} with |FE| > 5 (max={max_fe:.2f}, min={min_fe:.2f})", f"values: {np.sort(extreme_fe_hard)[:10].tolist()}") extreme_eah_hard = eah_vals[eah_vals > 5] max_eah = float(np.max(eah_vals)) if len(eah_vals) > 0 else 0 if len(extreme_eah_hard) > 0: sev = "CRITICAL" if max_eah > 10 else "HIGH" finding(sev, "outliers", "extreme_eah_hard", f"{len(extreme_eah_hard):,} with EaH > 5 (max={max_eah:.2f})", "") else: finding("PASS", "outliers", "extreme_eah_hard", "0", "") # --- Correlations --- print("\n--- Property Correlations ---") from scipy import stats # Build aligned arrays for correlation import pandas as pd df = pd.DataFrame({"fe": FE, "eah": EaH, "bg": BG}).dropna() fe_aligned = df["fe"].values[:50000] eah_aligned = df["eah"].values[:50000] bg_aligned = df["bg"].values[:50000] r1, _ = stats.pearsonr(fe_aligned, eah_aligned) print(f" FE vs EaH: r={r1:.4f} (n={len(fe_aligned):,})") r2, _ = stats.pearsonr(fe_aligned, bg_aligned) print(f" FE vs BG: r={r2:.4f} (n={len(fe_aligned):,})") r3, _ = stats.pearsonr(eah_aligned, bg_aligned) print(f" EaH vs BG: r={r3:.4f} (n={len(eah_aligned):,})") finding("PASS", "correlations", "fe_eah_correlation", f"r={r1:.4f}", "") finding("PASS", "correlations", "fe_bg_correlation", f"r={r2:.4f}", "") # --- Missing values per source --- print("\n--- Missing Values Per Source ---") for src in ["mp", "oqmd", "jarvis"]: subset = [e for e in entries if e.get("source") == src] for prop in ["formation_energy_per_atom", "energy_above_hull", "band_gap"]: missing = sum(1 for e in subset if e.get(prop) is None) if missing > 0: pct = 100 * missing / len(subset) sev = "CRITICAL" if pct > 50 else "HIGH" if pct > 10 else "MEDIUM" finding(sev, "missing", f"{src}_{prop}_missing", f"{missing:,}/{len(subset):,} ({pct:.1f}%)", "") print(f"\n{'=' * 60}") print(f" PHASE 3 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 3: Property Audit", "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), "total_entries": N, "properties": { "formation_energy": { "n": int(len(fe_vals)), "missing": fe_missing, "mean": float(np.mean(fe_vals)), "median": float(np.median(fe_vals)), "std": float(np.std(fe_vals)), "min": float(np.min(fe_vals)), "max": float(np.max(fe_vals)), "skewness": float(stats.skew(fe_vals)), "kurtosis": float(stats.kurtosis(fe_vals)), "outliers_iqr": int(len(fe_out)), }, "energy_above_hull": { "n": int(len(eah_vals)), "missing": eah_missing, "mean": float(np.mean(eah_vals)), "median": float(np.median(eah_vals)), "std": float(np.std(eah_vals)), "min": float(np.min(eah_vals)), "max": float(np.max(eah_vals)), "outliers_iqr": int(len(eah_out)), }, "band_gap": { "n": int(len(bg_vals)), "missing": bg_missing, "mean": float(np.mean(bg_vals)), "median": float(np.median(bg_vals)), "std": float(np.std(bg_vals)), "min": float(np.min(bg_vals)), "max": float(np.max(bg_vals)), "outliers_iqr": int(len(bg_out)), }, }, "findings": findings, "summary": dict(severity_counts), } with open(OUT / "phase3_property_audit.json", "w") as f: json.dump(report, f, indent=2) print(f"\n Report: {OUT / 'phase3_property_audit.json'}") if __name__ == "__main__": main()