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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """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()
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