[GENESIS] Upload risk_ml.py

risk_ml.py

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+#!/usr/bin/env python3
+"""
+GENESIS Risk ML Engine v10.1
+Predictive risk scoring based on infrastructure metrics.
+
+Features:
+- Multi-feature risk prediction (CPU, Memory, Network, Disk, Error Rate)
+- Gradient Boosted model for non-linear risk patterns
+- Confidence intervals for regulatory reporting
+- JSON output for audit trail integration
+"""
+
+import json
+import sys
+from datetime import datetime, timezone
+
+try:
+    import numpy as np
+    from sklearn.ensemble import GradientBoostingRegressor
+    from sklearn.model_selection import cross_val_score
+except ImportError:
+    print("Installing dependencies...")
+    import subprocess
+    subprocess.check_call([sys.executable, "-m", "pip", "install",
+                          "numpy", "scikit-learn", "pandas"])
+    import numpy as np
+    from sklearn.ensemble import GradientBoostingRegressor
+    from sklearn.model_selection import cross_val_score
+
+TRAINING_DATA = {
+    "cpu":        [20, 30, 40, 50, 55, 60, 65, 70, 75, 80, 85, 90, 92, 95, 98],
+    "memory":     [15, 25, 30, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95],
+    "network_io": [10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 65, 70, 75, 80, 90],
+    "disk_usage": [20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 92],
+    "error_rate": [ 0,  1,  2,  3,  4,  5,  7,  9, 12, 15, 20, 30, 40, 60, 80],
+    "risk_score": [ 5,  8, 12, 18, 22, 28, 35, 42, 52, 65, 72, 82, 88, 94, 99],
+}
+
+X = np.array([
+    TRAINING_DATA["cpu"],
+    TRAINING_DATA["memory"],
+    TRAINING_DATA["network_io"],
+    TRAINING_DATA["disk_usage"],
+    TRAINING_DATA["error_rate"],
+]).T
+
+y = np.array(TRAINING_DATA["risk_score"])
+
+model = GradientBoostingRegressor(
+    n_estimators=100,
+    max_depth=4,
+    learning_rate=0.1,
+    random_state=42
+)
+model.fit(X, y)
+
+cv_scores = cross_val_score(model, X, y, cv=3, scoring="r2")
+
+current_metrics = {
+    "cpu": float(sys.argv[1]) if len(sys.argv) > 1 else 75.0,
+    "memory": float(sys.argv[2]) if len(sys.argv) > 2 else 65.0,
+    "network_io": float(sys.argv[3]) if len(sys.argv) > 3 else 50.0,
+    "disk_usage": float(sys.argv[4]) if len(sys.argv) > 4 else 60.0,
+    "error_rate": float(sys.argv[5]) if len(sys.argv) > 5 else 12.0,
+}
+
+input_vector = np.array([[
+    current_metrics["cpu"],
+    current_metrics["memory"],
+    current_metrics["network_io"],
+    current_metrics["disk_usage"],
+    current_metrics["error_rate"],
+]])
+
+risk_score = float(np.clip(model.predict(input_vector)[0], 0, 100))
+
+feature_importance = dict(zip(
+    ["cpu", "memory", "network_io", "disk_usage", "error_rate"],
+    [round(float(x), 4) for x in model.feature_importances_]
+))
+
+if risk_score >= 80:
+    risk_level = "CRITICAL"
+elif risk_score >= 60:
+    risk_level = "HIGH"
+elif risk_score >= 40:
+    risk_level = "MEDIUM"
+elif risk_score >= 20:
+    risk_level = "LOW"
+else:
+    risk_level = "MINIMAL"
+
+result = {
+    "timestamp": datetime.now(timezone.utc).isoformat(),
+    "version": "10.1.0",
+    "engine": "GradientBoostingRegressor",
+    "input_metrics": current_metrics,
+    "risk_score": round(risk_score, 2),
+    "risk_level": risk_level,
+    "confidence": {
+        "cv_r2_mean": round(float(cv_scores.mean()), 4),
+        "cv_r2_std": round(float(cv_scores.std()), 4),
+    },
+    "feature_importance": feature_importance,
+    "model_params": {
+        "n_estimators": 100,
+        "max_depth": 4,
+        "training_samples": len(y),
+    },
+    "recommendation": (
+        "IMMEDIATE ACTION REQUIRED" if risk_level == "CRITICAL" else
+        "Escalate to operations team" if risk_level == "HIGH" else
+        "Monitor closely" if risk_level == "MEDIUM" else
+        "Normal operations"
+    ),
+}
+
+with open("risk_score.json", "w") as f:
+    json.dump(result, f, indent=2)
+
+with open("risk_score.txt", "w") as f:
+    f.write(str(round(risk_score)))
+
+print(json.dumps(result, indent=2))