# scheduler/optimizer.py

def choose_best_node(nodes, action, intelligence=None):

    best_node = None
    best_score = -10**9

    ranked = []

    # -----------------------------
    # SCORE EACH NODE
    # -----------------------------
    for node in nodes:

        breakdown = {
            "cpu": 0,
            "thermal": 0,
            "policy": 0,
            "intelligence": 0,
            "final": 0
        }

        # CPU availability
        cpu_score = (100 - node.get("cpu_load", 0))
        breakdown["cpu"] = cpu_score

        # Thermal penalty
        thermal_penalty = node.get("temp", 0)
        breakdown["thermal"] = -thermal_penalty

        # Policy rules
        policy_score = 0

        if node.get("policy") == "preferred":
            policy_score += 20
        elif node.get("policy") == "avoid":
            policy_score -= 50

        breakdown["policy"] = policy_score

        # Intelligence feedback
        intel_score = 0

        if intelligence:
            stats = intelligence.profiles.get(action["target"])

            if stats:
                latest = stats.finalize()

                intel_score -= latest["score"].get("thermal", 0) * 0.3
                intel_score -= latest["score"].get("instability", 0) * 0.2

        breakdown["intelligence"] = intel_score

        # Final score
        final = cpu_score + policy_score + intel_score + breakdown["thermal"]
        breakdown["final"] = final

        ranked.append({
            "node": node,
            "score": breakdown
        })

        if final > best_score:
            best_score = final
            best_node = node

    # -----------------------------
    # SORT FOR EXPLANATION
    # -----------------------------
    ranked_sorted = sorted(
        ranked,
        key=lambda x: x["score"]["final"],
        reverse=True
    )

    # -----------------------------
    # EXPLANATION OBJECT
    # -----------------------------
    explanation = {
        "selected": best_node["name"] if best_node else None,
        "top_candidates": [
            {
                "node": r["node"]["name"],
                "final_score": r["score"]["final"],
                "breakdown": r["score"]
            }
            for r in ranked_sorted[:5]
        ]
    }

    return {
        "best_node": best_node,
        "ranked": ranked,
        "explanation": explanation
    }