Function eval_hit_rate

scripts/train_predictor.py:555–563

(logits_np, E, k)

    print(f"Training for {epochs} epochs...\n")

    def eval_hit_rate(logits_np, E, k):
        preds = np.argsort(-logits_np, axis=1)[:, :k]
        hits = 0
        total = 0
        for i in range(len(E)):
            actual = set(int(x) for x in E[i] if x >= 0)  # drop -1 padding
            hits += len(actual & set(preds[i].tolist()))
            total += len(actual)  # real experts only, not padded K
        return hits, total

    def run_eval():
        model.eval()