def replay_actions()

in compiler_gym/random_search.py [0:0]
44 lines of code
4 McCabe index (conditional complexity)

def replay_actions(env: CompilerEnv, action_names: List[str], outdir: Path):
    logs_path = outdir / logs.BEST_ACTIONS_PROGRESS_NAME
    start_time = time()

    if isinstance(env, LlvmEnv):
        env.write_bitcode(outdir / "unoptimized.bc")

    with open(str(logs_path), "w") as f:
        ep_reward = 0
        for i, action in enumerate(action_names, start=1):
            _, reward, done, _ = env.step(env.action_space.names.index(action))
            assert not done
            ep_reward += reward
            print(
                f"Step [{i:03d} / {len(action_names):03d}]: reward={reward:.4f}   \t"
                f"episode={ep_reward:.4f}   \taction={action}"
            )
            progress = RandomSearchProgressLogEntry(
                runtime_seconds=time() - start_time,
                total_episode_count=1,
                total_step_count=i,
                num_passes=i,
                reward=reward,
            )
            print(progress.to_csv(), action, file=f, sep=",")

    if isinstance(env, LlvmEnv):
        env.write_bitcode(outdir / "optimized.bc")
        print(
            tabulate(
                [
                    (
                        "IR instruction count",
                        env.observation["IrInstructionCountO0"],
                        env.observation["IrInstructionCountOz"],
                        env.observation["IrInstructionCount"],
                    ),
                    (
                        "Object .text size (bytes)",
                        env.observation["ObjectTextSizeO0"],
                        env.observation["ObjectTextSizeOz"],
                        env.observation["ObjectTextSizeBytes"],
                    ),
                ],
                headers=("", "-O0", "-Oz", "final"),
            )
        )